diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 36eac55f9..2e3762719 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -1,5 +1,5 @@ name: Tests -on: [push] +on: [push, pull_request] jobs: Run-Tests: runs-on: ${{ matrix.os }} @@ -7,7 +7,8 @@ jobs: strategy: matrix: os: [ubuntu-latest] - python-version: [2.7, 3.7] + #python-version: [2.7, 3.7] + python-version: [3.7] steps: - name: Check out repository code diff --git a/.gitignore b/.gitignore index 6047937e2..6bf11798f 100644 --- a/.gitignore +++ b/.gitignore @@ -51,4 +51,7 @@ venv/ /examples/M1/*.png /examples/M1/*.pov /examples/HHTut/temp.json -.vscode \ No newline at end of file +.vscode +/examples/HHTut/HHTut_plot_2Dnet.png +/examples/HHTut/HHTut_regen.py +/examples/HHTut/HHTut_traces.png diff --git a/CHANGES.md b/CHANGES.md index 92add3670..380660df0 100644 --- a/CHANGES.md +++ b/CHANGES.md @@ -1,3 +1,70 @@ +# Version 1.0.1 + +**New features** + +- Added new plotting module with custom plotters + +- Separated analysis.plotSpikeRaster into analysis.prepareRaster and plotting.plotRaster + +- Separated analysis.plotSpikeHist into analysis.prepareSpikeData, analysis.prepareSpikeHist, plotting.plotSpikeHist and plotting.plotSpikeFreq + +- Separated analysis.plotLFP into analysis.prepareLFP, analysis.preparePSD, analysis.prepareSpectrogram, plotting.plotLFPTimeSeries, plotting.plotLFPPSD, plotting.plotLFPSpectrogram, and plotting.plotLFPLocations + +- Mapped old analysis.plots onto new ones + +- Interfaced with LFPkit to enable recording/plotting of dipole current moments and EEG signals + +- Added Current Source Density (CSD) analysis and plots + +- Enabled selecting a subset of cells to save individual LFP signal from via cfg.saveLFPCells = [...] + +- Added cfg.saveLFPPops to store LFP generated individually by each population + +- Function to return batch parameter combinations (used for GUI) + +- Added jupyter nb tutorials + +- Added hippocampus CA3 model example + +- Updated SONATA importing + +- Improved distributed saving and interval saving (more robust) + +- Added mapping of netParams values within cfg (used for batch sims in GUI) + +- Extended Action tests to pull requests (not just push) + + +**Bug fixes** + +- Fixed setting cfgMapping nested params when not string + +- Fixed defineCellShapes when sec['hObj'] is not a h.Section() + +- Fixed bug when using lognormal in string functions + +- Added rxdmath to avoid RxD error + +- Fixed bug in Tutorial 8 (batch) + +- When saving to JSON wait until file exists before returning (to avoid exiting before saving finished) + + +# Version 1.0.0.2 + +**New features** + +- Added function to get list of batch grid search parameter combinations + +- Save output of mpi_bulletin batches to .run and .err files + +- Fixed bug in loading exotic stim param values + +- Fixed bug in loading where sim was required to have rank attribute + +- Added quotes in batch.py to avoid path white spaces issue + + # Version 1.0.0.2 - Release for use with GUI @@ -1472,3 +1539,5 @@ First version that was uploaded to pypi. Includes following features: - Parameters/specifications - Instantiated networks - Simulation results + +- Distance-based redistribution of synapses (subConn) diff --git a/doc/build.py b/doc/build.py index 59092117b..5acb442c6 100644 --- a/doc/build.py +++ b/doc/build.py @@ -72,7 +72,7 @@ shutil.rmtree('build', ignore_errors=True) # All .rst files but those listed here will be deleted during this process -keep = ['about.rst', 'advanced.rst', 'index.rst', 'install.rst', 'reference.rst', 'tutorial.rst'] +keep = ['about.rst', 'advanced.rst', 'index.rst', 'install.rst', 'reference.rst', 'tutorial.rst', 'contrib.rst'] print('Deleting old .rst files.') for file in os.listdir('source'): diff --git a/doc/source/about.rst b/doc/source/about.rst index 3dea66e2c..1e8332ba9 100644 --- a/doc/source/about.rst +++ b/doc/source/about.rst @@ -88,6 +88,8 @@ Major Features Questions, suggestions, and contributions ----------------------------------------- +Two kinds of contributions are solicited: 1. direct contributions to NetPyNE code described below. 2. `Ancillary code contributions `_. + NetPyNE is open-source and available at `github.com/Neurosim-lab/netpyne `_. For questions or suggestions please use the `NetPyNE forum `_ , the `NEURON+NetPyNE forum `_ or `submit an issue to GitHub `_. @@ -135,58 +137,76 @@ About NetPyNE Make use of NetPyNE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -- Metzner C, Mäki-Marttunen T, Karni G, McMahon-Cole H, Steuber V. **The Effect of Alterations of Schizophrenia-Associated Genes on Gamma Band Oscillations.** `bioRxiv 2020.09.28.316737 `_. *2020* +- Hasegan D, Deible M, Earl C, D’Onofrio D, Hazan H, Anwar H, Neymotin SA. **Multi-timescale biological learning algorithms train spiking neuronal network motor control** bioRxiv 2021.11.20.469405, *2021*. doi: ``_. -- Gao P, Graham J, Zhou W, Jang J, Angulo S, Dura-Bernal S, Hines ML, Lytton WW, and Antic S **Local Glutamate-Mediated Dendritic Plateau Potentials Change the State of the Cortical Pyramidal Neuron.** *Journal of Neurophysiology (In Press). 2020* +- Bryson A, Berkovic SF, Petrou S, Grayden DB. **State transitions through inhibitory interneurons in a cortical network model.** PLoS Comput Biol 17(10): e1009521, *2021*. doi: ``_. -- Dura-Bernal S, Griffith EY, Barczak A, O’Connell MN, McGinnis T, Anwar H, Lytton WW, Lakatos p, Neymotin SA. **Biophysically-detailed multiscale model of macaque auditory thalamocortical circuits reproduces physiological oscillations.** *Computational Neurosience (CNS), 2020.* +- Pimentel1 JM, Moioli RC, de Araujo MFP, Ranieri CM, Romero RAF, Broz F and Vargas PA. **Neuro4PD: An Initial Neurorobotics Model of Parkinson’s Disease.** Front. Neurorobot. 15:640449, *2021*. doi: `<10.3389/fnbot.2021.640449>`_. -- Sivagnanam S, Gorman W, Doherty D, Neymotin S, Hovhannisyan H, Fang S, Lytton W, Dura-Bernal S **Simulating large-scale models of brain neuronal circuits using Google Cloud Platform**. *Practice and Experience in Advanced Research Computing, PEARC2020. 2020* +- Volk, V.L., Hamilton, L.D., Hume, D.R. et al. **Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling.** Scientific Reports 11, 22983, *2021*. doi: ``_. -- Neymotin, S.A., Daniels, D.S., Caldwell, B., McDougal, R.A., Carnevale, N.T., Jas, M., Moore, C.I., Hines, M.L., Hamalainen, M. and Jones, S.R., **Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data.** `eLife, 9, p.e51214. `_ *2020* +- Ranieri CM, Moioli RC, Vargas P, Romero RAF, **A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition**, arXiv:2107.12540 [cs.RO], *2021*. ``_. -- Lytton WW, Sherif MA, Dura-Bernal S, Neymotin S. **Multiscale modeling of a “mental” disease.** *Virtual Physiological Human Conference, VHP2020. 2020* +- Ranieri CM, Pimentel JM, Romano MR, Elias LA, Romero RAF, Lones MA, Araujo MFP, Vargas PA, Moioli RC, **A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys**, IEEE Access, vol. 9, pp. 122548-122567, *2021*. doi: ``_. -- Griffith EY, Dura-Bernal S, Barczak A, O’connell MN, Mcginnis TM, Lytton WW, Lakatos P, Neymotin SA. **Data-driven model of auditory thalamocortical system rhythms.** *Society for Neuroscience (SfN). *2019*. +- Metzner C, Mäki-Marttunen T, Karni G, McMahon-Cole H, Steuber V. **The Effect of Alterations of Schizophrenia-Associated Genes on Gamma Band Oscillations**, bioRxiv 2020.09.28.316737, *2020*. doi: ``_. -- Doherty DW, Dura-Bernal S, Lytton WW. **Computer models of mouse area M1 show avalanches for full model and subcircuits defined by layer or cell type.** *Society for Neuroscience (SfN), 2019*. +- Gao P, Graham J, Zhou W, Jang J, Angulo S, Dura-Bernal S, Hines ML, Lytton WW, and Antic S **Local Glutamate-Mediated Dendritic Plateau Potentials Change the State of the Cortical Pyramidal Neuron**, Journal of Neurophysiology (In Press). *2020*. + +- Sivagnanam S, Gorman W, Doherty D, Neymotin S, Hovhannisyan H, Fang S, Lytton W, Dura-Bernal S **Simulating large-scale models of brain neuronal circuits using Google Cloud Platform**, Practice and Experience in Advanced Research Computing, PEARC2020, *2020*. + +- Neymotin, S.A., Daniels, D.S., Caldwell, B., McDougal, R.A., Carnevale, N.T., Jas, M., Moore, C.I., Hines, M.L., Hamalainen, M. and Jones, S.R., **Human Neocortical Neurosolver (HNN), a new software tool for interpreting the cellular and network origin of human MEG/EEG data**, eLife, 9, p.e51214, *2020*. doi: ``_ . + +- Lytton WW, Sherif MA, Dura-Bernal S, Neymotin S. **Multiscale modeling of a “mental” disease**, Virtual Physiological Human Conference, VHP2020, *2020*. + +- Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Lytton WW. **Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit**, bioRxiv 201707, *2019*. doi: ``_. + +- Gerkin, R.C., Birgiolas, J., Jarvis, R.J., Omar, C. and Crook, S.M.. **NeuronUnit: A package for data-driven validation of neuron models using SciUnit**, bioRxiv, p.665331, *2019*. + +- Gleeson P, Cantarelli M, Quintana A, Earnsah M, Piasini E, Birgiolas J, Cannon RC, Cayco- Gajic A, Crook S, Davison AP, Dura-Bernal S, et al. **Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits**, Neuron, 10.1016/j.neuron.2019.05.019, *2019*. doi: ``_. -- Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Lytton WW. **Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit.** `bioRxiv 201707 `_ , *2019.* +- Dai K, Hernando J, Billeh JN, Gratiy SL, Planas J, Davison AP, Dura-Bernal S, Gleeson P, Devresse A, Gevaert M, King JG, Van Geit WAH, Povolotsky AV, Muller E, Courcol J-D, Arkhipov A . **The SONATA Data Format for Efficient Description of Large-Scale Network Models**, bioRxiv, 625491 [Preprint] doi: ``_. *Under review in PLoS Computational Biology. 2019* -- Gerkin, R.C., Birgiolas, J., Jarvis, R.J., Omar, C. and Crook, S.M.. **NeuronUnit: A package for data-driven validation of neuron models using SciUnit.** *bioRxiv, p.665331. 2019* +- Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Lytton WW **Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits**, bioRxiv 201707, *2018*. doi: ``_. -- Gleeson P, Cantarelli M, Quintana A, Earnsah M, Piasini E, Birgiolas J, Cannon RC, Cayco- Gajic A, Crook S, Davison AP, Dura-Bernal S, et al. **Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits.** `Neuron, 10.1016/j.neuron.2019.05.019 `_. *2019* -- Dai K, Hernando J, Billeh JN, Gratiy SL, Planas J, Davison AP, Dura-Bernal S, Gleeson P, Devresse A, Gevaert M, King JG, Van Geit WAH, Povolotsky AV, Muller E, Courcol J-D, Arkhipov A . **The SONATA Data Format for Efficient Description of Large-Scale Network Models.** `bioRxiv, 625491 [Preprint] `_. *Under review in PLoS Computational Biology. 2019* +Conference abstracts: -- Dura-Bernal S, Neymotin SA, Suter BA, Shepherd GMG, Lytton WW **Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits.** `bioRxiv 201707 `_ , *2018.* +- Dura-Bernal S, Griffith EY, Barczak A, O’Connell MN, McGinnis T, Anwar H, Lytton WW, Lakatos p, Neymotin SA. **Biophysically-detailed multiscale model of macaque auditory thalamocortical circuits reproduces physiological oscillations.** *Computational Neurosience (CNS), 2020*. -- Dura-Bernal S, Menzies RS, McLauchlan C, van Albada SJ, Kedziora DJ, Neymotin SA, Lytton WW, Kerr CC. **Effect of network size on computational capacity.** *Computational Neuroscience (CNS), 2016.* +- Griffith EY, Dura-Bernal S, Barczak A, O’connell MN, Mcginnis TM, Lytton WW, Lakatos P, Neymotin SA. **Data-driven model of auditory thalamocortical system rhythms.** *Society for Neuroscience (SfN), 2019*. -- Romaro C, Araujo Najman F, Dura-Bernal S, Roque AC. **Implementation of the Potjans-Diesmann cortical microcircuit model in NetPyNE/NEURON with rescaling option.** *Computational Neuroscience (CNS), 2018.* +- Doherty DW, Dura-Bernal S, Lytton WW. **Computer models of mouse area M1 show avalanches for full model and subcircuits defined by layer or cell type.** *Society for Neuroscience (SfN), 2019*. + +- Dura-Bernal S, Menzies RS, McLauchlan C, van Albada SJ, Kedziora DJ, Neymotin SA, Lytton WW, Kerr CC. **Effect of network size on computational capacity.** *Computational Neuroscience (CNS), 2016*. + +- Romaro C, Araujo Najman F, Dura-Bernal S, Roque AC. **Implementation of the Potjans-Diesmann cortical microcircuit model in NetPyNE/NEURON with rescaling option.** *Computational Neuroscience (CNS), 2018*. + +- Rodriguez F. **Dentate gyrus network model.** *Computational Neuroscience (CNS), 2018*. -- Rodriguez F. **Dentate gyrus network model.** *Computational Neuroscience (CNS), 2018.* Cite NetPyNE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -- Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar, P., Schürmann, F. and Segev, I., **An efficient analytical reduction of detailed nonlinear neuron models.** `Nature Communications, 11(1), pp.1-13 `_. *2020* +- Spreizer S, Senk J, Rotter S, Diesmann M, Weyers, **NEST Desktop, an Educational Application for Neuroscience** eNeuro 11 November 2021, 8 (6) ENEURO.0274-21.2021, *2021*. doi: ``_. -- Billeh, Y.N., Cai, B., Gratiy, S.L., Dai, K., Iyer, R., Gouwens, N.W., Abbasi-Asl, R., Jia, X., Siegle, J.H., Olsen, S.R. and Koch, C.,. **Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex.** `Neuron (In Press) NEURON-D-19-01027 `_. *2020* +- Amsalem, O., Eyal, G., Rogozinski, N., Gevaert, M., Kumbhar, P., Schürmann, F. and Segev, I., **An efficient analytical reduction of detailed nonlinear neuron models.** Nature Communications, 11(1), pp.1-13, *2020*. doi: ``_. -- Tran, H., Ranta, R., Le Cam, S. and Louis-Dorr, V., **Fast simulation of extracellular action potential signatures based on a morphological filtering approximation.** *Journal of Computational Neuroscience**, pp.1-20. *2020* +- Billeh, Y.N., Cai, B., Gratiy, S.L., Dai, K., Iyer, R., Gouwens, N.W., Abbasi-Asl, R., Jia, X., Siegle, J.H., Olsen, S.R. and Koch, C.,. **Systematic integration of structural and functional data into multi-scale models of mouse primary visual cortex.** Neuron (In Press) NEURON-D-19-01027, *2020*. doi: ``_. -- Gast, R., Rose, D., Salomon, C., Möller, H.E., Weiskopf, N. and Knösche, T.R.. **PyRates—A Python framework for rate-based neural simulations.** *PLoS ONE, 14(12). 2019* +- Tran, H., Ranta, R., Le Cam, S. and Louis-Dorr, V., **Fast simulation of extracellular action potential signatures based on a morphological filtering approximation.** Journal of Computational Neuroscience, pp.1-20, *2020*. doi: ``_. -- Tejada J, Roque AC, **Conductance-based models and the fragmentation problem: A case study based on hippocampal CA1 pyramidal cell models and epilepsy** `Epilepsy & Behavior, 106841 `_ 2019. +- Gast, R., Rose, D., Salomon, C., Möller, H.E., Weiskopf, N. and Knösche, T.R.. **PyRates—A Python framework for rate-based neural simulations.** PLoS ONE, 14(12), *2019*. doi: ``_. -- Kuhl E, Alber M, Tepole BA, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis GE, Lytton WW, Perdikaris P, Petzold L. **Multiscale modeling meets machine learning: What can we learn?** `arXiv:1911.11958 `_. [Preprint]. *Under review in Computer Methods in Applied Mechanics and Engineering. 2019* +- Tejada J, Roque AC, **Conductance-based models and the fragmentation problem: A case study based on hippocampal CA1 pyramidal cell models and epilepsy** `Epilepsy & Behavior, 106841, *2019*. doi: ``_. -- Alber M, Buganza A, Cannon W, De S, Dura-Bernal S, Garikipati K, Karmiadakis G, Lytton W, Perdikaris P, Petzold L, Kuhl E. **Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences.** `Nature Partner Journals (npj) Digital Medicine, 2, 115 `_. *2019* +- Kuhl E, Alber M, Tepole BA, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis GE, Lytton WW, Perdikaris P, Petzold L. **Multiscale modeling meets machine learning: What can we learn?** arXiv:1911.11958. doi: ``_. [Preprint]. *Under review in Computer Methods in Applied Mechanics and Engineering. 2019* +- Alber M, Buganza A, Cannon W, De S, Dura-Bernal S, Garikipati K, Karmiadakis G, Lytton W, Perdikaris P, Petzold L, Kuhl E. **Integrating Machine Learning and Multiscale Modeling: Perspectives, Challenges, and Opportunities in the Biological, Biomedical, and Behavioral Sciences.** Nature Partner Journals (npj) Digital Medicine, 2, 115, *2019*. doi: ``_. -Here is an `updated list from Google Scholar `_. + +See here an `updated list from Google Scholar `_. @@ -196,11 +216,17 @@ Courses Future ^^^^^^^^^^^^ -- June 2020: Building and simulating brain circuit models on Google Cloud, Google Office, NYC (delayed) +- June 2022: Building and simulating brain circuit models on Google Cloud, Google Office, NYC (delayed) Past ^^^^^^^^^ +- Dec 20201: Minicurso - Modelagem Computacional em NetPyNE usando Google Cloud Platform- Simpósio do PPGNCG (Brazilian course in portuguese) + +- July 2021: CNS’21 Building biophysically detailed neuronal models from molecules to networks with NEURON and NetPyNE. + +- May 2021: Online 2-week Course: Multiscale modeling of brain circuits using NetPyNE. + - July 2020: `CNS*2020 `_ Building mechanistic multiscale models: from molecules to networks using NEURON and NetPyNE, online conference - January 2020: VIII Latin American School on Computational Neuroscience (LASCON), Institute of Mathematics and Statistics, University of Sao Paulo, Brazil diff --git a/doc/source/code/HHCellFile.py b/doc/source/code/HHCellFile.py index 3d394db0e..3d5ebd23d 100644 --- a/doc/source/code/HHCellFile.py +++ b/doc/source/code/HHCellFile.py @@ -55,13 +55,13 @@ def createSections(self): def defineGeometry(self): """Set the 3D geometry of the cell.""" - self.soma.L = 18.8 - self.soma.diam = 18.8 - self.soma.Ra = 123.0 + self.soma.L = 18.8 # length of the soma section in microns + self.soma.diam = 18.8 # diameter of the soma section in microns + self.soma.Ra = 123.0 # axial resistivity of the soma section in ohm-cm - self.dend.L = 200.0 - self.dend.diam = 1.0 - self.dend.Ra = 100.0 + self.dend.L = 200.0 # length of the dend section in microns + self.dend.diam = 1.0 # diameter of the dend section in microns + self.dend.Ra = 100.0 # axial resistivity of the dend section in ohm-cm def defineBiophysics(self): """Assign the membrane properties across the cell.""" @@ -69,7 +69,7 @@ def defineBiophysics(self): self.soma.insert('hh') self.soma.gnabar_hh = 0.12 # Sodium conductance in S/cm2 self.soma.gkbar_hh = 0.036 # Potassium conductance in S/cm2 - self.soma.gl_hh = 0.003 # Leak conductance in S/cm2 + self.soma.gl_hh = 0.003 # Leak conductance in S/cm2 self.soma.el_hh = -70 # Reversal potential in mV self.dend.insert('pas') diff --git a/doc/source/code/tut2.py b/doc/source/code/tut2.py index bb39e97ae..c3370bb92 100644 --- a/doc/source/code/tut2.py +++ b/doc/source/code/tut2.py @@ -52,5 +52,5 @@ # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut2') diff --git a/doc/source/code/tut3.py b/doc/source/code/tut3.py index 9c979748b..2519d21e8 100644 --- a/doc/source/code/tut3.py +++ b/doc/source/code/tut3.py @@ -64,5 +64,5 @@ # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut3') diff --git a/doc/source/code/tut4.py b/doc/source/code/tut4.py index 09aa9a51a..8d975d257 100644 --- a/doc/source/code/tut4.py +++ b/doc/source/code/tut4.py @@ -77,5 +77,5 @@ # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut4') diff --git a/doc/source/code/tut5.py b/doc/source/code/tut5.py index faa2c326e..9a5bf4cca 100644 --- a/doc/source/code/tut5.py +++ b/doc/source/code/tut5.py @@ -80,5 +80,5 @@ # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut5') diff --git a/doc/source/code/tut6.py b/doc/source/code/tut6.py index 865d92888..b2b79538d 100644 --- a/doc/source/code/tut6.py +++ b/doc/source/code/tut6.py @@ -50,5 +50,5 @@ # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut6') diff --git a/doc/source/code/tut7.py b/doc/source/code/tut7.py index 7d6edeb2e..db6bf4a79 100644 --- a/doc/source/code/tut7.py +++ b/doc/source/code/tut7.py @@ -100,5 +100,5 @@ sim.analysis.plotRaster(syncLines=True) sim.analysis.plotTraces(include = [1]) -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut7') diff --git a/doc/source/code/tut8_analysis.py b/doc/source/code/tut8_analysis.py index d88b0740c..161c857b0 100644 --- a/doc/source/code/tut8_analysis.py +++ b/doc/source/code/tut8_analysis.py @@ -196,7 +196,7 @@ def plot2DRate(dataFolder, batchLabel, params, data, par1, par2, val, valLabel, # Function to read batch data and plot figure #-------------------------------------------------------------------- def readPlot(): - dataFolder = 'tauWeight_data/' + dataFolder = 'tut8_data/' batchLabel = 'tauWeight' params, data = readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None) diff --git a/doc/source/code/tut_artif.py b/doc/source/code/tut_artif.py index 0faad295c..1de6d0b93 100644 --- a/doc/source/code/tut_artif.py +++ b/doc/source/code/tut_artif.py @@ -14,14 +14,6 @@ ############################################################################### # NETWORK PARAMETERS ############################################################################### - -# Population parameters -netParams.popParams['PYR1'] = {'cellType': 'PYR', 'numCells': 100} # pop of HH cells -netParams.popParams['artif1'] = {'cellType': 'artif_NetStim', 'numCells': 100} # pop of NetStims -netParams.popParams['artif2'] = {'cellType': 'artif_IntFire2', 'numCells': 100} # pop of IntFire2 -netParams.popParams['artif3'] = {'cellType': 'artif_IntFire4', 'numCells': 100} # pop of IntFire4 -netParams.popParams['artif4'] = {'cellType': 'artif_VecStim', 'numCells': 100} # pop of Vecstims with 2 pulses - # Cell parameters ## PYR cell properties cellParams = Dict() @@ -29,44 +21,93 @@ cellParams.secs.soma.mechs.hh = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} netParams.cellParams['PYR'] = cellParams -netParams.cellParams['artif_NetStim'] = {'cellModel': 'NetStim', 'rate': 50, 'noise': 0.8, 'start': 1, 'seed': 2} # pop of NetStims -netParams.cellParams['artif_IntFire2'] = {'cellModel': 'IntFire2', 'ib': 0.0} # pop of IntFire2 -netParams.cellParams['artif3_IntFire4'] = {'cellModel': 'IntFire4', 'taue': 1.0} # pop of IntFire4 -netParams.cellParams['artif4_Vecstim'] = {'cellModel': 'VecStim', 'rate': 5, 'noise': 0.5, 'start': 50, - 'pulses': [{'start': 200, 'end': 300, 'rate': 60, 'noise':0.2}, {'start': 500, 'end': 800, 'rate': 30, 'noise': 0.5}]} # pop of Vecstims with 2 pulses +## IntFire2 artificial cell +netParams.cellParams['artif_IntFire2'] = { + 'cellModel': 'IntFire2', + 'ib': 0.0} + +## IntFire4 artificial cell +netParams.cellParams['artif_IntFire4'] = { + 'cellModel': 'IntFire4', + 'taue': 1.0} + +## NetStim artificial spike generator +netParams.cellParams['artif_NetStim'] = { + 'cellModel': 'NetStim'} + +## VecStim artificial spike generator +netParams.cellParams['artif_VecStim'] = { + 'cellModel': 'VecStim'} # pop of Vecstims with 2 pulses + + +# Population parameters +netParams.popParams['PYR1'] = { + 'cellType': 'PYR', + 'numCells': 100} # pop of HH cells + +netParams.popParams['pop_IntFire2'] = { + 'cellType': 'artif_IntFire2', + 'numCells': 100} # pop of IntFire2 + +netParams.popParams['pop_IntFire4'] = { + 'cellType': 'artif_IntFire4', + 'numCells': 100} # pop of IntFire4 + +netParams.popParams['pop_NetStim'] = { + 'cellType': 'artif_NetStim', + 'numCells': 100, + 'rate': 10, + 'noise': 0.8, + 'start': 1, + 'seed': 2} # pop of NEtSims + +netParams.popParams['pop_VecStim'] = { + 'cellType': 'artif_VecStim', + 'numCells': 100, + 'rate': 5, + 'noise': 0.5, + 'start': 50, + 'pulses': [{'start': 200, 'end': 300, 'rate': 60, 'noise':0.2}, + {'start': 500, 'end': 800, 'rate': 30, 'noise': 0.5}]} # pop of Vecstims with 2 pulses # Synaptic mechanism parameters netParams.synMechParams['AMPA'] = {'mod': 'Exp2Syn', 'tau1': 0.1, 'tau2': 1.0, 'e': 0} -# Stimulation parameters -netParams.stimSourceParams['background'] = {'type': 'NetStim', 'interval': 100, 'number': 1e5, 'start': 500, 'noise': 0.5} # stim using NetStims after 500ms -netParams.stimTargetParams['bkg->PYR1'] = {'source': 'background', 'conds': {'pop': 'PYR1'}, 'sec':'soma', 'loc': 0.5, 'weight': 0.5, 'delay': 1} +# Connections +netParams.connParams['NetStim->PYR1'] = { + 'preConds': {'pop': 'pop_NetStim'}, + 'postConds': {'pop': 'PYR1'}, + 'convergence': 3, + 'weight': 0.002, + 'synMech': 'AMPA', + 'delay': 'uniform(1,5)'} -# Connections -netParams.connParams['artif1->PYR1'] = { - 'preConds': {'pop': 'artif1'}, +netParams.connParams['VecStim->PYR1'] = { + 'preConds': {'pop': 'pop_VecStim'}, 'postConds': {'pop': 'PYR1'}, - 'convergence': 8, + 'probability': 0.4, 'weight': 0.005, 'synMech': 'AMPA', 'delay': 'uniform(1,5)'} -netParams.connParams['PYR1->artif2'] = { +netParams.connParams['PYR1->IntFire2'] = { 'preConds': {'pop': 'PYR1'}, - 'postConds': {'pop': 'artif2'}, + 'postConds': {'pop': 'pop_IntFire2'}, 'probability': 0.2, - 'weight': 0.2, + 'weight': 0.1, + 'synMech': 'AMPA', 'delay': 'uniform(1,5)'} -netParams.addConnParams('artif2->artif3', - {'preConds': {'pop': 'artif2'}, - 'postConds': {'pop': 'artif3'}, - 'divergence': 20, - 'weight': 0.05, - 'delay': 3}) + +netParams.connParams['IntFire2->IntFire4'] = { + 'preConds': {'pop': 'pop_IntFire2'}, + 'postConds': {'pop': 'pop_IntFire4'}, + 'probability': 0.1, + 'weight': 0.2, + 'delay': 'uniform(1,5)'} ############################################################################### @@ -84,7 +125,7 @@ simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}} # # Analysis and plotting -simConfig.analysis['plotRaster'] = True +simConfig.analysis['plotRaster'] = {'orderInverse': True} ############################################################################### diff --git a/doc/source/code/tut_import.py b/doc/source/code/tut_import.py index 82a8c85e9..45a3f05e4 100644 --- a/doc/source/code/tut_import.py +++ b/doc/source/code/tut_import.py @@ -182,5 +182,5 @@ # import pylab; pylab.show() # this line is only necessary in certain systems where figures appear empty -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the tutorial. sim.checkOutput('tut_import') diff --git a/doc/source/conf.py b/doc/source/conf.py index 89ed5b805..f4ca03571 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -39,6 +39,11 @@ 'autodocsumm', ] + +# Add custom sections to Napoleon +napoleon_custom_sections = [('NetPyNE Options', 'params_style'), ('Plot Options', 'params_style')] + + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -60,9 +65,9 @@ # built documents. # # The short X.Y version. -version = '1.0.0.2' +version = '1.0.1' # The full version, including alpha/beta/rc tags. -release = '1.0.0.2' +release = '1.0.1' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/doc/source/contrib.rst b/doc/source/contrib.rst new file mode 100644 index 000000000..7683c3288 --- /dev/null +++ b/doc/source/contrib.rst @@ -0,0 +1,16 @@ +Contribute ancillary code +======================================= + +Ancillary code contributions will most typically be additional analysis and/or plotting code to be used with NetPyNE output files. This is separate from +NetPyNE source code. Contribution to source code is also encouraged and is desribed `here `_. +Sometimes new analysis Python code may also require adding new output formats to NetPyNE so that both contributions may be made together. + +Other ancillary contributions might include new Tutorials for specific purposes or NEURON procedural code for augmenting the declarative code in a +netParams file for complex parameter settings. + +Code should be hosted by the user on a publicly-accessible github page with complete documentation (and strong code commenting). Submissions should then be made in the `NetPyNE forum +`_ with a brief description of the package for listing here. + +Contributed code list +--------------------- + diff --git a/doc/source/index.rst b/doc/source/index.rst index 953b72cb6..63e1e60c1 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -8,7 +8,7 @@ Welcome to the NetPyNE homepage! NetPyNE is an open-source Python package to facilitate the development, parallel simulation, analysis, and optimization of biological neuronal networks using the NEURON simulator. See our recent `publication in eLife `_ for more details. -To make NetPyNE more accessible, we have released a (beta version) graphical user interface (GUI). The GUI is freely available at `www.netpyne.org/gui `_ with documentation `here `_. +To make NetPyNE more accessible, we have released a (beta version) graphical user interface (GUI). The GUI is freely available at `gui.netpyne.org `_ with documentation `here `_. For a detailed overview of NetPyNE, including an interactive tutorial utilizing the GUI, please see our `CNS*2020 `_ tutorial: **Building mechanistic multiscale models: from molecules to networks using NEURON and NetPyNE**. The complete video is available `here `_ and slides are available `here `_. @@ -17,6 +17,8 @@ Check out our latest tutorial: **Virtual Environments, Jupyter Notebooks, and Ne Join our `NetPyNE mailing list `_ to receive updates on version releases and other major announcements. Get your questions answered and participate in the NetPyNE community at the `NetPyNE forum `_ or the `NetPyNE section of the NEURON forum `_. +NetPyNE is available as a resource through `Neuroscience Gateway Portal (NSG) `_; `Open Source Brain (OSB) `_ ; `Human Brain Project (HBP) EBRAINS `_; `NIH SPARC initiative `_. + The NetPyNE source code is available on `GitHub `_. diff --git a/doc/source/install.rst b/doc/source/install.rst index 283385b3f..5a1764b76 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -54,7 +54,7 @@ This version can also be used by developers interested in extending the package. Use a browser-based online version of NetPyNE GUI (beta version) ------------------------------------------------------ -The NetPyNE GUI is available online at: `www.netpyne.org/gui `_. There is a maximum number of simultaneous users for this online version, so if you can't log in, please try again later. +The NetPyNE GUI is available online at: `gui.netpyne.org `_. There is a maximum number of simultaneous users for this online version, so if you can't log in, please try again later. Note: the GUI also includes an interactive Python Jupyter Notebook (click "Python" icon at bottom-left) that you can use to directly run NetPyNE code/models (i.e. without using the actual graphical interface). diff --git a/doc/source/reference.rst b/doc/source/reference.rst index dfbfb407d..2b2ccaf4f 100644 --- a/doc/source/reference.rst +++ b/doc/source/reference.rst @@ -118,7 +118,8 @@ Each item of the ``cellParams`` ordered dictionary consists of a key and a value * **ions**: Dictionary of ions. The key contains the name of the ion (e.g. 'na' or 'k') - The value contains a dictionary with the properties of the ion (e.g. ``{'e': -70}``). + The value contains a dictionary with the properties of the ion for the particular section (e.g. ``{'e': -70}``). + Properties available are ``'e'``: reversal potential, ``'i'``: internal concentration of the ion at that section, and ``'o'``: the extracellular concentration of the ion at that section. * **pointps**: Dictionary of point processes (excluding synaptic mechanisms). The key contains an arbitrary label (e.g. 'Izhi') @@ -572,6 +573,71 @@ String-based functions add great flexibility and power to NetPyNE connectivity r # ... +Sub-cellular connectivity rules - Redistribution of synapses +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Once connections are defined via the ``connParams`` ordered dictionary, it may be necessary to redistribute synapses according to specific profiles along the dendritic tree. For this purpose, the ``subConnParams`` ordered dictionary set the rules and parameters governing this redistribution. Each item in this dictionary consists in a key and a value. The key is a label used as a reference for this redistribution rule. The value is a dictionary setting the parameters for this process and includes the following fields: + +* **preConds / postConds** - Set of conditions for the pre- and post-synaptic cells + + As in the ``connParams`` specifications, these fields provide attributes/tags to select already established connections that satisfy specific conditions on pre- and post-synaptic sides. They are defined as a dictionary with the proper attributes/tags and the required values, e.g. {'cellType': 'PYR'}, {'pop': ['Exc1', 'Exc2']}, {'ynorm': [0.1, 0.6]}. + +* **groupSynMechs (optional)** – List of synaptic mechanisms grouped together when redistributing synapses + + If omitted, post-synaptic locations of all connections (meeting preConds and postConds) are redistributed independently with a given profile (defined below). If a list is provided, synapses of common connections (same pre- and post-synaptic neurons for each mechanism) are relocated to the same location. For example, ['AMPA','NMDA']. + +* **sec (optional)** – List of sections admitting redistributed synapses + + If omitted, the section used to redistribute synapses is the soma or, if it does not exist, the first available section in the post-synaptic cell. For example, ['Adend1','Adend2', 'Adend3','Bdend']. + +* **density** - Type of redistribution. There are a number of predefined rules: + + * ``uniform`` - Each connection meeting conditions is uniformly redistributed along the provided sections, weighted according to their length. + + * dictionary with different options: + + * ``type`` - Type of synaptic density map. Available options: 1Dmap or 2Dmap. + + In addition, it should be included: + + * ``gridY`` – List of positions in y-coordinate (depth) + * ``gridX`` (only for 2Dmap) - List of positions in x-coordinate (or z). + * ``fixedSomaY`` (optional) - Absolute position y-coordinate of the soma, used to shift gridY (also provided in absolute coordinates). + * ``gridValues`` – one or two-dimensional list expressing the (relative) synaptic density in the coordinates defined by (gridX and) gridY. + + For example, + + .. code-block:: python + + netParams.subConnParams[...] = {'type':'1Dmap','gridY': [0,-200,-400,-600,-800], 'fixedSomaY':-700,'gridValues':[0,0.2,0.7,1.0,0]}. + + On the other hand, for this selection, post-synaptic cells need to have a 3d morphology. For simple sections, this can be automatically generated (cylinders along the y-axis oriented upwards) by setting ``netParams.defineCellShapes = True``. + + + * ``distance`` - Synapses relocated at a given distance (in allowed sections) from a reference. + + In addition, it may be included: + + * ``ref_sec`` (optional) – String + + Section used as a reference from which distance is computed. If omitted, the section used to reference distances is the soma or, if it does not exist, anything starting with 'som' or, otherwise, the first available section in the post-synaptic cell. + + * ``ref_seg`` (optional) - Numerical value + + Segment within the section used to reference the distances. If omitted, it is used a default value (0.5). + + * ``target_distance`` (optional) - Target distance from the reference where synapses will be reallocated + + If omitted, this value is set to 0. The chosen location will be the closest to this target, between the allowed sections. + + * ``coord`` (optional) - Coordinates' system used to compute distance. If omitted (or set to 'topol'), the distance is computed along the dendritic tree. Alternatively, it may be used 'cartesian' to calculate the distance in the euclidean space (distance from the reference to the target segment in the cartesian coordinate system). In this case, post-synaptic cells need to have a 3d morphology (or set ``netParams.defineCellShapes = True``). + + For example, + + .. code-block:: python + + netParams.subConnParams[...] = {'type':'distance','ref_sec': 'soma', 'ref_seg': 1,'target_distance': 500}. + + .. _stimulation: Stimulation parameters @@ -657,17 +723,143 @@ Reaction-Diffusion (RxD) parameters The ``rxdParams`` ordered dictionary can be used to define the different RxD components: - * **regions** - dictionary with RxD Regions (also used to define 'extracellular' regions) +* **regions** - Dictionary with RxD Regions (may be also used to define 'extracellular' regions) + + This component is mandatory and is required to define where the reactions are taken place. Each item of the ``rxdParams['regions']`` is a dictionary in which the key specifies the name of the region (to be used in further definitions) and the value is a dictionary containing the following fields: + + * ``cells``: List of cells relevant for the definition of intracellular domains where species, reactions and others need to be specified. This list can include cell gids (e.g. ``[1]`` or ``[0, 3]``), population labels (e.g. ``['S']`` or ``['all']``), or a mix (e.g. ``[['S',[0,2]]]`` or ``[('S',[0,2])]``). + + * ``secs``: List of sections to be included for the cells listed above (to be valid, they should be in the "secs" of the cells). For example, ['soma','Bdend']. + + Both ``cells`` and ``secs`` are used to specify the NEURON Sections where (intracellular) RxD is a relevant component. + + * ``nrn_region``: An option that defines whether the region corresponds to the intracellular/cytosolic domain of the cell (for which the transmembrane voltage is being computed) or not. Available options are: `'i'` (just inside the plasma membrane), `'o'` (just outside the plasma), or `None` (none of the above, for example, an intracellular organelle). + + * ``geometry``: This entry defines the geometry associated to the region. According to different options in NEURON, it can be either a string (`'inside'` or `'membrane'`) or a dictionary with two entries: the `'class'` indicating the kind of geometry (`'DistributedBoundary'`, `'FractionalVolume'`, `'FixedCrossSection'`, `'FixedPerimeter'`, `'ScalableBorder'`, `'Shell'`) and the `'args'` with the particular arguments neccessary to define it, structured in a dictionary. For example, + + .. code-block:: python + + netParams.rxdParams['regions'] = {'membrane_in':{'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}}}. + + * ``dimension``: This is an integer (1 or 3), indicating whether the simulation is 1D or 3D. + + * ``dx``: A float (or int) specifying the discretization. + + * ``extracellular``: Boolean option (``False`` if not specified) indicating whether the region represents the extracellular space or not. If ``True``, all the previous extries should not be specified. Instead, the entries corresponding to ``rxdParams['extracellular']`` (see next) has to be considered, but at the same level of the dictionary hierarchy. For example, ``rxdParams['regions']={'ext':{'extracellular':True, 'xlo': -100, ...}}``. + + + Example, + + .. code-block:: python + + netParams.rxdParams['regions'] = {'cyt':{'cells': ['all'], 'secs': ['soma','Bdend'], 'nrn_region': 'i'}} + + +* **extracellular** - Dictionary with the parameters neccessary to specify the RxD Extracellular region. + + * ``xlo``, ``ylo``, ``zlo``: Values indicating the left-bottom-back corner of the box specifying the extracellular domain. - * **species** - dictionary with RxD Species + * ``xhi``, ``yhi``, ``zhi``: Values indicating the right-upper-front corner of the box specifying the extracellular domain. - * **states** - dictionary with RxD States + * ``dx``: Value (int, float) specifying the discretization. In this case, the extracellular region, it could be a 3D-tuple if other than square voxels are needed. + + The previous entries are mandatory. Next ones are optional (default values are considered, see NEURON). + + * ``volume_fraction``: Value indicating the available space to diffuse. + + * ``tortuosity``: Value indicating how restricted are the stright pathways to diffuse. + + For example, + + .. code-block:: python + + netParams.rxdParams['extracellular'] = {'xlo':-100, 'ylo':-100, 'zlo':-100, 'xhi':100, 'yhi':100, 'zhi':100, 'dx':(0.2,0.2,0.4), 'volume_fraction':0.2, 'tortuosity': 1.6}. + + +* **species** - This component is also mandatory and it corresponds to a dictionary with all the definitions to specify relevant species and the domains where they are involved. The key is the name/label of the species and the value is a dictionary with the following entries: + + * ``regions``: A list of the regions (listed in ``rxdParams['regions']``) where the species are present. If it is a single region, it may be specified without listing. For example, ``'cyt'`` or ``['cyt','er']``. + + * ``d``: Diffusion coefficient of the species. + + * ``charge``: Signed charge, if any, of the species. + + * ``initial``: Initial state of the concentration field, in mM. It may be a single value for all of its definition domain or a string-based function, where the variable is a node (in RxD's framework) property. For example, ``'1 if (0.4 < node.x < 0.6) else 0'``. + + * ``ecs_boundary_conditions``: If an Extracellular region is defined, boundary conditions should be given. Options are ``None`` (default) for zero flux condition (Neumann type) or a value indicating the concentration at the boundary (Dirichlet). + + * ``atolscale``: A number (default = 1) indicating the scale factor for absolute tolerance in variable step integrations for this particular species' concentration. + + * ``name``: A string labeling this species. Important when RxD will be sharing species with hoc models, as this name has to be the same as the NEURON range variable. + + + Example, + + .. code-block:: python + + netParams.rxdParams['species'] = {'ca':{'regions': 'cyt', 'd': 0.25, 'charge': 2, 'name': 'ca', 'initial': '1 if node.sec in ['Bdend'] else 0'}} + + +* **states** - Dictionary declaring State variables that evolve, through other than reactions, during the simulation. The key is the name assigned to this variable and the value is a dictionary with the following entries: + + * ``regions``: A list of the regions where the State variable is relevant (i.e. it evolves there). If it is a single region, it may be specified without listing. + + * ``initial``: Initial state of this variable. Either a single-value valid in the entire domain (where this variable is specified) or a string-based function with node properties as the independent variable. + + * ``name``: A string internally labeling this variable. + + Example, + + .. code-block:: python + + netParams.rxdParams['states'] = {'mgate':{'regions': 'cyt', 'initial': 0.05, 'name': 'mgate'}} + + +* **reactions** - Dictionary specifying the reactions, who and where, under analysis. The key labels the reaction and the value is a dictionary with the following entries: + + * ``reactant``: A string declaring the left-hand side of the chemical reaction, with the species and the proper stechiometry. For example, ``ca + 2 * cl``, where 'ca' and 'cl' are defined in the 'species' entry and are available in the region where the reaction takes place (see next). + + * ``product``: The same, for the right-hand side of the chemical reaction. For example, ``cacl2``, where 'cacl2' is a species properly defined. + + * ``rate_f``: Rate for the forward reaction, for the scheme defined above. It can be either a numerical value or a string-based function (depending on species, etcetera; for example to implement a Hill equation). + + * ``rate_b``: Same as above, for the backward reaction. This entry is optional. + + * ``regions``: This entry is used to constrain the reaction to proceed only in a list of regions. If it is a single region, it may be specified without listing. If not provided, the reaction proceeds in all (plausible) regions. + + * ``custom_dynamics``: This boolean entry specifies whether law of mass-action for elementary reactions does apply or not. If 'True', dynamics of each species' concentration satisfy a mass-action scheme. + + Example, + + .. code-block:: python + + netParams.rxdParams['reactions'] = {'phosphorylation':{'reactant': 'E', 'product': 'EP', 'rate_f': 'kmax1 * E/ (k1 + E)', 'rate_b': 'kmax2 * EP/ (k2 + EP)','custom_dynamics': True}} + + +* **multicompartmentReactions** - Dictionary specifying reactions with species belonging to different regions. As in the previous case, the key labels the reaction and the value is a dictionary with exactly the same entries as before plus two further (optional) entries: + + * ``membrane``: The region (with a geometry compatible with a membrane or a border) involved in the passage of ions from one region to another. + + * ``membrane_flux``: This boolen entry indicates whether the reaction produces a current across the plasma membrane that should affect the membrane potential. +. + Note: Take into account that species appearing in the 'reactant' or 'product' entries should be specified along with the region from which they are taken in the reaction scheme. For example, ``'ca[cyt]'``. + +* **rates** - Dictionary specifying rates controlling the dynamics of selected species or states. The key labels the dynamical scheme and the value is a dictionary with the following entries: + + * ``species``: A string indicating which species or states is being considered. + + * ``rate``: Value for the rate in the dynamical equation governing the temporal evolution of the species/state. + + * ``regions``: This entry is used to constrain the dynamics to proceed only in a list of regions. If it is a single region, it may be specified without listing. + + * ``membrane_flux``: As before, a boolean entry specifying whether a current should be considered or not. If 'True' the 'region' entry should correspond to a unique region with a membrane-like geometry. + + Example, - * **reactions** - dictionary with RxD Reactions + .. code-block:: python - * **multicompartmentReactions** - dictionary with RxD MultiCompartmentReactions + netParams.rxdParams['rates'] = {'h_evol':{'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - h_gate) / tau'}} - * **rates** - dictionary with RxD Rates The parameters of each dictionary follow the same structure as described in the RxD package: https://www.neuron.yale.edu/neuron/static/docs/rxd/index.html @@ -717,7 +909,7 @@ Related to recording: * **recordSpikesGids** - List of cells to record spike times from (-1 to record from all). Can include cell gids (e.g. 5), population labels (e.g. 'S' to record from one cell of the 'S' population), or 'all', to record from all cells. (default: -1) * **recordStim** - Record spikes of cell stims (default: False) * **recordLFP** - 3D locations of local field potential (LFP) electrodes, e.g. [[50, 100, 50], [50, 200, 50]] (note the y coordinate represents depth, so will be represented as a negative value when plotted). The LFP signal in each electrode is obtained by summing the extracellular potential contributed by each neuronal segment, calculated using the "line source approximation" and assuming an Ohmic medium with conductivity |sigma| = 0.3 mS/mm. Stored in ``sim.allSimData['LFP']``. (default: False). -* **saveLFPCells** - Store LFP generated individually by each cell in ``sim.allSimData['LFPCells']`` +* **saveLFPCells** - Store LFP generated individually by each cell in ``sim.allSimData['LFPCells']``; can select a subset of cells to save e.g. [3, 'PYR', ('PV2', 5)] * **recordStep** - Step size in ms for data recording (default: 0.1) Related to file saving: @@ -1262,4 +1454,4 @@ Data saved to file * simConfig * netParams * net -* simData +* simData \ No newline at end of file diff --git a/examples/CA3model_3pops/00-Model_CA3.py b/examples/CA3model_3pops/00-Model_CA3.py new file mode 100644 index 000000000..dd7736e43 --- /dev/null +++ b/examples/CA3model_3pops/00-Model_CA3.py @@ -0,0 +1,348 @@ +from netpyne import specs, sim + +Dt = 0.1 +Tfinal = 1500 + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters +netParams.defaultThreshold = 0.0 +netParams.defineCellShapes = True # sets 3d geometry aligned along the y-axis + + +############################################################################### +## Cell types +############################################################################### +# Basket cell +BasketCell = {'secs':{}} +BasketCell['secs']['soma'] = {'geom': {}, 'mechs': {}} +BasketCell['secs']['soma']['geom'] = {'diam': 100, 'L': 31.831, 'nseg': 1, 'cm': 1} +BasketCell['secs']['soma']['mechs'] = {'pas': {'g': 0.1e-3, 'e': -65}, 'Nafbwb': {}, 'Kdrbwb': {}} +netParams.cellParams['BasketCell'] = BasketCell + + +# OLM cell +OlmCell = {'secs':{}} +OlmCell['secs']['soma'] = {'geom': {}, 'mechs': {}} +OlmCell['secs']['soma']['geom'] = {'diam': 100, 'L': 31.831, 'nseg': 1, 'cm': 1} +OlmCell['secs']['soma']['mechs'] = { + 'pas': {'g': 0.1e-3, 'e': -65}, + 'Nafbwb': {}, + 'Kdrbwb': {}, + 'Iholmw': {}, + 'Caolmw': {}, + 'ICaolmw': {}, + 'KCaolmw': {}} +netParams.cellParams['OlmCell'] = OlmCell + + +# Pyramidal cell +PyrCell = {'secs':{}} +PyrCell['secs']['soma'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'cm': 1, 'Ra': 150} +PyrCell['secs']['soma']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {}, + 'kacurrent': {}, + 'kdrcurrent': {}, + 'hcurrent': {}} +PyrCell['secs']['Bdend'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Bdend']['geom'] = {'diam': 2, 'L': 200, 'cm': 1, 'Ra': 150} +PyrCell['secs']['Bdend']['topol'] = {'parentSec': 'soma', 'parentX': 0, 'childX': 0} +PyrCell['secs']['Bdend']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {'ki': 1}, + 'kacurrent': {}, + 'kdrcurrent': {}, + 'hcurrent': {}} +PyrCell['secs']['Adend1'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Adend1']['geom'] = {'diam': 2, 'L': 150, 'cm': 1, 'Ra': 150} +PyrCell['secs']['Adend1']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # here there is a change: connected to end soma(1) instead of soma(0.5) +PyrCell['secs']['Adend1']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {'ki': 0.5}, + 'kacurrent': {'g': 0.072}, + 'kdrcurrent': {}, + 'hcurrent': {'v50': -82, 'g': 0.0002}} +PyrCell['secs']['Adend2'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Adend2']['geom'] = {'diam': 2, 'L': 150, 'cm': 1, 'Ra': 150} +PyrCell['secs']['Adend2']['topol'] = {'parentSec': 'Adend1', 'parentX': 1, 'childX': 0} +PyrCell['secs']['Adend2']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {'ki': 0.5}, + 'kacurrent': {'g': 0, 'gd': 0.120}, + 'kdrcurrent': {}, + 'hcurrent': {'v50': -90, 'g': 0.0004}} +PyrCell['secs']['Adend3'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Adend3']['geom'] = {'diam': 2, 'L': 150, 'cm': 2, 'Ra': 150} +PyrCell['secs']['Adend3']['topol'] = {'parentSec': 'Adend2', 'parentX': 1, 'childX': 0} +PyrCell['secs']['Adend3']['mechs'] = { + 'pas': {'g': 0.0000714, 'e': -70}, + 'nacurrent': {'ki': 0.5}, + 'kacurrent': {'g': 0, 'gd': 0.200}, + 'kdrcurrent': {}, + 'hcurrent': {'v50': -90, 'g': 0.0007}} +netParams.cellParams['PyrCell'] = PyrCell + + +############################################################################### +## Synaptic mechs +############################################################################### + +netParams.synMechParams['AMPAf'] = {'mod': 'MyExp2SynBB', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} +netParams.synMechParams['NMDA'] = {'mod': 'MyExp2SynNMDABB', 'tau1': 0.05, 'tau2': 5.3, 'tau1NMDA': 15, 'tau2NMDA': 150, 'r': 1, 'e': 0} +netParams.synMechParams['GABAf'] = {'mod': 'MyExp2SynBB', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} +netParams.synMechParams['GABAs'] = {'mod': 'MyExp2SynBB', 'tau1': 0.2, 'tau2': 20, 'e': -80} +netParams.synMechParams['GABAss'] = {'mod': 'MyExp2SynBB', 'tau1': 20, 'tau2': 40, 'e': -80} + + +############################################################################### +## Populations +############################################################################### +netParams.popParams['PYR'] = {'cellType': 'PyrCell', 'numCells': 800} +netParams.popParams['BC'] = {'cellType': 'BasketCell', 'numCells': 200} +netParams.popParams['OLM'] = {'cellType': 'OlmCell', 'numCells': 200} + + +############################################################################### +# Current-clamp to cells +############################################################################### +netParams.stimSourceParams['IClamp_PYR'] = {'type': 'IClamp', 'del': 2*Dt, 'dur': 1e9, 'amp': 50e-3} +netParams.stimSourceParams['IClamp_OLM'] = {'type': 'IClamp', 'del': 2*Dt, 'dur': 1e9, 'amp': -25e-3} + +netParams.stimTargetParams['IClamp_PYR->PYR'] = { + 'source': 'IClamp_PYR', + 'sec': 'soma', + 'loc': 0.5, + 'conds': {'pop': 'PYR'}} + +netParams.stimTargetParams['IClamp_OLM->OLM'] = { + 'source': 'IClamp_OLM', + 'sec': 'soma', + 'loc': 0.5, + 'conds': {'pop': 'OLM'}} + + +############################################################################### +# Setting connections +############################################################################### + +# PYR -> X, NMDA +netParams.connParams['PYR->BC_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'BC'}, + 'convergence': 100, + 'weight': 1.38e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'NMDA'} + +netParams.connParams['PYR->OLM_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'OLM'}, + 'convergence': 10, + 'weight': 0.7e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'NMDA'} + +netParams.connParams['PYR->PYR_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 25, + 'weight': 0.004e-3, + 'delay': 2, + 'sec': 'Bdend', + 'loc': 1.0, + 'synMech': 'NMDA'} + +# PYR -> X, AMPA +netParams.connParams['PYR->BC_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'BC'}, + 'convergence': 100, + 'weight': 0.36e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'AMPAf'} + +netParams.connParams['PYR->OLM_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'OLM'}, + 'convergence': 10, + 'weight': 0.36e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'AMPAf'} + +netParams.connParams['PYR->PYR_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 25, + 'weight': 0.02e-3, + 'delay': 2, + 'sec': 'Bdend', + 'loc': 1.0, + 'synMech': 'AMPAf'} + +# BC -> X, GABA +netParams.connParams['BC->BC_GABA'] = {'preConds': {'pop': 'BC'}, 'postConds': {'pop': 'BC'}, + 'convergence': 60, + 'weight':4.5e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'GABAf'} + +netParams.connParams['BC->PYR_GABA'] = {'preConds': {'pop': 'BC'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 50, + 'weight': 0.72e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'GABAf'} + + +# OLM -> PYR, GABA +netParams.connParams['OLM->PYR_GABA'] = {'preConds': {'pop': 'OLM'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 20, + 'weight': 72e-3, + 'delay': 2, + 'sec': 'Adend2', + 'loc': 0.5, + 'synMech': 'GABAs'} + + +############################################################################### +# Setting NetStims +############################################################################### +# to PYR +netParams.stimSourceParams['NetStim_PYR_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_SOMA_AMPA->PYR'] = { + 'source': 'NetStim_PYR_SOMA_AMPA', + 'conds': {'pop': 'PYR'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 4*0.05e-3, # different from published value + 'delay': 2*Dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_PYR_ADEND3_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_ADEND3_AMPA->PYR'] = { + 'source': 'NetStim_PYR_ADEND3_AMPA', + 'conds': {'pop': 'PYR'}, + 'sec': 'Adend3', + 'loc': 0.5, + 'weight': 4*0.05e-3, # different from published value + 'delay': 2*Dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_PYR_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_SOMA_GABA->PYR'] = { + 'source': 'NetStim_PYR_SOMA_GABA', + 'conds': {'pop': 'PYR'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.012e-3, + 'delay': 2*Dt, + 'synMech': 'GABAf'} + +netParams.stimSourceParams['NetStim_PYR_ADEND3_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_ADEND3_GABA->PYR'] = { + 'source': 'NetStim_PYR_ADEND3_GABA', + 'conds': {'pop': 'PYR'}, + 'sec': 'Adend3', + 'loc': 0.5, + 'weight': 0.012e-3, + 'delay': 2*Dt, + 'synMech': 'GABAf'} + +netParams.stimSourceParams['NetStim_PYR_ADEND3_NMDA'] = {'type': 'NetStim', 'interval': 100, 'number': int((1000/100.0)*Tfinal), 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_ADEND3_NMDA->PYR'] = { + 'source': 'NetStim_PYR_ADEND3_NMDA', + 'conds': {'pop': 'PYR'}, + 'sec': 'Adend3', + 'loc': 0.5, + 'weight': 6.5e-3, + 'delay': 2*Dt, + 'synMech': 'NMDA'} + +# to BC +netParams.stimSourceParams['NetStim_BC_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_BC_SOMA_AMPA->BC'] = { + 'source': 'NetStim_BC_SOMA_AMPA', + 'conds': {'pop': 'BC'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.02e-3, + 'delay': 2*Dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_BC_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_BC_SOMA_GABA->BC'] = { + 'source': 'NetStim_BC_SOMA_GABA', + 'conds': {'pop': 'BC'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.2e-3, + 'delay': 2*Dt, + 'synMech': 'GABAf'} + +# to OLM +netParams.stimSourceParams['NetStim_OLM_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_OLM_SOMA_AMPA->OLM'] = { + 'source': 'NetStim_OLM_SOMA_AMPA', + 'conds': {'pop': 'OLM'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.0625e-3, + 'delay': 2*Dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_OLM_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*Tfinal, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_OLM_SOMA_GABA->OLM'] = { + 'source': 'NetStim_OLM_SOMA_GABA', + 'conds': {'pop': 'OLM'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.2e-3, + 'delay': 2*Dt, + 'synMech': 'GABAf'} + +# Medial Septal inputs to BC and OLM cells +netParams.stimSourceParams['Septal'] = {'type': 'NetStim', 'interval': 150, 'number': int((1000/150)*Tfinal), 'start': 0, 'noise': 0} +netParams.stimTargetParams['Septal->BC'] = { + 'source': 'Septal', + 'conds': {'pop': 'BC'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 1.6e-3, + 'delay': 2*Dt, + 'synMech': 'GABAss'} + +netParams.stimTargetParams['Septal->OLM'] = { + 'source': 'Septal', + 'conds': {'pop': 'OLM'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 1.6e-3, + 'delay': 2*Dt, + 'synMech': 'GABAss'} + + + +############################################################################### +# Simulation options +############################################################################### +simConfig = specs.SimConfig() + +simConfig.duration = Tfinal +simConfig.dt = Dt +simConfig.params = {'v_init': -65.0} +simConfig.verbose = False +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record +simConfig.recordStim = True +simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = '00' # Set file output name +simConfig.savePickle = False # Save params, network and sim output to pickle file +simConfig.saveDat = False +simConfig.recordLFP = [[50, 50, 50]] + +simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'timeRange': [250,1250], 'saveFig': True} +simConfig.analysis['plotRaster'] = {'saveFig': True} +simConfig.analysis['plotTraces'] = {'include': [0, 1, 800, 801, 1000, 1001], 'saveFig': True} # Plot recorded traces for this list of cells +#simConfig.analysis['plotShape'] = {'includePre': ['all'],'includePost': [0,800,1000],'cvar':'numSyns','dist':0.7, 'saveFig': True} + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) diff --git a/examples/CA3model_3pops/CA1ih.mod b/examples/CA3model_3pops/CA1ih.mod new file mode 100644 index 000000000..93d435e30 --- /dev/null +++ b/examples/CA3model_3pops/CA1ih.mod @@ -0,0 +1,64 @@ +: $Id: CA1ih.mod,v 1.4 2010/12/13 21:35:47 samn Exp $ +TITLE Ih CA3 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX hcurrent + NONSPECIFIC_CURRENT ih + RANGE g, e, v50, htau, hinf + RANGE gfactor +} + +PARAMETER { + celsius (degC) + g= 0.0001 (mho/cm2) + e= -30 (mV) + v50=-82 (mV) + gfactor = 1 +} + +STATE { + h +} + +ASSIGNED { + ih (mA/cm2) + hinf + htau (ms) + v (mV) +} + +PROCEDURE iassign () { ih=g*h*(v-e)*gfactor } + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + h'= (hinf- h)/ htau +} + +INITIAL { + rates(v) + h = hinf + iassign() +} + +PROCEDURE rates(v (mV)) { + UNITSOFF + : HCN1 + :hinf = 1/(1+exp(0.151*(v-v50))) + :htau = exp((0.033*(v+75)))/(0.011*(1+exp(0.083*(v+75)))) + + : HCN2 + hinf = 1/(1+exp((v-v50)/10.5)) + htau = (1/(exp(-14.59-0.086*v)+exp(-1.87+0.0701*v))) + UNITSON +} + diff --git a/examples/CA3model_3pops/CA1ika.mod b/examples/CA3model_3pops/CA1ika.mod new file mode 100644 index 000000000..9e4fe6922 --- /dev/null +++ b/examples/CA3model_3pops/CA1ika.mod @@ -0,0 +1,85 @@ +: $Id: CA1ika.mod,v 1.2 2010/12/01 05:06:07 samn Exp $ +TITLE Ika CA1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX kacurrent + NONSPECIFIC_CURRENT ika, ikad + RANGE g, gd, e, ninf, ntau, ndinf, ndtau, linf, ltau +} + +PARAMETER { + celsius (degC) + g= 0.048 (mho/cm2) + gd= 0 (mho/cm2) + e= -90 (mV) +} + +STATE { + n + nd : distal + l +} + +ASSIGNED { + v (mV) + ika (mA/cm2) + ikad (mA/cm2) + ninf + ntau (ms) + ndinf + ndtau (ms) + linf + ltau (ms) +} + +PROCEDURE iassign () { + ika=g*n*l*(v-e) + ikad=gd*nd*l*(v-e) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + n'= (ninf- n)/ ntau + l'= (linf- l)/ ltau + nd'= (ndinf-nd)/ndtau +} + +INITIAL { + rates(v) + n = ninf + l = linf + iassign() +} + +PROCEDURE rates(v (mV)) { + LOCAL a, b + UNITSOFF + a = exp(-0.038*(1.5+1/(1+exp(v+40)/5))*(v-11)) + b = exp(-0.038*(0.825+1/(1+exp(v+40)/5))*(v-11)) + ntau=4*b/(1+a) + if (ntau<0.1) {ntau=0.1} + ninf=1/(1+a) + + a=exp(-0.038*(1.8+1/(1+exp(v+40)/5))*(v+1)) + b=exp(-0.038*(0.7+1/(1+exp(v+40)/5))*(v+1)) + ndtau=2*b/(1+a) + if (ndtau<0.1) {ndtau=0.1} + ndinf=1/(1+a) + + a = exp(0.11*(v+56)) + ltau=0.26*(v+50) + if (ltau<2) {ltau=2} + linf=1/(1+a) + UNITSON +} + diff --git a/examples/CA3model_3pops/CA1ikdr.mod b/examples/CA3model_3pops/CA1ikdr.mod new file mode 100644 index 000000000..4c5236362 --- /dev/null +++ b/examples/CA3model_3pops/CA1ikdr.mod @@ -0,0 +1,60 @@ +: $Id: CA1ikdr.mod,v 1.2 2010/12/01 05:10:52 samn Exp $ +TITLE IKDR CA1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX kdrcurrent + NONSPECIFIC_CURRENT ik + RANGE g, e, ninf, ntau +} + +PARAMETER { + celsius (degC) + g = 0.010 (mho/cm2) + e = -90 (mV) +} + +STATE { + n +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + ninf + ntau (ms) +} + +PROCEDURE iassign () { ik=g*n*(v-e) } + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + n'= (ninf- n)/ ntau +} + +INITIAL { + rates(v) + n = ninf + iassign() +} + +PROCEDURE rates(v (mV)) { + LOCAL a, b + UNITSOFF + a = exp(-0.11*(v-13)) + b = exp(-0.08*(v-13)) + ntau=50*b/(1+a) + if (ntau<2) {ntau=2} + ninf=1/(1+a) + UNITSON +} + diff --git a/examples/CA3model_3pops/CA1ina.mod b/examples/CA3model_3pops/CA1ina.mod new file mode 100644 index 000000000..d33ab9739 --- /dev/null +++ b/examples/CA3model_3pops/CA1ina.mod @@ -0,0 +1,89 @@ +: $Id: CA1ina.mod,v 1.4 2010/11/30 19:50:00 samn Exp $ +TITLE INa CA1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX nacurrent + NONSPECIFIC_CURRENT ina + RANGE g, e, vi, ki + RANGE minf,hinf,iinf,mtau,htau,itau : testing +} + +PARAMETER { + : v (mV) + celsius (degC) + g = 0.032 (mho/cm2) + e = 55 (mV) + vi = -60 (mV) + ki = 0.8 +} + +STATE { + m + h + I : i +} + +ASSIGNED { + i (mA/cm2) + ina (mA/cm2) + minf + mtau (ms) + hinf + htau (ms) + iinf + itau (ms) + v (mV) : testing +} + +: PROCEDURE iassign () { ina=g*m*m*m*h*i*(v-e) } +PROCEDURE iassign () { i=g*m*m*m*h*I*(v-e) ina=i} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + m' = (minf - m) / mtau + h' = (hinf - h) / htau + : i' = (iinf - i) / itau + I' = (iinf - I) / itau +} + +INITIAL { + rates(v) + h = hinf + m = minf + : i = iinf + I = iinf + iassign() : testing +} + + +PROCEDURE rates(v (mV)) { + LOCAL a, b + UNITSOFF + a = 0.4*(v+30)/(1-exp(-(v+30)/7.2)) + b = 0.124*(v+30)/(exp((v+30)/7.2)-1) + mtau=0.5/(a+b) + if (mtau<0.02) {mtau=0.02} + minf=a/(a+b) + a = 0.03*(v+45)/(1-exp(-(v+45)/1.5)) + b = 0.01*(v+45)/(exp((v+45)/1.5)-1) + htau=0.5/(a+b) + if (htau<0.5) {htau=0.5} + hinf=1/(1+exp((v+50)/4)) + a = exp(0.45*(v+66)) + b = exp(0.09*(v+66)) + itau=3000*b/(1+a) + if (itau<10) {itau=10} + iinf=(1+ki*exp((v-vi)/2))/(1+exp((v-vi)/2)) + UNITSON +} + diff --git a/examples/CA3model_3pops/MyExp2SynBB.mod b/examples/CA3model_3pops/MyExp2SynBB.mod new file mode 100644 index 000000000..9a68baef1 --- /dev/null +++ b/examples/CA3model_3pops/MyExp2SynBB.mod @@ -0,0 +1,67 @@ +: $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ +NEURON { +: THREADSAFE + POINT_PROCESS MyExp2SynBB + RANGE tau1, tau2, e, i, g, Vwt, gmax + NONSPECIFIC_CURRENT i +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) +} + +PARAMETER { + tau1=.1 (ms) <1e-9,1e9> + tau2 = 10 (ms) <1e-9,1e9> + e=0 (mV) + gmax = 1e9 (uS) + Vwt = 0 : weight for inputs coming in from vector +} + +ASSIGNED { + v (mV) + i (nA) + g (uS) + factor + etime (ms) +} + +STATE { + A (uS) + B (uS) +} + +INITIAL { + LOCAL tp + + Vwt = 0 : testing + + if (tau1/tau2 > .9999) { + tau1 = .9999*tau2 + } + A = 0 + B = 0 + tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1) + factor = -exp(-tp/tau1) + exp(-tp/tau2) + factor = 1/factor +} + +BREAKPOINT { + SOLVE state METHOD cnexp + g = B - A + if (g>gmax) {g=gmax}: saturation + i = g*(v - e) +} + +DERIVATIVE state { + A' = -A/tau1 + B' = -B/tau2 +} + +NET_RECEIVE(w (uS)) {LOCAL ww + ww=w + A = A + ww*factor + B = B + ww*factor +} diff --git a/examples/CA3model_3pops/MyExp2SynNMDABB.mod b/examples/CA3model_3pops/MyExp2SynNMDABB.mod new file mode 100644 index 000000000..01291643a --- /dev/null +++ b/examples/CA3model_3pops/MyExp2SynNMDABB.mod @@ -0,0 +1,108 @@ +: $Id: MyExp2SynNMDABB.mod,v 1.4 2010/12/13 21:28:02 samn Exp $ +NEURON { +: THREADSAFE + POINT_PROCESS MyExp2SynNMDABB + RANGE tau1, tau2, e, i, iNMDA, s, sNMDA, r, tau1NMDA, tau2NMDA, Vwt, smax, sNMDAmax + NONSPECIFIC_CURRENT i, iNMDA +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) +} + +PARAMETER { + tau1 = 0.1 (ms) <1e-9,1e9> + tau2 = 10 (ms) <1e-9,1e9> + tau1NMDA = 15 (ms) + tau2NMDA = 150 (ms) + e = 0 (mV) + mg = 1 + r = 1 + smax = 1e9 (1) + sNMDAmax = 1e9 (1) + + Vwt = 0 : weight for inputs coming in from vector +} + +ASSIGNED { + v (mV) + i (nA) + iNMDA (nA) + s (1) + sNMDA (1) + mgblock (1) + factor (1) + factor2 (1) + + etime (ms) +} + +STATE { + A (1) + B (1) + A2 (1) + B2 (1) +} + +INITIAL { + + LOCAL tp + + Vwt = 0 : testing + + if (tau1/tau2 > .9999) { + tau1 = .9999*tau2 + } + A = 0 + B = 0 + tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1) + factor = -exp(-tp/tau1) + exp(-tp/tau2) + factor = 1/factor + + if (tau1NMDA/tau2NMDA > .9999) { + tau1NMDA = .9999*tau2NMDA + } + A2 = 0 + B2 = 0 + tp = (tau1NMDA*tau2NMDA)/(tau2NMDA - tau1NMDA) * log(tau2NMDA/tau1NMDA) + factor2 = -exp(-tp/tau1NMDA) + exp(-tp/tau2NMDA) + factor2 = 1/factor2 +} + +BREAKPOINT { + SOLVE state METHOD cnexp + : Jahr Stevens 1990 J. Neurosci + mgblock = 1.0 / (1.0 + 0.28 * exp(-0.062(/mV) * v) ) + s = B - A + sNMDA = B2 - A2 + if (s >smax) {s =smax }: saturation + if (sNMDA>sNMDAmax) {sNMDA=sNMDAmax}: saturation + i = s * (v - e) + iNMDA = sNMDA * (v - e) * mgblock +} + +DERIVATIVE state { + A' = -A/tau1 + B' = -B/tau2 + A2' = -A2/tau1NMDA + B2' = -B2/tau2NMDA +} + +NET_RECEIVE(w (uS)) {LOCAL ww + ww=w + :printf("NMDA Spike: %g\n", t) + if(r>=0){ : if r>=0, g = AMPA + NMDA*r + A = A + factor *ww + B = B + factor *ww + A2 = A2 + factor2*ww*r + B2 = B2 + factor2*ww*r + }else{ + if(r>-1000){ : if r>-1, g = NMDA*r + A2 = A2 - factor2*ww*r + B2 = B2 - factor2*ww*r + } + : if r<0 and r<>-1, g = 0 + } +} diff --git a/examples/CA3model_3pops/aux_fun.inc b/examples/CA3model_3pops/aux_fun.inc new file mode 100644 index 000000000..ccb579afb --- /dev/null +++ b/examples/CA3model_3pops/aux_fun.inc @@ -0,0 +1,43 @@ +: $Id: aux_fun.inc,v 1.1 2009/11/04 01:24:52 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + + + +:------------------------------------------------------------------- +FUNCTION fun1(v(mV),V0(mV),A(/ms),B(mV))(/ms) { + + fun1 = A*exp((v-V0)/B) +} + +FUNCTION fun2(v(mV),V0(mV),A(/ms),B(mV))(/ms) { + + fun2 = A/(exp((v-V0)/B)+1) +} + +FUNCTION fun3(v(mV),V0(mV),A(/ms),B(mV))(/ms) { + + if(fabs((v-V0)/B)<1e-6) { + :if(v==V0) { + fun3 = A*B/1(mV) * (1- 0.5 * (v-V0)/B) + } else { + fun3 = A/1(mV)*(v-V0)/(exp((v-V0)/B)-1) + } +} + +FUNCTION min(x,y) { if (x<=y){ min = x }else{ min = y } } +FUNCTION max(x,y) { if (x>=y){ max = x }else{ max = y } } diff --git a/examples/CA3model_3pops/caolmw.mod b/examples/CA3model_3pops/caolmw.mod new file mode 100644 index 000000000..3ea21a7ef --- /dev/null +++ b/examples/CA3model_3pops/caolmw.mod @@ -0,0 +1,47 @@ +: $Id: caolmw.mod,v 1.2 2010/11/30 16:40:09 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mollar) = (1/liter) + (M) = (mollar) + (mM) = (millimollar) + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Caolmw + USEION ca READ ica, cai WRITE cai + RANGE alpha, tau +} + +PARAMETER { + alpha = 0.002 (cm2-M/mA-ms) + tau = 80 (ms) +} + +ASSIGNED { ica (mA/cm2) } + +INITIAL { cai = 0 } + +STATE { cai (mM) } + +BREAKPOINT { SOLVE states METHOD cnexp } + +DERIVATIVE states { cai' = -(1000) * alpha * ica - cai/tau } diff --git a/examples/CA3model_3pops/cfg.py b/examples/CA3model_3pops/cfg.py new file mode 100644 index 000000000..e96ea7232 --- /dev/null +++ b/examples/CA3model_3pops/cfg.py @@ -0,0 +1,21 @@ +from netpyne import specs + +cfg = specs.SimConfig() + +cfg.duration = 750 +cfg.dt = 0.1 +cfg.hparams = {'v_init': -65.0} +cfg.verbose = False +cfg.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} # Dict with traces to record +cfg.recordStim = False +cfg.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +cfg.filename = '00' # Set file output name +cfg.savePickle = False # Save params, network and sim output to pickle file +cfg.saveDat = False +cfg.printRunTime = 0.1 +cfg.recordLFP = [[50, 50, 50]] + +cfg.analysis['plotRaster'] = {'saveFig': True} +cfg.analysis['plotTraces'] = {'include': [0, 1, 800, 801, 1000, 1001], 'saveFig': True} # Plot recorded traces for this list of cells +cfg.analysis['plotLFPTimeSeries'] = {'showFig': True, 'saveFig': True} +#cfg.analysis['plotShape'] = {'includePre': ['all'],'includePost': [0,800,1000],'cvar':'numSyns','dist':0.7, 'saveFig': True} diff --git a/examples/CA3model_3pops/icaolmw.mod b/examples/CA3model_3pops/icaolmw.mod new file mode 100644 index 000000000..51112d099 --- /dev/null +++ b/examples/CA3model_3pops/icaolmw.mod @@ -0,0 +1,51 @@ +: $Id: icaolmw.mod,v 1.2 2010/11/30 16:44:13 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX ICaolmw + USEION ca WRITE ica + RANGE gca,eca +} + +PARAMETER { + gca = 1 (mS/cm2) + eca = 120 (mV) +} + +ASSIGNED { + ica (mA/cm2) + v (mV) +} + +PROCEDURE iassign () { ica = (1e-3) * gca * mcainf(v)^2 * (v-eca) } + +INITIAL { + iassign() +} + +BREAKPOINT { iassign() } + +FUNCTION mcainf(v(mV)) { mcainf = fun2(v, -20, 1, -9)*1(ms) } + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/iholmw.mod b/examples/CA3model_3pops/iholmw.mod new file mode 100644 index 000000000..ccd919202 --- /dev/null +++ b/examples/CA3model_3pops/iholmw.mod @@ -0,0 +1,60 @@ +: $Id: iholmw.mod,v 1.2 2010/11/30 16:34:22 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Iholmw + NONSPECIFIC_CURRENT i + RANGE gh,eh +} + +PARAMETER { + gh = 0.15 (mS/cm2) + eh = -40 (mV) +} + +ASSIGNED { + v (mV) + i (mA/cm2) +} + +STATE { q } + +PROCEDURE iassign () { i = (1e-3) * gh * q * (v-eh) } + +INITIAL { + q = qinf(v) + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { q' = (qinf(v)-q)/qtau(v) } + +FUNCTION qinf(v(mV)) { qinf = fun2(v, -80, 1, 10)*1(ms) } +FUNCTION qtau(v(mV))(ms) { qtau = 200(ms)/(exp((v+70(mV))/20(mV))+exp(-(v+70(mV))/20(mV))) + 5(ms) } + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/init.py b/examples/CA3model_3pops/init.py new file mode 100644 index 000000000..bfda5e710 --- /dev/null +++ b/examples/CA3model_3pops/init.py @@ -0,0 +1,11 @@ +""" +init.py + +Starting script to run NetPyNE-based CA3 model. +""" + +from netpyne import sim +from cfg import cfg +from netParams import netParams + +sim.createSimulateAnalyze(netParams, cfg) diff --git a/examples/CA3model_3pops/kcaolmw.mod b/examples/CA3model_3pops/kcaolmw.mod new file mode 100644 index 000000000..b2368787e --- /dev/null +++ b/examples/CA3model_3pops/kcaolmw.mod @@ -0,0 +1,52 @@ +: $Id: kcaolmw.mod,v 1.2 2010/11/30 16:47:18 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) + (mollar) = (1/liter) + (mM) = (millimollar) +} + +NEURON { + SUFFIX KCaolmw + USEION k WRITE ik + USEION ca READ cai + RANGE gkca,ek,kd +} + +PARAMETER { + gkca = 10 (mS/cm2) + ek = -90 (mV) + kd = 30 (mM) +} + +ASSIGNED { + cai (mM) + v (mV) + ik (mA/cm2) +} + +PROCEDURE iassign () { ik = (1e-3) * gkca * cai/(cai+kd) * (v-ek) } + +INITIAL { + iassign() +} + +BREAKPOINT { iassign() } diff --git a/examples/CA3model_3pops/kdrbwb.mod b/examples/CA3model_3pops/kdrbwb.mod new file mode 100644 index 000000000..fc52ae534 --- /dev/null +++ b/examples/CA3model_3pops/kdrbwb.mod @@ -0,0 +1,76 @@ +: $Id: kdrbwb.mod,v 1.4 2010/12/13 21:35:26 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Kdrbwb + USEION k WRITE ik + RANGE phin,gkdr,ek + RANGE taon,ninf +} + +PARAMETER { + gkdr = 9 (mS/cm2) + ek = -90 (mV) + phin = 5 +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + celsius (degC) + ninf (1) + taon (ms) +} + +STATE { n } + +PROCEDURE iassign () { ik = (1e-3) * gkdr * n^4 * (v-ek) } + +INITIAL { + rates(v) + n = ninf + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + n' = (ninf-n)/taon +} + +PROCEDURE rates(v(mV)) { LOCAL an, bn, q10 + q10 = phin:^((celsius-27.0(degC))/10.0(degC)) + + an = fun3(v, -34, -0.01, -10) + bn = fun1(v, -44, 0.125, -80) + + ninf = an/(an+bn) + taon = 1./((an+bn)*q10) +} + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/mod/CA1ih.mod b/examples/CA3model_3pops/mod/CA1ih.mod new file mode 100644 index 000000000..93d435e30 --- /dev/null +++ b/examples/CA3model_3pops/mod/CA1ih.mod @@ -0,0 +1,64 @@ +: $Id: CA1ih.mod,v 1.4 2010/12/13 21:35:47 samn Exp $ +TITLE Ih CA3 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX hcurrent + NONSPECIFIC_CURRENT ih + RANGE g, e, v50, htau, hinf + RANGE gfactor +} + +PARAMETER { + celsius (degC) + g= 0.0001 (mho/cm2) + e= -30 (mV) + v50=-82 (mV) + gfactor = 1 +} + +STATE { + h +} + +ASSIGNED { + ih (mA/cm2) + hinf + htau (ms) + v (mV) +} + +PROCEDURE iassign () { ih=g*h*(v-e)*gfactor } + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + h'= (hinf- h)/ htau +} + +INITIAL { + rates(v) + h = hinf + iassign() +} + +PROCEDURE rates(v (mV)) { + UNITSOFF + : HCN1 + :hinf = 1/(1+exp(0.151*(v-v50))) + :htau = exp((0.033*(v+75)))/(0.011*(1+exp(0.083*(v+75)))) + + : HCN2 + hinf = 1/(1+exp((v-v50)/10.5)) + htau = (1/(exp(-14.59-0.086*v)+exp(-1.87+0.0701*v))) + UNITSON +} + diff --git a/examples/CA3model_3pops/mod/CA1ika.mod b/examples/CA3model_3pops/mod/CA1ika.mod new file mode 100644 index 000000000..9e4fe6922 --- /dev/null +++ b/examples/CA3model_3pops/mod/CA1ika.mod @@ -0,0 +1,85 @@ +: $Id: CA1ika.mod,v 1.2 2010/12/01 05:06:07 samn Exp $ +TITLE Ika CA1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX kacurrent + NONSPECIFIC_CURRENT ika, ikad + RANGE g, gd, e, ninf, ntau, ndinf, ndtau, linf, ltau +} + +PARAMETER { + celsius (degC) + g= 0.048 (mho/cm2) + gd= 0 (mho/cm2) + e= -90 (mV) +} + +STATE { + n + nd : distal + l +} + +ASSIGNED { + v (mV) + ika (mA/cm2) + ikad (mA/cm2) + ninf + ntau (ms) + ndinf + ndtau (ms) + linf + ltau (ms) +} + +PROCEDURE iassign () { + ika=g*n*l*(v-e) + ikad=gd*nd*l*(v-e) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + n'= (ninf- n)/ ntau + l'= (linf- l)/ ltau + nd'= (ndinf-nd)/ndtau +} + +INITIAL { + rates(v) + n = ninf + l = linf + iassign() +} + +PROCEDURE rates(v (mV)) { + LOCAL a, b + UNITSOFF + a = exp(-0.038*(1.5+1/(1+exp(v+40)/5))*(v-11)) + b = exp(-0.038*(0.825+1/(1+exp(v+40)/5))*(v-11)) + ntau=4*b/(1+a) + if (ntau<0.1) {ntau=0.1} + ninf=1/(1+a) + + a=exp(-0.038*(1.8+1/(1+exp(v+40)/5))*(v+1)) + b=exp(-0.038*(0.7+1/(1+exp(v+40)/5))*(v+1)) + ndtau=2*b/(1+a) + if (ndtau<0.1) {ndtau=0.1} + ndinf=1/(1+a) + + a = exp(0.11*(v+56)) + ltau=0.26*(v+50) + if (ltau<2) {ltau=2} + linf=1/(1+a) + UNITSON +} + diff --git a/examples/CA3model_3pops/mod/CA1ikdr.mod b/examples/CA3model_3pops/mod/CA1ikdr.mod new file mode 100644 index 000000000..4c5236362 --- /dev/null +++ b/examples/CA3model_3pops/mod/CA1ikdr.mod @@ -0,0 +1,60 @@ +: $Id: CA1ikdr.mod,v 1.2 2010/12/01 05:10:52 samn Exp $ +TITLE IKDR CA1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX kdrcurrent + NONSPECIFIC_CURRENT ik + RANGE g, e, ninf, ntau +} + +PARAMETER { + celsius (degC) + g = 0.010 (mho/cm2) + e = -90 (mV) +} + +STATE { + n +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + ninf + ntau (ms) +} + +PROCEDURE iassign () { ik=g*n*(v-e) } + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + n'= (ninf- n)/ ntau +} + +INITIAL { + rates(v) + n = ninf + iassign() +} + +PROCEDURE rates(v (mV)) { + LOCAL a, b + UNITSOFF + a = exp(-0.11*(v-13)) + b = exp(-0.08*(v-13)) + ntau=50*b/(1+a) + if (ntau<2) {ntau=2} + ninf=1/(1+a) + UNITSON +} + diff --git a/examples/CA3model_3pops/mod/CA1ina.mod b/examples/CA3model_3pops/mod/CA1ina.mod new file mode 100644 index 000000000..d33ab9739 --- /dev/null +++ b/examples/CA3model_3pops/mod/CA1ina.mod @@ -0,0 +1,89 @@ +: $Id: CA1ina.mod,v 1.4 2010/11/30 19:50:00 samn Exp $ +TITLE INa CA1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX nacurrent + NONSPECIFIC_CURRENT ina + RANGE g, e, vi, ki + RANGE minf,hinf,iinf,mtau,htau,itau : testing +} + +PARAMETER { + : v (mV) + celsius (degC) + g = 0.032 (mho/cm2) + e = 55 (mV) + vi = -60 (mV) + ki = 0.8 +} + +STATE { + m + h + I : i +} + +ASSIGNED { + i (mA/cm2) + ina (mA/cm2) + minf + mtau (ms) + hinf + htau (ms) + iinf + itau (ms) + v (mV) : testing +} + +: PROCEDURE iassign () { ina=g*m*m*m*h*i*(v-e) } +PROCEDURE iassign () { i=g*m*m*m*h*I*(v-e) ina=i} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + m' = (minf - m) / mtau + h' = (hinf - h) / htau + : i' = (iinf - i) / itau + I' = (iinf - I) / itau +} + +INITIAL { + rates(v) + h = hinf + m = minf + : i = iinf + I = iinf + iassign() : testing +} + + +PROCEDURE rates(v (mV)) { + LOCAL a, b + UNITSOFF + a = 0.4*(v+30)/(1-exp(-(v+30)/7.2)) + b = 0.124*(v+30)/(exp((v+30)/7.2)-1) + mtau=0.5/(a+b) + if (mtau<0.02) {mtau=0.02} + minf=a/(a+b) + a = 0.03*(v+45)/(1-exp(-(v+45)/1.5)) + b = 0.01*(v+45)/(exp((v+45)/1.5)-1) + htau=0.5/(a+b) + if (htau<0.5) {htau=0.5} + hinf=1/(1+exp((v+50)/4)) + a = exp(0.45*(v+66)) + b = exp(0.09*(v+66)) + itau=3000*b/(1+a) + if (itau<10) {itau=10} + iinf=(1+ki*exp((v-vi)/2))/(1+exp((v-vi)/2)) + UNITSON +} + diff --git a/examples/CA3model_3pops/mod/MyExp2SynBB.mod b/examples/CA3model_3pops/mod/MyExp2SynBB.mod new file mode 100644 index 000000000..9a68baef1 --- /dev/null +++ b/examples/CA3model_3pops/mod/MyExp2SynBB.mod @@ -0,0 +1,67 @@ +: $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ +NEURON { +: THREADSAFE + POINT_PROCESS MyExp2SynBB + RANGE tau1, tau2, e, i, g, Vwt, gmax + NONSPECIFIC_CURRENT i +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) +} + +PARAMETER { + tau1=.1 (ms) <1e-9,1e9> + tau2 = 10 (ms) <1e-9,1e9> + e=0 (mV) + gmax = 1e9 (uS) + Vwt = 0 : weight for inputs coming in from vector +} + +ASSIGNED { + v (mV) + i (nA) + g (uS) + factor + etime (ms) +} + +STATE { + A (uS) + B (uS) +} + +INITIAL { + LOCAL tp + + Vwt = 0 : testing + + if (tau1/tau2 > .9999) { + tau1 = .9999*tau2 + } + A = 0 + B = 0 + tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1) + factor = -exp(-tp/tau1) + exp(-tp/tau2) + factor = 1/factor +} + +BREAKPOINT { + SOLVE state METHOD cnexp + g = B - A + if (g>gmax) {g=gmax}: saturation + i = g*(v - e) +} + +DERIVATIVE state { + A' = -A/tau1 + B' = -B/tau2 +} + +NET_RECEIVE(w (uS)) {LOCAL ww + ww=w + A = A + ww*factor + B = B + ww*factor +} diff --git a/examples/CA3model_3pops/mod/MyExp2SynNMDABB.mod b/examples/CA3model_3pops/mod/MyExp2SynNMDABB.mod new file mode 100644 index 000000000..01291643a --- /dev/null +++ b/examples/CA3model_3pops/mod/MyExp2SynNMDABB.mod @@ -0,0 +1,108 @@ +: $Id: MyExp2SynNMDABB.mod,v 1.4 2010/12/13 21:28:02 samn Exp $ +NEURON { +: THREADSAFE + POINT_PROCESS MyExp2SynNMDABB + RANGE tau1, tau2, e, i, iNMDA, s, sNMDA, r, tau1NMDA, tau2NMDA, Vwt, smax, sNMDAmax + NONSPECIFIC_CURRENT i, iNMDA +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) +} + +PARAMETER { + tau1 = 0.1 (ms) <1e-9,1e9> + tau2 = 10 (ms) <1e-9,1e9> + tau1NMDA = 15 (ms) + tau2NMDA = 150 (ms) + e = 0 (mV) + mg = 1 + r = 1 + smax = 1e9 (1) + sNMDAmax = 1e9 (1) + + Vwt = 0 : weight for inputs coming in from vector +} + +ASSIGNED { + v (mV) + i (nA) + iNMDA (nA) + s (1) + sNMDA (1) + mgblock (1) + factor (1) + factor2 (1) + + etime (ms) +} + +STATE { + A (1) + B (1) + A2 (1) + B2 (1) +} + +INITIAL { + + LOCAL tp + + Vwt = 0 : testing + + if (tau1/tau2 > .9999) { + tau1 = .9999*tau2 + } + A = 0 + B = 0 + tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1) + factor = -exp(-tp/tau1) + exp(-tp/tau2) + factor = 1/factor + + if (tau1NMDA/tau2NMDA > .9999) { + tau1NMDA = .9999*tau2NMDA + } + A2 = 0 + B2 = 0 + tp = (tau1NMDA*tau2NMDA)/(tau2NMDA - tau1NMDA) * log(tau2NMDA/tau1NMDA) + factor2 = -exp(-tp/tau1NMDA) + exp(-tp/tau2NMDA) + factor2 = 1/factor2 +} + +BREAKPOINT { + SOLVE state METHOD cnexp + : Jahr Stevens 1990 J. Neurosci + mgblock = 1.0 / (1.0 + 0.28 * exp(-0.062(/mV) * v) ) + s = B - A + sNMDA = B2 - A2 + if (s >smax) {s =smax }: saturation + if (sNMDA>sNMDAmax) {sNMDA=sNMDAmax}: saturation + i = s * (v - e) + iNMDA = sNMDA * (v - e) * mgblock +} + +DERIVATIVE state { + A' = -A/tau1 + B' = -B/tau2 + A2' = -A2/tau1NMDA + B2' = -B2/tau2NMDA +} + +NET_RECEIVE(w (uS)) {LOCAL ww + ww=w + :printf("NMDA Spike: %g\n", t) + if(r>=0){ : if r>=0, g = AMPA + NMDA*r + A = A + factor *ww + B = B + factor *ww + A2 = A2 + factor2*ww*r + B2 = B2 + factor2*ww*r + }else{ + if(r>-1000){ : if r>-1, g = NMDA*r + A2 = A2 - factor2*ww*r + B2 = B2 - factor2*ww*r + } + : if r<0 and r<>-1, g = 0 + } +} diff --git a/examples/CA3model_3pops/mod/aux_fun.inc b/examples/CA3model_3pops/mod/aux_fun.inc new file mode 100644 index 000000000..ccb579afb --- /dev/null +++ b/examples/CA3model_3pops/mod/aux_fun.inc @@ -0,0 +1,43 @@ +: $Id: aux_fun.inc,v 1.1 2009/11/04 01:24:52 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + + + +:------------------------------------------------------------------- +FUNCTION fun1(v(mV),V0(mV),A(/ms),B(mV))(/ms) { + + fun1 = A*exp((v-V0)/B) +} + +FUNCTION fun2(v(mV),V0(mV),A(/ms),B(mV))(/ms) { + + fun2 = A/(exp((v-V0)/B)+1) +} + +FUNCTION fun3(v(mV),V0(mV),A(/ms),B(mV))(/ms) { + + if(fabs((v-V0)/B)<1e-6) { + :if(v==V0) { + fun3 = A*B/1(mV) * (1- 0.5 * (v-V0)/B) + } else { + fun3 = A/1(mV)*(v-V0)/(exp((v-V0)/B)-1) + } +} + +FUNCTION min(x,y) { if (x<=y){ min = x }else{ min = y } } +FUNCTION max(x,y) { if (x>=y){ max = x }else{ max = y } } diff --git a/examples/CA3model_3pops/mod/caolmw.mod b/examples/CA3model_3pops/mod/caolmw.mod new file mode 100644 index 000000000..3ea21a7ef --- /dev/null +++ b/examples/CA3model_3pops/mod/caolmw.mod @@ -0,0 +1,47 @@ +: $Id: caolmw.mod,v 1.2 2010/11/30 16:40:09 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mollar) = (1/liter) + (M) = (mollar) + (mM) = (millimollar) + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Caolmw + USEION ca READ ica, cai WRITE cai + RANGE alpha, tau +} + +PARAMETER { + alpha = 0.002 (cm2-M/mA-ms) + tau = 80 (ms) +} + +ASSIGNED { ica (mA/cm2) } + +INITIAL { cai = 0 } + +STATE { cai (mM) } + +BREAKPOINT { SOLVE states METHOD cnexp } + +DERIVATIVE states { cai' = -(1000) * alpha * ica - cai/tau } diff --git a/examples/CA3model_3pops/mod/icaolmw.mod b/examples/CA3model_3pops/mod/icaolmw.mod new file mode 100644 index 000000000..51112d099 --- /dev/null +++ b/examples/CA3model_3pops/mod/icaolmw.mod @@ -0,0 +1,51 @@ +: $Id: icaolmw.mod,v 1.2 2010/11/30 16:44:13 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX ICaolmw + USEION ca WRITE ica + RANGE gca,eca +} + +PARAMETER { + gca = 1 (mS/cm2) + eca = 120 (mV) +} + +ASSIGNED { + ica (mA/cm2) + v (mV) +} + +PROCEDURE iassign () { ica = (1e-3) * gca * mcainf(v)^2 * (v-eca) } + +INITIAL { + iassign() +} + +BREAKPOINT { iassign() } + +FUNCTION mcainf(v(mV)) { mcainf = fun2(v, -20, 1, -9)*1(ms) } + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/mod/iholmw.mod b/examples/CA3model_3pops/mod/iholmw.mod new file mode 100644 index 000000000..ccd919202 --- /dev/null +++ b/examples/CA3model_3pops/mod/iholmw.mod @@ -0,0 +1,60 @@ +: $Id: iholmw.mod,v 1.2 2010/11/30 16:34:22 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Iholmw + NONSPECIFIC_CURRENT i + RANGE gh,eh +} + +PARAMETER { + gh = 0.15 (mS/cm2) + eh = -40 (mV) +} + +ASSIGNED { + v (mV) + i (mA/cm2) +} + +STATE { q } + +PROCEDURE iassign () { i = (1e-3) * gh * q * (v-eh) } + +INITIAL { + q = qinf(v) + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { q' = (qinf(v)-q)/qtau(v) } + +FUNCTION qinf(v(mV)) { qinf = fun2(v, -80, 1, 10)*1(ms) } +FUNCTION qtau(v(mV))(ms) { qtau = 200(ms)/(exp((v+70(mV))/20(mV))+exp(-(v+70(mV))/20(mV))) + 5(ms) } + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/mod/kcaolmw.mod b/examples/CA3model_3pops/mod/kcaolmw.mod new file mode 100644 index 000000000..b2368787e --- /dev/null +++ b/examples/CA3model_3pops/mod/kcaolmw.mod @@ -0,0 +1,52 @@ +: $Id: kcaolmw.mod,v 1.2 2010/11/30 16:47:18 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) + (mollar) = (1/liter) + (mM) = (millimollar) +} + +NEURON { + SUFFIX KCaolmw + USEION k WRITE ik + USEION ca READ cai + RANGE gkca,ek,kd +} + +PARAMETER { + gkca = 10 (mS/cm2) + ek = -90 (mV) + kd = 30 (mM) +} + +ASSIGNED { + cai (mM) + v (mV) + ik (mA/cm2) +} + +PROCEDURE iassign () { ik = (1e-3) * gkca * cai/(cai+kd) * (v-ek) } + +INITIAL { + iassign() +} + +BREAKPOINT { iassign() } diff --git a/examples/CA3model_3pops/mod/kdrbwb.mod b/examples/CA3model_3pops/mod/kdrbwb.mod new file mode 100644 index 000000000..fc52ae534 --- /dev/null +++ b/examples/CA3model_3pops/mod/kdrbwb.mod @@ -0,0 +1,76 @@ +: $Id: kdrbwb.mod,v 1.4 2010/12/13 21:35:26 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Kdrbwb + USEION k WRITE ik + RANGE phin,gkdr,ek + RANGE taon,ninf +} + +PARAMETER { + gkdr = 9 (mS/cm2) + ek = -90 (mV) + phin = 5 +} + +ASSIGNED { + v (mV) + ik (mA/cm2) + celsius (degC) + ninf (1) + taon (ms) +} + +STATE { n } + +PROCEDURE iassign () { ik = (1e-3) * gkdr * n^4 * (v-ek) } + +INITIAL { + rates(v) + n = ninf + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + n' = (ninf-n)/taon +} + +PROCEDURE rates(v(mV)) { LOCAL an, bn, q10 + q10 = phin:^((celsius-27.0(degC))/10.0(degC)) + + an = fun3(v, -34, -0.01, -10) + bn = fun1(v, -44, 0.125, -80) + + ninf = an/(an+bn) + taon = 1./((an+bn)*q10) +} + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/mod/nafbwb.mod b/examples/CA3model_3pops/mod/nafbwb.mod new file mode 100644 index 000000000..37281dc94 --- /dev/null +++ b/examples/CA3model_3pops/mod/nafbwb.mod @@ -0,0 +1,81 @@ +: $Id: nafbwb.mod,v 1.4 2010/12/13 21:35:08 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Nafbwb + USEION na WRITE ina + RANGE phih + RANGE gna, ena, taoh : testing +} + +PARAMETER { + gna = 35 (mS/cm2) + ena = 55 (mV) + phih = 5 +} + +ASSIGNED { + v (mV) + ina (mA/cm2) + minf (1) + hinf (1) + taoh (ms) + celsius (degC) +} + +STATE { h } + +PROCEDURE iassign () { ina = (1e-3) * gna * minf^3 * h * (v-ena) } + +INITIAL { + rates(v) + h = hinf + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + h' = (hinf-h)/taoh +} + +PROCEDURE rates(v(mV)) { LOCAL am, bm, ah, bh, q10 + + q10 = phih:^((celsius-27.0(degC))/10.0(degC)) + + am = fun3(v, -35, -0.1, -10) + bm = fun1(v, -60, 4, -18) + minf = am/(am+bm) + + ah = fun1(v, -58, 0.07, -20) + bh = fun2(v, -28, 1, -10) + hinf = ah/(ah+bh) + taoh = 1./((ah+bh)*q10) +} + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/nafbwb.mod b/examples/CA3model_3pops/nafbwb.mod new file mode 100644 index 000000000..37281dc94 --- /dev/null +++ b/examples/CA3model_3pops/nafbwb.mod @@ -0,0 +1,81 @@ +: $Id: nafbwb.mod,v 1.4 2010/12/13 21:35:08 samn Exp $ +COMMENT + +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +// +// NOTICE OF COPYRIGHT AND OWNERSHIP OF SOFTWARE +// +// Copyright 2007, The University Of Pennsylvania +// School of Engineering & Applied Science. +// All rights reserved. +// For research use only; commercial use prohibited. +// Distribution without permission of Maciej T. Lazarewicz not permitted. +// mlazarew@seas.upenn.edu +// +//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mS) = (millisiemens) +} + +NEURON { + SUFFIX Nafbwb + USEION na WRITE ina + RANGE phih + RANGE gna, ena, taoh : testing +} + +PARAMETER { + gna = 35 (mS/cm2) + ena = 55 (mV) + phih = 5 +} + +ASSIGNED { + v (mV) + ina (mA/cm2) + minf (1) + hinf (1) + taoh (ms) + celsius (degC) +} + +STATE { h } + +PROCEDURE iassign () { ina = (1e-3) * gna * minf^3 * h * (v-ena) } + +INITIAL { + rates(v) + h = hinf + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rates(v) + h' = (hinf-h)/taoh +} + +PROCEDURE rates(v(mV)) { LOCAL am, bm, ah, bh, q10 + + q10 = phih:^((celsius-27.0(degC))/10.0(degC)) + + am = fun3(v, -35, -0.1, -10) + bm = fun1(v, -60, 4, -18) + minf = am/(am+bm) + + ah = fun1(v, -58, 0.07, -20) + bh = fun2(v, -28, 1, -10) + hinf = ah/(ah+bh) + taoh = 1./((ah+bh)*q10) +} + +INCLUDE "aux_fun.inc" diff --git a/examples/CA3model_3pops/netParams.py b/examples/CA3model_3pops/netParams.py new file mode 100644 index 000000000..c3b582755 --- /dev/null +++ b/examples/CA3model_3pops/netParams.py @@ -0,0 +1,319 @@ +from netpyne import specs +from cfg import cfg + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters +netParams.defaultThreshold = 0.0 +netParams.defineCellShapes = True # sets 3d geometry aligned along the y-axis + + +############################################################################### +## Cell types +############################################################################### +# Basket cell +BasketCell = {'secs':{}} +BasketCell['secs']['soma'] = {'geom': {}, 'mechs': {}} +BasketCell['secs']['soma']['geom'] = {'diam': 100, 'L': 31.831, 'nseg': 1, 'cm': 1} +BasketCell['secs']['soma']['mechs'] = {'pas': {'g': 0.1e-3, 'e': -65}, 'Nafbwb': {}, 'Kdrbwb': {}} +netParams.cellParams['BasketCell'] = BasketCell + + +# OLM cell +OlmCell = {'secs':{}} +OlmCell['secs']['soma'] = {'geom': {}, 'mechs': {}} +OlmCell['secs']['soma']['geom'] = {'diam': 100, 'L': 31.831, 'nseg': 1, 'cm': 1} +OlmCell['secs']['soma']['mechs'] = { + 'pas': {'g': 0.1e-3, 'e': -65}, + 'Nafbwb': {}, + 'Kdrbwb': {}, + 'Iholmw': {}, + 'Caolmw': {}, + 'ICaolmw': {}, + 'KCaolmw': {}} +netParams.cellParams['OlmCell'] = OlmCell + + +# Pyramidal cell +PyrCell = {'secs':{}} +PyrCell['secs']['soma'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'cm': 1, 'Ra': 150} +PyrCell['secs']['soma']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {}, + 'kacurrent': {}, + 'kdrcurrent': {}, + 'hcurrent': {}} +PyrCell['secs']['Bdend'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Bdend']['geom'] = {'diam': 2, 'L': 200, 'cm': 1, 'Ra': 150} +PyrCell['secs']['Bdend']['topol'] = {'parentSec': 'soma', 'parentX': 0, 'childX': 0} +PyrCell['secs']['Bdend']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {'ki': 1}, + 'kacurrent': {}, + 'kdrcurrent': {}, + 'hcurrent': {}} +PyrCell['secs']['Adend1'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Adend1']['geom'] = {'diam': 2, 'L': 150, 'cm': 1, 'Ra': 150} +PyrCell['secs']['Adend1']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} # here there is a change: connected to end soma(1) instead of soma(0.5) +PyrCell['secs']['Adend1']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {'ki': 0.5}, + 'kacurrent': {'g': 0.072}, + 'kdrcurrent': {}, + 'hcurrent': {'v50': -82, 'g': 0.0002}} +PyrCell['secs']['Adend2'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Adend2']['geom'] = {'diam': 2, 'L': 150, 'cm': 1, 'Ra': 150} +PyrCell['secs']['Adend2']['topol'] = {'parentSec': 'Adend1', 'parentX': 1, 'childX': 0} +PyrCell['secs']['Adend2']['mechs'] = { + 'pas': {'g': 0.0000357, 'e': -70}, + 'nacurrent': {'ki': 0.5}, + 'kacurrent': {'g': 0, 'gd': 0.120}, + 'kdrcurrent': {}, + 'hcurrent': {'v50': -90, 'g': 0.0004}} +PyrCell['secs']['Adend3'] = {'geom': {}, 'mechs': {}} +PyrCell['secs']['Adend3']['geom'] = {'diam': 2, 'L': 150, 'cm': 2, 'Ra': 150} +PyrCell['secs']['Adend3']['topol'] = {'parentSec': 'Adend2', 'parentX': 1, 'childX': 0} +PyrCell['secs']['Adend3']['mechs'] = { + 'pas': {'g': 0.0000714, 'e': -70}, + 'nacurrent': {'ki': 0.5}, + 'kacurrent': {'g': 0, 'gd': 0.200}, + 'kdrcurrent': {}, + 'hcurrent': {'v50': -90, 'g': 0.0007}} +netParams.cellParams['PyrCell'] = PyrCell + + +############################################################################### +## Synaptic mechs +############################################################################### + +netParams.synMechParams['AMPAf'] = {'mod': 'MyExp2SynBB', 'tau1': 0.05, 'tau2': 5.3, 'e': 0} +netParams.synMechParams['NMDA'] = {'mod': 'MyExp2SynNMDABB', 'tau1': 0.05, 'tau2': 5.3, 'tau1NMDA': 15, 'tau2NMDA': 150, 'r': 1, 'e': 0} +netParams.synMechParams['GABAf'] = {'mod': 'MyExp2SynBB', 'tau1': 0.07, 'tau2': 9.1, 'e': -80} +netParams.synMechParams['GABAs'] = {'mod': 'MyExp2SynBB', 'tau1': 0.2, 'tau2': 20, 'e': -80} +netParams.synMechParams['GABAss'] = {'mod': 'MyExp2SynBB', 'tau1': 20, 'tau2': 40, 'e': -80} + + +############################################################################### +## Populations +############################################################################### +netParams.popParams['PYR'] = {'cellType': 'PyrCell', 'numCells': 800} +netParams.popParams['BC'] = {'cellType': 'BasketCell', 'numCells': 200} +netParams.popParams['OLM'] = {'cellType': 'OlmCell', 'numCells': 200} + + +############################################################################### +# Current-clamp to cells +############################################################################### +netParams.stimSourceParams['IClamp_PYR'] = {'type': 'IClamp', 'del': 2*cfg.dt, 'dur': 1e9, 'amp': 50e-3} +netParams.stimSourceParams['IClamp_OLM'] = {'type': 'IClamp', 'del': 2*cfg.dt, 'dur': 1e9, 'amp': -25e-3} + +netParams.stimTargetParams['IClamp_PYR->PYR'] = { + 'source': 'IClamp_PYR', + 'sec': 'soma', + 'loc': 0.5, + 'conds': {'pop': 'PYR'}} + +netParams.stimTargetParams['IClamp_OLM->OLM'] = { + 'source': 'IClamp_OLM', + 'sec': 'soma', + 'loc': 0.5, + 'conds': {'pop': 'OLM'}} + + +############################################################################### +# Setting connections +############################################################################### + +# PYR -> X, NMDA +netParams.connParams['PYR->BC_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'BC'}, + 'convergence': 100, + 'weight': 1.38e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'NMDA'} + +netParams.connParams['PYR->OLM_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'OLM'}, + 'convergence': 10, + 'weight': 0.7e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'NMDA'} + +netParams.connParams['PYR->PYR_NMDA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 25, + 'weight': 0.004e-3, + 'delay': 2, + 'sec': 'Bdend', + 'loc': 1.0, + 'synMech': 'NMDA'} + +# PYR -> X, AMPA +netParams.connParams['PYR->BC_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'BC'}, + 'convergence': 100, + 'weight': 0.36e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'AMPAf'} + +netParams.connParams['PYR->OLM_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'OLM'}, + 'convergence': 10, + 'weight': 0.36e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'AMPAf'} + +netParams.connParams['PYR->PYR_AMPA'] = {'preConds': {'pop': 'PYR'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 25, + 'weight': 0.02e-3, + 'delay': 2, + 'sec': 'Bdend', + 'loc': 1.0, + 'synMech': 'AMPAf'} + +# BC -> X, GABA +netParams.connParams['BC->BC_GABA'] = {'preConds': {'pop': 'BC'}, 'postConds': {'pop': 'BC'}, + 'convergence': 60, + 'weight':4.5e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'GABAf'} + +netParams.connParams['BC->PYR_GABA'] = {'preConds': {'pop': 'BC'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 50, + 'weight': 0.72e-3, + 'delay': 2, + 'sec': 'soma', + 'loc': 0.5, + 'synMech': 'GABAf'} + + +# OLM -> PYR, GABA +netParams.connParams['OLM->PYR_GABA'] = {'preConds': {'pop': 'OLM'}, 'postConds': {'pop': 'PYR'}, + 'convergence': 20, + 'weight': 72e-3, + 'delay': 2, + 'sec': 'Adend2', + 'loc': 0.5, + 'synMech': 'GABAs'} + + +############################################################################### +# Setting NetStims +############################################################################### +# to PYR +netParams.stimSourceParams['NetStim_PYR_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_SOMA_AMPA->PYR'] = { + 'source': 'NetStim_PYR_SOMA_AMPA', + 'conds': {'pop': 'PYR'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 4*0.05e-3, # different from published value + 'delay': 2*cfg.dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_PYR_ADEND3_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_ADEND3_AMPA->PYR'] = { + 'source': 'NetStim_PYR_ADEND3_AMPA', + 'conds': {'pop': 'PYR'}, + 'sec': 'Adend3', + 'loc': 0.5, + 'weight': 4*0.05e-3, # different from published value + 'delay': 2*cfg.dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_PYR_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_SOMA_GABA->PYR'] = { + 'source': 'NetStim_PYR_SOMA_GABA', + 'conds': {'pop': 'PYR'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.012e-3, + 'delay': 2*cfg.dt, + 'synMech': 'GABAf'} + +netParams.stimSourceParams['NetStim_PYR_ADEND3_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_ADEND3_GABA->PYR'] = { + 'source': 'NetStim_PYR_ADEND3_GABA', + 'conds': {'pop': 'PYR'}, + 'sec': 'Adend3', + 'loc': 0.5, + 'weight': 0.012e-3, + 'delay': 2*cfg.dt, + 'synMech': 'GABAf'} + +netParams.stimSourceParams['NetStim_PYR_ADEND3_NMDA'] = {'type': 'NetStim', 'interval': 100, 'number': int((1000/100.0)*cfg.duration), 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_PYR_ADEND3_NMDA->PYR'] = { + 'source': 'NetStim_PYR_ADEND3_NMDA', + 'conds': {'pop': 'PYR'}, + 'sec': 'Adend3', + 'loc': 0.5, + 'weight': 6.5e-3, + 'delay': 2*cfg.dt, + 'synMech': 'NMDA'} + +# to BC +netParams.stimSourceParams['NetStim_BC_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_BC_SOMA_AMPA->BC'] = { + 'source': 'NetStim_BC_SOMA_AMPA', + 'conds': {'pop': 'BC'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.02e-3, + 'delay': 2*cfg.dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_BC_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_BC_SOMA_GABA->BC'] = { + 'source': 'NetStim_BC_SOMA_GABA', + 'conds': {'pop': 'BC'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.2e-3, + 'delay': 2*cfg.dt, + 'synMech': 'GABAf'} + +# to OLM +netParams.stimSourceParams['NetStim_OLM_SOMA_AMPA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_OLM_SOMA_AMPA->OLM'] = { + 'source': 'NetStim_OLM_SOMA_AMPA', + 'conds': {'pop': 'OLM'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.0625e-3, + 'delay': 2*cfg.dt, + 'synMech': 'AMPAf'} + +netParams.stimSourceParams['NetStim_OLM_SOMA_GABA'] = {'type': 'NetStim', 'interval': 1, 'number': 1000*cfg.duration, 'start': 0, 'noise': 1} +netParams.stimTargetParams['NetStim_OLM_SOMA_GABA->OLM'] = { + 'source': 'NetStim_OLM_SOMA_GABA', + 'conds': {'pop': 'OLM'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 0.2e-3, + 'delay': 2*cfg.dt, + 'synMech': 'GABAf'} + +# Medial Septal inputs to BC and OLM cells +netParams.stimSourceParams['Septal'] = {'type': 'NetStim', 'interval': 150, 'number': int((1000/150)*cfg.duration), 'start': 0, 'noise': 0} +netParams.stimTargetParams['Septal->BC'] = { + 'source': 'Septal', + 'conds': {'pop': 'BC'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 1.6e-3, + 'delay': 2*cfg.dt, + 'synMech': 'GABAss'} + +netParams.stimTargetParams['Septal->OLM'] = { + 'source': 'Septal', + 'conds': {'pop': 'OLM'}, + 'sec': 'soma', + 'loc': 0.5, + 'weight': 1.6e-3, + 'delay': 2*cfg.dt, + 'synMech': 'GABAss'} diff --git a/examples/HHTut/HHTut.py b/examples/HHTut/HHTut.py index 0ab3123f9..b1ac9a37d 100644 --- a/examples/HHTut/HHTut.py +++ b/examples/HHTut/HHTut.py @@ -72,6 +72,7 @@ simConfig.filename = 'HHTut' # Set file output name simConfig.saveFileStep = 1000 # step size in ms to save data to disk simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file +simConfig.saveJson = True # Analysis and plotting simConfig.analysis['plotRaster'] = {'saveData': 'raster_data.json', 'saveFig': True, 'showFig': True} # Plot raster diff --git a/examples/HHTut/HHTut_export.py b/examples/HHTut/HHTut_export.py index 585755a56..76295bc96 100644 --- a/examples/HHTut/HHTut_export.py +++ b/examples/HHTut/HHTut_export.py @@ -5,6 +5,19 @@ print("********************\n*\n* Note: setting noise to 1, since noise can only be 0 or 1 in NeuroML export currently!\n*\n********************") np.stimSourceParams['bkg']['noise'] = 1 +HHTut.simConfig.verbose = True sim.createExportNeuroML2(netParams = np, simConfig = HHTut.simConfig, reference = 'HHTut') # create and export network to NeuroML 2 + + +###### Validate the NeuroML ###### + +from neuroml.utils import validate_neuroml2 +validate_neuroml2('HHTut.net.nml') + + +###### Export also to Python ###### + +from netpyne.conversion import createPythonScript +createPythonScript('HHTut_regen.py', HHTut.netParams, HHTut.simConfig) diff --git a/examples/HybridTut/HybridTut_export.py b/examples/HybridTut/HybridTut_export.py index e0bef8b90..22ccaf74a 100644 --- a/examples/HybridTut/HybridTut_export.py +++ b/examples/HybridTut/HybridTut_export.py @@ -8,3 +8,9 @@ sim.createExportNeuroML2(netParams = np, simConfig = HybridTut.simConfig, reference = 'HybridTut') # create and export network to NeuroML 2 + + +###### Validate the NeuroML ###### + +from neuroml.utils import validate_neuroml2 +validate_neuroml2('HybridTut.net.nml') diff --git a/examples/HybridTut/HybridTut_run.py b/examples/HybridTut/HybridTut_run.py index ee327ae23..7d3ebb476 100644 --- a/examples/HybridTut/HybridTut_run.py +++ b/examples/HybridTut/HybridTut_run.py @@ -3,5 +3,5 @@ sim.createSimulateAnalyze(netParams = HybridTut.netParams, simConfig = HybridTut.simConfig) # create and simulate network -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. sim.checkOutput('HybridTut') diff --git a/examples/LFPrecording/cell_lfp.py b/examples/LFPrecording/cell_lfp.py index 1df45d59f..d11a7d301 100644 --- a/examples/LFPrecording/cell_lfp.py +++ b/examples/LFPrecording/cell_lfp.py @@ -27,16 +27,16 @@ simConfig.duration = 0.05*1e3 # Duration of the simulation, in ms simConfig.dt = 0.1 # Internal integration timestep to use simConfig.verbose = False # Show detailed messages -simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}, 'Ina_soma':{'sec':'soma','loc':0.5,'var':'ina'}} simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'cell_lfp' # Set file output name simConfig.recordLFP = [[x, y, 35] for y in range(280, 1000, 150) for x in [30, 90]] -simConfig.analysis['plotTraces'] = {'include': [('E',0)], 'oneFigPer':'cell', 'overlay': True, 'figSize': (5,3),'saveFig': True} # Plot recorded traces for this list of cells +simConfig.analysis['plotTraces'] = {'include': [('E',0)], 'oneFigPer':'cell', 'overlay': False, 'figSize': (5,3),'saveFig': True} # Plot recorded traces for this list of cells simConfig.analysis['plotLFP'] = {'includeAxon': False, 'plots': ['timeSeries', 'locations'], 'figSize': (5,9), 'saveFig': True} #simConfig.analysis['getCSD'] = {'timeRange': [10,45],'spacing_um': 150, 'vaknin': True} -simConfig.analysis['plotCSD'] = {'timeRange': [10,45]} +#simConfig.analysis['plotCSD'] = {'timeRange': [10,45]} #sim.analysis.getCSD(...args...) #simConfig.analysis['plotCSD'] = {} @@ -45,5 +45,19 @@ sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) #sim.analysis.plotCSD() -# check model output -sim.checkOutput('cell_lfp') +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. +#sim.checkOutput('cell_lfp') + + +allData =sim.allSimData +Isoma = allData['Ina_soma'] +print('Isoma length: ' + str(len(Isoma))) + +Vsoma = allData['V_soma'] +print('Vsoma length: ' + str(len(Vsoma))) + + + + + + diff --git a/examples/LFPrecording/net_lfp.py b/examples/LFPrecording/net_lfp.py index b05d40c2e..753fc7186 100644 --- a/examples/LFPrecording/net_lfp.py +++ b/examples/LFPrecording/net_lfp.py @@ -47,20 +47,40 @@ # Simulation configuration simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration -simConfig.duration = 300 #3.0*1e3 # Duration of the simulation, in ms +simConfig.duration = 1.0*1e3 # Duration of the simulation, in ms simConfig.dt = 0.1 # Internal integration timestep to use simConfig.verbose = False # Show detailed messages simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) simConfig.filename = 'net_lfp' # Set file output name - +simConfig.savePickle = True simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/5.0), int(netParams.sizeY), int(netParams.sizeY/5.0))] +simConfig.saveLFPPops = ['I2', 'E4'] +simConfig.savePickle = True #simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig':True, 'figSize': (9,3)} # Plot a raster -simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'timeRange': [100,3000], 'saveFig': True} +simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'timeRange': [100,1000], 'saveFig': True} # optional: 'pop': 'E4' #simConfig.analysis['getCSD'] = {'spacing_um': 200, 'timeRange': [100,3000], 'vaknin': True} #simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'timeRange':[100,900], 'minFreq': 10, 'maxFreq':60, 'norm':1, 'plots': ['spectrogram'], 'showFig': True} -simConfig.analysis['plotCSD'] = {'timeRange':[100,200]} +#simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'timeRange':[100,900], 'plots': ['spectrogram'], 'showFig': True} +#simConfig.analysis['plotCSD'] = True #{'timeRange':[100,200]} # Create network and run simulation -sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) + +#sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) #sim.analysis.plotCSD(timeRange=[100,3000]) +sim.initialize( + simConfig = simConfig, + netParams = netParams) # create network object and set cfg and net params +sim.net.createPops() # instantiate network populations +sim.net.createCells() # instantiate network cells based on defined populations +sim.net.connectCells() # create connections between cells based on params +sim.net.addStims() # add network stimulation +sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc) +sim.runSim() # run parallel Neuron simulation +#sim.gatherData() # gather spiking data and cell info from each node + +# distributed saving (to avoid errors with large output data) +sim.saveDataInNodes() +sim.gatherDataFromFiles(saveMerged=True) + +sim.analysis.plotData() # plot spike raster etc diff --git a/examples/M1/M1.py b/examples/M1/M1.py index 330526347..6a83afe0f 100644 --- a/examples/M1/M1.py +++ b/examples/M1/M1.py @@ -22,7 +22,7 @@ ############################################################################### # Simulation parameters -simConfig.duration = 10*1e3 # Duration of the simulation, in ms +simConfig.duration = 2*1e3 # Duration of the simulation, in ms simConfig.dt = 0.1 # Internal integration timestep to use simConfig.seeds = {'conn': 1, 'stim': 1, 'loc': 1} # Seeds for randomizers (connectivity, input stimulation and cell locations) simConfig.createNEURONObj = 1 # create HOC objects when instantiating network @@ -69,8 +69,8 @@ netParams.sizeZ = 50 # z-dimension (horizontal depth) size in um ## General connectivity parameters -netParams.scaleConnWeight = 0.05 # Connection weight scale factor -netParams.scaleConnWeightNetStims = 1.0 # Connection weight scale factor for NetStims +netParams.scaleConnWeight = 0.01 # Connection weight scale factor +netParams.scaleConnWeightNetStims = 0.1 # Connection weight scale factor for NetStims netParams.defaultDelay = 2.0 # default conn delay (ms) netParams.propVelocity = 100.0 # propagation velocity (um/ms) netParams.probLambda = 100.0 # length constant (lambda) for connection probability decay (um) diff --git a/examples/M1/M1_export.py b/examples/M1/M1_export.py index d55a0fe3a..42bcd4564 100644 --- a/examples/M1/M1_export.py +++ b/examples/M1/M1_export.py @@ -11,3 +11,8 @@ reference = 'M1', connections=True, stimulations=True) # create and export network to NeuroML 2 + +###### Validate the NeuroML ###### + +from neuroml.utils import validate_neuroml2 +validate_neuroml2('M1.net.nml') diff --git a/examples/M1/M1_run.py b/examples/M1/M1_run.py index 28c54d614..2948d865f 100644 --- a/examples/M1/M1_run.py +++ b/examples/M1/M1_run.py @@ -3,5 +3,5 @@ sim.createSimulateAnalyze(netParams = M1.netParams, simConfig = M1.simConfig) # create and simulate network -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. sim.checkOutput('M1') diff --git a/examples/M1detailed/cells/CT6_reduced_cellParams.pkl b/examples/M1detailed/cells/CT6_reduced_cellParams.pkl index 0f9fcc963..ae281b3d2 100644 Binary files a/examples/M1detailed/cells/CT6_reduced_cellParams.pkl and b/examples/M1detailed/cells/CT6_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/IT2_reduced_cellParams.pkl b/examples/M1detailed/cells/IT2_reduced_cellParams.pkl index 0cda23189..3e53ec1f5 100644 Binary files a/examples/M1detailed/cells/IT2_reduced_cellParams.pkl and b/examples/M1detailed/cells/IT2_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/IT4_reduced_cellParams.pkl b/examples/M1detailed/cells/IT4_reduced_cellParams.pkl index 106b1d270..cd75f5973 100644 Binary files a/examples/M1detailed/cells/IT4_reduced_cellParams.pkl and b/examples/M1detailed/cells/IT4_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/IT5A_full_cellParams.pkl b/examples/M1detailed/cells/IT5A_full_cellParams.pkl index 567eaf4da..270e206f0 100644 Binary files a/examples/M1detailed/cells/IT5A_full_cellParams.pkl and b/examples/M1detailed/cells/IT5A_full_cellParams.pkl differ diff --git a/examples/M1detailed/cells/IT5A_reduced_cellParams.pkl b/examples/M1detailed/cells/IT5A_reduced_cellParams.pkl index 259cc97f7..cf5b00322 100644 Binary files a/examples/M1detailed/cells/IT5A_reduced_cellParams.pkl and b/examples/M1detailed/cells/IT5A_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/IT5B_reduced_cellParams.pkl b/examples/M1detailed/cells/IT5B_reduced_cellParams.pkl index 239754ff4..cda09dabd 100644 Binary files a/examples/M1detailed/cells/IT5B_reduced_cellParams.pkl and b/examples/M1detailed/cells/IT5B_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/IT6_reduced_cellParams.pkl b/examples/M1detailed/cells/IT6_reduced_cellParams.pkl index fb9eeff7b..3718d0c33 100644 Binary files a/examples/M1detailed/cells/IT6_reduced_cellParams.pkl and b/examples/M1detailed/cells/IT6_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/PT5B_full_cellParams.pkl b/examples/M1detailed/cells/PT5B_full_cellParams.pkl index af312da2d..5006415fb 100644 Binary files a/examples/M1detailed/cells/PT5B_full_cellParams.pkl and b/examples/M1detailed/cells/PT5B_full_cellParams.pkl differ diff --git a/examples/M1detailed/cells/PT5B_reduced_cellParams.pkl b/examples/M1detailed/cells/PT5B_reduced_cellParams.pkl index dbe98d2ea..b90a68899 100644 Binary files a/examples/M1detailed/cells/PT5B_reduced_cellParams.pkl and b/examples/M1detailed/cells/PT5B_reduced_cellParams.pkl differ diff --git a/examples/M1detailed/cells/PV_simple_cellParams.pkl b/examples/M1detailed/cells/PV_simple_cellParams.pkl index 91bcf90ff..f2e3d0e4d 100644 Binary files a/examples/M1detailed/cells/PV_simple_cellParams.pkl and b/examples/M1detailed/cells/PV_simple_cellParams.pkl differ diff --git a/examples/M1detailed/cells/SOM_simple_cellParams.pkl b/examples/M1detailed/cells/SOM_simple_cellParams.pkl index d702ea434..a928edcd9 100644 Binary files a/examples/M1detailed/cells/SOM_simple_cellParams.pkl and b/examples/M1detailed/cells/SOM_simple_cellParams.pkl differ diff --git a/examples/M1detailed/cfg.py b/examples/M1detailed/cfg.py index 21ee9d38f..e81dd67d1 100644 --- a/examples/M1detailed/cfg.py +++ b/examples/M1detailed/cfg.py @@ -22,6 +22,9 @@ cfg.dt = 0.05 cfg.seeds = {'conn': 4321, 'stim': 1234, 'loc': 4321} cfg.hParams = {'celsius': 34, 'v_init': -80} +cfg.coreneuron = False # run via coreneuron (alpha support) +cfg.gpu = False # needs coreneuron enabled +cfg.random123 = False # enable random123 for coreneuron compatibility cfg.verbose = 0 cfg.createNEURONObj = True cfg.createPyStruct = True @@ -53,7 +56,7 @@ cfg.saveFolder = '.' cfg.savePickle = False cfg.saveJson = True -cfg.saveDataInclude = ['simData', 'simConfig', 'netParams']#, 'net'] +cfg.saveDataInclude = ['simData', 'simConfig', 'netParams', 'net'] cfg.backupCfgFile = None #['cfg.py', 'backupcfg/'] cfg.gatherOnlySimData = False cfg.saveCellSecs = True diff --git a/examples/M1detailed/init.py b/examples/M1detailed/init.py index 7ac3ad94d..ce87506ee 100644 --- a/examples/M1detailed/init.py +++ b/examples/M1detailed/init.py @@ -19,4 +19,5 @@ print("Starting sim ...") sim.createSimulateAnalyze(netParams, cfg) +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. sim.checkOutput('M1detailed') diff --git a/examples/M1detailed/mod/catcb.mod b/examples/M1detailed/mod/catcb.mod index 303d13400..3fe92df5f 100755 --- a/examples/M1detailed/mod/catcb.mod +++ b/examples/M1detailed/mod/catcb.mod @@ -8,7 +8,7 @@ NEURON { USEION ca READ cai, eca RANGE gcatbar, iCa RANGE gcatbar, ica - GLOBAL hinf, minf + RANGE hinf, minf } UNITS { diff --git a/examples/M1detailed/mod/gabab.mod b/examples/M1detailed/mod/gabab.mod index 768744b47..cfe7e51a5 100644 --- a/examples/M1detailed/mod/gabab.mod +++ b/examples/M1detailed/mod/gabab.mod @@ -105,7 +105,8 @@ NEURON { RANGE R, G, g NONSPECIFIC_CURRENT i GLOBAL Cmax, Cdur - GLOBAL K1, K2, K3, K4, KD, Erev, warn, cutoff + GLOBAL K1, K2, K3, K4, KD, Erev, cutoff + RANGE warn } UNITS { (nA) = (nanoamp) @@ -196,14 +197,14 @@ NET_RECEIVE(weight, r0, t0 (ms)) { r0 = weight*(Rinf + (r0 - Rinf)*exp(-(t - t0)/Rtau)) t0 = t synon = synon - weight - state_discontinuity(Ron, Ron - r0) - state_discontinuity(Roff, Roff + r0) + Ron = Ron - r0 + Roff = Roff + r0 }else{ : at beginning of Cdur pulse so turn on r0 = weight*r0*exp(-Beta*(t - t0)) t0 = t synon = synon + weight - state_discontinuity(Ron, Ron + r0) - state_discontinuity(Roff, Roff - r0) + Ron = Ron + r0 + Roff = Roff - r0 :come again in Cdur net_send(Cdur, 1) } diff --git a/examples/M1detailed/mod/h_migliore.mod b/examples/M1detailed/mod/h_migliore.mod index f65df095e..821c69359 100644 --- a/examples/M1detailed/mod/h_migliore.mod +++ b/examples/M1detailed/mod/h_migliore.mod @@ -31,7 +31,7 @@ NEURON { NONSPECIFIC_CURRENT i NONSPECIFIC_CURRENT lk RANGE gbar, vhalfl, elk, clk, glk, ehd - GLOBAL linf,taul + RANGE linf,taul } diff --git a/examples/M1detailed/mod/kapcb.mod b/examples/M1detailed/mod/kapcb.mod index 53935544a..36823b32b 100755 --- a/examples/M1detailed/mod/kapcb.mod +++ b/examples/M1detailed/mod/kapcb.mod @@ -47,14 +47,13 @@ ASSIGNED { :parameters needed to solve DE taun (ms) ko (mM) ki (mM) + qt } STATE { :the unknown parameters to be solved in the DEs n l } -LOCAL qt - INITIAL { :initialize the following parameter using rates() qt = q10^((celsius-24)/10(degC)) : temprature adjustment factor rates(v) diff --git a/examples/M1detailed/mod/kapin.mod b/examples/M1detailed/mod/kapin.mod index 0dae89109..55b086d19 100755 --- a/examples/M1detailed/mod/kapin.mod +++ b/examples/M1detailed/mod/kapin.mod @@ -50,6 +50,7 @@ ASSIGNED { :parameters needed to solve DE linf taul (ms) taun (ms) + qt } @@ -57,8 +58,6 @@ STATE { :the unknown parameters to be solved in the DEs n l } -LOCAL qt - INITIAL { :initialize the following parameter using rates() qt = q10^((celsius-24)/10(degC)) : temprature adjustment factor rates(v) diff --git a/examples/M1detailed/mod/vecstim.mod b/examples/M1detailed/mod/vecstim.mod index 9dcb4d0b9..cee0b0f71 100644 --- a/examples/M1detailed/mod/vecstim.mod +++ b/examples/M1detailed/mod/vecstim.mod @@ -1,57 +1,86 @@ -: $Id: vecstim.mod,v 1.3 2010/12/13 21:29:27 samn Exp $ : Vector stream of events +COMMENT +A VecStim is an artificial spiking cell that generates +events at times that are specified in a Vector. + +HOC Example: + +// assumes spt is a Vector whose elements are all > 0 +// and are sorted in monotonically increasing order +objref vs +vs = new VecStim() +vs.play(spt) +// now launch a simulation, and vs will produce spike events +// at the times contained in spt + +Python Example: + +from neuron import h +spt = h.Vector(10).indgen(1, 0.2) +vs = h.VecStim() +vs.play(spt) + +def pr(): + print (h.t) + +nc = h.NetCon(vs, None) +nc.record(pr) + +cvode = h.CVode() +h.finitialize() +cvode.solve(20) + +ENDCOMMENT + NEURON { - THREADSAFE - ARTIFICIAL_CELL VecStim + THREADSAFE + ARTIFICIAL_CELL VecStim + BBCOREPOINTER ptr } ASSIGNED { index etime (ms) - space + ptr } + INITIAL { index = 0 element() if (index > 0) { - if (etime - t>=0) { - net_send(etime - t, 1) - } else { - printf("Event in the stimulus vector at time %g is omitted since has value less than t=%g!\n", etime, t) - net_send(0, 2) - } + net_send(etime - t, 1) } } NET_RECEIVE (w) { - if (flag == 1) { net_event(t) } - if (flag == 1 || flag == 2) { + if (flag == 1) { + net_event(t) element() - if (index > 0) { - if (etime - t>=0) { - net_send(etime - t, 1) - } else { - printf("Event in the stimulus vector at time %g is omitted since has value less than t=%g!\n", etime, t) - net_send(0, 2) - } + if (index > 0) { + net_send(etime - t, 1) } } } +DESTRUCTOR { VERBATIM -extern double* vector_vec(); -extern int vector_capacity(); -extern void* vector_arg(); +#if !NRNBBCORE + void* vv = (void*)(_p_ptr); + if (vv) { + hoc_obj_unref(*vector_pobj(vv)); + } +#endif ENDVERBATIM +} PROCEDURE element() { VERBATIM { void* vv; int i, size; double* px; i = (int)index; if (i >= 0) { - vv = *((void**)(&space)); + vv = (void*)(_p_ptr); if (vv) { size = vector_capacity(vv); px = vector_vec(vv); @@ -71,13 +100,66 @@ ENDVERBATIM PROCEDURE play() { VERBATIM - void** vv; - vv = (void**)(&space); - *vv = (void*)0; +#if !NRNBBCORE + { + void** pv; + void* ptmp = NULL; if (ifarg(1)) { - *vv = vector_arg(1); + ptmp = vector_arg(1); + hoc_obj_ref(*vector_pobj(ptmp)); } + pv = (void**)(&_p_ptr); + if (*pv) { + hoc_obj_unref(*vector_pobj(*pv)); + } + *pv = ptmp; + } +#endif ENDVERBATIM } - +VERBATIM +static void bbcore_write(double* xarray, int* iarray, int* xoffset, int* ioffset, _threadargsproto_) { + int i, dsize, *ia; + double *xa, *dv; + dsize = 0; + if (_p_ptr) { + dsize = vector_capacity(_p_ptr); + } + if (xarray) { + void* vec = _p_ptr; + ia = iarray + *ioffset; + xa = xarray + *xoffset; + ia[0] = dsize; + if (dsize) { + dv = vector_vec(vec); + for (i = 0; i < dsize; ++i) { + xa[i] = dv[i]; + } + } + } + *ioffset += 1; + *xoffset += dsize; +} + +static void bbcore_read(double* xarray, int* iarray, int* xoffset, int* ioffset, _threadargsproto_) { + int dsize, i, *ia; + double *xa, *dv; + dsize = 0; + if (xarray) { + xa = xarray + *xoffset; + ia = iarray + *ioffset; + dsize = ia[0]; + if(!_p_ptr) { + _p_ptr = vector_new1(dsize); + } + dv = vector_vec(_p_ptr); + for (i = 0; i < dsize; ++i) { + dv[i] = xa[i]; + } + } + *xoffset += dsize; + *ioffset += 1; +} + +ENDVERBATIM diff --git a/examples/M1detailed/netParams.py b/examples/M1detailed/netParams.py index c3770ab04..dccc75b1e 100644 --- a/examples/M1detailed/netParams.py +++ b/examples/M1detailed/netParams.py @@ -54,7 +54,7 @@ cellParamLabels = ['IT5A_full', 'IT2_reduced', 'IT4_reduced', 'IT5A_reduced', 'IT5B_reduced', 'PT5B_reduced', 'IT6_reduced', 'CT6_reduced', 'PV_simple', 'SOM_simple']#, 'PT5B_full'] # # list of cell rules to load from file loadCellParams = cellParamLabels -saveCellParams = False #True +saveCellParams = False for ruleLabel in loadCellParams: netParams.loadCellParamsRule(label=ruleLabel, fileName='cells/'+ruleLabel+'_cellParams.pkl') @@ -67,6 +67,8 @@ cellRule['secs']['axon']['geom']['pt3d'][1][1] = cellRule['secs']['axon']['geom']['pt3d'][1][1]*0.01 sec = cellRule['secs']['Bdend'] cellRule['secs']['Bdend']['geom']['pt3d'][1] = [0.5*sec['geom']['L'], -0.5*sec['geom']['L'], 0, sec['geom']['diam']] + + #------------------------------------------------------------------------------ # Specification of cell rules not previously loaded # Includes importing from hoc template or python class, and setting additional params diff --git a/examples/NeuroMLImport/LEMS_SimpleNet.xml b/examples/NeuroMLImport/LEMS_SimpleNet.xml index 30731b566..b37af5f87 100644 --- a/examples/NeuroMLImport/LEMS_SimpleNet.xml +++ b/examples/NeuroMLImport/LEMS_SimpleNet.xml @@ -2,7 +2,7 @@ @@ -20,9 +20,9 @@ - - - + + + diff --git a/examples/NeuroMLImport/RS.mod b/examples/NeuroMLImport/RS.mod index e8cc4e351..b35853f95 100644 --- a/examples/NeuroMLImport/RS.mod +++ b/examples/NeuroMLImport/RS.mod @@ -3,9 +3,9 @@ TITLE Mod file for component: Component(id=RS type=izhikevich2007Cell) COMMENT This NEURON file has been generated by org.neuroml.export (see https://github.com/NeuroML/org.neuroml.export) - org.neuroml.export v1.7.0 - org.neuroml.model v1.7.0 - jLEMS v0.10.2 + org.neuroml.export v1.8.0 + org.neuroml.model v1.8.0 + jLEMS v0.10.5 ENDCOMMENT diff --git a/examples/NeuroMLImport/SimpleNet.json b/examples/NeuroMLImport/SimpleNet.json new file mode 100644 index 000000000..9207aaddd --- /dev/null +++ b/examples/NeuroMLImport/SimpleNet.json @@ -0,0 +1,447 @@ +{ + "net": { + "cells": [ + { + "conns": [], + "gid": 0, + "secLists": {}, + "secs": { + "soma": { + "geom": { + "L": 10, + "cm": 31.830988618379067, + "diam": 10 + }, + "pointps": { + "RS": { + "mod": "RS" + } + }, + "threshold": 0.0, + "topol": {} + } + }, + "stims": [ + { + "label": "Stim0_0_RS_pop_0_soma_0_5", + "loc": 0.5, + "originalFormat": "NeuroML2_stochastic_input", + "sec": "soma", + "source": "Stim0_0_RS_pop_0_soma_0_5", + "type": "poissonFiringSyn" + } + ], + "tags": { + "cellLabel": 0, + "cellType": "RS", + "label": [ + "RS" + ], + "pop": "RS_pop", + "x": 96.64535, + "xnorm": 0.9664535, + "y": 44.07326, + "ynorm": 0.4407326, + "z": 0.74915, + "znorm": 0.0074915 + } + }, + { + "conns": [], + "gid": 1, + "secLists": {}, + "secs": { + "soma": { + "geom": { + "L": 10, + "cm": 31.830988618379067, + "diam": 10 + }, + "pointps": { + "RS": { + "mod": "RS" + } + }, + "threshold": 0.0, + "topol": {} + } + }, + "stims": [ + { + "label": "Stim0_1_RS_pop_1_soma_0_5", + "loc": 0.5, + "originalFormat": "NeuroML2_stochastic_input", + "sec": "soma", + "source": "Stim0_1_RS_pop_1_soma_0_5", + "type": "poissonFiringSyn" + } + ], + "tags": { + "cellLabel": 1, + "cellType": "RS", + "label": [ + "RS" + ], + "pop": "RS_pop", + "x": 91.0976, + "xnorm": 0.910976, + "y": 93.9269, + "ynorm": 0.939269, + "z": 58.22276, + "znorm": 0.5822276 + } + }, + { + "conns": [], + "gid": 2, + "secLists": {}, + "secs": { + "soma": { + "geom": { + "L": 10, + "cm": 31.830988618379067, + "diam": 10 + }, + "pointps": { + "RS": { + "mod": "RS" + } + }, + "threshold": 0.0, + "topol": {} + } + }, + "stims": [ + { + "label": "Stim0_2_RS_pop_2_soma_0_5", + "loc": 0.5, + "originalFormat": "NeuroML2_stochastic_input", + "sec": "soma", + "source": "Stim0_2_RS_pop_2_soma_0_5", + "type": "poissonFiringSyn" + } + ], + "tags": { + "cellLabel": 2, + "cellType": "RS", + "label": [ + "RS" + ], + "pop": "RS_pop", + "x": 67.15635, + "xnorm": 0.6715635, + "y": 8.39382, + "ynorm": 0.0839382, + "z": 76.64809, + "znorm": 0.7664808999999999 + } + } + ], + "params": { + "cellParams": { + "RS": { + "label": "RS", + "secs": { + "soma": { + "geom": { + "L": 10, + "cm": 31.830988618379067, + "diam": 10 + }, + "pointps": { + "RS": { + "mod": "RS" + } + }, + "threshold": 0.0 + } + } + } + }, + "cellsVisualizationSpacingMultiplier": [ + 1, + 1, + 1 + ], + "connParams": {}, + "correctBorder": false, + "defaultDelay": 1, + "defaultThreshold": 10, + "defaultWeight": 1, + "defineCellShapes": false, + "popParams": { + "RS_pop": { + "cellType": "RS", + "cellsList": [ + { + "cellLabel": 0, + "x": 96.64535, + "y": 44.07326, + "z": 0.74915 + }, + { + "cellLabel": 1, + "x": 91.0976, + "y": 93.9269, + "z": 58.22276 + }, + { + "cellLabel": 2, + "x": 67.15635, + "y": 8.39382, + "z": 76.64809 + } + ], + "numCells": 3, + "originalFormat": "NeuroML2", + "pop": "RS_pop" + } + }, + "popTagsCopiedToCells": [ + "cellModel", + "cellType" + ], + "propVelocity": 500.0, + "rotateCellsRandomly": false, + "rxdParams": {}, + "scale": 1, + "scaleConnWeight": 1, + "scaleConnWeightModels": false, + "scaleConnWeightNetStims": 1, + "shape": "cuboid", + "sizeX": 100, + "sizeY": 100, + "sizeZ": 100, + "stimSourceParams": { + "Stim0_0_RS_pop_0_soma_0_5": { + "label": "Stim0_0_RS_pop_0_soma_0_5", + "originalFormat": "NeuroML2_stochastic_input", + "type": "poissonFiringSyn" + }, + "Stim0_1_RS_pop_1_soma_0_5": { + "label": "Stim0_1_RS_pop_1_soma_0_5", + "originalFormat": "NeuroML2_stochastic_input", + "type": "poissonFiringSyn" + }, + "Stim0_2_RS_pop_2_soma_0_5": { + "label": "Stim0_2_RS_pop_2_soma_0_5", + "originalFormat": "NeuroML2_stochastic_input", + "type": "poissonFiringSyn" + } + }, + "stimTargetParams": { + "Stim0_0_RS_pop_0_soma_0_5": { + "conds": { + "cellList": [ + 0 + ], + "pop": "RS_pop" + }, + "loc": 0.5, + "sec": "soma", + "source": "Stim0_0_RS_pop_0_soma_0_5" + }, + "Stim0_1_RS_pop_1_soma_0_5": { + "conds": { + "cellList": [ + 1 + ], + "pop": "RS_pop" + }, + "loc": 0.5, + "sec": "soma", + "source": "Stim0_1_RS_pop_1_soma_0_5" + }, + "Stim0_2_RS_pop_2_soma_0_5": { + "conds": { + "cellList": [ + 2 + ], + "pop": "RS_pop" + }, + "loc": 0.5, + "sec": "soma", + "source": "Stim0_2_RS_pop_2_soma_0_5" + } + }, + "subConnParams": {}, + "synMechParams": {} + }, + "pops": { + "RS_pop": { + "cellGids": [ + 0, + 1, + 2 + ], + "tags": { + "cellType": "RS", + "cellsList": [ + { + "cellLabel": 0, + "x": 96.64535, + "y": 44.07326, + "z": 0.74915 + }, + { + "cellLabel": 1, + "x": 91.0976, + "y": 93.9269, + "z": 58.22276 + }, + { + "cellLabel": 2, + "x": 67.15635, + "y": 8.39382, + "z": 76.64809 + } + ], + "numCells": 3, + "originalFormat": "NeuroML2", + "pop": "RS_pop" + } + } + } + }, + "netpyne_changeset": "", + "netpyne_version": "1.0.0.2", + "simConfig": { + "addSynMechs": true, + "allowConnsWithWeight0": true, + "allowSelfConns": false, + "analysis": {}, + "backupCfgFile": [], + "cache_efficient": false, + "checkErrors": false, + "checkErrorsVerbose": false, + "compactConnFormat": false, + "connRandomSecFromList": true, + "createNEURONObj": true, + "createPyStruct": true, + "cvode_active": false, + "cvode_atol": 0.001, + "distributeSynsUniformly": true, + "dt": 0.025, + "duration": 1.0, + "filename": "SimpleNet", + "gatherOnlySimData": false, + "hParams": { + "celsius": 32.0, + "clamp_resist": 0.001, + "v_init": -65.0 + }, + "includeParamsLabel": true, + "invertedYCoord": true, + "oneSynPerNetcon": true, + "plotCells": [ + "all" + ], + "plotRaster": true, + "printPopAvgRates": false, + "printRunTime": false, + "printSynsAfterRule": false, + "pt3dRelativeToCellLocation": true, + "rand123GlobalIndex": null, + "recordCells": [ + "all" + ], + "recordCellsSpikes": -1, + "recordDipoles": false, + "recordLFP": [], + "recordStep": 0.1, + "recordStim": false, + "recordTime": true, + "recordTraces": { + "Vsoma": { + "loc": 0.5, + "sec": "soma", + "var": "v" + } + }, + "saveCSV": false, + "saveCellConns": true, + "saveCellSecs": true, + "saveDat": true, + "saveDataInclude": [ + "netParams", + "netCells", + "netPops", + "simConfig", + "simData" + ], + "saveDpk": false, + "saveFolder": "", + "saveHDF5": false, + "saveJson": true, + "saveLFPCells": false, + "saveMat": false, + "savePickle": false, + "saveTiming": false, + "seeds": { + "conn": 1, + "loc": 1, + "stim": 1 + }, + "simLabel": "", + "timestampFilename": false, + "timing": true, + "tstop": 1000.0, + "use_fast_imem": false, + "verbose": true + }, + "simData": { + "Vsoma": { + "cell_0": [ + -60.0, + -59.978353611390716, + -59.97865460481149, + -59.97895128804392, + -59.97924372302133, + -59.97953197078967, + -59.97981609152018, + -59.98009614452178, + -59.98037218825339, + -59.98064428033601 + ], + "cell_1": [ + -60.0, + -59.978353611390716, + -59.97865460481149, + -59.97895128804392, + -59.97924372302133, + -59.97953197078967, + -59.97981609152018, + -59.98009614452178, + -59.98037218825339, + -59.98064428033601 + ], + "cell_2": [ + -60.0, + -59.978353611390716, + -59.97865460481149, + -59.97895128804392, + -59.97924372302133, + -59.97953197078967, + -59.97981609152018, + -59.98009614452178, + -59.98037218825339, + -59.98064428033601 + ] + }, + "avgRate": 0, + "spkid": [], + "spkt": [], + "t": [ + 0.0, + 0.1, + 0.2, + 0.30000000000000004, + 0.4, + 0.5, + 0.6, + 0.7, + 0.7999999999999999, + 0.8999999999999999 + ] + } +} \ No newline at end of file diff --git a/examples/NeuroMLImport/SimpleNet.net.nml b/examples/NeuroMLImport/SimpleNet.net.nml index 5688dd0b0..e22dd892b 100644 --- a/examples/NeuroMLImport/SimpleNet.net.nml +++ b/examples/NeuroMLImport/SimpleNet.net.nml @@ -1,9 +1,9 @@ - + This NeuroML 2 file was generated by OpenCortex v0.1.11 using: - libNeuroML v0.2.45 - pyNeuroML v0.3.14 + libNeuroML v0.2.56 + pyNeuroML v0.5.13 diff --git a/examples/NeuroMLImport/SimpleNet_import.py b/examples/NeuroMLImport/SimpleNet_import.py index 113256b78..c27ffcf97 100644 --- a/examples/NeuroMLImport/SimpleNet_import.py +++ b/examples/NeuroMLImport/SimpleNet_import.py @@ -8,7 +8,7 @@ simConfig = specs.SimConfig() # dictionary to store simConfig # Simulation parameters -simConfig.duration = 10000 # Duration of the simulation, in ms +simConfig.duration = 1 # Duration of the simulation, in ms simConfig.dt = 0.025 # Internal integration timestep to use simConfig.verbose = True @@ -16,9 +16,10 @@ simConfig.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}} simConfig.filename = 'SimpleNet' # Set file output name simConfig.saveDat = True # save traces +simConfig.saveJson = True simConfig.plotRaster = True # Whether or not to plot a raster simConfig.plotCells = ['all'] # plot recorded traces for this list of cells -sim.importNeuroML2SimulateAnalyze(nml2_file_name,simConfig) +sim.importNeuroML2SimulateAnalyze(nml2_file_name, simConfig) diff --git a/examples/NeuroMLImport/poissonFiringSyn.mod b/examples/NeuroMLImport/poissonFiringSyn.mod index 3023fa078..1d4ad2d27 100644 --- a/examples/NeuroMLImport/poissonFiringSyn.mod +++ b/examples/NeuroMLImport/poissonFiringSyn.mod @@ -3,9 +3,9 @@ TITLE Mod file for component: Component(id=poissonFiringSyn type=poissonFiringSy COMMENT This NEURON file has been generated by org.neuroml.export (see https://github.com/NeuroML/org.neuroml.export) - org.neuroml.export v1.7.0 - org.neuroml.model v1.7.0 - jLEMS v0.10.2 + org.neuroml.export v1.8.0 + org.neuroml.model v1.8.0 + jLEMS v0.10.5 ENDCOMMENT diff --git a/examples/PTcell/init.py b/examples/PTcell/init.py index b4c287cc2..9734dea88 100644 --- a/examples/PTcell/init.py +++ b/examples/PTcell/init.py @@ -18,5 +18,5 @@ sim.createSimulateAnalyze(netParams, cfg) #SimulateAnalyze(netParams, cfg) -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. sim.checkOutput('PTcell') diff --git a/examples/RL_arm/main.py b/examples/RL_arm/main.py index 55a1b8377..8d80e44a5 100644 --- a/examples/RL_arm/main.py +++ b/examples/RL_arm/main.py @@ -89,15 +89,15 @@ sim.arm.setup(sim) # pass framework as argument # Function to run at intervals during simulation -def runArm(t): +def runArm(simTime): # turn off RL and explor movs for last testing trial - if t >= sim.trainTime: + if simTime >= sim.trainTime: sim.useRL = False sim.explorMovs = False if sim.useArm: - sim.arm.run(t, sim) # run virtual arm apparatus (calculate command, move arm, feedback) - if sim.useRL and (t - sim.timeoflastRL >= sim.RLinterval): # if time for next RL + sim.arm.run(simTime, sim) # run virtual arm apparatus (calculate command, move arm, feedback) + if sim.useRL and (simTime - sim.timeoflastRL >= sim.RLinterval): # if time for next RL sim.timeoflastRL = h.t vec = h.Vector() if sim.rank == 0: @@ -108,7 +108,7 @@ def runArm(t): sim.pc.broadcast(vec, 0) critic = vec.to_python()[0] if critic != 0: # if critic signal indicates punishment (-1) or reward (+1) - print('t=',t,'- adjusting weights based on RL critic value:', critic) + print('t=',simTime,'- adjusting weights based on RL critic value:', critic) for cell in sim.net.cells: for conn in cell.conns: STDPmech = conn.get('hSTDP') # check if has STDP mechanism diff --git a/examples/RL_arm/params.py b/examples/RL_arm/params.py index 19d398407..a863d05ae 100644 --- a/examples/RL_arm/params.py +++ b/examples/RL_arm/params.py @@ -291,7 +291,7 @@ simConfig.createNEURONObj = True # create HOC objects when instantiating network simConfig.createPyStruct = True # create Python structure (simulator-independent) when instantiating network simConfig.timing = True # show timing and save to file -simConfig.verbose = True # show detailed messages +simConfig.verbose = False # show detailed messages # Recording simConfig.recordCells = ['all'] # list of cells to record from @@ -309,9 +309,9 @@ # Saving simConfig.filename = 'simdata' # Set file output name simConfig.saveFileStep = 1000 # step size in ms to save data to disk -simConfig.savePickle = False # Whether or not to write spikes etc. to a .mat file +simConfig.savePickle = True # Whether or not to write spikes etc. to a .mat file simConfig.saveJson = False # Whether or not to write spikes etc. to a .mat file -simConfig.saveMat = True # Whether or not to write spikes etc. to a .mat file +simConfig.saveMat = False # Whether or not to write spikes etc. to a .mat file simConfig.saveTxt = False # save spikes and conn to txt file simConfig.saveDpk = False # save to a .dpk pickled file diff --git a/examples/batchCell/batch.py b/examples/batchCell/batch.py index 0d430fce9..a5a269ecf 100644 --- a/examples/batchCell/batch.py +++ b/examples/batchCell/batch.py @@ -16,7 +16,7 @@ def runBatch(b, label, setup='mpi_bulletin'): if setup == 'mpi_bulletin': b.runCfg = {'type': 'mpi_bulletin', 'script': 'init.py', - 'skip': True} + 'skip': False} elif setup == 'hpc_slurm_comet': b.runCfg = {'type': 'hpc_slurm', @@ -45,15 +45,16 @@ def runBatchComet(b, label): def batchNa(): params = {'dendNa': [0.025, 0.03, 0.035, 0.4], ('IClamp1', 'amp'): list(np.arange(-2.0, 8.0, 0.5)/10.0)} - initCfg = {'duration': 1.1, 'tau1NMDA': 15} + initCfg = {'duration': 1*1e3, 'tau1NMDA': 15} b = Batch(params=params, initCfg=initCfg) runBatch(b, 'batchNa', setup='mpi_bulletin') def batchNMDA(): params = {'tau1NMDA': [10, 15, 20, 25], - ('NetStim1', 'weight'): list(np.arange(1.0, 10.0, 1.0)/1e4)} + ('NetStim1', 'weight'): [0.0, 0.5, 0.75]} #list(np.arange(1.0, 10.0, 1.0)/1e4)} initCfg = {'duration': 1.1} + b = Batch(params=params, initCfg=initCfg) runBatch(b, 'batchNMDA', setup='mpi_bulletin') diff --git a/examples/batchCell/cfg.py b/examples/batchCell/cfg.py index c001301f6..1d3978c75 100644 --- a/examples/batchCell/cfg.py +++ b/examples/batchCell/cfg.py @@ -17,7 +17,7 @@ ############################################################################### # Run parameters ############################################################################### -cfg.duration = 1.0*1e3 +cfg.duration = 30.0*1e3 cfg.dt = 0.05 cfg.seeds = {'conn': 4321, 'stim': 1234, 'loc': 4321} cfg.hParams = {'celsius': 34, 'v_init': -80} diff --git a/examples/batchCell/init.py b/examples/batchCell/init.py index 7fadf23ac..296c0abb6 100644 --- a/examples/batchCell/init.py +++ b/examples/batchCell/init.py @@ -9,5 +9,25 @@ """ from netpyne import sim -cfg, netParams = sim.readCmdLineArgs() # read cfg and netParams from command line arguments -sim.createSimulateAnalyze(simConfig = cfg, netParams = netParams) + +cfg, netParams = sim.readCmdLineArgs(simConfigDefault='cfg.py', netParamsDefault='netParams.py') + +# sim.createSimulateAnalyze(netParams, cfg) +#​ +sim.initialize( + simConfig = cfg, + netParams = netParams) # create network object and set cfg and net params +sim.net.createPops() # instantiate network populations +sim.net.createCells() # instantiate network cells based on defined populations +sim.net.connectCells() # create connections between cells based on params +sim.net.addStims() # add network stimulation +sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc) +sim.runSim() # run parallel Neuron simulation +sim.gatherData() # gather spiking data and cell info from each node + +# distributed saving (to avoid errors with large output data) +#sim.saveDataInNodes() +#sim.gatherDataFromFiles(saveMerged=True) + +sim.saveData() # gather spiking data and cell info from each node +sim.analysis.plotData() \ No newline at end of file diff --git a/examples/batchCell/mod/parameters.multi b/examples/batchCell/mod/parameters.multi deleted file mode 100644 index 7fcba688f..000000000 --- a/examples/batchCell/mod/parameters.multi +++ /dev/null @@ -1,72 +0,0 @@ -:DEFAULT clampex -DEFAULT misc -DEFAULT stats -DEFAULT infot - -DEFAULT vecst -DEFAULT nstim -DEFAULT samnutils - -DEFAULT MyExp2SynBB -DEFAULT MyExp2SynNMDABB -DEFAULT izhi2007a -DEFAULT izhi2007b - -: Ben Suter ion channels -DEFAULT h_BS -DEFAULT kap_BS -DEFAULT kdmc_BS -DEFAULT kdr_BS -DEFAULT nax_BS - -: Mainen/Sej cell ion channels -DEFAULT naz -DEFAULT km -DEFAULT kv -DEFAULT kca -DEFAULT Nca -DEFAULT cadad - -: mig ion channels -DEFAULT cat_mig -DEFAULT can_mig -DEFAULT cal_mig - -: calcium activated k channel -DEFAULT cagk - -: k channel with ca and voltage dependence - responsible for fast part of AHP -DEFAULT IC - -: from Ben -DEFAULT savedist - -: Harnett 2015 H channel -DEFAULT h_harnett - -: Kole 2006 H channel -DEFAULT h_kole - -: BK channel with ca and voltage dependence -DEFAULT kBK - -: ih used for basket cells -DEFAULT HCN1 - -: channels for LTS1 -DEFAULT na2_mh -: ar is HCN - anomalous rectifier -DEFAULT ar_traub -DEFAULT cat_traub -DEFAULT cal_mh - -: persistent sodium channel -DEFAULT nap_sidi -: ican (calcium dependent cation non-specific channel) -DEFAULT ican_sidi - -: based on destexhe -DEFAULT gabab - -: homeostatic synapse -DEFAULT hsyn diff --git a/examples/batchCell/netParams.py b/examples/batchCell/netParams.py index a968a6644..36b442ee2 100644 --- a/examples/batchCell/netParams.py +++ b/examples/batchCell/netParams.py @@ -10,7 +10,7 @@ try: from __main__ import cfg # import SimConfig object with params from parent module except: - from tut8_cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module + from cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module ############################################################################### # diff --git a/examples/batchCellMapping/README.md b/examples/batchCellMapping/README.md new file mode 100644 index 000000000..b7fcb3054 --- /dev/null +++ b/examples/batchCellMapping/README.md @@ -0,0 +1,57 @@ +# Running batch simulations using NetPyNE + +Includes two toy examples using the 6-compartment M1 Corticospinal cell. +1) f-I curve as a function of the amplitude of an IClamp and dendritic Na conductance. +2) EPSP amplitude as a function of the weight of aNetStim and the rise time of an NMDA synapse. + +The code runs the required batch simulations in parallel using MPI and plots the corresponding figures. + +## Requirements +- NEURON with MPI support +- NetPyNE + +## Quick steps to run batch sims and plot results + +1) Compile mod files: + cd mod + mkmod + +2) Run batch simulations: + ./runbatch [num_cores] + +3) Plot results: + ipython -i analysis.py + + +## Code structure: + +* netParams.py: Defines the network model. Includes "fixed" parameter values of cells, synapses, connections, stimulation, etc. Changes to this file should only be made for relatively stable improvements of the model. Parameter values that will be varied systematically to explore or tune the model should be included here by referencing the appropiate variable in the simulation configuration (cfg) module. Only a single netParams file is required for each batch of simulations. + +* cfg.py: Simulation configuration module. Includes parameter values for each simulation run such as duration, dt, recording parameters etc. Also includes the model parameters that are being varied to explore or tune the network. When running a batch, NetPyNE will create one cfg file for each parameter configuration. + +* init.py: Sequence of commands to run a single simulation. Can be executed via 'ipython init.py'. When running a batch, NetPyNE will call init.py multiple times, pass a different cfg file for each of the parameter configurations explored. + +* batch.py: Defines the parameters and parameter values explored in the batch, as well as the run configuration (e.g. using MPI in a multicore machine or PBS Torque for HPCs). Includes examples of how to create 2 different batch simulations: + 1) batchNa(): explore the effect of changing the amplitude of an IClamp and the dendritic Na conductance. + 2) batchNMDA(): explore the effect of changing the weight of NetStim and the rise time of an NMDA synapse. + +To run, add a call to either of these functions in __main__(), or import the batch module and run interactively. + +To add new batch explorations, follow the format of the above example, making sure that the parameters being explored are included in cfg.py and referenced appropiately in netParams.py + +* runbatch: Shell script to run the batch simulation using mpi. Alternatively, can run "manually" using: +mpiexec -np [num_cores] nrniv -python -mpi batch.py + +* analysis.py: Functions to read and plot figures from the batch simulation results. Can call readPlotNa() or readPlotNMDA() from __main__() or import analysis and call interactively. + +* utils.py: Support functions to read output data from batch simulations, and others. Eventually, some of these functions will be moved to NetPyNE. + +* /cells: Folder containing cell model. In this case only includes SPI6.py. NetPyNE will import the cell specifications from this file. + +* /data: Folder to store output of simulations. + +* /mod: Folder containing mod files (need to clean up to keep just mod files required for this model) + + +For further information please contact: salvadordura@gmail.com + diff --git a/examples/batchCellMapping/analysis.py b/examples/batchCellMapping/analysis.py new file mode 100644 index 000000000..fc11f8567 --- /dev/null +++ b/examples/batchCellMapping/analysis.py @@ -0,0 +1,99 @@ +""" +analysis.py + +Functions to read and plot figures from the batch simulation results. +Can call readPlotNa() or readPlotNMDA() from __main__() or import analysis and call interactively. +""" + +import utils +import json +import matplotlib.pyplot as plt + + +def plotfINa(dataFolder, batchLabel, params, data): + utils.setPlotFormat(numColors = 8) + + Pvals = params[0]['values'] + Ivals = params[1]['values'] + Pvalsdic = {val: i for i,val in enumerate(Pvals)} + Ivalsdic = {val: i for i,val in enumerate(Ivals)} + + rates = [[0 for x in range(len(Pvals))] for y in range(len(Ivals))] + for key, d in data.items(): + rate = len(d['simData']['spkt']) + Pindex = Pvalsdic[d['paramValues'][0]] + Iindex = Ivalsdic[d['paramValues'][1]] + rates[Iindex][Pindex] = rate + print(d['paramValues']) + print(rate) + + filename = '%s/%s/%s_fIcurve.json' % (dataFolder, batchLabel, batchLabel) + with open(filename, 'w') as fileObj: + json.dump(rates, fileObj) + + plt.figure() + + handles = plt.plot(rates, marker='o', markersize=10) + plt.xlabel('Somatic current injection (nA)') + plt.xticks(list(range(len(Ivals)))[::2], Ivals[::2]) + plt.ylabel('Frequency (Hz)') + plt.legend(handles, params[0]['values'], title = 'dend Na', loc=2) + plt.savefig('%s/%s/%s_fIcurve.png' % (dataFolder, batchLabel, batchLabel)) + plt.show() + + +def plotNMDA(dataFolder, batchLabel, params, data, somaLabel='soma', stimRange=[5000,10000]): + utils.setPlotFormat(numColors = 8) + + Pvals = params[0]['values'] + Wvals = params[1]['values'] + Pvalsdic = {val: i for i,val in enumerate(Pvals)} + Wvalsdic = {val: i for i,val in enumerate(Wvals)} + + epsps = [[0 for x in range(len(Pvals))] for y in range(len(Wvals))] + for key, d in data.items(): + cellLabel = list(d['simData']['V_soma'].keys())[0] + vsoma = d['simData']['V_'+somaLabel][cellLabel] + epsp = max(vsoma[stimRange[0]:stimRange[1]]) - vsoma[stimRange[0]-1] + + Pindex = Pvalsdic[d['paramValues'][0]] + Windex = Wvalsdic[d['paramValues'][1]] + epsps[Windex][Pindex] = epsp + print(d['paramValues']) + print(epsp) + + filename = '%s/%s/%s_epsp.json' % (dataFolder, batchLabel, batchLabel) + with open(filename, 'w') as fileObj: + json.dump(epsps, fileObj) + + plt.figure() + + handles = plt.plot(epsps, marker='o', markersize=10) + plt.xlabel('Weight (of NetStim connection)') + plt.ylabel('Somatic EPSP amplitude (mV) in response to 1 NetStim spike') + plt.xticks(list(range(len(Wvals)))[::2], Wvals[::2]) + plt.legend(handles, params[0]['values'], title = 'NMDA tau1 (ms)', loc=2) + plt.savefig('%s/%s/%s_epsp.png' % (dataFolder, batchLabel, batchLabel)) + plt.show() + + +def readPlotNa(): + dataFolder = 'data/' + batchLabel = 'batchNa' + + params, data = utils.readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None) + plotfINa(dataFolder, batchLabel, params, data) + + +def readPlotNMDA(): + dataFolder = 'data/' + batchLabel = 'batchNMDA' + + params, data = utils.readBatchData(dataFolder, batchLabel, loadAll=0, saveAll=1, vars=None, maxCombs=None) + plotNMDA(dataFolder, batchLabel, params, data) + + +# Main code +if __name__ == '__main__': + readPlotNa() + # readPlotNMDA() diff --git a/examples/batchCellMapping/batch.py b/examples/batchCellMapping/batch.py new file mode 100644 index 000000000..db2b9ef96 --- /dev/null +++ b/examples/batchCellMapping/batch.py @@ -0,0 +1,73 @@ +""" +batch.py + +Batch simulation for M1 model using NetPyNE +""" +from netpyne.batch import Batch +import numpy as np + + +def runBatch(b, label, setup='mpi_bulletin'): + + b.batchLabel = label + b.saveFolder = 'data/'+b.batchLabel + b.method = 'grid' + + if setup == 'mpi_bulletin': + b.runCfg = {'type': 'mpi_bulletin', + 'script': 'init.py', + 'skip': True} + + elif setup == 'hpc_slurm_comet': + b.runCfg = {'type': 'hpc_slurm', + 'allocation': 'csd403', + 'walltime': '6:00:00', + 'nodes': 1, + 'coresPerNode': 24, + 'email': 'salvadordura@gmail.com', + 'folder': '/home/salvadord/netpyne/examples/batchCell', # startup folder + 'script': 'init.py', + 'mpiCommand': 'ibrun'} # specific command for Comet + + b.run() # run batch + +def runBatchComet(b, label): + b.batchLabel = label + b.saveFolder = 'data/'+b.batchLabel + b.method = 'grid' + b.runCfg = {'type': 'mpi_bulletin', + 'script': 'init.py', + 'skip': True} + + b.run() # run batch + + +def batchNa(): + params = {'dendNa': [0.025, 0.03, 0.035, 0.4], + ('IClamp1', 'amp'): list(np.arange(-2.0, 8.0, 0.5)/10.0)} + initCfg = {'duration': 1.1, 'tau1NMDA': 15} + b = Batch(params=params, initCfg=initCfg) + runBatch(b, 'batchNa', setup='mpi_bulletin') + + +def batchNMDA(): + params = {'tau1NMDA': [10, 15, 20, 25], + ('NetStim1', 'weight'): list(np.arange(1.0, 10.0, 1.0)/1e4)} + initCfg = {'duration': 1.1} + b = Batch(params=params, initCfg=initCfg) + runBatch(b, 'batchNMDA', setup='mpi_bulletin') + + +def batchNMDAMapping(): + params = {'tau1NMDA': [10, 15, 20, 25]} + initCfg = {'duration': 1100} + + from cfg import cfg + from netParams import netParams + b = Batch(params=params, initCfg=initCfg, cfg=cfg, netParams=netParams) + runBatch(b, 'batchNMDAMapping', setup='mpi_bulletin') + +# Main code +if __name__ == '__main__': + # batchNa() + batchNMDAMapping() diff --git a/examples/batchCellMapping/cells/SPI6.py b/examples/batchCellMapping/cells/SPI6.py new file mode 100644 index 000000000..286c092f5 --- /dev/null +++ b/examples/batchCellMapping/cells/SPI6.py @@ -0,0 +1,208 @@ +# simplified corticospinal cell model (6 compartment) + +from neuron import h +from math import exp,log + +h.load_file('stdrun.hoc') + +Vrest = -88.5366550238 +h.v_init = -75.0413649414 +h.celsius = 34.0 # for in vitro opt + +# geom properties +somaL = 48.4123467666 +somaDiam = 28.2149102762 +axonL = 594.292937602 +axonDiam = 1.40966286462 +apicL = 261.904636003 +apicDiam = 1.5831889597 +bdendL = 299.810775175 +bdendDiam = 2.2799248874 + +# passive properties +axonCap = 1.01280903702 +somaCap = 1.78829677463 +apicCap = 1.03418636866 +bdendCap = 1.89771901209 +rall = 114.510490019 +axonRM = 3945.2107187 +somaRM = 18501.7540916 +apicRM = 10751.193413 +bdendRM = 13123.00174 + +# Na, K reversal potentials calculated from BenS internal/external solutions via Nernst eq. +p_ek = -104.0 # these reversal potentials for in vitro conditions +p_ena = 42.0 +# h-current +h.erev_ih = -37.0 # global +gbar_h = 0.000140956438043 +h_gbar_tuft = 0.00565 # mho/cm^2 (based on Harnett 2015 J Neurosci) + +# d-current +gbar_kdmc = 0.000447365630734 +kdmc_gbar_axonm = 20 +# spiking currents +gbar_nax = 0.0345117294903 +nax_gbar_axonm = 5.0 +nax_gbar_somam = 1.0 +nax_gbar_dendm = 1.0 +gbar_kdr = 0.0131103978049 +kdr_gbar_axonm = 5.0 +# A few kinetic params changed vis-a-vis kdr.mod defaults: +kdr_vhalfn = 11.6427471384 +gbar_kap = 0.0898600246397 +kap_gbar_axonm = 5.0 +# A few kinetic params changed vis-a-vis kap.mod defaults: +kap_vhalfn = 32.7885075379 +kap_tq = -52.0967985869 +kap_vhalfl = -59.7867409796 # global!! + +# other ion channel parameters +cal_gcalbar = 4.41583533572e-06 +can_gcanbar = 4.60717910591e-06 +calginc = 1.0 +h_lambda = 325.0 +kBK_gpeak = 5.09733585163e-05 +kBK_caVhminShift = 43.8900261407 +cadad_depth = 0.119408607923 +cadad_taur = 99.1146852282 + +############################################################################### +# SPI6 Cell +############################################################################### +class SPI6 (): + "Simplified Corticospinal Cell Model" + def __init__(self,x=0,y=0,z=0,ID=0): + self.x,self.y,self.z=x,y,z + self.ID=ID + self.all_sec = [] + self.add_comp('soma') + self.set_morphology() + self.insert_conductances() + self.set_props() + + def add_comp(self, name): + self.__dict__[name] = h.Section(name=name)#,cell=self) + self.all_sec.append(self.__dict__[name]) + + def set_morphology(self): + self.add_comp('axon') + self.add_comp('Bdend') + self.add_comp('Adend1') + self.add_comp('Adend2') + self.add_comp('Adend3') + self.apic = [self.Adend1, self.Adend2, self.Adend3] + self.basal = [self.Bdend] + self.alldend = [self.Adend1, self.Adend2, self.Adend3, self.Bdend] + self.set_geom() + self.axon.connect(self.soma, 0.0, 0.0) + self.Bdend.connect(self.soma, 0.5, 0.0) # soma 0.5 to Bdend 0 + self.Adend1.connect(self.soma, 1.0, 0.0) + self.Adend2.connect(self.Adend1, 1.0, 0.0) + self.Adend3.connect(self.Adend2, 1.0, 0.0) + + def set_geom (self): + self.axon.L = axonL; self.axon.diam = axonDiam; + self.soma.L = somaL; self.soma.diam = somaDiam + for sec in self.apic: sec.L,sec.diam = apicL,apicDiam + self.Bdend.L = bdendL; self.Bdend.diam = bdendDiam + + def activeoff (self): + for sec in self.all_sec: sec.gbar_nax=sec.gbar_kdr=sec.gbar_kap=0.0 + + def set_axong (self): + axon = self.axon + axon.gbar_kdmc = gbar_kdmc * kdmc_gbar_axonm + axon.gbar_nax = gbar_nax * nax_gbar_axonm + axon.gbar_kdr = gbar_kdr * kdr_gbar_axonm + axon.gbar_kap = gbar_kap * kap_gbar_axonm + + def set_calprops (self,sec): + sec.gcalbar_cal = cal_gcalbar + sec.gcanbar_can = can_gcanbar + sec.gpeak_kBK = kBK_gpeak + sec.caVhmin_kBK = -46.08 + kBK_caVhminShift + sec.depth_cadad = cadad_depth + sec.taur_cadad = cadad_taur + + def set_somag (self): + sec = self.soma + sec.gbar_ih = gbar_h # Ih + self.set_calprops(sec) + sec.gbar_kdmc = gbar_kdmc + sec.gbar_nax = gbar_nax * nax_gbar_somam + + def set_bdendg (self): + sec = self.Bdend + sec.gbar_ih = gbar_h # Ih + self.set_calprops(sec) + sec.gbar_nax = gbar_nax * nax_gbar_dendm + + def set_apicg (self): + h.distance(0,0.5,sec=self.soma) # middle of soma is origin for distance + self.nexusdist = nexusdist = 300.0 + self.h_gbar_tuftm = h_gbar_tuftm = h_gbar_tuft / gbar_h + self.h_lambda = h_lambda = nexusdist / log(h_gbar_tuftm) + for sec in self.apic: + self.set_calprops(sec) + for seg in sec: + d = h.distance(seg.x,sec=sec) + if d <= nexusdist: seg.gbar_ih = gbar_h * exp(d/h_lambda) + else: seg.gbar_ih = h_gbar_tuft + sec.gbar_nax = gbar_nax * nax_gbar_dendm + self.apic[1].gcalbar_cal = cal_gcalbar * calginc # middle apical dend gets more iL + + # set properties + def set_props (self): + self.set_geom() + # cm - can differ across locations + self.axon.cm = axonCap + self.soma.cm = somaCap + self.Bdend.cm = bdendCap + for sec in self.apic: sec.cm = apicCap + # g_pas == 1.0/rm - can differ across locations + self.axon.g_pas = 1.0/axonRM + self.soma.g_pas = 1.0/somaRM + self.Bdend.g_pas = 1.0/bdendRM + for sec in self.apic: sec.g_pas = 1.0/apicRM + for sec in self.all_sec: + sec.ek = p_ek # K+ current reversal potential (mV) + sec.ena = p_ena # Na+ current reversal potential (mV) + sec.Ra = rall; sec.e_pas = Vrest # passive + sec.gbar_nax = gbar_nax # Na + sec.gbar_kdr = gbar_kdr # KDR + sec.vhalfn_kdr = kdr_vhalfn # KDR kinetics + sec.gbar_kap = gbar_kap # K-A + sec.vhalfn_kap = kap_vhalfn # K-A kinetics + sec.vhalfl_kap = kap_vhalfl + sec.tq_kap = kap_tq + self.set_somag() + self.set_bdendg() + self.set_apicg() + self.set_axong() + + def insert_conductances (self): + for sec in self.all_sec: + sec.insert('k_ion') + sec.insert('na_ion') + sec.insert('pas') # passive + sec.insert('nax') # Na current + sec.insert('kdr') # K delayed rectifier current + sec.insert('kap') # K-A current + for sec in [self.Adend3, self.Adend2, self.Adend1, self.Bdend, self.soma]: + sec.insert('ih') # h-current + sec.insert('ca_ion') # calcium channels + sec.insert('cal') # cal_mig.mod + sec.insert('can') # can_mig.mod + sec.insert('cadad') # cadad.mod - calcium decay + sec.insert('kBK') # kBK.mod - ca and v dependent k channel + for sec in [self.soma, self.axon]: sec.insert('kdmc') # K-D current in soma & axon only + +# +def prmstr (p,s,fctr=2.0,shift=5.0): + if p == 0.0: + print(s,'=',str(p-shift),str(p+shift),str(p),'True') + elif p < 0.0: + print(s, '=',str(p*fctr),str(p/fctr),str(p),'True') + else: + print(s, ' = ' , str(p/fctr), str(p*fctr), str(p), 'True') diff --git a/examples/batchCellMapping/cfg.py b/examples/batchCellMapping/cfg.py new file mode 100644 index 000000000..a98d3f400 --- /dev/null +++ b/examples/batchCellMapping/cfg.py @@ -0,0 +1,76 @@ +""" +cfg.py + +Simulation configuration +""" + +from netpyne import specs + +cfg = specs.SimConfig() + +############################################################################### +# +# SIMULATION CONFIGURATION +# +############################################################################### + +############################################################################### +# Run parameters +############################################################################### +cfg.duration = 1.0*1e3 +cfg.dt = 0.05 +cfg.seeds = {'conn': 4321, 'stim': 1234, 'loc': 4321} +cfg.hParams = {'celsius': 34, 'v_init': -80} +cfg.verbose = 0 +cfg.cvode_active = False +cfg.printRunTime = 0.1 +cfg.printPopAvgRates = True + + +############################################################################### +# Recording +############################################################################### +cfg.recordTraces = {'V_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'v'}} +cfg.recordStims = False +cfg.recordStep = 0.1 + + +############################################################################### +# Saving +############################################################################### +cfg.simLabel = 'sim1' +cfg.saveFolder = 'data' +cfg.savePickle = False +cfg.saveJson = True +cfg.saveDataInclude = ['simData', 'simConfig', 'netParams', 'net'] + + +############################################################################### +# Analysis and plotting +############################################################################### +cfg.analysis['plotTraces'] = {'include': ['PT5B'], 'oneFigPer': 'cell', 'saveFig': True, + 'showFig': False, 'figSize': (10,8), 'timeRange': [0,cfg.duration]} + + +############################################################################### +# Parameters +############################################################################### +cfg.dendNa = 0.0345117294903 +cfg.tau1NMDA = 15 + + +############################################################################### +# Current inputs +############################################################################### +cfg.addIClamp = 1 + +cfg.IClamp1 = {'pop': 'PT5B', 'sec': 'soma', 'loc': 0.5, 'start': 1, 'dur': 1000, 'amp': 0.0} + + +############################################################################### +# NetStim inputs +############################################################################### +cfg.addNetStim = 1 + +cfg.NetStim1 = {'pop': 'PT5B', 'sec': 'soma', 'loc': 0.5, 'synMech': 'NMDA', 'start': 500, + 'interval': 1000, 'noise': 0.0, 'number': 1, 'weight': 0.01, 'delay': 1} diff --git a/examples/batchCellMapping/init.py b/examples/batchCellMapping/init.py new file mode 100644 index 000000000..7fadf23ac --- /dev/null +++ b/examples/batchCellMapping/init.py @@ -0,0 +1,13 @@ +""" +init.py + +Starting script to run NetPyNE-based model. + +Usage: python init.py # Run simulation, optionally plot a raster + +MPI usage: mpiexec -n 4 nrniv -python -mpi init.py +""" + +from netpyne import sim +cfg, netParams = sim.readCmdLineArgs() # read cfg and netParams from command line arguments +sim.createSimulateAnalyze(simConfig = cfg, netParams = netParams) diff --git a/examples/batchCellMapping/mod/HCN1.mod b/examples/batchCellMapping/mod/HCN1.mod new file mode 100644 index 000000000..4ed0564dc --- /dev/null +++ b/examples/batchCellMapping/mod/HCN1.mod @@ -0,0 +1,67 @@ +: $Id: HCN1.mod,v 1.4 2013/01/02 15:01:55 samn Exp $ + +TITLE HCN1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX HCN1 + NONSPECIFIC_CURRENT ih + RANGE gbar, g, e, v50, htau, hinf + RANGE gfactor, htaufactor +} + +PARAMETER { + celsius (degC) + gbar = 0.0001 (mho/cm2) + e= -30 (mV) + v50= -73 (mV) + gfactor = 1 + htaufactor = 1.0 : 4.78 +} + +STATE { + h +} + +ASSIGNED { + ih (mA/cm2) + hinf + htau (ms) + v (mV) + g (mho/cm2) +} + +PROCEDURE giassign () { + : ih=g*h*(v-e)*gfactor + g = gbar*h*gfactor + ih = g*(v-e) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + giassign() +} + +DERIVATIVE states { + rates(v) + h'= (hinf- h)/ htau +} + +INITIAL { + rates(v) + h = hinf + giassign() +} + +PROCEDURE rates(v (mV)) { + UNITSOFF + : HCN1 + hinf = 1/(1+exp(0.151*(v-v50))) + htau = htaufactor*exp((0.033*(v+75)))/(0.011*(1+exp(0.083*(v+75)))) + UNITSON +} + diff --git a/examples/batchCellMapping/mod/IC.mod b/examples/batchCellMapping/mod/IC.mod new file mode 100644 index 000000000..b489e1b7d --- /dev/null +++ b/examples/batchCellMapping/mod/IC.mod @@ -0,0 +1,88 @@ +TITLE Ca-dependent potassium current +: +: Ca++ dependent K+ current IC responsible for +: action potentials AHP's +: Differential equations +: +: Model of Yamada, Koch & Adams, in: Methods in Neuronal Modeling, +: Ed. by Koch & Segev, MIT press, 1989. +: +: This current models the "fast" IK[Ca]: +: - potassium current +: - activated by intracellular calcium +: - VOLTAGE DEPENDENT +: +: Written by Alain Destexhe, Salk Institute, Sept 18, 1992 +: +: should be considered 'BK' - fast, big conductance + +NEURON { + SUFFIX ikc + USEION k READ ek WRITE ik + USEION ca READ cai + RANGE gkbar, ik + RANGE m_inf, tau_m + RANGE taumin + GLOBAL ascale,bscale,vfctr +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + v (mV) + celsius (degC) + ek (mV) + cai (mM) + gkbar = .003 (mho/cm2) : taken from + taumin = 0.1 + ascale = 250.0 + bscale = 0.1 + vfctr = 24.0 +} + +STATE { + m +} + +INITIAL { + evaluate_fct(v,cai) + m = m_inf +} + +ASSIGNED { + ik (mA/cm2) + m_inf + tau_m (ms) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gkbar * m * (v - ek) +} + +DERIVATIVE states { + evaluate_fct(v,cai) + m' = (m_inf - m) / tau_m +} + +UNITSOFF +PROCEDURE evaluate_fct(v(mV),cai(mM)) { LOCAL a,b,tadj +: +: activation kinetics of Yamada et al were at 22 deg. C +: transformation to 36 deg assuming Q10=3 +: + tadj = 3 ^ ((celsius-22.0)/10) + + a = ascale * cai * exp(v/vfctr) + b = bscale * exp(-v/vfctr) + + tau_m = 1.0 / (a + b) / tadj + if(tau_m < taumin){ tau_m = taumin } + m_inf = a / (a + b) +} +UNITSON diff --git a/examples/batchCellMapping/mod/MyExp2SynBB.mod b/examples/batchCellMapping/mod/MyExp2SynBB.mod new file mode 100644 index 000000000..9a68baef1 --- /dev/null +++ b/examples/batchCellMapping/mod/MyExp2SynBB.mod @@ -0,0 +1,67 @@ +: $Id: MyExp2SynBB.mod,v 1.4 2010/12/13 21:27:51 samn Exp $ +NEURON { +: THREADSAFE + POINT_PROCESS MyExp2SynBB + RANGE tau1, tau2, e, i, g, Vwt, gmax + NONSPECIFIC_CURRENT i +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) +} + +PARAMETER { + tau1=.1 (ms) <1e-9,1e9> + tau2 = 10 (ms) <1e-9,1e9> + e=0 (mV) + gmax = 1e9 (uS) + Vwt = 0 : weight for inputs coming in from vector +} + +ASSIGNED { + v (mV) + i (nA) + g (uS) + factor + etime (ms) +} + +STATE { + A (uS) + B (uS) +} + +INITIAL { + LOCAL tp + + Vwt = 0 : testing + + if (tau1/tau2 > .9999) { + tau1 = .9999*tau2 + } + A = 0 + B = 0 + tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1) + factor = -exp(-tp/tau1) + exp(-tp/tau2) + factor = 1/factor +} + +BREAKPOINT { + SOLVE state METHOD cnexp + g = B - A + if (g>gmax) {g=gmax}: saturation + i = g*(v - e) +} + +DERIVATIVE state { + A' = -A/tau1 + B' = -B/tau2 +} + +NET_RECEIVE(w (uS)) {LOCAL ww + ww=w + A = A + ww*factor + B = B + ww*factor +} diff --git a/examples/batchCellMapping/mod/MyExp2SynNMDABB.mod b/examples/batchCellMapping/mod/MyExp2SynNMDABB.mod new file mode 100755 index 000000000..e225a3017 --- /dev/null +++ b/examples/batchCellMapping/mod/MyExp2SynNMDABB.mod @@ -0,0 +1,92 @@ +: $Id: MyExp2SynNMDABB.mod,v 1.4 2010/12/13 21:28:02 samn Exp $ +NEURON { +: THREADSAFE + POINT_PROCESS MyExp2SynNMDABB + RANGE e, i, iNMDA, s, sNMDA, r, tau1NMDA, tau2NMDA, Vwt, smax, sNMDAmax, g + NONSPECIFIC_CURRENT iNMDA + USEION ca READ cai,cao WRITE ica + GLOBAL fracca + RANGE ica +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) + FARADAY = (faraday) (coulomb) + R = (k-mole) (joule/degC) +} + +PARAMETER { + tau1NMDA = 15 (ms) + tau2NMDA = 150 (ms) + e = 0 (mV) + r = 1 + smax = 1e9 (1) + sNMDAmax = 1e9 (1) + Vwt = 0 : weight for inputs coming in from vector + fracca = 0.13 : fraction of current that is ca ions; Srupuston &al 95 +} + +ASSIGNED { + v (mV) + iNMDA (nA) + sNMDA (1) + mgblock (1) + factor2 (1) + ica (nA) + cai (mM) + cao (mM) + g (umho) +} + +STATE { + A2 (1) + B2 (1) +} + +INITIAL { + LOCAL tp + Vwt = 0 : testing + if (tau1NMDA/tau2NMDA > .9999) { + tau1NMDA = .9999*tau2NMDA + } + A2 = 0 + B2 = 0 + tp = (tau1NMDA*tau2NMDA)/(tau2NMDA - tau1NMDA) * log(tau2NMDA/tau1NMDA) + factor2 = -exp(-tp/tau1NMDA) + exp(-tp/tau2NMDA) + factor2 = 1/factor2 +} + +BREAKPOINT { + LOCAL iTOT + SOLVE state METHOD cnexp + : Jahr Stevens 1990 J. Neurosci + mgblock = 1.0 / (1.0 + 0.28 * exp(-0.062(/mV) * v) ) + sNMDA = B2 - A2 + if (sNMDA>sNMDAmax) {sNMDA=sNMDAmax}: saturation + + :iTOT = sNMDA * (v - e) * mgblock + :iNMDA = iTOT * (1-fracca) + :ica = iTOT * fracca + + iNMDA = sNMDA * (v - e) * mgblock * (1-fracca) + if(fracca>0.0){ica = sNMDA * ghkg(v,cai,cao,2) * mgblock * fracca} + g = sNMDA * mgblock +} + +INCLUDE "ghk.inc" + +DERIVATIVE state { + A2' = -A2/tau1NMDA + B2' = -B2/tau2NMDA +} + +NET_RECEIVE(w (uS)) {LOCAL ww + ww=w + :printf("NMDA Spike: %g\n", t) + if(r>=0){ : if r>=0, g = NMDA*r + A2 = A2 + factor2*ww*r + B2 = B2 + factor2*ww*r + } +} diff --git a/examples/batchCellMapping/mod/Nca.mod b/examples/batchCellMapping/mod/Nca.mod new file mode 100644 index 000000000..33add8cd9 --- /dev/null +++ b/examples/batchCellMapping/mod/Nca.mod @@ -0,0 +1,124 @@ +: $Id: Nca.mod,v 1.7 2004/06/08 21:07:12 billl Exp $ + +COMMENT +26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error. +Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal + +ca.mod +Uses fixed eca instead of GHK eqn + +HVA Ca current +Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13: +4609-4621. + +Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu + +ENDCOMMENT + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX Nca + USEION ca READ eca WRITE ica + RANGE i, m, h, gca, gmax + RANGE minf, hinf, mtau, htau + GLOBAL q10, temp, tadj, vmin, vmax, vshift +} + +PARAMETER { + gmax = 0.1 (pS/um2) : 0.12 mho/cm2 + vshift = 0 (mV) : voltage shift (affects all) + + cao = 2.5 (mM) : external ca concentration + cai (mM) + + temp = 23 (degC) : original temp + q10 = 2.3 : temperature sensitivity + + v (mV) + dt (ms) + celsius (degC) + vmin = -120 (mV) + vmax = 100 (mV) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) + FARADAY = (faraday) (coulomb) + R = (k-mole) (joule/degC) + PI = (pi) (1) +} + +ASSIGNED { + i (mA/cm2) + ica (mA/cm2) + gca (pS/um2) + eca (mV) + minf hinf + mtau (ms) htau (ms) + tadj +} + + +STATE { m h } + +INITIAL { + tadj = q10^((celsius - temp)/10) + rates(v+vshift) + m = minf + h = hinf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gca = tadj*gmax*m*m*h + i = (1e-4) * gca * (v - eca) + ica = i +} + +LOCAL mexp, hexp + +:PROCEDURE states() { + : rates(v+vshift) + : m = m + mexp*(minf-m) + : h = h + hexp*(hinf-h) + : VERBATIM + : return 0; + : ENDVERBATIM + :} + + DERIVATIVE states { + rates(v+vshift) + m' = (minf-m)/mtau + h' = (hinf-h)/htau + } + + PROCEDURE rates(vm) { + LOCAL a, b + + a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1) + b = 0.94*exp((-75-vm)/17) + + mtau = 1/tadj/(a+b) + minf = a/(a+b) + + :"h" inactivation + + a = 0.000457*exp((-13-vm)/50) + b = 0.0065/(exp((-vm-15)/28) + 1) + + htau = 1/tadj/(a+b) + hinf = a/(a+b) + } + + FUNCTION efun(z) { + if (fabs(z) < 1e-4) { + efun = 1 - z/2 + }else{ + efun = z/(exp(z) - 1) + } + } diff --git a/examples/batchCellMapping/mod/ar_traub.mod b/examples/batchCellMapping/mod/ar_traub.mod new file mode 100644 index 000000000..93fe5898f --- /dev/null +++ b/examples/batchCellMapping/mod/ar_traub.mod @@ -0,0 +1,47 @@ +TITLE Anomalous rectifier current for RD Traub, J Neurophysiol 89:909-921, 2003 + +COMMENT + + Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu) + +ENDCOMMENT + +INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) } + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) +} +NEURON { + SUFFIX ar + NONSPECIFIC_CURRENT i + RANGE gbar, i +} +PARAMETER { + gbar = 0.0 (mho/cm2) + v (mV) + erev = -35 (mV) +} +ASSIGNED { + i (mA/cm2) + minf (1) + mtau (ms) +} +STATE { + m +} +BREAKPOINT { + SOLVE states METHOD cnexp + i = gbar * m * ( v - erev ) +} +INITIAL { + minf = 1 / ( 1 + exp( ( v + 75 ) / 5.5 ) ) + mtau = 1 / ( exp( -14.6 - 0.086 * v ) + exp( -1.87 + 0.07 * v ) ) + m = minf + : m = 0.25 : ?? +} +DERIVATIVE states { + minf = 1 / ( 1 + exp( ( v + 75 ) / 5.5 ) ) + mtau = 1 / ( exp( -14.6 - 0.086 * v ) + exp( -1.87 + 0.07 * v ) ) + m' = ( minf - m ) / mtau +} diff --git a/examples/batchCellMapping/mod/cadad.mod b/examples/batchCellMapping/mod/cadad.mod new file mode 100644 index 000000000..46466753c --- /dev/null +++ b/examples/batchCellMapping/mod/cadad.mod @@ -0,0 +1,74 @@ +: $Id: cadad.mod,v 1.4 2002/11/08 15:42:37 billl Exp $ +TITLE Fast mechanism for submembranal Ca++ concentration (cai) +: +: Takes into account: +: +: - increase of cai due to calcium currents +: - extrusion of calcium with a simple first order equation +: +: This mechanism is compatible with the calcium pump "cad" and has the +: same name and parameters; however the parameters specific to the pump +: are dummy here. +: +: Parameters: +: +: - depth: depth of the shell just beneath the membran (in um) +: - cainf: equilibrium concentration of calcium (2e-4 mM) +: - taur: time constant of calcium extrusion (must be fast) +: - kt,kd: dummy parameters +: +: Written by Alain Destexhe, Salk Institute, 1995 +: + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX cadad + USEION ca READ ica, cai WRITE cai + RANGE depth,kt,kd,cainf,taur +} + +UNITS { + (molar) = (1/liter) : moles do not appear in units + (mM) = (millimolar) + (um) = (micron) + (mA) = (milliamp) + (msM) = (ms mM) +} + +CONSTANT { + FARADAY = 96489 (coul) : moles do not appear in units + : FARADAY = 96.489 (k-coul) : moles do not appear in units +} + +PARAMETER { + depth = 1 (um) : depth of shell + taur = 5 (ms) : rate of calcium removal + cainf = 2.4e-4 (mM) + kt = 0 (mM/ms) : dummy + kd = 0 (mM) : dummy +} + +STATE { + cai (mM) +} + +INITIAL { + cai = cainf +} + +ASSIGNED { + ica (mA/cm2) + drive_channel (mM/ms) +} + +BREAKPOINT { + SOLVE state METHOD cnexp +} + +DERIVATIVE state { + drive_channel = - (10000) * ica / (2 * FARADAY * depth) + if (drive_channel <= 0.) { drive_channel = 0. } : cannot pump inward + cai' = drive_channel + (cainf-cai)/taur +} + diff --git a/examples/batchCellMapping/mod/cagk.mod b/examples/batchCellMapping/mod/cagk.mod new file mode 100644 index 000000000..662d43764 --- /dev/null +++ b/examples/batchCellMapping/mod/cagk.mod @@ -0,0 +1,87 @@ +TITLE CaGk +: Calcium activated K channel. +: Modified from Moczydlowski and Latorre (1983) J. Gen. Physiol. 82 + +UNITS { + (molar) = (1/liter) +} + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (mM) = (millimolar) +} + + +NEURON { + SUFFIX cagk + USEION ca READ cai + USEION k READ ek WRITE ik + RANGE gbar,gkca,ik + RANGE oinf, tau +} + +UNITS { + FARADAY = (faraday) (kilocoulombs) + R = 8.313424 (joule/degC) +} + +PARAMETER { + celsius (degC) + v (mV) + gbar=.01 (mho/cm2) : Maximum Permeability + cai (mM) + ek (mV) + + d1 = .84 + d2 = 1. + k1 = .48e-3 (mM) + k2 = .13e-6 (mM) + abar = .28 (/ms) + bbar = .48 (/ms) + st=1 (1) +} + +ASSIGNED { + ik (mA/cm2) + oinf + tau (ms) + gkca (mho/cm2) +} + +INITIAL { + rate(v,cai) + o=oinf +} + +STATE { o } : fraction of open channels + +BREAKPOINT { + SOLVE state METHOD cnexp + gkca = gbar*o^st + ik = gkca*(v - ek) +} + +DERIVATIVE state { : exact when v held constant; integrates over dt step + rate(v, cai) + o' = (oinf - o)/tau +} + +FUNCTION alp(v (mV), c (mM)) (1/ms) { :callable from hoc + alp = c*abar/(c + exp1(k1,d1,v)) +} + +FUNCTION bet(v (mV), c (mM)) (1/ms) { :callable from hoc + bet = bbar/(1 + c/exp1(k2,d2,v)) +} + +FUNCTION exp1(k (mM), d, v (mV)) (mM) { :callable from hoc + exp1 = k*exp(-2*d*FARADAY*v/R/(273.15 + celsius)) +} + +PROCEDURE rate(v (mV), c (mM)) { :callable from hoc + LOCAL a + a = alp(v,c) + tau = 1/(a + bet(v, c)) + oinf = a*tau +} diff --git a/examples/batchCellMapping/mod/cal_mh.mod b/examples/batchCellMapping/mod/cal_mh.mod new file mode 100644 index 000000000..79ad9c924 --- /dev/null +++ b/examples/batchCellMapping/mod/cal_mh.mod @@ -0,0 +1,139 @@ + +COMMENT +26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error. + Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal + +ca.mod +Uses fixed eca instead of GHK eqn + +HVA Ca current +Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13: +4609-4621. + +Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu + +ENDCOMMENT + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX ca + USEION ca READ eca WRITE ica + RANGE m, h, gca, gbar + RANGE minf, hinf, mtau, htau + RANGE q10, temp, tadj, vmin, vmax, vshift +} + +PARAMETER { + gbar = 0.1 (pS/um2) : 0.12 mho/cm2 + vshift = 0 (mV) : voltage shift (affects all) + + cao = 2.5 (mM) : external ca concentration + cai (mM) + + temp = 23 (degC) : original temp + q10 = 2.3 : temperature sensitivity + + v (mV) + dt (ms) + celsius (degC) + vmin = -120 (mV) + vmax = 100 (mV) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) + FARADAY = (faraday) (coulomb) + R = (k-mole) (joule/degC) + PI = (pi) (1) +} + +ASSIGNED { + ica (mA/cm2) + gca (pS/um2) + eca (mV) + minf hinf + mtau (ms) htau (ms) + tadj +} + + +STATE { m h } + +INITIAL { + trates(v+vshift) + m = minf + h = hinf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gca = tadj*gbar*m*m*h + ica = (1e-4) * gca * (v - eca) +} + +LOCAL mexp, hexp + +:PROCEDURE states() { +: trates(v+vshift) +: m = m + mexp*(minf-m) +: h = h + hexp*(hinf-h) +: VERBATIM +: return 0; +: ENDVERBATIM +:} + +DERIVATIVE states { + trates(v+vshift) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE trates(v) { + + + : TABLE minf, hinf, mtau, htau + : DEPEND celsius, temp + : + : FROM vmin TO vmax WITH 199 + + rates(v): not consistently executed from here if usetable == 1 + +: tinc = -dt * tadj + +: mexp = 1 - exp(tinc/mtau) +: hexp = 1 - exp(tinc/htau) +} + + +PROCEDURE rates(vm) { + LOCAL a, b + + tadj = q10^((celsius - temp)/10) + + a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1) + b = 0.94*exp((-75-vm)/17) + + mtau = 1/tadj/(a+b) + minf = a/(a+b) + + :"h" inactivation + + a = 0.000457*exp((-13-vm)/50) + b = 0.0065/(exp((-vm-15)/28) + 1) + + htau = 1/tadj/(a+b) + hinf = a/(a+b) +} + +FUNCTION efun(z) { + if (fabs(z) < 1e-4) { + efun = 1 - z/2 + }else{ + efun = z/(exp(z) - 1) + } +} diff --git a/examples/batchCellMapping/mod/cal_mig.mod b/examples/batchCellMapping/mod/cal_mig.mod new file mode 100644 index 000000000..9069e8643 --- /dev/null +++ b/examples/batchCellMapping/mod/cal_mig.mod @@ -0,0 +1,131 @@ +TITLE L-calcium channel +: L-type calcium channel with [Ca]i inactivation +: from Jaffe, D. B., Ross, W. N., Lisman, J. E., Laser-Ross, N., Miyakawa, H., and Johnston, D. A. A model for dendritic Ca2 +: accumulation in hippocampal pyramidal neurons based on fluorescence imaging measurements. J. Neurophysiol. 71:1O65-1077 1994. +: conduction density estimate of 50-200 pS/mu2; 0.0025 S/cm2 (5-20 channels of 10 each) +: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 +: hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). +: adapted from http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=3263&file=\ca3_db\cal2.mod +: this version from https://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=148094&file=\kv72-R213QW-mutations\cal2.mod +: Miceli F, Soldovieri MV, Ambrosino P, Barrese V, Migliore M, Cilio MR, Taglialatela M (2013) Genotype-phenotype +: correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits. PNAS 110:4386-4391 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + + FARADAY = 96520 (coul) + R = 8.3134 (joule/degC) + KTOMV = .0853 (mV/degC) +} + +PARAMETER { + v (mV) + celsius (degC) + gcalbar=.003 (mho/cm2) + ki=.001 (mM) + cai = 50.e-6 (mM) + cao = 2 (mM) + q10 = 5 + mmin=0.2 + tfa = 1 + a0m =0.1 + zetam = 2 + vhalfm = 4 + gmm=0.1 + USEGHK=1 + erev = 100 +} + + +NEURON { + SUFFIX cal + USEION ca READ cai,cao WRITE ica + RANGE gcalbar,cai, ica, gcal, ggk + RANGE minf,tau + GLOBAL USEGHK +} + +STATE { + m +} + +ASSIGNED { + ica (mA/cm2) + gcal (mho/cm2) + minf + tau (ms) + ggk +} + +INITIAL { + rate(v) + m = minf +} + +BREAKPOINT { + SOLVE state METHOD cnexp + gcal = gcalbar*m*m*h2(cai) + if (USEGHK == 1) { + ggk=ghk(v,cai,cao) + } else { + ggk=v-erev + } + ica = gcal*ggk +} + +FUNCTION h2(cai(mM)) { + h2 = ki/(ki+cai) +} + + +FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) { + LOCAL nu,f + f = KTF(celsius)/2 + nu = v/f + ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu) +} + +FUNCTION KTF(celsius (DegC)) (mV) { + KTF = ((25./293.15)*(celsius + 273.15)) +} + + +FUNCTION efun(z) { + if (fabs(z) < 1e-4) { + efun = 1 - z/2 + }else{ + efun = z/(exp(z) - 1) + } +} + +FUNCTION alp(v(mV)) (1/ms) { + alp = 15.69*(-1.0*v+81.5)/(exp((-1.0*v+81.5)/10.0)-1.0) +} + +FUNCTION bet(v(mV)) (1/ms) { + bet = 0.29*exp(-v/10.86) +} + +FUNCTION alpmt(v(mV)) { + alpmt = exp(0.0378*zetam*(v-vhalfm)) +} + +FUNCTION betmt(v(mV)) { + betmt = exp(0.0378*zetam*gmm*(v-vhalfm)) +} + +DERIVATIVE state { + rate(v) + m' = (minf - m)/tau +} + +PROCEDURE rate(v (mV)) { :callable from hoc + LOCAL a, b, qt + qt=q10^((celsius-25)/10) + a = alp(v) + b = 1/((a + bet(v))) + minf = a*b + tau = betmt(v)/(qt*a0m*(1+alpmt(v))) + if (tau O (Alpha,Beta) + + If the binding is fast, the fraction of open channels is given by: + + O = G^n / ( G^n + KD ) + + where KD = Beta / Alpha is the dissociation constant + +----------------------------------------------------------------------------- + + Parameters estimated from patch clamp recordings of GABAB PSP's in + rat hippocampal slices (Otis et al, J. Physiol. 463: 391-407, 1993). + +----------------------------------------------------------------------------- + + PULSE MECHANISM + + Kinetic synapse with release mechanism as a pulse. + + Warning: for this mechanism to be equivalent to the model with diffusion + of transmitter, small pulses must be used... + + For a detailed model of GABAB: + + Destexhe, A. and Sejnowski, T.J. G-protein activation kinetics and + spill-over of GABA may account for differences between inhibitory responses + in the hippocampus and thalamus. Proc. Natl. Acad. Sci. USA 92: + 9515-9519, 1995. + + For a review of models of synaptic currents: + + Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Kinetic models of + synaptic transmission. In: Methods in Neuronal Modeling (2nd edition; + edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1996. + + This simplified model was introduced in: + + Destexhe, A., Bal, T., McCormick, D.A. and Sejnowski, T.J. + Ionic mechanisms underlying synchronized oscillations and propagating + waves in a model of ferret thalamic slices. Journal of Neurophysiology + 76: 2049-2070, 1996. + + See also http://www.cnl.salk.edu/~alain + + + + Alain Destexhe, Salk Institute and Laval University, 1995 + +----------------------------------------------------------------------------- +ENDCOMMENT + + + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + POINT_PROCESS GABAB + RANGE R, G, g + NONSPECIFIC_CURRENT i + GLOBAL Cmax, Cdur + GLOBAL K1, K2, K3, K4, KD, Erev, warn, cutoff +} +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (umho) = (micromho) + (mM) = (milli/liter) +} + +PARAMETER { + + Cmax = 0.5 (mM) : max transmitter concentration + Cdur = 0.3 (ms) : transmitter duration (rising phase) +: +: From Kfit with long pulse (5ms 0.5mM) +: + K1 = 0.52 (/ms mM) : forward binding rate to receptor + K2 = 0.0013 (/ms) : backward (unbinding) rate of receptor + K3 = 0.098 (/ms) : rate of G-protein production + K4 = 0.033 (/ms) : rate of G-protein decay + KD = 100 : dissociation constant of K+ channel + n = 4 : nb of binding sites of G-protein on K+ + Erev = -95 (mV) : reversal potential (E_K) + warn = 0 : too large G warning has/has not been issued + cutoff = 1e12 +} + + +ASSIGNED { + v (mV) : postsynaptic voltage + i (nA) : current = g*(v - Erev) + g (umho) : conductance + Gn + R : fraction of activated receptor + edc + synon + Rinf + Rtau (ms) + Beta (/ms) +} + +STATE { + Ron Roff + G : fraction of activated G-protein +} + + +INITIAL { + R = 0 + G = 0 + Ron = 0 + Roff = 0 + synon = 0 + Rinf = K1*Cmax/(K1*Cmax + K2) + Rtau = 1/(K1*Cmax + K2) + Beta = K2 + +} + +BREAKPOINT { + SOLVE bindkin METHOD derivimplicit + if (G < cutoff) { + Gn = G*G*G*G : ^n = 4 + g = Gn / (Gn+KD) + } else { + if(!warn){ + printf("gabab.mod WARN: G = %g too large\n", G) + warn = 1 + } + g = 1 + } + i = g*(v - Erev) +} + + +DERIVATIVE bindkin { + Ron' = synon*K1*Cmax - (K1*Cmax + K2)*Ron + Roff' = -K2*Roff + R = Ron + Roff + G' = K3 * R - K4 * G +} + +: following supports both saturation from single input and +: summation from multiple inputs +: Note: automatic initialization of all reference args to 0 except first + +NET_RECEIVE(weight, r0, t0 (ms)) { + if (flag == 1) { : at end of Cdur pulse so turn off + r0 = weight*(Rinf + (r0 - Rinf)*exp(-(t - t0)/Rtau)) + t0 = t + synon = synon - weight + state_discontinuity(Ron, Ron - r0) + state_discontinuity(Roff, Roff + r0) + }else{ : at beginning of Cdur pulse so turn on + r0 = weight*r0*exp(-Beta*(t - t0)) + t0 = t + synon = synon + weight + state_discontinuity(Ron, Ron + r0) + state_discontinuity(Roff, Roff - r0) + :come again in Cdur + net_send(Cdur, 1) + } +} diff --git a/examples/batchCellMapping/mod/ghk.inc b/examples/batchCellMapping/mod/ghk.inc new file mode 100644 index 000000000..95babfc11 --- /dev/null +++ b/examples/batchCellMapping/mod/ghk.inc @@ -0,0 +1,40 @@ +COMMENT + GHK function that returns effective driving force + Slope at low voltages is 1 + z needs to be set as a PARAMETER +ENDCOMMENT + +FUNCTION ghkg(v(mV), ci(mM), co(mM), z) (mV) { + LOCAL xi, f, exi, fxi + f = R*(celsius+273.15)/(z*(1e-3)*FARADAY) + xi = v/f + exi = exp(xi) + if (fabs(xi) < 1e-4) { + fxi = 1 - xi/2 + }else{ + fxi = xi/(exi - 1) + } + ghkg = f*((ci/co)*exi - 1)*fxi +} + +FUNCTION ghk(v(mV), ci(mM), co(mM), z) (.001 coul/cm3) { + LOCAL xi, f, exi, fxi + f = R*(celsius+273.15)/(z*(1e-3)*FARADAY) + xi = v/f + exi = exp(xi) + if (fabs(xi) < 1e-4) { + fxi = 1 - xi/2 + }else{ + fxi = xi/(exi - 1) + } + ghk = (.001)*z*FARADAY*(ci*exi - co)*fxi +} + + + + + + + + + diff --git a/examples/batchCellMapping/mod/h_BS.mod b/examples/batchCellMapping/mod/h_BS.mod new file mode 100644 index 000000000..eeca578ac --- /dev/null +++ b/examples/batchCellMapping/mod/h_BS.mod @@ -0,0 +1,88 @@ +TITLE I-h channel from Magee 1998 for distal dendrites +: modified to take into account Sonia's exp. Apr.2008 M.Migliore +: thread-safe 2010-05-18 Ben Suter +: 2010-11-07 Ben Suter, removing "hd" from parameter names, changing suffix from "hd" to "h" +: Parameters fit to pre-ZD current-clamp step responses from experiment BS0284 (traces and reconstruction from single corticospinal neuron) +: 2011-09-18 Ben Suter, set default parameter values to those found from MRF optimization for BS0284 model +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius = 34.0 (degC) + erev = -37.0 (mV) + gbar = 0.0001 (mho/cm2) + vhalfl = -78.474 (mV) : was -81 + kl = -6 : was -8 + vhalft = -66.139 (mV) : was -62 + a0t = 0.009 (/ms) : was 0.0077696 + zetat = 20 (1) : was 5 + gmt = 0.01 (1) : was 0.057127 + q10 = 4.5 + qtl = 1 + taumin = 2.0 (ms) : minimal value of time constant +} + +NEURON { + SUFFIX h + NONSPECIFIC_CURRENT i + RANGE gbar, vhalfl + RANGE linf, taul, g + GLOBAL taumin +} + +STATE { + l +} + +ASSIGNED { + i (mA/cm2) + linf + taul + g +} + +INITIAL { + rate(v) + l = linf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*l + i = g*(v-erev) +} + +FUNCTION alpt(v(mV)) { + alpt = exp(0.0378*zetat*(v-vhalft)) +} + +FUNCTION bett(v(mV)) { + bett = exp(0.0378*zetat*gmt*(v-vhalft)) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rate(v) + l' = (linf - l)/taul +} + +PROCEDURE rate(v (mV)) { :callable from hoc + LOCAL a,qt + qt = q10^((celsius-33)/10) + a = alpt(v) + linf = 1/(1 + exp(-(v-vhalfl)/kl)) + taul = bett(v)/(qtl*qt*a0t*(1+a)) + 1e-8 + if(taul < taumin) { taul = taumin } : min value of time constant +} diff --git a/examples/batchCellMapping/mod/h_harnett.mod b/examples/batchCellMapping/mod/h_harnett.mod new file mode 100644 index 000000000..84b0c64f7 --- /dev/null +++ b/examples/batchCellMapping/mod/h_harnett.mod @@ -0,0 +1,69 @@ +TITLE I-h channel from Harnett 2015 - J Neurosci + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius (degC) + erev = -30 (mV) + gbar = 0.0001 (mho/cm2) + vhalf = -100.6 (mV) + k = 6.4 + bA = 9.63 : for activation tau - (note - only 1 tau) + bD = 1.30 : for deactivation tau + mA = 0.0458 : for activation tau + mD = -0.0447 : for deactivation tau + q10 = 2.2 + taumin = 2.0 (ms) : minimal value of time constant +} + +NEURON { + SUFFIX h15 + NONSPECIFIC_CURRENT i + RANGE gbar, minf, tau, g, m + GLOBAL taumin, k, bA, bD, mA, mD, vhalf +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) + minf + tau + g +} + +INITIAL { + rate(v) + m = minf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*m + i = g*(v-erev) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rate(v) + m' = (minf - m) / tau +} + +PROCEDURE rate(v (mV)) { :callable from hoc - leads to segfault in python + LOCAL qt + qt = q10^((celsius-26.0)/10.0) + + if(v <= -92.0046199111992) { + tau = exp(bA + mA * v) / qt + } else { + tau = exp(bD + mD * v) / qt + } + if(tau < taumin) { tau = taumin } + + minf = 1.0/(1.0 + exp((v-vhalf)/k)) +} diff --git a/examples/batchCellMapping/mod/h_kole.mod b/examples/batchCellMapping/mod/h_kole.mod new file mode 100644 index 000000000..3971e5125 --- /dev/null +++ b/examples/batchCellMapping/mod/h_kole.mod @@ -0,0 +1,76 @@ +TITLE Ih-current +: modified from http://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=64195&file=%5cStochastic%5cStochastic_Na%5cih.mod +: /u/samn/papers/jnsci_26_1677.pdf +: +: @article{kole2006single, +: title={Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output}, +: author={Kole, M.H.P. and Hallermann, S. and Stuart, G.J.}, +: journal={The Journal of neuroscience}, +: volume={26}, +: number={6}, +: pages={1677--1687}, +: year={2006}, +: publisher={Soc Neuroscience} +: } + +COMMENT +Author: Stefan Hallermann; modified by Sam Neymotin (parameterized) +Provides deterministic Ih-currents as described in Kole et al. (2006). +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + erev=-45 (mV) :ih-reversal potential + gbar=0.00015 (S/cm2) :default Ih conductance; exponential distribution is set in Ri18init.hoc + q10 = 2.2 + ascale = 0.00643 + bscale = 0.193 + ashift = 154.9 + aslope = 11.9 + bslope = 33.1 +} + +NEURON { + THREADSAFE + SUFFIX ih + NONSPECIFIC_CURRENT i + RANGE i,gbar,ascale,bscale,ashift,aslope,bslope +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) +} + +INITIAL { LOCAL a,b + a = alpha(v) + b = beta(v) + m = a / (a + b) +} + +BREAKPOINT { + SOLVE state METHOD cnexp + i = gbar*m*(v-erev) +} + +: tau = 1 / (alpha + beta) +FUNCTION alpha(v(mV)) { + alpha = ascale*(v+ashift)/(exp((v+ashift)/aslope)-1) + :parameters are estimated by direct fitting of HH model to activation time constants and voltage activation curve recorded at 34C +} + +FUNCTION beta(v(mV)) { + beta = bscale*exp(v/bslope) +} + +DERIVATIVE state { + m' = (1-m)*alpha(v) - m*beta(v) +} diff --git a/examples/batchCellMapping/mod/hsyn.mod b/examples/batchCellMapping/mod/hsyn.mod new file mode 100644 index 000000000..d23794a36 --- /dev/null +++ b/examples/batchCellMapping/mod/hsyn.mod @@ -0,0 +1,130 @@ + +NEURON { + POINT_PROCESS hsyn + RANGE tau1, tau2, e, i, g, gmax + NONSPECIFIC_CURRENT i + : following variables used in synaptic scaling + RANGE scalefactor : synaptic weight scaling factor + RANGE firingrate : Cell firing rate + RANGE activity : Slow-varying cell activity value + RANGE maxscale : Maximum scaling factor + RANGE targetrate : Target firing rate + RANGE rateinc : rate increase step + RANGE ratedectau : rate decay tau + RANGE scaling : is scaling getting adjusted for this synapse? default is on + RANGE scaleratefctr : Scaling weight growth/decay factor + GLOBAL verbose +} + +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (uS) = (microsiemens) +} + +PARAMETER { + tau1=.1 (ms) <1e-9,1e9> + tau2 = 10 (ms) <1e-9,1e9> + e=0 (mV) + gmax = 1e9 (uS) + : default values for synaptic scaling + scaling = 1 : synaptic scaling defaults to ON + rateinc = 1.0 + ratedectau = 1000.0 + scaleratefctr = 0.000001 : how quickly scaling (weights) change + maxscale = 1000.0 : Max scaling factor + targetrate = -1 : Cell's target activity (Hz) + verbose = 0 +} + +ASSIGNED { + v (mV) + i (nA) + g (uS) + factor +} + +STATE { + A (uS) + B (uS) + activity : postsynaptic excitatory events (in MHz) + firingrate : somatic firing rate (in MHz) + scalefactor : activity-dependent scaling factor, by which to multiply AMPA weights +} + +INITIAL { + LOCAL tp + if (tau1/tau2 > .9999) { tau1 = .9999*tau2 } + A = 0 + B = 0 + tp = (tau1*tau2)/(tau2 - tau1) * log(tau2/tau1) + factor = 1.0 / (-exp(-tp/tau1) + exp(-tp/tau2)) + + activity = 0 : Sensor for this cell's recent activity (Hz) + firingrate = 0 : firing rate (Hz) + scalefactor = 1.0 : Default scaling factor for this cell's AMPA synapses + if (scaling==0) { + targetrate = -1 : set cell's target rate to invalid + } +} + +PROCEDURE resetscaling () { + activity = 0 : Sensor for this cell's recent activity (default 0MHz i.e. cycles per ms) + firingrate = 0 + scalefactor = 1.0 : Default scaling factor for this cell's AMPA synapses + targetrate = -1 : Cell's target activity (MHz i.e. cycles per ms) +} + +BREAKPOINT { + if (scaling && targetrate < 0) { + : If scaling has just been turned on, set goal activity to historical average firing rate + : This is only meaningful if sensor has had a chance to measure correct activity over + : a relatively long period of time, so don't set scaling = 1 until at least ~800s. + targetrate = firingrate : Take current activity sensor value + } + SOLVE state METHOD cnexp + g = B - A + if (g>gmax) {g=gmax}: saturation + i = g*(v - e) +} + +FUNCTION scalederivfunc () { + if (scaling) { + scalederivfunc = scaleratefctr * (targetrate - firingrate) + } else { + scalederivfunc = 0.0 : this ensures that scalefactor will not change; no conditionals allowed around deriv + } +} + +DERIVATIVE state { + A' = -A/tau1 + B' = -B/tau2 + : activity' = -activity/ratedectau + firingrate' = -firingrate/ratedectau + scalefactor' = scalederivfunc() + if (scalefactor > maxscale) { + scalefactor = maxscale + } else if (scalefactor < 0.0) { + scalefactor = 0.0 + } +} + +NET_RECEIVE(w) { LOCAL winc + if (verbose) { printf("in NET_RECEIVE@t=%g: w=%g",t,w) } + if (w < 0) { : negative weight event signifies spike from soma + if (verbose) { printf("spike at t = %g\n",t)} + : Update the cell's firing rate value, assuming this is called at the same + : time as a somatic spike + firingrate = firingrate + rateinc + } else { + if (e >= 0) { : Scale AMPA receptors by scalefactor (Turrigiano, 2008) + A = A + w * factor * scalefactor + B = B + w * factor * scalefactor + : activity = activity + rateinc : Update the cell's activity sensor value + } else { : Scale GABA receptors by 1/scalefactor to model BDNF (Chandler and Grossberg, 2012) + A = A + w * factor / scalefactor + B = B + w * factor / scalefactor + } + } +} + diff --git a/examples/batchCellMapping/mod/ican_sidi.mod b/examples/batchCellMapping/mod/ican_sidi.mod new file mode 100644 index 000000000..f2d69a3ff --- /dev/null +++ b/examples/batchCellMapping/mod/ican_sidi.mod @@ -0,0 +1,110 @@ +TITLE Slow Ca-dependent cation current +: from +: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=144089&file=/PFCcell/mechanism/ican.mod +: +: Ca++ dependent nonspecific cation current ICAN +: Differential equations +: +: Model based on a first order kinetic scheme +: +: + n cai <-> (alpha,beta) +: +: Following this model, the activation fct will be half-activated at +: a concentration of Cai = (beta/alpha)^(1/n) = cac (parameter) +: +: The mod file is here written for the case n=2 (2 binding sites) +: --------------------------------------------- +: +: Kinetics based on: Partridge & Swandulla, TINS 11: 69-72, 1988. +: +: This current has the following properties: +: - inward current (non specific for cations Na, K, Ca, ...) +: - activated by intracellular calcium +: - NOT voltage dependent +: +: A minimal value for the time constant has been added +: +: Ref: Destexhe et al., J. Neurophysiology 72: 803-818, 1994. +: See also: http://www.cnl.salk.edu/~alain , http://cns.fmed.ulaval.ca +: + +: Updated by Kiki Sidiropoulou (2010) so that dADP has slow inactivation kinetics and it +: is activated after 5 spikes + +: Updated by Sam Neymotin (2016) to avoid using n ion and get rid of 'mystart' rule; also +: make sure that INITIAL block assigns currents + +NEURON { + SUFFIX ican + NONSPECIFIC_CURRENT i + USEION ca READ cai + USEION na WRITE ina + RANGE gbar, m_inf, tau_m + GLOBAL beta, cac, taumin +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + v (mV) + celsius = 36 (degC) + erev = -20 (mV) : reversal potential + cai (mM) : initial [Ca]i + gbar = 0.0001 (mho/cm2) + beta = 0.0001 (1/ms) : backward rate constant + cac = 0.0004 (mM) + : middle point of activation fct, for ip3 as somacar, for current injection + taumin = 0.1 (ms) : minimal value of time constant +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) + ina (mA/cm2) + m_inf + tau_m (ms) + tadj + g (mho/cm2) +} + +PROCEDURE iassign () { + g = gbar * m * m + i = g * (v - erev) + ina = 0.7 * i +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + evaluate_fct(v,cai) + m' = (m_inf - m) / tau_m +} + +UNITSOFF +INITIAL { + : activation kinetics are assumed to be at 22 deg. C + : Q10 is assumed to be 3 + tadj = 3.0 ^ ((celsius-22.0)/10) + evaluate_fct(v,cai) + m = m_inf + iassign() +} + +PROCEDURE evaluate_fct(v(mV),cai(mM)) { LOCAL alpha2 + alpha2 = beta * (cai/cac)^2 + tau_m = 1 / (alpha2 + beta) / tadj + m_inf = alpha2 / (alpha2 + beta) + if(tau_m < taumin) { tau_m = taumin } : min value of time constant +} +UNITSON diff --git a/examples/batchCell/mod/infot.mod b/examples/batchCellMapping/mod/infot.unused similarity index 100% rename from examples/batchCell/mod/infot.mod rename to examples/batchCellMapping/mod/infot.unused diff --git a/examples/batchCellMapping/mod/izhi2007a.mod b/examples/batchCellMapping/mod/izhi2007a.mod new file mode 100644 index 000000000..7423db534 --- /dev/null +++ b/examples/batchCellMapping/mod/izhi2007a.mod @@ -0,0 +1,248 @@ +COMMENT +A "simple" implementation of the Izhikevich neuron with AMPA, NMDA, +GABA_A, and GABA_B receptor dynamics. Equations and parameter values are taken from + Izhikevich EM (2007). + "Dynamical systems in neuroscience" + MIT Press + +Equation for synaptic inputs taken from + Izhikevich EM, Edelman GM (2008). + "Large-scale model of mammalian thalamocortical systems." + PNAS 105(9) 3593-3598. + +Example usage (in Python): + from neuron import h + dummycell = h.Section() # Since Izhi is a point process, it needs to be in a section + izhl = [h.Izhi2007(0.5) for i in range(2)] # Create two new Izhikevich cells + connection = h.NetCon(izhl[0], izhl[1]) # Connect them + izhl[0].Iin = 70 # activate 1 cell + +Cell types available are based on Izhikevich, 2007 book: + 1. RS - Layer 5 regular spiking pyramidal cell (fig 8.12 from 2007 book) + 2. IB - Layer 5 intrinsically bursting cell (fig 8.19 from 2007 book) + 3. CH - Cat primary visual cortex chattering cell (fig8.23 from 2007 book) + 4. LTS - Rat barrel cortex Low-threshold spiking interneuron (fig 8.25 from 2007 book) + 5. FS - Rat visual cortex layer 5 fast-spiking interneuron (fig 8.27 from 2007 book) + 6. TC - Cat dorsal LGN thalamocortical (TC) cell (fig 8.31 from 2007 book) + 7. RTN - Rat reticular thalamic nucleus (RTN) cell (fig 8.32 from 2007 book) +ENDCOMMENT + +: Declare name of object and variables +NEURON { + POINT_PROCESS Izhi2007a + RANGE C, k, vr, vt, vpeak, a, b, c, d, Iin, tauAMPA, tauNMDA, tauGABAA, tauGABAB, tauOpsin, celltype, alive, cellid, verbose + RANGE V, u, gAMPA, gNMDA, gGABAA, gGABAB, gOpsin, I + RANGE factor, eventflag, delta, t0 +} + +: Specify units that have physiological interpretations (NB: ms is already declared) +UNITS { + (mV) = (millivolt) + (uM) = (micrometer) +} + +: Parameters from Izhikevich 2007, MIT Press for regular spiking pyramidal cell +PARAMETER { + C = 1 : Capacitance + k = 0.7 + vr = -60 (mV) : Resting membrane potential + vt = -40 (mV) : Membrane threhsold + vpeak = 35 (mV) : Peak voltage + a = 0.03 + b = -2 + c = -50 + d = 100 + Iin = 0 + Vpre = 0 + tauAMPA = 5 (ms) : Receptor time constant, AMPA + tauNMDA = 150 (ms) : Receptor time constant, NMDA + tauGABAA = 6 (ms) : Receptor time constant, GABAA + tauGABAB = 150 (ms) : Receptor time constant, GABAB + tauOpsin = 50 (ms) : Receptor time constant, opsin, from Mattis et al. (2011) + celltype = 1 : A flag for indicating what kind of cell it is, used for changing the dynamics slightly (see list of cell types in initial comment). + alive = 1 : A flag for deciding whether or not the cell is alive -- if it's dead, acts normally except it doesn't fire spikes + cellid = -1 : A parameter for storing the cell ID, if required (useful for diagnostic information) + verbose = 0 : Whether or not to print diagnostic information to file -- WARNING, do not modify this manually -- it's set by useverbose() +} + +: Variables used for internal calculations +ASSIGNED { + factor : Voltage factor used for calculating the current + eventflag : For diagnostic information + V (mV) : Membrane voltage + u (mV) : Slow current/recovery variable + gAMPA : AMPA conductance + gNMDA : NMDA conductance + gGABAA : GABAA conductance + gGABAB : GABAB conductance + gOpsin : Opsin conductance + I : Total current + delta : Time step + t0 : Previous time +} + +: Initial conditions +INITIAL { + V = vr + u = 0.0 + t0 = t + gAMPA = 0 + gNMDA = 0 + gGABAA = 0 + gGABAB = 0 + gOpsin = 0 + I = 0 + delta = 0 + net_send(0,1) : Required for the WATCH statement to be active +} + + +: Function for printing diagnostic information to a file -- usage example: cell.useverbose(2,"logfile.txt") +VERBATIM +char filename[1000]; // Allocate some memory for the filename +ENDVERBATIM +PROCEDURE useverbose() { : Create user-accessible function + VERBATIM + #include // Basic input-output + verbose = (float) *getarg(1); // Set verbosity -- 0 = none, 1 = events, 2 = events + timesteps + strcpy(filename, gargstr(2)); // Copy input filename into memory + ENDVERBATIM +} + +: Define neuron dynamics +BREAKPOINT { + delta = t-t0 : Find time difference + + : Receptor dynamics -- the correct form is gAMPA = gAMPA*exp(-delta/tauAMPA), but this is 30% slower and, in the end, not really any more physiologically realistic + gAMPA = gAMPA - delta*gAMPA/tauAMPA : "Exponential" decays -- fast excitatory (AMPA) + gNMDA = gNMDA - delta*gNMDA/tauNMDA : Slow excitatory (NMDA) + gGABAA = gGABAA - delta*gGABAA/tauGABAA : Fast inhibitory (GABA_A) + gGABAB = gGABAB - delta*gGABAB/tauGABAB : Slow inhibitory (GABA_B) + gOpsin = gOpsin - delta*gOpsin/tauOpsin : Optogenetic (opsin) + + : Calculate current + factor = ((V+80)/60)*((V+80)/60) + I = gAMPA*(V-0) + gNMDA*factor/(1+factor)*(V-0) + gGABAA*(V+70) + gGABAB*(V+90) + gOpsin*(V-0) : Treat the opsin channel like an AMPA channel + + : Calculate neuronal dynamics; -I since I = -I_{syn}, which is really what I is as I've defined it above + Vpre = V + V = V + delta*(k*(V-vr)*(V-vt) - u - I + Iin)/(C*100) : Calculate voltage + + if (Vpre<=c && V>vpeak) {V=c+1} : if just spiked, wait at least 1 timestep before increasing V>vpeak again, so V reset value takes effect; WATCH statement requires V to cross the vpeak threshod) + + : Cell-type specific dynamics + if (celltype<5) { + u = u + delta*a*(b*(V-vr)-u) : Calculate recovery variable + } + else { + : For FS neurons, include nonlinear U(v): U(v) = 0 when v=vb (d=vb=-55) + if (celltype==5) { + if (V-65) {b=0} + else {b=15} + u = u + delta*a*(b*(V-vr)-u) : Calculate recovery variable + } + + : For TRN neurons, reset b + if (celltype==7) { + if (V>-65) {b=2} + else {b=10} + u = u + delta*a*(b*(V-vr)-u) : Calculate recovery variable + } + } + + t0=t : Reset last time so delta can be calculated in the next time step + + : Print diagnostic inormation to a file + if (verbose>1) { : Verbose turned on? + VERBATIM + FILE *outfile; // Declare file object + outfile=fopen(filename,"a"); // Open file for appending + fprintf(outfile,"%8.2f cell=%6.0f delta=%8.2f gAMPA=%8.2f gNMDA=%8.2f gGABAA=%8.2f gGABAB=%8.2f gOpsin=%8.2f factor=%8.2f I=%8.2f V=%8.2f u=%8.2f (timestep)\n",t,cellid,delta,gAMPA,gNMDA,gGABAA,gGABAB,gOpsin,factor,I,V,u); + fclose(outfile); // Close file + ENDVERBATIM + } +} + +: Input received +NET_RECEIVE (wAMPA, wNMDA, wGABAA, wGABAB, wOpsin) { + INITIAL { wAMPA=wAMPA wNMDA=wNMDA wGABAA=wGABAA wGABAB=wGABAB wOpsin=wOpsin} : Insanely stupid but required, otherwise reset to 0, + + : Check if spike occurred + if (flag == 1) { : Fake event from INITIAL block + if (celltype < 4 || celltype == 5 || celltype == 7) { : default + WATCH (V>vpeak) 2 : Check if threshold has been crossed, and if so, set flag=2 + } + else if (celltype == 4) { : LTS cell + WATCH (V>(vpeak-0.1*u)) 2 : Check if threshold has been crossed, and if so, set flag=2 + } + else if (celltype == 6) { : TC cell + WATCH (V>(vpeak+0.1*u)) 2 : Check if threshold has been crossed, and if so, set flag=2 + } + } + + : Event created by WATCH statement -- i.e. threshold crossed + else if (flag == 2) { + if (alive) {net_event(t)} : Send spike event if the cell is alive + + : For RS, IB and CH neurons, and RTN + if (celltype < 4 || celltype == 7) { + V = c : Reset voltage + u = u+d : Reset recovery variable + } + : For LTS neurons + else if (celltype == 4) { + V = c+0.04*u : Reset voltage + if ((u+d)<670) {u=u+d} : Reset recovery variable + else {u=670} + } + : For FS neurons (only update v) + else if (celltype == 5) { + V = c : Reset voltage + } + : For TC neurons (only update v) + else if (celltype == 6) { + V = c-0.1*u : Reset voltage + u = u+d : Reset recovery variable + } + + gAMPA = 0 : Reset conductances -- not mentioned in Izhikevich's paper but necessary to stop things from exploding! + gNMDA = 0 + gGABAA = 0 + gGABAB = 0 + gOpsin = 0 + } + + : Actual input, calculate receptor dynamics + else { + gAMPA = gAMPA + wAMPA + gNMDA = gNMDA + wNMDA + gGABAA = gGABAA + wGABAA + gGABAB = gGABAB + wGABAB + gOpsin = gOpsin + wOpsin + } + + : Print diagnostic information to a file + if (verbose>0) { : Verbose turned on? + eventflag = flag + VERBATIM + FILE *outfile; // Declare file object +//if(cellid>=0 && cellid < 300) { + outfile=fopen(filename,"a"); // Open file for appending + fprintf(outfile,"t=%8.2f cell=%6.0f flag=%1.0f gAMPA=%8.2f gNMDA=%8.2f gGABAA=%8.2f gGABAB=%8.2f gOpsin=%8.2f V=%8.2f u=%8.2f (event)\n",t, cellid,eventflag,gAMPA,gNMDA,gGABAA,gGABAB,gOpsin,V,u); + fclose(outfile); // Close file +//} + ENDVERBATIM + } + + +} diff --git a/examples/batchCellMapping/mod/izhi2007b.mod b/examples/batchCellMapping/mod/izhi2007b.mod new file mode 100644 index 000000000..d76c93bce --- /dev/null +++ b/examples/batchCellMapping/mod/izhi2007b.mod @@ -0,0 +1,172 @@ +COMMENT + +A "simple" implementation of the Izhikevich neuron. +Equations and parameter values are taken from + Izhikevich EM (2007). + "Dynamical systems in neuroscience" + MIT Press + +Equation for synaptic inputs taken from + Izhikevich EM, Edelman GM (2008). + "Large-scale model of mammalian thalamocortical systems." + PNAS 105(9) 3593-3598. + +Example usage (in Python): + from neuron import h + sec = h.Section(name=sec) # section will be used to calculate v + izh = h.Izhi2007b(0.5) + def initiz () : sec.v=-60 + fih=h.FInitializeHandler(initz) + izh.Iin = 70 # current clamp + +Cell types available are based on Izhikevich, 2007 book: + 1. RS - Layer 5 regular spiking pyramidal cell (fig 8.12 from 2007 book) + 2. IB - Layer 5 intrinsically bursting cell (fig 8.19 from 2007 book) + 3. CH - Cat primary visual cortex chattering cell (fig 8.23 from 2007 book) + 4. LTS - Rat barrel cortex Low-threshold spiking interneuron (fig 8.25 from 2007 book) + 5. FS - Rat visual cortex layer 5 fast-spiking interneuron (fig 8.27 from 2007 book) + 6. TC - Cat dorsal LGN thalamocortical (TC) cell (fig 8.31 from 2007 book) + 7. RTN - Rat reticular thalamic nucleus (RTN) cell (fig 8.32 from 2007 book) + +ENDCOMMENT + +: Declare name of object and variables +NEURON { + POINT_PROCESS Izhi2007b + RANGE C, k, vr, vt, vpeak, u, a, b, c, d, Iin, celltype, alive, cellid, verbose, derivtype, delta, t0 + NONSPECIFIC_CURRENT i +} + +: Specify units that have physiological interpretations (NB: ms is already declared) +UNITS { + (mV) = (millivolt) + (uM) = (micrometer) +} + +: Parameters from Izhikevich 2007, MIT Press for regular spiking pyramidal cell +PARAMETER { + C = 1 : Capacitance + k = 0.7 + vr = -60 (mV) : Resting membrane potential + vt = -40 (mV) : Membrane threhsold + vpeak = 35 (mV) : Peak voltage + a = 0.03 + b = -2 + c = -50 + d = 100 + Iin = 0 + celltype = 1 : A flag for indicating what kind of cell it is, used for changing the dynamics slightly (see list of cell types in initial comment). + alive = 1 : A flag for deciding whether or not the cell is alive -- if it's dead, acts normally except it doesn't fire spikes + cellid = -1 : A parameter for storing the cell ID, if required (useful for diagnostic information) +} + +: Variables used for internal calculations +ASSIGNED { + v (mV) + i (nA) + u (mV) : Slow current/recovery variable + delta + t0 + derivtype +} + +: Initial conditions +INITIAL { + u = 0.0 + derivtype=2 + net_send(0,1) : Required for the WATCH statement to be active; v=vr initialization done there +} + +: Define neuron dynamics +BREAKPOINT { + delta = t-t0 : Find time difference + if (celltype<5) { + u = u + delta*a*(b*(v-vr)-u) : Calculate recovery variable + } + else { + : For FS neurons, include nonlinear U(v): U(v) = 0 when v=vb (d=vb=-55) + if (celltype==5) { + if (v-65) {b=0} + else {b=15} + u = u + delta*a*(b*(v-vr)-u) : Calculate recovery variable + } + + : For TRN neurons, reset b + if (celltype==7) { + if (v>-65) {b=2} + else {b=10} + u = u + delta*a*(b*(v-vr)-u) : Calculate recovery variable + } + } + + t0=t : Reset last time so delta can be calculated in the next time step + i = -(k*(v-vr)*(v-vt) - u + Iin)/C/1000 +} + +: Input received +NET_RECEIVE (w) { + : Check if spike occurred + if (flag == 1) { : Fake event from INITIAL block + if (celltype == 4) { : LTS cell + WATCH (v>(vpeak-0.1*u)) 2 : Check if threshold has been crossed, and if so, set flag=2 + } else if (celltype == 6) { : TC cell + WATCH (v>(vpeak+0.1*u)) 2 + } else { : default for all other types + WATCH (v>vpeak) 2 + } + : additional WATCHfulness + if (celltype==6 || celltype==7) { + WATCH (v> -65) 3 : change b param + WATCH (v< -65) 4 : change b param + } + if (celltype==5) { + WATCH (v> d) 3 : going up + WATCH (v< d) 4 : coming down + } + v = vr : initialization can be done here + : FLAG 2 Event created by WATCH statement -- threshold crossed for spiking + } else if (flag == 2) { + if (alive) {net_event(t)} : Send spike event if the cell is alive + : For LTS neurons + if (celltype == 4) { + v = c+0.04*u : Reset voltage + if ((u+d)<670) {u=u+d} : Reset recovery variable + else {u=670} + } + : For FS neurons (only update v) + else if (celltype == 5) { + v = c : Reset voltage + } + : For TC neurons (only update v) + else if (celltype == 6) { + v = c-0.1*u : Reset voltage + u = u+d : Reset recovery variable + } else {: For RS, IB and CH neurons, and RTN + v = c : Reset voltage + u = u+d : Reset recovery variable + } + : FLAG 3 Event created by WATCH statement -- v exceeding set point for param reset + } else if (flag == 3) { + : For TC neurons + if (celltype == 5) { derivtype = 1 : if (v>d) u'=a*((0.025*(v-d)*(v-d)*(v-d))-u) + } else if (celltype == 6) { b=0 + } else if (celltype == 7) { b=2 + } + : FLAG 4 Event created by WATCH statement -- v dropping below a setpoint for param reset + } else if (flag == 4) { + if (celltype == 5) { derivtype = 2 : if (v + + : Calcium dependence of opening probability (Gong 2001) + caPh = 2e-3 (mM) : conc. with half maximum open probaility + caPk = 1 : Steepness of calcium dependence curve + caPmax = 1 : max and + caPmin = 0 : min open probability + + : Calcium dependence of Vh shift (Womack 2002) + caVhh = 2e-3 (mM) : Conc. for half of the Vh shift + caVhk = -0.94208 : Steepness of the Vh-calcium dependence curve + caVhmax = 155.67 (mV) : max and + caVhmin = -46.08 (mV) : min Vh + + : Voltage dependence of open probability (Gong 2001) + : must not be zero + k = 17 (mV) + + : Timeconstant of channel kinetics + : no data for a description of a calcium&voltage dependence + : some points (room temp) in Behassine 05 & Womack 02 + tau = 1 (ms) <1e-12, 1e9> + scale = 100 : scaling to incorporate higher ca conc near ca channels + + pinfmin = 0.0 : cutoff for pinf - less than that set pinf to 0.0 + +} + + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + cai (mM) + caiScaled (mM) + pinf (1) +} + + +STATE { + p +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gpeak*p* (v - ek) +} + +DERIVATIVE states { + rate(v, cai) + p' = (pinf - p)/tau +} + +INITIAL { + rate(v, cai) + p = pinf +} + +PROCEDURE rate(v(mV), ca(mM)) { + caiScaled = ca*scale + pinf = P0ca(caiScaled) / ( 1 + exp( (Vhca(caiScaled)-v)/k ) ) + if(pinf < pinfmin) { pinf = 0.0 } +} + +FUNCTION P0ca(ca(mM)) (1) { + + if (ca < 1E-18) { :check for division by zero + P0ca = caPmin + } else { + P0ca = caPmin + ( (caPmax - caPmin) / ( 1 + (caPh/ca)^caPk )) + } +} + +FUNCTION Vhca(ca(mM)) (mV) { + + if (ca < 1E-18) { :check for division by zero + Vhca = caVhmax + } else { + Vhca = caVhmin + ( (caVhmax - caVhmin ) / ( 1 + ((caVhh/ca)^caVhk)) ) + } +} + diff --git a/examples/batchCellMapping/mod/kap_BS.mod b/examples/batchCellMapping/mod/kap_BS.mod new file mode 100644 index 000000000..501817d5b --- /dev/null +++ b/examples/batchCellMapping/mod/kap_BS.mod @@ -0,0 +1,124 @@ +TITLE K-A channel from Klee Ficker and Heinemann +: modified to account for Dax A Current --- M.Migliore Jun 1997 +: modified to be used with cvode M.Migliore 2001 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter, removing "ka" from parameter names, reformatting, setting sh = 0 (was 24 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius (degC) + ek + + sh = 0 + gbar = 0.008 (mho/cm2) + vhalfn = 11 (mV) + vhalfl = -56 (mV) + a0l = 0.05 (/ms) + a0n = 0.05 (/ms) + zetan = -1.5 (1) + zetal = 3 (1) + gmn = 0.55 (1) + gml = 1 (1) + lmin = 5 (mS) + nmin = 0.4 (mS) + pw = -1 (1) + tq = -40 + qq = 5 + q10 = 5 + qtl = 1 +} + + +NEURON { + SUFFIX kap + USEION k READ ek WRITE ik + RANGE gbar, g, sh, tq, vhalfn, vhalfl, ik : +: GLOBAL ninf,linf,taul,taun,lmin +} + +STATE { + n + l +} + +ASSIGNED { + ik (mA/cm2) + ninf + linf + taul + taun + g +} + +INITIAL { + rates(v) + n=ninf + l=linf +} + + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*n*l + ik = g*(v-ek) +} + + +FUNCTION alpn(v(mV)) { + LOCAL zeta + zeta=zetan+pw/(1+exp((v-tq-sh)/qq)) + alpn = exp(1.e-3*zeta*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betn(v(mV)) { + LOCAL zeta + zeta=zetan+pw/(1+exp((v-tq-sh)/qq)) + betn = exp(1.e-3*zeta*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION alpl(v(mV)) { + alpl = exp(1.e-3*zetal*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betl(v(mV)) { + betl = exp(1.e-3*zetal*gml*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rates(v) + n' = (ninf - n) / taun + l' = (linf - l) / taul +} + +PROCEDURE rates(v (mV)) { :callable from hoc + LOCAL a,qt + qt = q10^((celsius-24)/10) + + a = alpn(v) + ninf = 1/(1 + a) + taun = betn(v)/(qt*a0n*(1+a)) + if (taun 1e-6) { + trap0 = a * (v - th) / (1 - exp(-(v - th)/q)) + } else { + trap0 = a * q + } +} diff --git a/examples/batchCell/mod/misc.mod b/examples/batchCellMapping/mod/misc.unused similarity index 100% rename from examples/batchCell/mod/misc.mod rename to examples/batchCellMapping/mod/misc.unused diff --git a/examples/batchCellMapping/mod/na2_mh.mod b/examples/batchCellMapping/mod/na2_mh.mod new file mode 100644 index 000000000..62895f978 --- /dev/null +++ b/examples/batchCellMapping/mod/na2_mh.mod @@ -0,0 +1,132 @@ + +COMMENT + +26 Ago 2002 Modification of original channel to allow variable time +step and to correct an initialization error. + Done by Michael Hines(michael.hines@yale.e) and Ruggero +Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational +Neuroscience. Obidos, Portugal +11 Jan 2007 + Glitch in trap where (v/th) was where (v-th)/q is. (thanks Ronald +van Elburg!) + +na.mod + +Sodium channel, Hodgkin-Huxley style kinetics. + +Kinetics were fit to data from Huguenard et al. (1988) and Hamill et +al. (1991) + +qi is not well constrained by the data, since there are no points +between -80 and -55. So this was fixed at 5 while the thi1,thi2,Rg,Rd +were optimized using a simplex least square proc + +voltage dependencies are shifted approximately from the best +fit to give higher threshold + +Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu + +ENDCOMMENT + +NEURON { + SUFFIX na + USEION na READ ena WRITE ina + RANGE m, h, gna, gbar, vshift + GLOBAL tha, thi1, thi2, qa, qi, qinf, thinf + RANGE minf, hinf, mtau, htau + GLOBAL Ra, Rb, Rd, Rg + RANGE q10, temp, tadj, vmin, vmax +} + +PARAMETER { + gbar = 1000 (pS/um2) : 0.12 mho/cm2 + vshift = -10 (mV) : voltage shift (affects all) + + tha = -35 (mV) : v 1/2 for act (-42) + qa = 9 (mV) : act slope + Ra = 0.182 (/ms) : open (v) + Rb = 0.124 (/ms) : close (v) + + thi1 = -50 (mV) : v 1/2 for inact + thi2 = -75 (mV) : v 1/2 for inact + qi = 5 (mV) : inact tau slope + thinf = -65 (mV) : inact inf slope + qinf = 6.2 (mV) : inact inf slope + Rg = 0.0091 (/ms) : inact (v) + Rd = 0.024 (/ms) : inact recov (v) + + temp = 23 (degC) : original temp + q10 = 2.3 : temperature sensitivity + + v (mV) + dt (ms) + celsius (degC) + vmin = -120 (mV) + vmax = 100 (mV) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) +} + +ASSIGNED { + ina (mA/cm2) + gna (pS/um2) + ena (mV) + minf hinf + mtau (ms) htau (ms) + tadj +} + + +STATE { m h } + +INITIAL { + rates(v+vshift) + m = minf + h = hinf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gna = tadj*gbar*m*m*m*h + ina = (1e-4) * gna * (v - ena) +} + +DERIVATIVE states { :Computes state variables m, h, and n + rates(v+vshift) : at the current v and dt. + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE rates(vm) { + LOCAL a, b + + a = trap0(vm,tha,Ra,qa) + b = trap0(-vm,-tha,Rb,qa) + + tadj = q10^((celsius - temp)/10) + + mtau = 1/tadj/(a+b) + minf = a/(a+b) + + :"h" inactivation + + a = trap0(vm,thi1,Rd,qi) + b = trap0(-vm,-thi2,Rg,qi) + htau = 1/tadj/(a+b) + hinf = 1/(1+exp((vm-thinf)/qinf)) +} + + +FUNCTION trap0(v,th,a,q) { + if (fabs((v-th)/q) > 1e-6) { + trap0 = a * (v - th) / (1 - exp(-(v - th)/q)) + } else { + trap0 = a * q + } +} diff --git a/examples/batchCellMapping/mod/nap_sidi.mod b/examples/batchCellMapping/mod/nap_sidi.mod new file mode 100644 index 000000000..f45f4e488 --- /dev/null +++ b/examples/batchCellMapping/mod/nap_sidi.mod @@ -0,0 +1,109 @@ +: Persistent Na+ channel +: adapted from +: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=144089&file=/PFCcell/mechanism/nap.mod + +NEURON { + SUFFIX nap + USEION na READ ena WRITE ina + RANGE gbar + RANGE DA_alphamshift,DA_betamshift + RANGE DA_alphahfactor, DA_betahfactor +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + gbar= 0.0022 (mho/cm2) <0,1e9> + ena (mV) : do not set global ena here + DA_alphamshift=0 : 2 for 100% DA, 0 otherwise + DA_betamshift=0 : 5 for 100% DA,0 otherwise + DA_alphahfactor=0: -.8e-5 for DA, 0 otherwise + DA_betahfactor=0 : 0.014286-0.02 for DA, 0 otherwise +} + +STATE { + m h +} + +ASSIGNED { + ina (mA/cm2) + minf hinf + mtau (ms) + htau (ms) + g (mho/cm2) +} + +PROCEDURE iassign () { + g = gbar*m*h + ina = g*(v-ena) +} + +INITIAL { + rate(v) + m = minf + h = hinf + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rate(v) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +UNITSOFF + +FUNCTION malf( v){ LOCAL va + va=v+12+DA_alphamshift + if (fabs(va)<1e-04){ + va = va + 0.00001 + } + malf = (-0.2816*va)/(-1+exp(-va/9.3)) +} + +FUNCTION mbet(v(mV))(/ms) { LOCAL vb + vb=v-15+DA_betamshift + if (fabs(vb)<1e-04){ + vb = vb + 0.00001 + } + mbet = (0.2464*vb)/(-1+exp(vb/6)) +} + +FUNCTION half(v(mV))(/ms) { LOCAL vc + vc=v+42.8477 + if (fabs(vc)<1e-04){ + vc=vc+0.00001 + } + half= (2.8e-5+DA_alphahfactor)*(exp(-vc/4.0248)) +} + +FUNCTION hbet(v(mV))(/ms) { LOCAL vd + vd=v-413.9284 + if (fabs(vd)<1e-04){ + vd=vd+0.00001 + } + hbet= (0.02+DA_betahfactor)/(1+exp(-vd/148.2589)) +} + +PROCEDURE rate(v (mV)) {LOCAL msum, hsum, ma, mb, ha, hb + ma=malf(v) mb=mbet(v) ha=half(v) hb=hbet(v) + + msum = ma+mb + minf = ma/msum + mtau = 1/msum + + hsum = ha+hb + hinf = ha/hsum + htau = 1/hsum +} + +UNITSON diff --git a/examples/batchCellMapping/mod/nax_BS.mod b/examples/batchCellMapping/mod/nax_BS.mod new file mode 100644 index 000000000..b9b4cfd7b --- /dev/null +++ b/examples/batchCellMapping/mod/nax_BS.mod @@ -0,0 +1,115 @@ +TITLE nax +: Na current for axon. No slow inact. +: M.Migliore Jul. 1997 +: added sh to account for higher threshold M.Migliore, Apr.2002 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter reformatting, renaming thegna to g, setting sh = 0 (was 8 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +NEURON { + SUFFIX nax + USEION na READ ena WRITE ina + RANGE gbar, ina, sh +: GLOBAL minf, hinf, mtau, htau,thinf, qinf, Rb, Rg, qg +} + +PARAMETER { + v (mV) + celsius (degC) + ena (mV) : must be explicitly def. in hoc + + sh = 0 (mV) + gbar = 0.010 (mho/cm2) + + tha = -30 (mV) : v 1/2 for act + qa = 7.2 (mV) : act slope (4.5) + Ra = 0.4 (/ms) : open (v) + Rb = 0.124 (/ms) : close (v) + + thi1 = -45 (mV) : v 1/2 for inact + thi2 = -45 (mV) : v 1/2 for inact + qd = 1.5 (mV) : inact tau slope + qg = 1.5 (mV) + mmin = 0.02 + hmin = 0.5 + q10 = 2 + Rg = 0.01 (/ms) : inact recov (v) + Rd = 0.03 (/ms) : inact (v) + + thinf = -50 (mV) : inact inf slope + qinf = 4 (mV) : inact inf slope +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) +} + +ASSIGNED { + ina (mA/cm2) + g (mho/cm2) + minf + hinf + mtau (ms) + htau (ms) +} + +STATE { m h} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*m*m*m*h + ina = g * (v - ena) +} + +INITIAL { + trates(v,sh) + m = minf + h = hinf +} + +DERIVATIVE states { + trates(v,sh) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE trates(vm,sh2) { + LOCAL a, b, qt + qt = q10^((celsius-24)/10) + + a = trap0(vm,tha+sh2,Ra,qa) + b = trap0(-vm,-tha-sh2,Rb,qa) + mtau = 1/(a+b)/qt + if (mtau 1e-6) { + trap0 = a * (v - th) / (1 - exp(-(v - th)/q)) + } else { + trap0 = a * q + } +} diff --git a/examples/batchCellMapping/mod/naz.mod b/examples/batchCellMapping/mod/naz.mod new file mode 100644 index 000000000..1bad29078 --- /dev/null +++ b/examples/batchCellMapping/mod/naz.mod @@ -0,0 +1,134 @@ +: $Id: naz.mod,v 1.8 2004/07/27 18:41:01 billl Exp $ + +COMMENT +26 Ago 2002 Modification of original channel to allow variable time step and to + correct an initialization error. +Done by Michael Hines(michael.hines@yale.e) and Ruggero + Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational + Neuroscience. Obidos, Portugal + +na.mod + +Sodium channel, Hodgkin-Huxley style kinetics. + +Kinetics were fit to data from Huguenard et al. (1988) and Hamill et +al. (1991) + +qi is not well constrained by the data, since there are no points +between -80 and -55. So this was fixed at 5 while the thi1,thi2,Rg,Rd +were optimized using a simplex least square proc + +voltage dependencies are shifted approximately from the best +fit to give higher threshold + +Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu + +ENDCOMMENT + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX naz + USEION na READ ena WRITE ina + RANGE m, h, gna, gmax, i + GLOBAL tha, thi1, thi2, qa, qi, qinf, thinf + GLOBAL minf, hinf, mtau, htau + GLOBAL Ra, Rb, Rd, Rg + GLOBAL q10, temp, tadj, vmin, vmax, vshift +} + +PARAMETER { + gmax = 1000 (pS/um2) : 0.12 mho/cm2 + vshift = -10 (mV) : voltage shift (affects all) + + tha = -35 (mV) : v 1/2 for act (-42) + qa = 9 (mV) : act slope + Ra = 0.182 (/ms) : open (v) + Rb = 0.124 (/ms) : close (v) + + thi1 = -50 (mV) : v 1/2 for inact + thi2 = -75 (mV) : v 1/2 for inact + qi = 5 (mV) : inact tau slope + thinf = -65 (mV) : inact inf slope + qinf = 6.2 (mV) : inact inf slope + Rg = 0.0091 (/ms) : inact (v) + Rd = 0.024 (/ms) : inact recov (v) + + temp = 23 (degC) : original temp + q10 = 2.3 : temperature sensitivity + + v (mV) + dt (ms) + celsius (degC) + vmin = -120 (mV) + vmax = 100 (mV) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) +} + +ASSIGNED { + ina (mA/cm2) + i (mA/cm2) + gna (pS/um2) + ena (mV) + minf hinf + mtau (ms) htau (ms) + tadj +} + + +STATE { m h } + +INITIAL { + tadj = q10^((celsius - temp)/10) + rates(v+vshift) + m = minf + h = hinf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gna = tadj*gmax*m*m*m*h + i = (1e-4) * gna * (v - ena) + ina = i +} + +LOCAL mexp, hexp + +DERIVATIVE states { :Computes state variables m, h, and n + rates(v+vshift) : at the current v and dt. + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE rates(vm) { + LOCAL a, b + + a = trap0(vm,tha,Ra,qa) + b = trap0(-vm,-tha,Rb,qa) + + mtau = 1/tadj/(a+b) + minf = a/(a+b) + + :"h" inactivation + + a = trap0(vm,thi1,Rd,qi) + b = trap0(vm,thi2,-Rg,-qi) + htau = 1/tadj/(a+b) + hinf = 1/(1+exp((vm-thinf)/qinf)) +} + + +FUNCTION trap0(v,th,a,q) { + if (fabs(v-th) > 1e-6) { + trap0 = a * (v - th) / (1 - exp(-(v - th)/q)) + } else { + trap0 = a * q + } +} diff --git a/examples/batchCellMapping/mod/nstim.mod b/examples/batchCellMapping/mod/nstim.mod new file mode 100644 index 000000000..3dcd44213 --- /dev/null +++ b/examples/batchCellMapping/mod/nstim.mod @@ -0,0 +1,91 @@ +: $Id: nstim.mod,v 1.24 2006/04/03 19:18:18 billl Exp $ + +NEURON { + ARTIFICIAL_CELL NStim + RANGE interval, number, start, end + RANGE noise,type,id +} + +PARAMETER { + interval = 10 (ms) <1e-9,1e9>: time between spikes (msec) + number = 10 <0,1e9> : number of spikes + start = 50 (ms) : start of first spike + noise = 0 <0,1> : amount of randomeaness (0.0 - 1.0) + end = 1e9 (ms) : time to terminate train +} + +ASSIGNED { + event (ms) + on + endt (ms) + type + id +} + +CONSTRUCTOR { + VERBATIM + { if (ifarg(1)) { id= *getarg(1); } else { id= -1; } + if (ifarg(2)) { type= *getarg(2); } else { type= 1; } + } + ENDVERBATIM +} + +PROCEDURE seed (x) { + set_seed(x) +} + +INITIAL { + on = 0 + if (noise < 0) { noise = 0 } + if (noise > 1) { noise = 1 } + if (interval <= 0.) { interval = .01 (ms) } + if (start>=0 && number>0 && end>0) { + event = start + invl(interval) - interval*(1. - noise) + if (event < 0) { event = 0 } + net_send(event, 3) + } +} + +PROCEDURE init_sequence (t(ms)) { + if (number > 0) { + on = 1 + event = t + endt = t + 1e-6 + interval*(number-1) + } +} + +FUNCTION invl (mean (ms)) (ms) { + if (noise == 0) { + invl = mean + } else { + invl = (1. - noise)*mean + noise*mean*exprand(1) + } +} + +NET_RECEIVE (w) { + if (flag == 0) { : external event + if (w > 0 && on == 0) { : turn on spike sequence + init_sequence(t) + net_send(0, 1) + } else if (w < 0 && on == 1) { : turn off spiking + on = 0 + } + } + if (flag == 3) { : from INITIAL + if (on == 0) { + init_sequence(t) + net_send(0, 1) + } + } + if (flag == 1 && on == 1) { + net_event(t) + event = event + invl(interval) + if (event > endt || event > end) { + on = 0 + } else { + net_send(event - t, 1) + } + } +} + +FUNCTION fflag () { fflag=1 } diff --git a/examples/batchCell/mod/samnutils.mod b/examples/batchCellMapping/mod/samnutils.unused similarity index 100% rename from examples/batchCell/mod/samnutils.mod rename to examples/batchCellMapping/mod/samnutils.unused diff --git a/examples/batchCellMapping/mod/savedist.mod b/examples/batchCellMapping/mod/savedist.mod new file mode 100644 index 000000000..d4168565c --- /dev/null +++ b/examples/batchCellMapping/mod/savedist.mod @@ -0,0 +1,14 @@ +TITLE Mech to store distance from origin as workaround for multisplit non-uniform densities +: 2011-09-18 Ben Suter, initial version per email from Michael Hines +: 2016-11-31 Ernie Forzano, changed suffix dist to savedist because import3d() and this mod file +: were referencing dist and causing compile error. +: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + +NEURON { + SUFFIX savedist + RANGE x +} + +PARAMETER { + x = 0 (micron) +} diff --git a/examples/batchCell/mod/stats.mod b/examples/batchCellMapping/mod/stats.unused similarity index 100% rename from examples/batchCell/mod/stats.mod rename to examples/batchCellMapping/mod/stats.unused diff --git a/examples/batchCell/mod/vecst.mod b/examples/batchCellMapping/mod/vecst.unused similarity index 100% rename from examples/batchCell/mod/vecst.mod rename to examples/batchCellMapping/mod/vecst.unused diff --git a/examples/batchCellMapping/netParams.py b/examples/batchCellMapping/netParams.py new file mode 100644 index 000000000..6c7d1d084 --- /dev/null +++ b/examples/batchCellMapping/netParams.py @@ -0,0 +1,86 @@ + +""" +netParams.py + +High-level specifications for M1 network model using NetPyNE +""" + +from netpyne import specs + +try: + from __main__ import cfg # import SimConfig object with params from parent module +except: + from cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module + +############################################################################### +# +# NETWORK PARAMETERS +# +############################################################################### + +netParams = specs.NetParams() # object of class NetParams to store the network parameters + +############################################################################### +# Cell parameters +############################################################################### + +# PT cell params (6-comp) +cellRule = netParams.importCellParams(label='PT_6comp', conds={'cellType': 'PT', 'cellModel': 'HH_reduced'}, + fileName='cells/SPI6.py', cellName='SPI6') + +cellRule['secLists']['alldend'] = ['Bdend', 'Adend1', 'Adend2', 'Adend3'] # define section lists +cellRule['secLists']['apicdend'] = ['Adend1', 'Adend2', 'Adend3'] + +# for secName,sec in cellRule['secs'].items(): +# sec['vinit'] = -75.0413649414 # set vinit for all secs +# if secName in cellRule['secLists']['alldend']: +# sec['mechs']['nax']['gbar'] = cfg.dendNa # set dend Na gmax for all dends + + +############################################################################### +# Population parameters +############################################################################### +netParams.popParams['PT5B'] = {'cellModel': 'HH_reduced', 'cellType': 'PT', 'numCells': 1} + + +############################################################################### +# Synaptic mechanism parameters +############################################################################### +netParams.synMechParams['NMDA'] = {'mod': 'MyExp2SynNMDABB', 'tau1NMDA': 1, 'tau2NMDA': 150, 'e': 0} + + +############################################################################### +# Current inputs (IClamp) +############################################################################### +if cfg.addIClamp: + for iclabel in [k for k in dir(cfg) if k.startswith('IClamp')]: + ic = getattr(cfg, iclabel, None) # get dict with params + + # add stim source + netParams.stimSourceParams[iclabel] = {'type': 'IClamp', 'delay': ic['start'], 'dur': ic['dur'], 'amp': ic['amp']} + + # connect stim source to target + netParams.stimTargetParams[iclabel+'_'+ic['pop']] = \ + {'source': iclabel, 'conds': {'pop': ic['pop']}, 'sec': ic['sec'], 'loc': ic['loc']} + + +############################################################################### +# NetStim inputs +############################################################################### +if cfg.addNetStim: + for nslabel in [k for k in dir(cfg) if k.startswith('NetStim')]: + ns = getattr(cfg, nslabel, None) + + # add stim source + netParams.stimSourceParams[nslabel] = {'type': 'NetStim', 'start': ns['start'], 'interval': ns['interval'], + 'noise': ns['noise'], 'number': ns['number']} + + # connect stim source to target + netParams.stimTargetParams[nslabel+'_'+ns['pop']] = \ + {'source': nslabel, 'conds': {'pop': ns['pop']}, 'sec': ns['sec'], 'loc': ns['loc'], + 'synMech': ns['synMech'], 'weight': ns['weight'], 'delay': ns['delay']} + +############################################################################### +# NetStim mapping to cfg +############################################################################### +netParams.mapping['tau1NMDA'] = ['synMechParams', 'NMDA', 'tau1NMDA'] \ No newline at end of file diff --git a/examples/batchCellMapping/runbatch b/examples/batchCellMapping/runbatch new file mode 100755 index 000000000..4603773d4 --- /dev/null +++ b/examples/batchCellMapping/runbatch @@ -0,0 +1,9 @@ +#!/bin/bash +# Runs simulation, including MPI. + +rm *.pyc + +numprocesses=$1; if [ -z $numprocesses ]; then numprocesses=4; fi # Number of processes to use +shift # Eliminate first argument + +mpiexec -np $numprocesses nrniv -python -mpi batch.py $@ # Run the model diff --git a/examples/batchCellMapping/utils.py b/examples/batchCellMapping/utils.py new file mode 100644 index 000000000..4fc2762cd --- /dev/null +++ b/examples/batchCellMapping/utils.py @@ -0,0 +1,119 @@ +""" +utils.py + +General functions to analyse simulation data +""" +import json +import pickle +import numpy as np +from pylab import * +from itertools import product + +from pprint import pprint +from netpyne import specs + + +def readBatchData(dataFolder, batchLabel, loadAll=False, saveAll=True, vars=None, maxCombs=None, listCombs=None): + # load from previously saved file with all data + if loadAll: + print('\nLoading single file with all data...') + filename = '%s/%s/%s_allData.json' % (dataFolder, batchLabel, batchLabel) + with open(filename, 'r') as fileObj: + dataLoad = json.load(fileObj, object_pairs_hook=specs.OrderedDict) + params = dataLoad['params'] + data = dataLoad['data'] + return params, data + + if isinstance(listCombs, str): + filename = str(listCombs) + with open(filename, 'r') as fileObj: + dataLoad = json.load(fileObj) + listCombs = dataLoad['paramsMatch'] + + # read the batch file and cfg + batchFile = '%s/%s/%s_batch.json' % (dataFolder, batchLabel, batchLabel) + with open(batchFile, 'r') as fileObj: + b = json.load(fileObj)['batch'] + + # read params labels and ranges + params = b['params'] + + # read vars from all files - store in dict + if b['method'] == 'grid': + labelList, valuesList = list(zip(*[(p['label'], p['values']) for p in params])) + valueCombinations = product(*(valuesList)) + indexCombinations = product(*[list(range(len(x))) for x in valuesList]) + data = {} + print('Reading data...') + missing = 0 + for i,(iComb, pComb) in enumerate(zip(indexCombinations, valueCombinations)): + if (not maxCombs or i<= maxCombs) and (not listCombs or list(pComb) in listCombs): + print(i, iComb) + # read output file + iCombStr = ''.join([''.join('_'+str(i)) for i in iComb]) + simLabel = b['batchLabel']+iCombStr + outFile = b['saveFolder']+'/'+simLabel+'.json' + try: + with open(outFile, 'r') as fileObj: + output = json.load(fileObj, object_pairs_hook=specs.OrderedDict) + + # save output file in data dict + data[iCombStr] = {} + data[iCombStr]['paramValues'] = pComb # store param values + if not vars: vars = list(output.keys()) + for key in vars: + data[iCombStr][key] = output[key] + + except: + missing = missing + 1 + output = {} + else: + missing = missing + 1 + + print('%d files missing' % (missing)) + + # save + if saveAll: + print('Saving to single file with all data') + filename = '%s/%s/%s_allData.json' % (dataFolder, batchLabel, batchLabel) + dataSave = {'params': params, 'data': data} + with open(filename, 'w') as fileObj: + json.dump(dataSave, fileObj) + + return params, data + + +def compare(source_file, target_file, source_key=None, target_key=None): + from deepdiff import DeepDiff + with open(source_file, 'r') as fileObj: + if source_file.endswith('.json'): + source = json.load(fileObj, object_pairs_hook=specs.OrderedDict) + elif source_file.endswith('.pkl'): + source = pickle.load(fileObj) + if source_key: source = source[source_key] + + with open(target_file, 'r') as fileObj: + if target_file.endswith('.json'): + target = json.load(fileObj, object_pairs_hook=specs.OrderedDict) + elif source_file.endswith('.pkl'): + target = pickle.load(fileObj) + if target_key: target = target[target_key] + + ddiff = DeepDiff(source, target) + pprint(ddiff) + return ddiff + + +def setPlotFormat(numColors=8): + plt.style.use('ggplot') + #plt.style.use(['dark_background', 'presentation']) + + plt.rcParams['font.size'] = 12 + plt.rcParams['axes.titlesize'] = 14 + plt.rcParams['axes.labelsize'] = 12 + plt.rcParams['legend.fontsize'] = 'large' + + NUM_COLORS = numColors + colormap = plt.get_cmap('gist_rainbow') + colorlist = [colormap(1.*i/NUM_COLORS) for i in range(NUM_COLORS)] + plt.rc('axes', prop_cycle=(cycler('color', colorlist))) diff --git a/examples/batch_rxd_net/CSTR_cellParams.json b/examples/batch_rxd_net/CSTR_cellParams.json new file mode 100644 index 000000000..bf2b64e7f --- /dev/null +++ b/examples/batch_rxd_net/CSTR_cellParams.json @@ -0,0 +1,560 @@ +{ + "secs": { + "soma": { + "ions": { + "na": { + "i": 10.0, + "e": 42.0, + "o": 140.0 + }, + "ca": { + "i": 0.00005, + "e": 132.4579341637009, + "o": 2.0 + }, + "k": { + "i": 54.4, + "e": -104.0, + "o": 2.5 + } + }, + "mechs": { + "kBK": { + "tau": 1.0, + "caPmax": 1.0, + "caPmin": 0.0, + "caVhh": 0.002, + "caVhmin": -2.1899738592999967, + "gpeak": 0.01529200755489, + "caPh": 0.002, + "caPk": 1.0, + "k": 17.0, + "caVhmax": 155.67 + }, + "pas": { + "e": -90.7942106535, + "g": 0.00008772270604035548 + }, + "cat": { + "gcatbar": 0.0 + }, + "ih": { + "aslope": 7.12195833953, + "gbar": 0.00000999986721729, + "ascale": 0.00276508832116, + "bslope": 28.5415800392, + "bscale": 0.154401580683, + "ashift": 118.919921764 + }, + "kap": { + "vhalfn": 32.7885075379, + "gbar": 0.0898600246397, + "sh": 0.0, + "vhalfl": -59.7867409796, + "tq": -52.0967985869 + }, + "can": { + "gcanbar": 0.00000460717910591 + }, + "cal": { + "gcalbar": 0.00000441583533572 + }, + "nax": { + "gbar": 0.0345117294903, + "sh": 0.0 + }, + "kdr": { + "gbar": 0.0131103978049, + "sh": 0.0, + "vhalfn": 11.6427471384 + } + }, + "geom": { + "diam": 13.5574965259, + "cm": 1.98838095104, + "nseg": 1, + "L": 12.53064375245, + "Ra": 88.8088669637, + "pt3d": [ + [ + 0, + 0, + 0, + 13.5574965259 + ], + [ + 0, + 12.53064375245, + 0, + 13.5574965259 + ] + ] + }, + "topol": {}, + "vinit": -75.8801688855 + }, + "Adend1": { + "ions": { + "na": { + "i": 10.0, + "e": 42.0, + "o": 140.0 + }, + "ca": { + "i": 0.00005, + "e": 132.4579341637009, + "o": 2.0 + }, + "k": { + "i": 54.4, + "e": -104.0, + "o": 2.5 + } + }, + "mechs": { + "kBK": { + "tau": 1.0, + "caPmax": 1.0, + "caPmin": 0.0, + "caVhh": 0.002, + "caVhmin": -2.1899738592999967, + "gpeak": 0.01529200755489, + "caPh": 0.002, + "caPk": 1.0, + "k": 17.0, + "caVhmax": 155.67 + }, + "pas": { 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7.12195833953, + "gbar": 0.00000999986721729, + "ascale": 0.00276508832116, + "bslope": 28.5415800392, + "bscale": 0.154401580683, + "ashift": 118.919921764 + }, + "kap": { + "vhalfn": 32.7885075379, + "gbar": 0.0898600246397, + "sh": 0.0, + "vhalfl": -59.7867409796, + "tq": -52.0967985869 + }, + "can": { + "gcanbar": 0.00000460717910591 + }, + "cal": { + "gcalbar": 0.00000441583533572 + }, + "nax": { + "gbar": 0.0345117294903, + "sh": 0.0 + }, + "kdr": { + "gbar": 0.0131103978049, + "sh": 0.0, + "vhalfn": 11.6427471384 + } + }, + "geom": { + "diam": 2.86090460606, + "cm": 1.9931856141, + "nseg": 1, + "L": 58.9231269108, + "Ra": 88.8088669637, + "pt3d": [ + [ + 0, + 130.37689757405, + 0, + 2.86090460606 + ], + [ + 0, + 189.30002448484998, + 0, + 2.86090460606 + ] + ] + }, + "topol": { + "childX": 0.0, + "parentSec": "Adend2", + "parentX": 1.0 + }, + "vinit": -75.8801688855 + }, + "axon": { + "ions": { + "na": { + "i": 10.0, + "e": 42.0, + "o": 140.0 + }, + "k": { + "i": 54.4, + "e": -104.0, + "o": 2.5 + } + }, + "mechs": { + "nax": { + "gbar": 0.1725586474515, + "sh": 0.0 + }, + "kap": { + "vhalfn": 32.7885075379, + "gbar": 0.4493001231985, + "sh": 0.0, + "vhalfl": -59.7867409796, + "tq": -52.0967985869 + }, + "pas": { + "e": -90.7942106535, + "g": 0.0004120813684418219 + }, + "kdr": { + "gbar": 0.0655519890245, + "sh": 0.0, + "vhalfn": 11.6427471384 + } + }, + "geom": { + "diam": 0.552948640016, + "cm": 1.98999103645, + "nseg": 1, + "L": 119.72972477280001, + "Ra": 88.8088669637, + "pt3d": [ + [ + 0, + 0, + 0, + 0.552948640016 + ], + [ + 0, + -119.72972477280001, + 0, + 0.552948640016 + ] + ] + }, + "topol": { + "childX": 0.0, + "parentSec": "soma", + "parentX": 0.0 + }, + "vinit": -75.8801688855 + }, + "Bdend": { + "ions": { + "na": { + "i": 10.0, + "e": 42.0, + "o": 140.0 + }, + "ca": { + "i": 0.00005, + "e": 132.4579341637009, + "o": 2.0 + }, + "k": { + "i": 54.4, + "e": -104.0, + "o": 2.5 + } + }, + "mechs": { + "kBK": { + "tau": 1.0, + "caPmax": 1.0, + "caPmin": 0.0, + "caVhh": 0.002, + "caVhmin": -2.1899738592999967, + "gpeak": 0.01529200755489, + "caPh": 0.002, + "caPk": 1.0, + "k": 17.0, + "caVhmax": 155.67 + }, + "pas": { + "e": -90.7942106535, + "g": 0.00004343420865783602 + }, + "cat": { + "gcatbar": 0.0 + }, + "ih": { + "aslope": 7.12195833953, + "gbar": 0.00000999986721729, + "ascale": 0.00276508832116, + "bslope": 28.5415800392, + "bscale": 0.154401580683, + "ashift": 118.919921764 + }, + "kap": { + "vhalfn": 32.7885075379, + "gbar": 0.0898600246397, + "sh": 0.0, + "vhalfl": -59.7867409796, + "tq": -52.0967985869 + }, + "can": { + "gcanbar": 0.00000460717910591 + }, + "cal": { + "gcalbar": 0.00000441583533572 + }, + "nax": { + "gbar": 0.0345117294903, + "sh": 0.0 + }, + "kdr": { + "gbar": 0.0131103978049, + "sh": 0.0, + "vhalfn": 11.6427471384 + } + }, + "geom": { + "diam": 1.02617341611, + "cm": 1.98526067401, + "nseg": 1, + "L": 72.960082572, + "Ra": 88.8088669637, + "pt3d": [ + [ + 0, + 6.53064375245, + 0, + 1.02617341611 + ], + [ + 72.960082572, + 6.53064375245, + 0, + 1.02617341611 + ] + ] + }, + "topol": { + "childX": 0.0, + "parentSec": "soma", + "parentX": 0.5 + }, + "vinit": -75.8801688855 + } + }, + "globals": { + "celsius": 34.0, + "v_init": -75.8801688855, + "erev_ih": -37.0 + }, + "secLists": {}, + "conds": { + "cellType": [ + "E", + "I" + ] + } +} \ No newline at end of file diff --git a/examples/batch_rxd_net/README.md b/examples/batch_rxd_net/README.md new file mode 100644 index 000000000..676d82c22 --- /dev/null +++ b/examples/batch_rxd_net/README.md @@ -0,0 +1,30 @@ +# RxD network example +## Description +A 3-layer network with intra- and extra-cellular RxD and LFP recording illustrating multiscale effects. + +Changing the initial intracellular ip3 (e.g. from 0 to 0.1) concentration leads to a chain of effects that reduces network firing and the LFP signal: +high ip3 -> ER Ca released to Cyt -> kBK channels open -> less firing + +![rxd_net](https://github.com/Neurosim-lab/netpyne/blob/development/examples/rxd_net/rxdfig.png) + +Developed using NetPyNE (www.netpyne.org) + +## Setup and execution + +Requires NEURON with RxD and Python. + +1. Type or ./compile or the equivalent `nrnivmodl mod`. This should create a directory called either i686 or x86_64, depending on your computer's architecture. + +2. To run type: `python -i init.py` + +## Overview of file structure: + +* /init.py: Main executable; calls functions from other modules. Sets what parameter file to use. + +* /netParams.py: Network parameters + +* /cfg.py: Simulation configuration + + +For further information please contact: salvadordura@gmail.com + diff --git a/examples/batch_rxd_net/batch.py b/examples/batch_rxd_net/batch.py new file mode 100644 index 000000000..6014bd961 --- /dev/null +++ b/examples/batch_rxd_net/batch.py @@ -0,0 +1,33 @@ +""" +batch.py + +Batch simulation for M1 model using NetPyNE +""" +import sys +from netpyne.batch import Batch + + +def batchRxd(): + init_list = [0.0, 1.0] + gip3r_list = [12040 * 50, 12040 * 150] + + params = {'ip3_init' : init_list, + 'gip3r' : gip3r_list} + + initCfg = {'duration' : 0.5*1e3} + + b = Batch(params=params, initCfg=initCfg) + + b.batchLabel = 'batchRxd' + b.saveFolder = 'data/'+b.batchLabel + b.method = 'grid' + + b.runCfg = {'type': 'mpi_bulletin', + 'script': 'init.py'} + + b.run() + + +# Main code +if __name__ == '__main__': + batchRxd() diff --git a/examples/batch_rxd_net/cfg.py b/examples/batch_rxd_net/cfg.py new file mode 100644 index 000000000..c07d8dc80 --- /dev/null +++ b/examples/batch_rxd_net/cfg.py @@ -0,0 +1,46 @@ +from netpyne import specs + +#------------------------------------------------------------------------------ +# +# SIMULATION CONFIGURATION +# +#------------------------------------------------------------------------------ +# parameters +cfg = specs.SimConfig() # object of class cfg to store simulation configuration + + +# Run parameters +cfg.duration = 1.0*1e3 # Duration of the simulation, in ms +cfg.hParams['v_init'] = -65 # set v_init to -65 mV +cfg.dt = 0.1 # Internal integration timestep to use +cfg.verbose = False # Show detailed messages +cfg.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) +# from neuron import h +# pc = h.ParallelContext() +# pcid = pc.id() +# cfg.filename = 'data/batchRxd/rxd_net_' + str(pcid) #+ str(cfg.ip3_init) + '_' + str(cfg.gip3r) # Set file output name +cfg.saveJson = True +cfg.saveDataInclude = ['simData'] + + # Network dimensions +cfg.sizeX = 100 +cfg.sizeY = 500 +cfg.sizeZ = 100 + +# Recording/plotting parameters +cfg.recordTraces = {'V_soma':{'sec': 'soma','loc': 0.5,'var': 'v'}, + 'ik_soma': {'sec': 'soma', 'loc': 0.5, 'var': 'ik'}, + 'cai_soma': {'sec': 'soma', 'loc':0.5, 'var': 'cai'}, + 'cao_soma': {'sec': 'soma', 'loc':0.5, 'var': 'cao'}} + +cfg.recordLFP = [[-15, y, 1.0*cfg.sizeZ] for y in range(int(cfg.sizeY/3), int(cfg.sizeY), int(cfg.sizeY/3))] + +cfg.analysis['plotTraces']={'include': [0], 'saveFig' : False} +cfg.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig': False, 'figSize': (9,3)} # Plot a raster +cfg.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'NFFT': 256, 'noverlap': 48, 'nperseg': 64, 'saveFig': False} +# cfg.analysis['plotRxDConcentration'] = {'speciesLabel': 'ca', 'regionLabel': 'ecs', 'saveFig' : False} + +## Change ip3_init from 0 to 0.1 to observe multiscale effect: +## high ip3 -> ER Ca released to Cyt -> kBK channels open -> less firing +cfg.ip3_init = 0.0 +cfg.gip3r = 12040 * 100 \ No newline at end of file diff --git a/examples/batch_rxd_net/init.py b/examples/batch_rxd_net/init.py new file mode 100644 index 000000000..2c2da8aa5 --- /dev/null +++ b/examples/batch_rxd_net/init.py @@ -0,0 +1,26 @@ +""" +init.py + +Starting script to run NetPyNE-based RxD model. + +Usage: + python init.py # Run simulation, optionally plot a raster + +MPI usage: + mpiexec -n 4 nrniv -python -mpi init.py +""" +from netpyne import sim +cfg, netParams = sim.readCmdLineArgs() + +# -------------------------------- +# Instantiate network +# -------------------------------- +sim.initialize(netParams, cfg) # create network object and set cfg and net params +sim.net.createPops() # instantiate network populations +sim.net.createCells() # instantiate network cells based on defined populations +sim.net.connectCells() # create connections between cells based on params +sim.net.addStims() # add external stimulation to cells (IClamps etc) +sim.net.addRxD(nthreads=4) # add reaction-diffusion (RxD) +sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc) +sim.simulate() +sim.analyze() diff --git a/examples/batch_rxd_net/mod/HCN1.mod b/examples/batch_rxd_net/mod/HCN1.mod new file mode 100644 index 000000000..4ed0564dc --- /dev/null +++ b/examples/batch_rxd_net/mod/HCN1.mod @@ -0,0 +1,67 @@ +: $Id: HCN1.mod,v 1.4 2013/01/02 15:01:55 samn Exp $ + +TITLE HCN1 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +NEURON { + SUFFIX HCN1 + NONSPECIFIC_CURRENT ih + RANGE gbar, g, e, v50, htau, hinf + RANGE gfactor, htaufactor +} + +PARAMETER { + celsius (degC) + gbar = 0.0001 (mho/cm2) + e= -30 (mV) + v50= -73 (mV) + gfactor = 1 + htaufactor = 1.0 : 4.78 +} + +STATE { + h +} + +ASSIGNED { + ih (mA/cm2) + hinf + htau (ms) + v (mV) + g (mho/cm2) +} + +PROCEDURE giassign () { + : ih=g*h*(v-e)*gfactor + g = gbar*h*gfactor + ih = g*(v-e) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + giassign() +} + +DERIVATIVE states { + rates(v) + h'= (hinf- h)/ htau +} + +INITIAL { + rates(v) + h = hinf + giassign() +} + +PROCEDURE rates(v (mV)) { + UNITSOFF + : HCN1 + hinf = 1/(1+exp(0.151*(v-v50))) + htau = htaufactor*exp((0.033*(v+75)))/(0.011*(1+exp(0.083*(v+75)))) + UNITSON +} + diff --git a/examples/batch_rxd_net/mod/IC.mod b/examples/batch_rxd_net/mod/IC.mod new file mode 100644 index 000000000..b489e1b7d --- /dev/null +++ b/examples/batch_rxd_net/mod/IC.mod @@ -0,0 +1,88 @@ +TITLE Ca-dependent potassium current +: +: Ca++ dependent K+ current IC responsible for +: action potentials AHP's +: Differential equations +: +: Model of Yamada, Koch & Adams, in: Methods in Neuronal Modeling, +: Ed. by Koch & Segev, MIT press, 1989. +: +: This current models the "fast" IK[Ca]: +: - potassium current +: - activated by intracellular calcium +: - VOLTAGE DEPENDENT +: +: Written by Alain Destexhe, Salk Institute, Sept 18, 1992 +: +: should be considered 'BK' - fast, big conductance + +NEURON { + SUFFIX ikc + USEION k READ ek WRITE ik + USEION ca READ cai + RANGE gkbar, ik + RANGE m_inf, tau_m + RANGE taumin + GLOBAL ascale,bscale,vfctr +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + v (mV) + celsius (degC) + ek (mV) + cai (mM) + gkbar = .003 (mho/cm2) : taken from + taumin = 0.1 + ascale = 250.0 + bscale = 0.1 + vfctr = 24.0 +} + +STATE { + m +} + +INITIAL { + evaluate_fct(v,cai) + m = m_inf +} + +ASSIGNED { + ik (mA/cm2) + m_inf + tau_m (ms) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gkbar * m * (v - ek) +} + +DERIVATIVE states { + evaluate_fct(v,cai) + m' = (m_inf - m) / tau_m +} + +UNITSOFF +PROCEDURE evaluate_fct(v(mV),cai(mM)) { LOCAL a,b,tadj +: +: activation kinetics of Yamada et al were at 22 deg. C +: transformation to 36 deg assuming Q10=3 +: + tadj = 3 ^ ((celsius-22.0)/10) + + a = ascale * cai * exp(v/vfctr) + b = bscale * exp(-v/vfctr) + + tau_m = 1.0 / (a + b) / tadj + if(tau_m < taumin){ tau_m = taumin } + m_inf = a / (a + b) +} +UNITSON diff --git a/examples/batch_rxd_net/mod/IKsin.mod b/examples/batch_rxd_net/mod/IKsin.mod new file mode 100755 index 000000000..0bce29e1a --- /dev/null +++ b/examples/batch_rxd_net/mod/IKsin.mod @@ -0,0 +1,97 @@ +: Slowly inactivating K+ channel + +NEURON { + SUFFIX IKsin + USEION k READ ki, ko WRITE ik + RANGE gKsbar, ik, gk + +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (mM) = (milli/liter) + +} +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} +PARAMETER { + v (mV) + dt (ms) + gKsbar= 0.00014 (mho/cm2) <0,1e9> + +} + + +STATE { + a b +} + + +ASSIGNED { + ik (mA/cm2) + ainf binf + atau (ms) + btau (ms) + gk (mho/cm2) + ek (mV) + ki (mM) + ko (mM) +} + + + +INITIAL { + rate(v) + a = ainf + b = binf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + + gk = gKsbar * a * b + ek = 25 * log(ko/ki) + ik = gk*(v-ek) + +} + +DERIVATIVE states { + rate(v) + + a' = (ainf-a)/atau + b' = (binf-b)/btau +} +UNITSOFF + +PROCEDURE rate(v (mV)) {LOCAL va, vb, vc, vd + + + va = v + 34 + vb = v + 65 + vd = v + 63.6 + + +if (fabs(va)<1e-04){ va = va+0.00001 } + ainf = 1/(1 + exp(-va/6.5)) + atau = 10 + :atau=6 + + +if (fabs(vb)<1e-04){ vb = vb+0.00001 } + binf = 1/(1 + exp(vb/6.6)) + + +if (fabs(vd)<1e-04){ vd = vd+0.00001 } + btau = 200 + 3200 / (1 + exp(-vd/4)) + :btau = 200 + 3200 / (1 + exp(-vd/4)) +} + + +UNITSON + + + + + + + diff --git a/examples/batch_rxd_net/mod/ar_traub.mod b/examples/batch_rxd_net/mod/ar_traub.mod new file mode 100644 index 000000000..93fe5898f --- /dev/null +++ b/examples/batch_rxd_net/mod/ar_traub.mod @@ -0,0 +1,47 @@ +TITLE Anomalous rectifier current for RD Traub, J Neurophysiol 89:909-921, 2003 + +COMMENT + + Implemented by Maciej Lazarewicz 2003 (mlazarew@seas.upenn.edu) + +ENDCOMMENT + +INDEPENDENT { t FROM 0 TO 1 WITH 1 (ms) } + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) +} +NEURON { + SUFFIX ar + NONSPECIFIC_CURRENT i + RANGE gbar, i +} +PARAMETER { + gbar = 0.0 (mho/cm2) + v (mV) + erev = -35 (mV) +} +ASSIGNED { + i (mA/cm2) + minf (1) + mtau (ms) +} +STATE { + m +} +BREAKPOINT { + SOLVE states METHOD cnexp + i = gbar * m * ( v - erev ) +} +INITIAL { + minf = 1 / ( 1 + exp( ( v + 75 ) / 5.5 ) ) + mtau = 1 / ( exp( -14.6 - 0.086 * v ) + exp( -1.87 + 0.07 * v ) ) + m = minf + : m = 0.25 : ?? +} +DERIVATIVE states { + minf = 1 / ( 1 + exp( ( v + 75 ) / 5.5 ) ) + mtau = 1 / ( exp( -14.6 - 0.086 * v ) + exp( -1.87 + 0.07 * v ) ) + m' = ( minf - m ) / mtau +} diff --git a/examples/batch_rxd_net/mod/cadad.mod b/examples/batch_rxd_net/mod/cadad.mod new file mode 100644 index 000000000..46466753c --- /dev/null +++ b/examples/batch_rxd_net/mod/cadad.mod @@ -0,0 +1,74 @@ +: $Id: cadad.mod,v 1.4 2002/11/08 15:42:37 billl Exp $ +TITLE Fast mechanism for submembranal Ca++ concentration (cai) +: +: Takes into account: +: +: - increase of cai due to calcium currents +: - extrusion of calcium with a simple first order equation +: +: This mechanism is compatible with the calcium pump "cad" and has the +: same name and parameters; however the parameters specific to the pump +: are dummy here. +: +: Parameters: +: +: - depth: depth of the shell just beneath the membran (in um) +: - cainf: equilibrium concentration of calcium (2e-4 mM) +: - taur: time constant of calcium extrusion (must be fast) +: - kt,kd: dummy parameters +: +: Written by Alain Destexhe, Salk Institute, 1995 +: + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX cadad + USEION ca READ ica, cai WRITE cai + RANGE depth,kt,kd,cainf,taur +} + +UNITS { + (molar) = (1/liter) : moles do not appear in units + (mM) = (millimolar) + (um) = (micron) + (mA) = (milliamp) + (msM) = (ms mM) +} + +CONSTANT { + FARADAY = 96489 (coul) : moles do not appear in units + : FARADAY = 96.489 (k-coul) : moles do not appear in units +} + +PARAMETER { + depth = 1 (um) : depth of shell + taur = 5 (ms) : rate of calcium removal + cainf = 2.4e-4 (mM) + kt = 0 (mM/ms) : dummy + kd = 0 (mM) : dummy +} + +STATE { + cai (mM) +} + +INITIAL { + cai = cainf +} + +ASSIGNED { + ica (mA/cm2) + drive_channel (mM/ms) +} + +BREAKPOINT { + SOLVE state METHOD cnexp +} + +DERIVATIVE state { + drive_channel = - (10000) * ica / (2 * FARADAY * depth) + if (drive_channel <= 0.) { drive_channel = 0. } : cannot pump inward + cai' = drive_channel + (cainf-cai)/taur +} + diff --git a/examples/batch_rxd_net/mod/cadyn.mod b/examples/batch_rxd_net/mod/cadyn.mod new file mode 100755 index 000000000..58aa6177d --- /dev/null +++ b/examples/batch_rxd_net/mod/cadyn.mod @@ -0,0 +1,52 @@ +: simple first-order model of calcium dynamics + +NEURON { + SUFFIX cadyn + USEION ca READ cai,ica WRITE cai + RANGE ca + GLOBAL depth,cainf,taur + +} + +UNITS { + (molar) = (1/liter) + (mM) = (milli/liter) + (um) = (micron) + (mA) = (milliamp) + (msM) = (ms mM) + FARADAY = (faraday) (coul) +} + +PARAMETER { + depth = .1 (um) + taur = 200 (ms) : rate of calcium removal for stress conditions + cainf = 50e-6(mM) :changed oct2 + cai (mM) +} + +ASSIGNED { + ica (mA/cm2) + drive_channel (mM/ms) +} + +STATE { + ca (mM) +} + + +BREAKPOINT { + SOLVE state METHOD euler +} + +DERIVATIVE state { + + drive_channel = - (10000) * ica / (2 * FARADAY * depth) + if (drive_channel <= 0.) { drive_channel = 0. } : cannot pump inward + ca' = drive_channel/18 + (cainf -ca)/taur*11 + cai = ca +} + + +INITIAL { + ca = cainf +} diff --git a/examples/batch_rxd_net/mod/cagk.mod b/examples/batch_rxd_net/mod/cagk.mod new file mode 100644 index 000000000..662d43764 --- /dev/null +++ b/examples/batch_rxd_net/mod/cagk.mod @@ -0,0 +1,87 @@ +TITLE CaGk +: Calcium activated K channel. +: Modified from Moczydlowski and Latorre (1983) J. Gen. Physiol. 82 + +UNITS { + (molar) = (1/liter) +} + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (mM) = (millimolar) +} + + +NEURON { + SUFFIX cagk + USEION ca READ cai + USEION k READ ek WRITE ik + RANGE gbar,gkca,ik + RANGE oinf, tau +} + +UNITS { + FARADAY = (faraday) (kilocoulombs) + R = 8.313424 (joule/degC) +} + +PARAMETER { + celsius (degC) + v (mV) + gbar=.01 (mho/cm2) : Maximum Permeability + cai (mM) + ek (mV) + + d1 = .84 + d2 = 1. + k1 = .48e-3 (mM) + k2 = .13e-6 (mM) + abar = .28 (/ms) + bbar = .48 (/ms) + st=1 (1) +} + +ASSIGNED { + ik (mA/cm2) + oinf + tau (ms) + gkca (mho/cm2) +} + +INITIAL { + rate(v,cai) + o=oinf +} + +STATE { o } : fraction of open channels + +BREAKPOINT { + SOLVE state METHOD cnexp + gkca = gbar*o^st + ik = gkca*(v - ek) +} + +DERIVATIVE state { : exact when v held constant; integrates over dt step + rate(v, cai) + o' = (oinf - o)/tau +} + +FUNCTION alp(v (mV), c (mM)) (1/ms) { :callable from hoc + alp = c*abar/(c + exp1(k1,d1,v)) +} + +FUNCTION bet(v (mV), c (mM)) (1/ms) { :callable from hoc + bet = bbar/(1 + c/exp1(k2,d2,v)) +} + +FUNCTION exp1(k (mM), d, v (mV)) (mM) { :callable from hoc + exp1 = k*exp(-2*d*FARADAY*v/R/(273.15 + celsius)) +} + +PROCEDURE rate(v (mV), c (mM)) { :callable from hoc + LOCAL a + a = alp(v,c) + tau = 1/(a + bet(v, c)) + oinf = a*tau +} diff --git a/examples/batch_rxd_net/mod/cal_mh.mod b/examples/batch_rxd_net/mod/cal_mh.mod new file mode 100644 index 000000000..79ad9c924 --- /dev/null +++ b/examples/batch_rxd_net/mod/cal_mh.mod @@ -0,0 +1,139 @@ + +COMMENT +26 Ago 2002 Modification of original channel to allow variable time step and to correct an initialization error. + Done by Michael Hines(michael.hines@yale.e) and Ruggero Scorcioni(rscorcio@gmu.edu) at EU Advance Course in Computational Neuroscience. Obidos, Portugal + +ca.mod +Uses fixed eca instead of GHK eqn + +HVA Ca current +Based on Reuveni, Friedman, Amitai and Gutnick (1993) J. Neurosci. 13: +4609-4621. + +Author: Zach Mainen, Salk Institute, 1994, zach@salk.edu + +ENDCOMMENT + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX ca + USEION ca READ eca WRITE ica + RANGE m, h, gca, gbar + RANGE minf, hinf, mtau, htau + RANGE q10, temp, tadj, vmin, vmax, vshift +} + +PARAMETER { + gbar = 0.1 (pS/um2) : 0.12 mho/cm2 + vshift = 0 (mV) : voltage shift (affects all) + + cao = 2.5 (mM) : external ca concentration + cai (mM) + + temp = 23 (degC) : original temp + q10 = 2.3 : temperature sensitivity + + v (mV) + dt (ms) + celsius (degC) + vmin = -120 (mV) + vmax = 100 (mV) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) + FARADAY = (faraday) (coulomb) + R = (k-mole) (joule/degC) + PI = (pi) (1) +} + +ASSIGNED { + ica (mA/cm2) + gca (pS/um2) + eca (mV) + minf hinf + mtau (ms) htau (ms) + tadj +} + + +STATE { m h } + +INITIAL { + trates(v+vshift) + m = minf + h = hinf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gca = tadj*gbar*m*m*h + ica = (1e-4) * gca * (v - eca) +} + +LOCAL mexp, hexp + +:PROCEDURE states() { +: trates(v+vshift) +: m = m + mexp*(minf-m) +: h = h + hexp*(hinf-h) +: VERBATIM +: return 0; +: ENDVERBATIM +:} + +DERIVATIVE states { + trates(v+vshift) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE trates(v) { + + + : TABLE minf, hinf, mtau, htau + : DEPEND celsius, temp + : + : FROM vmin TO vmax WITH 199 + + rates(v): not consistently executed from here if usetable == 1 + +: tinc = -dt * tadj + +: mexp = 1 - exp(tinc/mtau) +: hexp = 1 - exp(tinc/htau) +} + + +PROCEDURE rates(vm) { + LOCAL a, b + + tadj = q10^((celsius - temp)/10) + + a = 0.055*(-27 - vm)/(exp((-27-vm)/3.8) - 1) + b = 0.94*exp((-75-vm)/17) + + mtau = 1/tadj/(a+b) + minf = a/(a+b) + + :"h" inactivation + + a = 0.000457*exp((-13-vm)/50) + b = 0.0065/(exp((-vm-15)/28) + 1) + + htau = 1/tadj/(a+b) + hinf = a/(a+b) +} + +FUNCTION efun(z) { + if (fabs(z) < 1e-4) { + efun = 1 - z/2 + }else{ + efun = z/(exp(z) - 1) + } +} diff --git a/examples/batch_rxd_net/mod/cal_mig.mod b/examples/batch_rxd_net/mod/cal_mig.mod new file mode 100644 index 000000000..9069e8643 --- /dev/null +++ b/examples/batch_rxd_net/mod/cal_mig.mod @@ -0,0 +1,131 @@ +TITLE L-calcium channel +: L-type calcium channel with [Ca]i inactivation +: from Jaffe, D. B., Ross, W. N., Lisman, J. E., Laser-Ross, N., Miyakawa, H., and Johnston, D. A. A model for dendritic Ca2 +: accumulation in hippocampal pyramidal neurons based on fluorescence imaging measurements. J. Neurophysiol. 71:1O65-1077 1994. +: conduction density estimate of 50-200 pS/mu2; 0.0025 S/cm2 (5-20 channels of 10 each) +: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 +: hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). +: adapted from http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=3263&file=\ca3_db\cal2.mod +: this version from https://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=148094&file=\kv72-R213QW-mutations\cal2.mod +: Miceli F, Soldovieri MV, Ambrosino P, Barrese V, Migliore M, Cilio MR, Taglialatela M (2013) Genotype-phenotype +: correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits. PNAS 110:4386-4391 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + + FARADAY = 96520 (coul) + R = 8.3134 (joule/degC) + KTOMV = .0853 (mV/degC) +} + +PARAMETER { + v (mV) + celsius (degC) + gcalbar=.003 (mho/cm2) + ki=.001 (mM) + cai = 50.e-6 (mM) + cao = 2 (mM) + q10 = 5 + mmin=0.2 + tfa = 1 + a0m =0.1 + zetam = 2 + vhalfm = 4 + gmm=0.1 + USEGHK=1 + erev = 100 +} + + +NEURON { + SUFFIX cal + USEION ca READ cai,cao WRITE ica + RANGE gcalbar,cai, ica, gcal, ggk + RANGE minf,tau + GLOBAL USEGHK +} + +STATE { + m +} + +ASSIGNED { + ica (mA/cm2) + gcal (mho/cm2) + minf + tau (ms) + ggk +} + +INITIAL { + rate(v) + m = minf +} + +BREAKPOINT { + SOLVE state METHOD cnexp + gcal = gcalbar*m*m*h2(cai) + if (USEGHK == 1) { + ggk=ghk(v,cai,cao) + } else { + ggk=v-erev + } + ica = gcal*ggk +} + +FUNCTION h2(cai(mM)) { + h2 = ki/(ki+cai) +} + + +FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) { + LOCAL nu,f + f = KTF(celsius)/2 + nu = v/f + ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu) +} + +FUNCTION KTF(celsius (DegC)) (mV) { + KTF = ((25./293.15)*(celsius + 273.15)) +} + + +FUNCTION efun(z) { + if (fabs(z) < 1e-4) { + efun = 1 - z/2 + }else{ + efun = z/(exp(z) - 1) + } +} + +FUNCTION alp(v(mV)) (1/ms) { + alp = 15.69*(-1.0*v+81.5)/(exp((-1.0*v+81.5)/10.0)-1.0) +} + +FUNCTION bet(v(mV)) (1/ms) { + bet = 0.29*exp(-v/10.86) +} + +FUNCTION alpmt(v(mV)) { + alpmt = exp(0.0378*zetam*(v-vhalfm)) +} + +FUNCTION betmt(v(mV)) { + betmt = exp(0.0378*zetam*gmm*(v-vhalfm)) +} + +DERIVATIVE state { + rate(v) + m' = (minf - m)/tau +} + +PROCEDURE rate(v (mV)) { :callable from hoc + LOCAL a, b, qt + qt=q10^((celsius-25)/10) + a = alp(v) + b = 1/((a + bet(v))) + minf = a*b + tau = betmt(v)/(qt*a0m*(1+alpmt(v))) + if (tau O (Alpha,Beta) + + If the binding is fast, the fraction of open channels is given by: + + O = G^n / ( G^n + KD ) + + where KD = Beta / Alpha is the dissociation constant + +----------------------------------------------------------------------------- + + Parameters estimated from patch clamp recordings of GABAB PSP's in + rat hippocampal slices (Otis et al, J. Physiol. 463: 391-407, 1993). + +----------------------------------------------------------------------------- + + PULSE MECHANISM + + Kinetic synapse with release mechanism as a pulse. + + Warning: for this mechanism to be equivalent to the model with diffusion + of transmitter, small pulses must be used... + + For a detailed model of GABAB: + + Destexhe, A. and Sejnowski, T.J. G-protein activation kinetics and + spill-over of GABA may account for differences between inhibitory responses + in the hippocampus and thalamus. Proc. Natl. Acad. Sci. USA 92: + 9515-9519, 1995. + + For a review of models of synaptic currents: + + Destexhe, A., Mainen, Z.F. and Sejnowski, T.J. Kinetic models of + synaptic transmission. In: Methods in Neuronal Modeling (2nd edition; + edited by Koch, C. and Segev, I.), MIT press, Cambridge, 1996. + + This simplified model was introduced in: + + Destexhe, A., Bal, T., McCormick, D.A. and Sejnowski, T.J. + Ionic mechanisms underlying synchronized oscillations and propagating + waves in a model of ferret thalamic slices. Journal of Neurophysiology + 76: 2049-2070, 1996. + + See also http://www.cnl.salk.edu/~alain + + + + Alain Destexhe, Salk Institute and Laval University, 1995 + +----------------------------------------------------------------------------- +ENDCOMMENT + + + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + POINT_PROCESS GABAB + RANGE R, G, g + NONSPECIFIC_CURRENT i + GLOBAL Cmax, Cdur + GLOBAL K1, K2, K3, K4, KD, Erev, warn, cutoff +} +UNITS { + (nA) = (nanoamp) + (mV) = (millivolt) + (umho) = (micromho) + (mM) = (milli/liter) +} + +PARAMETER { + + Cmax = 0.5 (mM) : max transmitter concentration + Cdur = 0.3 (ms) : transmitter duration (rising phase) +: +: From Kfit with long pulse (5ms 0.5mM) +: + K1 = 0.52 (/ms mM) : forward binding rate to receptor + K2 = 0.0013 (/ms) : backward (unbinding) rate of receptor + K3 = 0.098 (/ms) : rate of G-protein production + K4 = 0.033 (/ms) : rate of G-protein decay + KD = 100 : dissociation constant of K+ channel + n = 4 : nb of binding sites of G-protein on K+ + Erev = -95 (mV) : reversal potential (E_K) + warn = 0 : too large G warning has/has not been issued + cutoff = 1e12 +} + + +ASSIGNED { + v (mV) : postsynaptic voltage + i (nA) : current = g*(v - Erev) + g (umho) : conductance + Gn + R : fraction of activated receptor + edc + synon + Rinf + Rtau (ms) + Beta (/ms) +} + +STATE { + Ron Roff + G : fraction of activated G-protein +} + + +INITIAL { + R = 0 + G = 0 + Ron = 0 + Roff = 0 + synon = 0 + Rinf = K1*Cmax/(K1*Cmax + K2) + Rtau = 1/(K1*Cmax + K2) + Beta = K2 + +} + +BREAKPOINT { + SOLVE bindkin METHOD derivimplicit + if (G < cutoff) { + Gn = G*G*G*G : ^n = 4 + g = Gn / (Gn+KD) + } else { + if(!warn){ + printf("gabab.mod WARN: G = %g too large\n", G) + warn = 1 + } + g = 1 + } + i = g*(v - Erev) +} + + +DERIVATIVE bindkin { + Ron' = synon*K1*Cmax - (K1*Cmax + K2)*Ron + Roff' = -K2*Roff + R = Ron + Roff + G' = K3 * R - K4 * G +} + +: following supports both saturation from single input and +: summation from multiple inputs +: Note: automatic initialization of all reference args to 0 except first + +NET_RECEIVE(weight, r0, t0 (ms)) { + if (flag == 1) { : at end of Cdur pulse so turn off + r0 = weight*(Rinf + (r0 - Rinf)*exp(-(t - t0)/Rtau)) + t0 = t + synon = synon - weight + state_discontinuity(Ron, Ron - r0) + state_discontinuity(Roff, Roff + r0) + }else{ : at beginning of Cdur pulse so turn on + r0 = weight*r0*exp(-Beta*(t - t0)) + t0 = t + synon = synon + weight + state_discontinuity(Ron, Ron + r0) + state_discontinuity(Roff, Roff - r0) + :come again in Cdur + net_send(Cdur, 1) + } +} diff --git a/examples/batch_rxd_net/mod/ghk.inc b/examples/batch_rxd_net/mod/ghk.inc new file mode 100644 index 000000000..95babfc11 --- /dev/null +++ b/examples/batch_rxd_net/mod/ghk.inc @@ -0,0 +1,40 @@ +COMMENT + GHK function that returns effective driving force + Slope at low voltages is 1 + z needs to be set as a PARAMETER +ENDCOMMENT + +FUNCTION ghkg(v(mV), ci(mM), co(mM), z) (mV) { + LOCAL xi, f, exi, fxi + f = R*(celsius+273.15)/(z*(1e-3)*FARADAY) + xi = v/f + exi = exp(xi) + if (fabs(xi) < 1e-4) { + fxi = 1 - xi/2 + }else{ + fxi = xi/(exi - 1) + } + ghkg = f*((ci/co)*exi - 1)*fxi +} + +FUNCTION ghk(v(mV), ci(mM), co(mM), z) (.001 coul/cm3) { + LOCAL xi, f, exi, fxi + f = R*(celsius+273.15)/(z*(1e-3)*FARADAY) + xi = v/f + exi = exp(xi) + if (fabs(xi) < 1e-4) { + fxi = 1 - xi/2 + }else{ + fxi = xi/(exi - 1) + } + ghk = (.001)*z*FARADAY*(ci*exi - co)*fxi +} + + + + + + + + + diff --git a/examples/batch_rxd_net/mod/h_BS.mod b/examples/batch_rxd_net/mod/h_BS.mod new file mode 100644 index 000000000..eeca578ac --- /dev/null +++ b/examples/batch_rxd_net/mod/h_BS.mod @@ -0,0 +1,88 @@ +TITLE I-h channel from Magee 1998 for distal dendrites +: modified to take into account Sonia's exp. Apr.2008 M.Migliore +: thread-safe 2010-05-18 Ben Suter +: 2010-11-07 Ben Suter, removing "hd" from parameter names, changing suffix from "hd" to "h" +: Parameters fit to pre-ZD current-clamp step responses from experiment BS0284 (traces and reconstruction from single corticospinal neuron) +: 2011-09-18 Ben Suter, set default parameter values to those found from MRF optimization for BS0284 model +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius = 34.0 (degC) + erev = -37.0 (mV) + gbar = 0.0001 (mho/cm2) + vhalfl = -78.474 (mV) : was -81 + kl = -6 : was -8 + vhalft = -66.139 (mV) : was -62 + a0t = 0.009 (/ms) : was 0.0077696 + zetat = 20 (1) : was 5 + gmt = 0.01 (1) : was 0.057127 + q10 = 4.5 + qtl = 1 + taumin = 2.0 (ms) : minimal value of time constant +} + +NEURON { + SUFFIX h + NONSPECIFIC_CURRENT i + RANGE gbar, vhalfl + RANGE linf, taul, g + GLOBAL taumin +} + +STATE { + l +} + +ASSIGNED { + i (mA/cm2) + linf + taul + g +} + +INITIAL { + rate(v) + l = linf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*l + i = g*(v-erev) +} + +FUNCTION alpt(v(mV)) { + alpt = exp(0.0378*zetat*(v-vhalft)) +} + +FUNCTION bett(v(mV)) { + bett = exp(0.0378*zetat*gmt*(v-vhalft)) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rate(v) + l' = (linf - l)/taul +} + +PROCEDURE rate(v (mV)) { :callable from hoc + LOCAL a,qt + qt = q10^((celsius-33)/10) + a = alpt(v) + linf = 1/(1 + exp(-(v-vhalfl)/kl)) + taul = bett(v)/(qtl*qt*a0t*(1+a)) + 1e-8 + if(taul < taumin) { taul = taumin } : min value of time constant +} diff --git a/examples/batch_rxd_net/mod/h_harnett.mod b/examples/batch_rxd_net/mod/h_harnett.mod new file mode 100644 index 000000000..84b0c64f7 --- /dev/null +++ b/examples/batch_rxd_net/mod/h_harnett.mod @@ -0,0 +1,69 @@ +TITLE I-h channel from Harnett 2015 - J Neurosci + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius (degC) + erev = -30 (mV) + gbar = 0.0001 (mho/cm2) + vhalf = -100.6 (mV) + k = 6.4 + bA = 9.63 : for activation tau - (note - only 1 tau) + bD = 1.30 : for deactivation tau + mA = 0.0458 : for activation tau + mD = -0.0447 : for deactivation tau + q10 = 2.2 + taumin = 2.0 (ms) : minimal value of time constant +} + +NEURON { + SUFFIX h15 + NONSPECIFIC_CURRENT i + RANGE gbar, minf, tau, g, m + GLOBAL taumin, k, bA, bD, mA, mD, vhalf +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) + minf + tau + g +} + +INITIAL { + rate(v) + m = minf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*m + i = g*(v-erev) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rate(v) + m' = (minf - m) / tau +} + +PROCEDURE rate(v (mV)) { :callable from hoc - leads to segfault in python + LOCAL qt + qt = q10^((celsius-26.0)/10.0) + + if(v <= -92.0046199111992) { + tau = exp(bA + mA * v) / qt + } else { + tau = exp(bD + mD * v) / qt + } + if(tau < taumin) { tau = taumin } + + minf = 1.0/(1.0 + exp((v-vhalf)/k)) +} diff --git a/examples/batch_rxd_net/mod/h_kole.mod b/examples/batch_rxd_net/mod/h_kole.mod new file mode 100644 index 000000000..3971e5125 --- /dev/null +++ b/examples/batch_rxd_net/mod/h_kole.mod @@ -0,0 +1,76 @@ +TITLE Ih-current +: modified from http://senselab.med.yale.edu/ModelDB/showmodel.cshtml?model=64195&file=%5cStochastic%5cStochastic_Na%5cih.mod +: /u/samn/papers/jnsci_26_1677.pdf +: +: @article{kole2006single, +: title={Single Ih channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output}, +: author={Kole, M.H.P. and Hallermann, S. and Stuart, G.J.}, +: journal={The Journal of neuroscience}, +: volume={26}, +: number={6}, +: pages={1677--1687}, +: year={2006}, +: publisher={Soc Neuroscience} +: } + +COMMENT +Author: Stefan Hallermann; modified by Sam Neymotin (parameterized) +Provides deterministic Ih-currents as described in Kole et al. (2006). +ENDCOMMENT + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + erev=-45 (mV) :ih-reversal potential + gbar=0.00015 (S/cm2) :default Ih conductance; exponential distribution is set in Ri18init.hoc + q10 = 2.2 + ascale = 0.00643 + bscale = 0.193 + ashift = 154.9 + aslope = 11.9 + bslope = 33.1 +} + +NEURON { + THREADSAFE + SUFFIX ih + NONSPECIFIC_CURRENT i + RANGE i,gbar,ascale,bscale,ashift,aslope,bslope +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) +} + +INITIAL { LOCAL a,b + a = alpha(v) + b = beta(v) + m = a / (a + b) +} + +BREAKPOINT { + SOLVE state METHOD cnexp + i = gbar*m*(v-erev) +} + +: tau = 1 / (alpha + beta) +FUNCTION alpha(v(mV)) { + alpha = ascale*(v+ashift)/(exp((v+ashift)/aslope)-1) + :parameters are estimated by direct fitting of HH model to activation time constants and voltage activation curve recorded at 34C +} + +FUNCTION beta(v(mV)) { + beta = bscale*exp(v/bslope) +} + +DERIVATIVE state { + m' = (1-m)*alpha(v) - m*beta(v) +} diff --git a/examples/batch_rxd_net/mod/h_migliore.mod b/examples/batch_rxd_net/mod/h_migliore.mod new file mode 100644 index 000000000..f65df095e --- /dev/null +++ b/examples/batch_rxd_net/mod/h_migliore.mod @@ -0,0 +1,102 @@ +TITLE I-h channel from Magee 1998 for distal dendrites +: default values are for dendrites and low Na +: plus leakage, M.Migliore Mar 2010 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + +} + +PARAMETER { + v (mV) + ehd = -30 (mV) + celsius (degC) + gbar=.0001 (mho/cm2) + vhalfl=-90 (mV) + vhalft=-75 (mV) + a0t=0.0046 (/ms) + zetal=4 (1) + zetat=2.2 (1) + gmt=.4 (1) + q10=4.5 + qtl=1 + clk=0 + elk = -70 (mV) +} + + +NEURON { + THREADSAFE SUFFIX hd + NONSPECIFIC_CURRENT i + NONSPECIFIC_CURRENT lk + RANGE gbar, vhalfl, elk, clk, glk, ehd + GLOBAL linf,taul +} + + +STATE { + l +} + +ASSIGNED { + i (mA/cm2) + lk (mA/cm2) + linf + taul + ghd + glk +} + +INITIAL { + rate(v) + l=linf +} + + +BREAKPOINT { + SOLVE states METHOD cnexp + ghd = gbar*l + i = ghd*(v-ehd) + lk = clk*gbar*(v-elk) +} + + +FUNCTION alpl(v(mV)) { + alpl = exp(0.0378*zetal*(v-vhalfl)) +} + +FUNCTION alpt(v(mV)) { + alpt = exp(0.0378*zetat*(v-vhalft)) +} + +FUNCTION bett(v(mV)) { + bett = exp(0.0378*zetat*gmt*(v-vhalft)) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rate(v) + l' = (linf - l)/taul +} + +PROCEDURE rate(v (mV)) { :callable from hoc + LOCAL a,qt + qt=q10^((celsius-33)/10) + a = alpt(v) + linf = 1/(1+ alpl(v)) + taul = bett(v)/(qtl*qt*a0t*(1+a)) +} + + + + + + + + + + + + + + diff --git a/examples/batch_rxd_net/mod/hin.mod b/examples/batch_rxd_net/mod/hin.mod new file mode 100755 index 000000000..0492ca91b --- /dev/null +++ b/examples/batch_rxd_net/mod/hin.mod @@ -0,0 +1,76 @@ +TITLE H-current that uses Na ions +: Updated to use Cvode by Yiota Poirazi 12/1/2005 + +NEURON { + SUFFIX hin + RANGE gbar,vhalf, K, taun, ninf, g, ihi + USEION hi READ ehi WRITE ihi VALENCE 1 + +} + +UNITS { + (um) = (micrometer) + (mA) = (milliamp) + (uA) = (microamp) + (mV) = (millivolt) + (pmho) = (picomho) + (mmho) = (millimho) +} + +:INDEPENDENT {t FROM 0 TO 1 WITH 100 (ms)} + +PARAMETER { + ena = 55 (mV) + ehi = -10 (mV) + K = 10.0 (mV) + gbar = 0 (mho/cm2) : initialize conductance to zero + vhalf = -90 (mV) : half potential +} + + +STATE { + n +} + +ASSIGNED { + v +: ina (mA/cm2) + ihi (mA/cm2) + ninf + taun (ms) + g +} + + +INITIAL { + rates() + n = ninf + g = gbar*n + ihi = g*(v-ehi) +} + + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*n + ihi = g*(v-ehi) +} + +DERIVATIVE states { + rates() + n' = (ninf - n)/taun +} + +PROCEDURE rates() { + + if (v > -10) { + taun = 1 + } else { + taun = 2*(1/(exp((v+145)/-17.5)+exp((v+16.8)/16.5)) + 10) :h activation tau +5 + + } + ninf = 1 - (1 / (1 + exp((vhalf - v)/K))) :steady state value +} + + + diff --git a/examples/batch_rxd_net/mod/ican_sidi.mod b/examples/batch_rxd_net/mod/ican_sidi.mod new file mode 100644 index 000000000..f2d69a3ff --- /dev/null +++ b/examples/batch_rxd_net/mod/ican_sidi.mod @@ -0,0 +1,110 @@ +TITLE Slow Ca-dependent cation current +: from +: https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=144089&file=/PFCcell/mechanism/ican.mod +: +: Ca++ dependent nonspecific cation current ICAN +: Differential equations +: +: Model based on a first order kinetic scheme +: +: + n cai <-> (alpha,beta) +: +: Following this model, the activation fct will be half-activated at +: a concentration of Cai = (beta/alpha)^(1/n) = cac (parameter) +: +: The mod file is here written for the case n=2 (2 binding sites) +: --------------------------------------------- +: +: Kinetics based on: Partridge & Swandulla, TINS 11: 69-72, 1988. +: +: This current has the following properties: +: - inward current (non specific for cations Na, K, Ca, ...) +: - activated by intracellular calcium +: - NOT voltage dependent +: +: A minimal value for the time constant has been added +: +: Ref: Destexhe et al., J. Neurophysiology 72: 803-818, 1994. +: See also: http://www.cnl.salk.edu/~alain , http://cns.fmed.ulaval.ca +: + +: Updated by Kiki Sidiropoulou (2010) so that dADP has slow inactivation kinetics and it +: is activated after 5 spikes + +: Updated by Sam Neymotin (2016) to avoid using n ion and get rid of 'mystart' rule; also +: make sure that INITIAL block assigns currents + +NEURON { + SUFFIX ican + NONSPECIFIC_CURRENT i + USEION ca READ cai + USEION na WRITE ina + RANGE gbar, m_inf, tau_m + GLOBAL beta, cac, taumin +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + v (mV) + celsius = 36 (degC) + erev = -20 (mV) : reversal potential + cai (mM) : initial [Ca]i + gbar = 0.0001 (mho/cm2) + beta = 0.0001 (1/ms) : backward rate constant + cac = 0.0004 (mM) + : middle point of activation fct, for ip3 as somacar, for current injection + taumin = 0.1 (ms) : minimal value of time constant +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) + ina (mA/cm2) + m_inf + tau_m (ms) + tadj + g (mho/cm2) +} + +PROCEDURE iassign () { + g = gbar * m * m + i = g * (v - erev) + ina = 0.7 * i +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + evaluate_fct(v,cai) + m' = (m_inf - m) / tau_m +} + +UNITSOFF +INITIAL { + : activation kinetics are assumed to be at 22 deg. C + : Q10 is assumed to be 3 + tadj = 3.0 ^ ((celsius-22.0)/10) + evaluate_fct(v,cai) + m = m_inf + iassign() +} + +PROCEDURE evaluate_fct(v(mV),cai(mM)) { LOCAL alpha2 + alpha2 = beta * (cai/cac)^2 + tau_m = 1 / (alpha2 + beta) / tadj + m_inf = alpha2 / (alpha2 + beta) + if(tau_m < taumin) { tau_m = taumin } : min value of time constant +} +UNITSON diff --git a/examples/batch_rxd_net/mod/kBK.mod b/examples/batch_rxd_net/mod/kBK.mod new file mode 100644 index 000000000..353571e71 --- /dev/null +++ b/examples/batch_rxd_net/mod/kBK.mod @@ -0,0 +1,108 @@ +: from https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=168148&file=/stadler2014_layerV/kBK.mod +TITLE large-conductance calcium-activated potassium channel (BK) + :Mechanism according to Gong et al 2001 and Womack&Khodakakhah 2002, + :adapted for Layer V cells on the basis of Benhassine&Berger 2005. + :NB: concentrations in mM + +NEURON { + SUFFIX kBK + USEION k READ ek WRITE ik + USEION ca READ cai + RANGE gpeak, gkact, caPh, caPk, caPmax, caPmin + RANGE caVhh, CaVhk, caVhmax, caVhmin, k, tau + GLOBAL pinfmin : cutoff - if pinf < pinfmin, set to 0.; by default cutoff not used (pinfmin==0) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + + +PARAMETER { + :maximum conductance (Benhassine 05) + gpeak = 268e-4 (mho/cm2) <0, 1e9> + + : Calcium dependence of opening probability (Gong 2001) + caPh = 2e-3 (mM) : conc. with half maximum open probaility + caPk = 1 : Steepness of calcium dependence curve + caPmax = 1 : max and + caPmin = 0 : min open probability + + : Calcium dependence of Vh shift (Womack 2002) + caVhh = 2e-3 (mM) : Conc. for half of the Vh shift + caVhk = -0.94208 : Steepness of the Vh-calcium dependence curve + caVhmax = 155.67 (mV) : max and + caVhmin = -46.08 (mV) : min Vh + + : Voltage dependence of open probability (Gong 2001) + : must not be zero + k = 17 (mV) + + : Timeconstant of channel kinetics + : no data for a description of a calcium&voltage dependence + : some points (room temp) in Behassine 05 & Womack 02 + tau = 1 (ms) <1e-12, 1e9> + scale = 100 : scaling to incorporate higher ca conc near ca channels + + pinfmin = 0.0 : cutoff for pinf - less than that set pinf to 0.0 + +} + + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + cai (mM) + caiScaled (mM) + pinf (1) +} + + +STATE { + p +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gpeak*p* (v - ek) +} + +DERIVATIVE states { + rate(v, cai) + p' = (pinf - p)/tau +} + +INITIAL { + rate(v, cai) + p = pinf +} + +PROCEDURE rate(v(mV), ca(mM)) { + caiScaled = ca*scale + pinf = P0ca(caiScaled) / ( 1 + exp( (Vhca(caiScaled)-v)/k ) ) + if(pinf < pinfmin) { pinf = 0.0 } +} + +FUNCTION P0ca(ca(mM)) (1) { + + if (ca < 1E-18) { :check for division by zero + P0ca = caPmin + } else { + P0ca = caPmin + ( (caPmax - caPmin) / ( 1 + (caPh/ca)^caPk )) + } +} + +FUNCTION Vhca(ca(mM)) (mV) { + + if (ca < 1E-18) { :check for division by zero + Vhca = caVhmax + } else { + Vhca = caVhmin + ( (caVhmax - caVhmin ) / ( 1 + ((caVhh/ca)^caVhk)) ) + } +} + diff --git a/examples/batch_rxd_net/mod/kap_BS.mod b/examples/batch_rxd_net/mod/kap_BS.mod new file mode 100644 index 000000000..501817d5b --- /dev/null +++ b/examples/batch_rxd_net/mod/kap_BS.mod @@ -0,0 +1,124 @@ +TITLE K-A channel from Klee Ficker and Heinemann +: modified to account for Dax A Current --- M.Migliore Jun 1997 +: modified to be used with cvode M.Migliore 2001 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter, removing "ka" from parameter names, reformatting, setting sh = 0 (was 24 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius (degC) + ek + + sh = 0 + gbar = 0.008 (mho/cm2) + vhalfn = 11 (mV) + vhalfl = -56 (mV) + a0l = 0.05 (/ms) + a0n = 0.05 (/ms) + zetan = -1.5 (1) + zetal = 3 (1) + gmn = 0.55 (1) + gml = 1 (1) + lmin = 5 (mS) + nmin = 0.4 (mS) + pw = -1 (1) + tq = -40 + qq = 5 + q10 = 5 + qtl = 1 +} + + +NEURON { + SUFFIX kap + USEION k READ ek WRITE ik + RANGE gbar, g, sh, tq, vhalfn, vhalfl, ik : +: GLOBAL ninf,linf,taul,taun,lmin +} + +STATE { + n + l +} + +ASSIGNED { + ik (mA/cm2) + ninf + linf + taul + taun + g +} + +INITIAL { + rates(v) + n=ninf + l=linf +} + + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*n*l + ik = g*(v-ek) +} + + +FUNCTION alpn(v(mV)) { + LOCAL zeta + zeta=zetan+pw/(1+exp((v-tq-sh)/qq)) + alpn = exp(1.e-3*zeta*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betn(v(mV)) { + LOCAL zeta + zeta=zetan+pw/(1+exp((v-tq-sh)/qq)) + betn = exp(1.e-3*zeta*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION alpl(v(mV)) { + alpl = exp(1.e-3*zetal*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betl(v(mV)) { + betl = exp(1.e-3*zetal*gml*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rates(v) + n' = (ninf - n) / taun + l' = (linf - l) / taul +} + +PROCEDURE rates(v (mV)) { :callable from hoc + LOCAL a,qt + qt = q10^((celsius-24)/10) + + a = alpn(v) + ninf = 1/(1 + a) + taun = betn(v)/(qt*a0n*(1+a)) + if (taunost (k3,k4) + ~ost<->ist (k1,0.0) + ~ist<->cst (k2,0.0) + CONSERVE cst+ost+ist=1 +} + +:change feb8th for pfc +:PROCEDURE rates( v(mV), cani(mM)) { +PROCEDURE rates( v(mV), cai(mM)) { +: k1=alp( 0.1, v, -10.0, 1.0 ) : original + k1=alp( 0.01, v, -10.0, 1.0 ) :increases the current + k2=alp( 0.1, v, -120.0, -10.0 ) :original (0.1, -120, -10) +: k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* ( cai*1.0(/mM) )^3 :original + k3=alpha( 0.001, 1.0, v, -20.0, 7.0 ) *1.0e8* (cai*1.0(/mM) )^3 + :k3 changes the attenuation + k4=alp( 0.2, v, -44.0, -5.0 ) :original +: k4=alp( 0.2, v, -44.0, -5.0 ) +} + +FUNCTION alpha( tmin(ms), tmax(ms), v(mV), vhalf(mV), k(mV) )(/ms){ + alpha = 1.0 / ( tmin + 1.0 / ( 1.0 / (tmax-tmin) + exp((v-vhalf)/k)*1.0(/ms) ) ) +} + +FUNCTION alp( tmin(ms), v(mV), vhalf(mV), k(mV) )(/ms){ + alp = 1.0 / ( tmin + exp( -(v-vhalf) / k )*1.0(ms) ) +} + + + + + + + + + diff --git a/examples/batch_rxd_net/mod/kdmc_BS.mod b/examples/batch_rxd_net/mod/kdmc_BS.mod new file mode 100644 index 000000000..8fa27bd98 --- /dev/null +++ b/examples/batch_rxd_net/mod/kdmc_BS.mod @@ -0,0 +1,93 @@ +TITLE K-D channel with activation for motor cortex +: K-D current with activation, for motor cortex pyramidal neurons, per Miller et al. (2008) +: Based on K-A current K-A current for Mitral Cells from Wang et al (1996), by M.Migliore Jan. 2002 +: 2011-02-25 Ben Suter, first version, using MM's kamt.mod as a starting template +: 2011-09-18 Ben Suter, set default parameter values to those found from MRF optimization for BS0284 model +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +NEURON { + THREADSAFE + SUFFIX kdmc + USEION k READ ek WRITE ik + RANGE gbar, minf, mtau, hinf, htau, ik + GLOBAL taumin +} + +PARAMETER { + gbar = 0.002 (mho/cm2) + + celsius + ek (mV) : must be explicitly def. in hoc + v (mV) + + : activation + vhalfmt = -25 : original -20 : rough estimate from Miller et al (2008) Fig. 3D I-V curve + km = 14 : manual fit to match this I-V curve + + : inactivation + : NOTE: These values are still quite arbitrary (but get about the correct htau at -40 and -30 mV + vhalfh = -5 : original -55 + zetah = 0.02 : original 0.05 + gmh = 0.2 : original 0.7 + a0h = 0.00058 : original 0.00055 + taumin = 0.1 (ms) : minimal value of time constant + + vhalfht = -100 : original -88 : measured by Storm (1988) + kh = 8 : manual fit to match inactivation curve in Storm (1988) and Johnston+Wu textbook + + q10 = 3 +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) +} + +ASSIGNED { + ik (mA/cm2) + minf mtau (ms) + hinf htau (ms) +} + + +STATE { m h } + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gbar*m*h*(v - ek) +} + +INITIAL { + trates(v) + m = minf + h = hinf +} + +DERIVATIVE states { + trates(v) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE trates(v) { + LOCAL qt + qt = q10^((celsius-34)/10) + + minf = 1/(1 + exp(-(v-vhalfmt)/km)) + mtau = 1 + + hinf = 1/(1 + exp((v-vhalfht)/kh)) + htau = exp(zetah*gmh*(v-vhalfh)) / (qt*a0h*(1 + exp(zetah*(v-vhalfh)))) + if(htau < taumin) { htau = taumin } : min value of time constant +} diff --git a/examples/batch_rxd_net/mod/kdr_BS.mod b/examples/batch_rxd_net/mod/kdr_BS.mod new file mode 100644 index 000000000..971fbbf92 --- /dev/null +++ b/examples/batch_rxd_net/mod/kdr_BS.mod @@ -0,0 +1,89 @@ +TITLE K-DR channel +: from Klee Ficker and Heinemann +: modified to account for Dax et al. +: M.Migliore 1997 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter, removing "kdr" from parameter names, reformatting, setting sh = 0 (was 24 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius (degC) + ek (mV) : must be explicitely def. in hoc + + gbar = 0.003 (mho/cm2) + vhalfn = 13 (mV) + a0n = 0.0075 (/ms) + zetan = -3 (1) + gmn = 0.7 (1) + nmax = 20 (1) + q10 = 1 + sh = 0 +} + +NEURON { + THREADSAFE + SUFFIX kdr + USEION k READ ek WRITE ik + RANGE g, gbar, sh, ninf, taun, vhalfn, ik +} + +STATE { + n +} + +ASSIGNED { + ik (mA/cm2) + ninf + g + taun +} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*n + ik = g*(v-ek) +} + +INITIAL { + rates(v) + n=ninf +} + +FUNCTION alpn(v(mV)) { + alpn = exp(1.e-3*zetan*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betn(v(mV)) { + betn = exp(1.e-3*zetan*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rates(v) + n' = (ninf - n)/taun +} + +PROCEDURE rates(v (mV)) { :callable from hoc + LOCAL a,qt + qt = q10^((celsius-24)/10) + + a = alpn(v) + ninf = 1/(1+a) + taun = betn(v)/(qt*a0n*(1+a)) + if (taun + + +} + +STATE { + n +} + +ASSIGNED { + ik (mA/cm2) + inf + tau (ms) + gk (mho/cm2) + ek (mV) + ki (mM) + ko (mM) + +} + + +INITIAL { + rate(v) + n = inf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gk= gkdrbar*n*n*n*n + ek = 25 * log(ko/ki) + ik = gk*(v-ek) + +} + +DERIVATIVE states { + rate(v) + n' = (inf-n)/tau +} + +UNITSOFF + +FUNCTION alf(v){ LOCAL va + + va=v-13 + if (fabs(va)<1e-04){ + va=va+0.0001 + alf= (-0.018*va)/(-1+exp(-(va/25))) + } else { + alf = (-0.018*(v-13))/(-1+exp(-((v-13)/25))) + } +} + + +FUNCTION bet(v) { LOCAL vb + + vb=v-23 + if (fabs(vb)<1e-04){ + vb=vb+0.0001 + bet= (0.0054*vb)/(-1+exp(vb/12)) + } else { + bet = (0.0054*(v-23))/(-1+exp((v-23)/12)) + } +} + + + + + + +PROCEDURE rate(v (mV)) {LOCAL q10, sum, aa, ab + + aa=alf(v) ab=bet(v) + + sum = aa+ab + inf = aa/sum + tau = 1/(sum) + + +} + +UNITSON + + + diff --git a/examples/batch_rxd_net/mod/misc.h b/examples/batch_rxd_net/mod/misc.h new file mode 100644 index 000000000..8a4ed6038 --- /dev/null +++ b/examples/batch_rxd_net/mod/misc.h @@ -0,0 +1,134 @@ +// $Id: misc.h,v 1.38 2011/11/02 15:26:48 billl Exp $ + +#include +#include +#include /* contains LONG_MAX */ +#include +#include +#include +#include + +#if !defined(t) + #define _pval pval +#endif + +typedef struct LISTVEC { + int isz; + Object* pL; + double** pv; + unsigned int* plen; + unsigned int* pbuflen; +} ListVec; + +typedef struct BVEC { + int size; + int bufsize; + short *x; + Object* o; +} bvec; + +#define BYTEHEADER int _II__; char *_IN__; char _OUT__[16]; int BYTESWAP_FLAG=0; +#define BYTESWAP(_X__,_TYPE__) \ + if (BYTESWAP_FLAG == 1) { \ + _IN__ = (char *) &(_X__); \ + for (_II__=0;_II__ (Y) ? (X) : (Y)) + +//square root of 2 * PI +#define SQRT2PI 2.5066282746310002416 +//ln(2), base e log of 2 +#define LG2 0.69314718055994530941723212145818 +#define VRRY 200 +#define ISVEC(_OB__) (strncmp(hoc_object_name(_OB__),"Vector",6)==0) +#define dmaxuint 4294967295. // for 32 bits + +// Andre Fentons cast designations +typedef unsigned char ui1; /* one byte unsigned integer */ +typedef char si1; /* one byte signed integer */ +typedef unsigned short ui2; /* two byte unsigned integer */ +typedef short si2; /* two byte signed integer */ +typedef unsigned int ui4; /* four byte unsigned integer */ +typedef int si4; /* four byte signed integer */ +typedef float sf4; /* four byte signed floating point number */ +typedef double sf8; /* eight byte signed floating point number */ + +extern double ERR,GET,SET,OK,NOP,ALL,NEG,POS,CHK,NOZ,GTH,GTE,LTH,LTE,EQU; +extern double EQV,EQW,EQX,NEQ,SEQ,RXP,IBE,EBI,IBI,EBE; + +extern double *vector_newsize(); +extern unsigned int dcrsz; +extern double *dcr; +extern double *dcrset(int); +extern unsigned int scrsz; +extern unsigned int *scr; +extern unsigned int *scrset(int); +extern unsigned int iscrsz; +extern int *iscr; +extern int *iscrset(int); +extern double BVBASE; +extern double* hoc_pgetarg(); +extern void hoc_notify_iv(); +extern double hoc_call_func(Symbol*, int narg); +extern FILE* hoc_obj_file_arg(int narg); +extern Object** hoc_objgetarg(); +char *gargstr(); +char** hoc_pgargstr(); +extern void vector_resize(); +extern int vector_instance_px(); +extern void* vector_arg(); +extern double* vector_vec(); +extern int vector_buffer_size(void*); +extern double hoc_epsilon; +extern int stoprun; +extern void set_seed(); +extern void dshuffle(double* x,int nx); +extern void mcell_ran4_init(u_int32_t); +extern double mcell_ran4(u_int32_t *idx1, double *x, unsigned int n, double range); +extern int nrn_mlh_gsort(); +extern int ivoc_list_count(Object*); +extern Object* ivoc_list_item(Object*, int); +extern int list_vector_px2(); +extern int hoc_is_double_arg(int narg); +extern int hoc_is_str_arg(int narg); +extern int hoc_is_object_arg(int narg); +extern int hoc_is_pdouble_arg(int narg); +extern Symbol *hoc_get_symbol(char *); +extern Symbol *hoc_lookup(const char*); +extern Point_process* ob2pntproc(Object*); + +extern char* hoc_object_name(Object*); +extern int cmpdfn(); +extern int openvec(int, double **); +int list_vector_px(); +double *list_vector_resize(); +static void hxe() { hoc_execerror("",0); } +extern void FreeListVec(ListVec** pp); +extern ListVec* AllocListVec(Object* p); +extern ListVec* AllocILV(Object*, int, double *); +void FillListVec(ListVec* p,double dval); +void ListVecResize(ListVec* p,int newsz); +extern short *nrn_artcell_qindex_; +extern double nrn_event_queue_stats(double*); +extern void clear_event_queue(); + +static double sc[6]; +static FILE* testout; + +//* in vecst.mod +extern int** getint2D(int rows,int cols); +extern void freeint2D(int*** ppp,int rows); +extern double** getdouble2D(int rows,int cols); +extern void freedouble2D(double*** ppp,int rows); +extern double ismono1 (double *x, int n, int flag); + +//* in stats.mod +double kcorfast(double* input1, double* input2, double* i1d , double* i2d,int n,double* ps); +double Rktau (double* x, double* y, int n); // R version +double kcorfast (double* input1, double* input2, double* i1d , double* i2d,int n,double* ps); diff --git a/examples/batch_rxd_net/mod/nafx.mod b/examples/batch_rxd_net/mod/nafx.mod new file mode 100755 index 000000000..80ba6e79a --- /dev/null +++ b/examples/batch_rxd_net/mod/nafx.mod @@ -0,0 +1,170 @@ +: Fast Na+ channel +: added the 's' attenuation system from hha2.mod +: Kiki Sidiropoulou +: September 27, 2007 + +NEURON { + SUFFIX Nafx + USEION na READ ena WRITE ina + RANGE gnafbar, ina, gna, ar2 +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + +} + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +PARAMETER { + v (mV) + dt (ms) + gnafbar = 0 (mho/cm2) + :gnafbar= 0.086 (mho/cm2) <0,1e9> + ena = 55 (mV) + + :PARAMETERS FOR S ATTENUATION SYSTEM + taumin = 30 (ms) :min activation time for "s" attenuation system + vhalfr =-60 (mV) :half potential for "s" attenuation system, -60 + vvh=-58 (mV) + vvs = 2 (mV) + a0r = 0.0003 (/ms) + b0r = 0.0003 (/ms) + : a0r = 0.0003 (ms) + :b0r = 0.0003 (ms) + zetar = 12 + zetas = 12 + gmr = 0.2 + ar2 = 1.0 :initialized parameter for location-dependent + :Na-conductance attenuation, "s", (ar=1 -> zero attenuation) +} +STATE { + m h s +} +ASSIGNED { + celsius (degC) + ina (mA/cm2) + minf + hinf + sinf + mtau (ms) + htau (ms) + stau (ms) + gna (mho/cm2) + +} + + + +INITIAL { + rate(v, ar2) + m = minf + h = hinf + s = sinf +} + +BREAKPOINT { + SOLVE states METHOD cnexp + gna = gnafbar*m*m*m*h*s + ina = gna*(v-55) + +} + +DERIVATIVE states { + rate(v, ar2) + m' = (minf-m)/mtau + h' = (hinf-h)/htau + s' = (sinf-s)/stau +} + +UNITSOFF + +FUNCTION malf( v){ LOCAL va + va=v+28 + :va=v+28 + if (fabs(va)<1e-04){ + malf= -0.2816*(-9.3 + va*0.5) + :malf= -0.2816*(-9.3 + va*0.5) + }else{ + malf = -0.2816*(v+28)/(-1+exp(-va/9.3)) + } +} + + +FUNCTION mbet(v(mV))(/ms) { LOCAL vb + vb=v+1 + :vb=v+1 + if (fabs(vb)<1e-04){ + mbet = 0.2464*(6+vb*0.5) + :mbet = 0.2464*(6 + vb*0.5) + }else{ + mbet = 0.2464*(v+1)/(-1+exp(vb/6)) :/(-1+exp((v+1)/6)) + } + } + + +FUNCTION half(v(mV))(/ms) { LOCAL vc + :vc=v+15.1 + vc=v+40.1 :changed to 40.1 by kiki + if (fabs(vc)<1e-04){ + half=0.098*(20 + vc*0.5) + }else{ + half=0.098/exp(vc+43.1/20) :43.1, also spike train attenuation +} +} + + +FUNCTION hbet(v(mV))(/ms) { LOCAL vd + :vd=v+13.1 + vd=v+13.1 :decreasing it increases the peak current + if (fabs(vd)<1e-04){ + hbet=1.4*(10 + vd*0.5) + }else{ + hbet=1.4/(1+exp(-(vd-13.1)/10)) :13.1 increasing it, increases the spike train attenuation and increases spike width +} +} + + +:FUNCTIONS FOR S +FUNCTION alpv(v(mV)) { + alpv = 1/(1+exp((v-vvh)/vvs)) +} + + +FUNCTION alpr(v(mV)) { :used in "s" activation system tau + + alpr = exp(1.e-3*zetar*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betr(v(mV)) { :used in "s" activation system tau + + betr = exp(1.e-3*zetar*gmr*(v-vhalfr)*9.648e4/(8.315*(273.16+celsius))) +} + + + +PROCEDURE rate(v (mV),ar2) {LOCAL q10, msum, hsum, ma, mb, ha, hb,c + + + ma=malf(v) mb=mbet(v) ha=half(v) hb=hbet(v) + + msum = ma+mb + minf = ma/msum + mtau = 1/(msum) + + + hsum=ha+hb + hinf=ha/hsum + htau = 1 / (hsum) + + stau = betr(v)/(a0r*(1+alpr(v))) + if (stau + ena (mV) : do not set global ena here + DA_alphamshift=0 : 2 for 100% DA, 0 otherwise + DA_betamshift=0 : 5 for 100% DA,0 otherwise + DA_alphahfactor=0: -.8e-5 for DA, 0 otherwise + DA_betahfactor=0 : 0.014286-0.02 for DA, 0 otherwise +} + +STATE { + m h +} + +ASSIGNED { + ina (mA/cm2) + minf hinf + mtau (ms) + htau (ms) + g (mho/cm2) +} + +PROCEDURE iassign () { + g = gbar*m*h + ina = g*(v-ena) +} + +INITIAL { + rate(v) + m = minf + h = hinf + iassign() +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + rate(v) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +UNITSOFF + +FUNCTION malf( v){ LOCAL va + va=v+12+DA_alphamshift + if (fabs(va)<1e-04){ + va = va + 0.00001 + } + malf = (-0.2816*va)/(-1+exp(-va/9.3)) +} + +FUNCTION mbet(v(mV))(/ms) { LOCAL vb + vb=v-15+DA_betamshift + if (fabs(vb)<1e-04){ + vb = vb + 0.00001 + } + mbet = (0.2464*vb)/(-1+exp(vb/6)) +} + +FUNCTION half(v(mV))(/ms) { LOCAL vc + vc=v+42.8477 + if (fabs(vc)<1e-04){ + vc=vc+0.00001 + } + half= (2.8e-5+DA_alphahfactor)*(exp(-vc/4.0248)) +} + +FUNCTION hbet(v(mV))(/ms) { LOCAL vd + vd=v-413.9284 + if (fabs(vd)<1e-04){ + vd=vd+0.00001 + } + hbet= (0.02+DA_betahfactor)/(1+exp(-vd/148.2589)) +} + +PROCEDURE rate(v (mV)) {LOCAL msum, hsum, ma, mb, ha, hb + ma=malf(v) mb=mbet(v) ha=half(v) hb=hbet(v) + + msum = ma+mb + minf = ma/msum + mtau = 1/msum + + hsum = ha+hb + hinf = ha/hsum + htau = 1/hsum +} + +UNITSON diff --git a/examples/batch_rxd_net/mod/nax_BS.mod b/examples/batch_rxd_net/mod/nax_BS.mod new file mode 100644 index 000000000..b9b4cfd7b --- /dev/null +++ b/examples/batch_rxd_net/mod/nax_BS.mod @@ -0,0 +1,115 @@ +TITLE nax +: Na current for axon. No slow inact. +: M.Migliore Jul. 1997 +: added sh to account for higher threshold M.Migliore, Apr.2002 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter reformatting, renaming thegna to g, setting sh = 0 (was 8 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +NEURON { + SUFFIX nax + USEION na READ ena WRITE ina + RANGE gbar, ina, sh +: GLOBAL minf, hinf, mtau, htau,thinf, qinf, Rb, Rg, qg +} + +PARAMETER { + v (mV) + celsius (degC) + ena (mV) : must be explicitly def. in hoc + + sh = 0 (mV) + gbar = 0.010 (mho/cm2) + + tha = -30 (mV) : v 1/2 for act + qa = 7.2 (mV) : act slope (4.5) + Ra = 0.4 (/ms) : open (v) + Rb = 0.124 (/ms) : close (v) + + thi1 = -45 (mV) : v 1/2 for inact + thi2 = -45 (mV) : v 1/2 for inact + qd = 1.5 (mV) : inact tau slope + qg = 1.5 (mV) + mmin = 0.02 + hmin = 0.5 + q10 = 2 + Rg = 0.01 (/ms) : inact recov (v) + Rd = 0.03 (/ms) : inact (v) + + thinf = -50 (mV) : inact inf slope + qinf = 4 (mV) : inact inf slope +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) +} + +ASSIGNED { + ina (mA/cm2) + g (mho/cm2) + minf + hinf + mtau (ms) + htau (ms) +} + +STATE { m h} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*m*m*m*h + ina = g * (v - ena) +} + +INITIAL { + trates(v,sh) + m = minf + h = hinf +} + +DERIVATIVE states { + trates(v,sh) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE trates(vm,sh2) { + LOCAL a, b, qt + qt = q10^((celsius-24)/10) + + a = trap0(vm,tha+sh2,Ra,qa) + b = trap0(-vm,-tha-sh2,Rb,qa) + mtau = 1/(a+b)/qt + if (mtau 1e-6) { + trap0 = a * (v - th) / (1 - exp(-(v - th)/q)) + } else { + trap0 = a * q + } +} diff --git a/examples/batch_rxd_net/mod/savedist.mod b/examples/batch_rxd_net/mod/savedist.mod new file mode 100644 index 000000000..d4168565c --- /dev/null +++ b/examples/batch_rxd_net/mod/savedist.mod @@ -0,0 +1,14 @@ +TITLE Mech to store distance from origin as workaround for multisplit non-uniform densities +: 2011-09-18 Ben Suter, initial version per email from Michael Hines +: 2016-11-31 Ernie Forzano, changed suffix dist to savedist because import3d() and this mod file +: were referencing dist and causing compile error. +: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + +NEURON { + SUFFIX savedist + RANGE x +} + +PARAMETER { + x = 0 (micron) +} diff --git a/examples/batch_rxd_net/mod/vecstim.mod b/examples/batch_rxd_net/mod/vecstim.mod new file mode 100644 index 000000000..9dcb4d0b9 --- /dev/null +++ b/examples/batch_rxd_net/mod/vecstim.mod @@ -0,0 +1,83 @@ +: $Id: vecstim.mod,v 1.3 2010/12/13 21:29:27 samn Exp $ +: Vector stream of events + +NEURON { + THREADSAFE + ARTIFICIAL_CELL VecStim +} + +ASSIGNED { + index + etime (ms) + space +} + +INITIAL { + index = 0 + element() + if (index > 0) { + if (etime - t>=0) { + net_send(etime - t, 1) + } else { + printf("Event in the stimulus vector at time %g is omitted since has value less than t=%g!\n", etime, t) + net_send(0, 2) + } + } +} + +NET_RECEIVE (w) { + if (flag == 1) { net_event(t) } + if (flag == 1 || flag == 2) { + element() + if (index > 0) { + if (etime - t>=0) { + net_send(etime - t, 1) + } else { + printf("Event in the stimulus vector at time %g is omitted since has value less than t=%g!\n", etime, t) + net_send(0, 2) + } + } + } +} + +VERBATIM +extern double* vector_vec(); +extern int vector_capacity(); +extern void* vector_arg(); +ENDVERBATIM + +PROCEDURE element() { +VERBATIM + { void* vv; int i, size; double* px; + i = (int)index; + if (i >= 0) { + vv = *((void**)(&space)); + if (vv) { + size = vector_capacity(vv); + px = vector_vec(vv); + if (i < size) { + etime = px[i]; + index += 1.; + }else{ + index = -1.; + } + }else{ + index = -1.; + } + } + } +ENDVERBATIM +} + +PROCEDURE play() { +VERBATIM + void** vv; + vv = (void**)(&space); + *vv = (void*)0; + if (ifarg(1)) { + *vv = vector_arg(1); + } +ENDVERBATIM +} + + diff --git a/examples/batch_rxd_net/netParams.py b/examples/batch_rxd_net/netParams.py new file mode 100644 index 000000000..5eaba021d --- /dev/null +++ b/examples/batch_rxd_net/netParams.py @@ -0,0 +1,122 @@ +from netpyne import specs + +# from cfg import cfg +try: + from __main__ import cfg # import SimConfig object with params from parent module +except: + from cfg import cfg # if no simConfig in parent module, import directly from tut8_cfg module + + +#------------------------------------------------------------------------------ +# +# NETWORK PARAMETERS +# +#------------------------------------------------------------------------------ + +netParams = specs.NetParams() # object of class NetParams to store the network parameters + +netParams.sizeX = cfg.sizeX # x-dimension (horizontal length) size in um +netParams.sizeY = cfg.sizeY # y-dimension (vertical height or cortical depth) size in um +netParams.sizeZ = cfg.sizeZ # z-dimension (horizontal length) size in um +netParams.propVelocity = 100.0 # propagation velocity (um/ms) +netParams.probLengthConst = 150.0 # length constant for conn probability (um) + +#------------------------------------------------------------------------------ +## Population parameters +netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 10, 'yRange': [50,150], 'cellModel': 'HH'} +netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 10, 'yRange': [50,150], 'cellModel': 'HH'} +netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 10, 'yRange': [150,300], 'cellModel': 'HH'} +netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 10, 'yRange': [150,300], 'cellModel': 'HH'} +netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 10, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'} +netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 10, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'} + +#------------------------------------------------------------------------------ +## Cell property rules +netParams.loadCellParamsRule(label='CellRule', fileName='CSTR_cellParams.json') + +#------------------------------------------------------------------------------ +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0} # NMDA synaptic mechanism +netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75} # GABA synaptic mechanism + +#------------------------------------------------------------------------------ +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 25, 'noise': 0.8} +netParams.stimTargetParams['bkg->all'] = {'source': 'bkg', 'conds': {'cellType': ['E','I']}, 'weight': 0.01, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'} + +#------------------------------------------------------------------------------ +## Cell connectivity rules +netParams.connParams['E->all'] = { + 'preConds': {'cellType': 'E'}, 'postConds': {'y': [50,500]}, # E -> all (100-1000 um) + 'probability': 0.1, # probability of connection + 'weight': '0.04*post_ynorm', # synaptic weight + 'delay': 'dist_3D/propVelocity', # transmission delay (ms) + 'sec': ['Adend1', 'Adend2', 'Adend3'], + 'synMech': 'exc'} # synaptic mechanism + +netParams.connParams['I->E'] = { + 'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']}, # I -> E + 'probability': '0.3*exp(-dist_3D/probLengthConst)', # probability of connection + 'weight': 0.01, # synaptic weight + 'delay': 'dist_3D/propVelocity', # transmission delay (ms) + 'sec': ['soma','Bdend'], + 'synMech': 'inh'} # synaptic mechanism + + +#------------------------------------------------------------------------------ +## RxD params + +### constants +constants = {'ip3_init': cfg.ip3_init, # initial ip3 concentration + 'caDiff': 0.08, # calcium diffusion coefficient + 'ip3Diff': 1.41, # ip3 diffusion coefficient + 'caci_init': 1e-5, # intracellular calcium initial concentration + 'caco_init': 2.0, # extracellular calcium initial concentration + 'gip3r': cfg.gip3r, # ip3 receptors density + 'gserca': 0.3913, # SERCA conductance + 'gleak': 6.020, # ER leak channel conductance + 'kserca': 0.1, # SERCA reaction constant + 'kip3': 0.15, # ip3 reaction constant + 'kact': 0.4, # + 'ip3rtau': 2000, # ip3 receptors time constant + 'fc': 0.8, # fraction of cytosol + 'fe': 0.2, # fraction of ER + 'margin': 20} # extracellular volume additional margin + +netParams.rxdParams['constants'] = constants + +### regions +regions = {} +regions['cyt'] = {'cells': 'all', 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}} +regions['er'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}} +regions['cyt_er_membrane'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}} + +margin = 20 # extracellular volume additional margin +x, y, z = [0-margin, 100+margin], [-500-margin, 0+margin], [0-margin, 100+margin] +regions['ecs'] = {'extracellular': True, 'xlo': x[0], 'ylo': y[0], 'zlo': z[0], 'xhi': x[1], 'yhi': y[1], 'zhi': z[1], 'dx': 5, 'volume_fraction': 0.2, 'tortuosity': 1.6} + +netParams.rxdParams['regions'] = regions + +### species +species = {} +species['ca'] = {'regions': ['cyt', 'er', 'ecs'], 'd': constants['caDiff'], 'charge': 2, + 'initial': 'caco_init if isinstance(node,rxd.node.NodeExtracellular) else (0.0017 - caci_init * fc) / fe if node.region == er else caci_init'} +species['ip3'] = {'regions': ['cyt'], 'd': constants['ip3Diff'], 'initial': constants['ip3_init']} +netParams.rxdParams['species'] = species + +### states +netParams.rxdParams['states'] = {'ip3r_gate_state': {'regions': ['cyt_er_membrane'], 'initial': 0.8}} + +### reactions +minf = 'ip3[cyt] * 1000. * ca[cyt] / (ip3[cyt] + kip3) / (1000. * ca[cyt] + kact)' +h_gate = 'ip3r_gate_state[cyt_er_membrane]' +kip3 = 'gip3r * (%s * %s) ** 3' % (minf, h_gate) + +mcReactions = {} +mcReactions['serca'] = {'reactant': 'ca[cyt]', 'product': 'ca[er]', 'rate_f': 'gserca / ((kserca / (1000. * ca[cyt])) ** 2 + 1)', 'membrane': 'cyt_er_membrane', 'custom_dynamics': True} +mcReactions['leak'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': constants['gleak'], 'rate_b': constants['gleak'], 'membrane': 'cyt_er_membrane'} +mcReactions['ip3r'] = {'reactant': 'ca[er]', 'product': 'ca[cyt]', 'rate_f': kip3, 'rate_b': kip3, 'membrane': 'cyt_er_membrane'} +netParams.rxdParams['multicompartmentReactions'] = mcReactions + +### rates +netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate)}} diff --git a/examples/dipoleRecording/IT2_reduced_cellParams.json b/examples/dipoleRecording/IT2_reduced_cellParams.json new file mode 100644 index 000000000..165a7b99e --- /dev/null +++ b/examples/dipoleRecording/IT2_reduced_cellParams.json @@ -0,0 +1,633 @@ +{ + "secs": { + "soma": { + "ions": { + "na": { + "i": 10.0, + "e": 42.0, + "o": 140.0 + }, + "ca": { + "i": 0.00005, + "e": 132.4579341637009, + "o": 2.0 + }, + "k": { + "i": 54.4, + "e": -104.0, + "o": 2.5 + } + }, + "mechs": { + "kBK": { + "tau": 1.0, + "caPmax": 1.0, + "caPmin": 0.0, + "caVhh": 0.002, + "caVhmin": 43.919142291200004, + "gpeak": 0.0000445651933019, + "caPh": 0.002, + "caPk": 1.0, + "k": 17.0, + "caVhmax": 155.67 + }, + "pas": { + "e": -87.1335623948, + "g": 0.00009442294539558377 + }, + "cat": { + "gcatbar": 9.29455717585E-7 + }, + "ih": { + "aslope": 7.09800576233, + "ascale": 0.00320887293027, + "bscale": 0.285307415701, + "gbar": 0.000033176340367, + "ashift": 119.696272155, + "bslope": 23.2995848558 + }, + "kap": { + "tq": -49.7149526489, + "gbar": 0.0240195239098, + "sh": 0.0, + "vhalfl": -36.7754836348, + "vhalfn": 32.179925527 + }, + "can": { + "gcanbar": 8.13137955053E-7 + }, + "kdr": { + "gbar": 0.00833766634808, + "sh": 0.0, + "vhalfn": 11.6427471384 + }, + "cal": { + "gcalbar": 0.00000239132864454 + }, + "nax": { + "gbar": 0.0768253702194, + "sh": 0.0 + }, + "cadad": { + "taur": 99.1146852282, + "depth": 0.119408607923, + "kd": 0.0, + "cainf": 0.00024, + "kt": 0.0 + } + }, + "weightNorm": [ + 0.0012489909542124195 + ], + "geom": { + "diam": 18.2149102762, + "cm": 2.4998269977, + "nseg": 1, + "L": 18.4123467666, + "Ra": 70.0015514222, + "pt3d": [ + [ + 0, + 40.0, + 0, + 18.2149102762 + ], + [ + 0, + 48.4123467666, + 0, + 18.2149102762 + ] + ] + }, + "topol": {}, + "vinit": -88.7986728464 + }, + "Adend1": { + "ions": { + "na": { + "i": 10.0, + "e": 42.0, + "o": 140.0 + }, + "ca": { + "i": 0.00005, + "e": 132.4579341637009, + "o": 2.0 + }, + "k": { + "i": 54.4, + "e": -104.0, + "o": 2.5 + } + }, + "mechs": { + "kBK": { + "tau": 1.0, + "caPmax": 1.0, + "caPmin": 0.0, + "caVhh": 0.002, + "caVhmin": 43.919142291200004, + "gpeak": 0.0000445651933019, + "caPh": 0.002, + "caPk": 1.0, + "k": 17.0, + "caVhmax": 155.67 + }, + "pas": { + "e": -87.1335623948, + "g": 0.00007199592136286027 + }, + "cat": { + "gcatbar": 9.29455717585E-7 + }, + "ih": { + "aslope": 7.09800576233, + "ascale": 0.00320887293027, + "bscale": 0.285307415701, + "gbar": 0.000033176340367, + "ashift": 119.696272155, + "bslope": 23.2995848558 + }, + "kap": { + "tq": -49.7149526489, + "gbar": 0.0240195239098, + "sh": 0.0, + "vhalfl": -36.7754836348, + "vhalfn": 32.179925527 + }, + "can": { + "gcanbar": 8.13137955053E-7 + }, + "kdr": { + "gbar": 0.00833766634808, + "sh": 0.0, + "vhalfn": 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"dend_34", "dend_35", "dend_36", "dend_37", "dend_30", "dend_31", "dend_32", "dend_33", "apic_45", "apic_44", "apic_47", "apic_46", "apic_41", "apic_40", "apic_43", "apic_42", "apic_49", "apic_48", "apic_31", "apic_52", "apic_53", "apic_50", "apic_51", "apic_56", "apic_57", "apic_54", "apic_55", "apic_58", "apic_59", "apic_6"], "apic_upper": ["apic_27", "apic_26", "apic_25", "apic_24", "apic_29", "apic_28", "apic_34", "apic_35", "apic_36", "apic_37", "apic_30", "apic_32", "apic_33", "apic_38", "apic_39", "apic_69", "apic_68", "apic_63", "apic_62", "apic_61", "apic_60", "apic_67", "apic_66", "apic_65", "apic_64", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77", "apic_45", "apic_44", "apic_47", "apic_46", "apic_41", "apic_40", "apic_43", "apic_42", "apic_49", "apic_48", "apic_31", "apic_53", "apic_50", "apic_51", "apic_56", "apic_57", "apic_54", "apic_55", "apic_58", "apic_59"], "apical_tuft": ["apic_26", "apic_27", "apic_28", "apic_29", "apic_30", "apic_31", "apic_34", "apic_35", "apic_36", "apic_37", "apic_38", "apic_39", "apic_40", "apic_41", "apic_42", "apic_43", "apic_44", "apic_45", "apic_47", "apic_48", "apic_49", "apic_50", "apic_54", "apic_55", "apic_56", "apic_57", "apic_58", "apic_59", "apic_60", "apic_61", "apic_62", "apic_63", "apic_64", "apic_65", "apic_66", "apic_67", "apic_68", "apic_69", "apic_70", "apic_71", "apic_72", "apic_73", "apic_74", "apic_75", "apic_76", "apic_77"], "apical_beforebranchpoint": ["apic_0", "apic_1", "apic_2", "apic_3", "apic_4", "apic_5", "apic_6", "apic_7", "apic_8", "apic_9", "apic_10", "apic_11", "apic_12", "apic_13", "apic_14", "apic_15", "apic_16", "apic_17", "apic_18", "apic_19", "apic_20", "apic_21", "apic_22", "apic_79", "apic_80", "apic_81", "apic_82", "apic_83", "apic_84", "apic_85", "apic_86", "apic_87", "apic_88", "apic_89", "apic_90", "apic_91", "apic_92", "apic_93", "apic_94", "apic_95", "apic_96", "apic_97", "apic_98", "apic_99", "apic_100", "apic_101", "apic_102"]}, "conds": {"cellModel": "HH_full", "cellType": "PT"}} \ No newline at end of file diff --git a/examples/dipoleRecording/cell_dipole.py b/examples/dipoleRecording/cell_dipole.py new file mode 100644 index 000000000..8ebecd074 --- /dev/null +++ b/examples/dipoleRecording/cell_dipole.py @@ -0,0 +1,50 @@ +from netpyne import specs, sim + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + +netParams.sizeX = 200 # x-dimension (horizontal length) size in um +netParams.sizeY = 1000 # y-dimension (vertical height or cortical depth) size in um +netParams.sizeZ = 20 # z-dimension (horizontal length) size in um + +## Population parameters +netParams.popParams['E'] = {'cellType': 'E', 'numCells': 1, 'yRange': [700,800], 'cellModel': 'HH'} + +## Cell property rules +netParams.loadCellParamsRule(label='Erule', fileName='PT5B_full_cellParams.json') +netParams.cellParams['Erule']['conds'] = {'cellType': ['E']} + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0} # NMDA synaptic mechanism + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 50, 'noise': 0.0} +netParams.stimTargetParams['bkg->all'] = {'source': 'bkg', 'conds': {'cellType': ['E']}, 'weight': 10.0, 'sec': 'soma', 'delay': 15, 'synMech': 'exc'} + + +# Simulation options +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration +simConfig.duration = 0.05*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.1 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'}} +simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'cell_dipole' # Set file output name + +simConfig.recordDipole = True +simConfig.saveDipoleCells = ['all'] +simConfig.saveDipolePops = ['E'] + + +simConfig.analysis['plotTraces'] = {'include': [('E',0)], 'oneFigPer':'cell', 'overlay': True, 'figSize': (5,3),'saveFig': True} # Plot recorded traces for this list of cells +#simConfig.analysis['plotLFP'] = {'includeAxon': False, 'plots': ['timeSeries', 'locations'], 'figSize': (5,9), 'saveFig': True} +#simConfig.analysis['getCSD'] = {'timeRange': [10,45],'spacing_um': 150, 'vaknin': True} +#simConfig.analysis['plotCSD'] = {'timeRange': [10,45]} +#sim.analysis.getCSD(...args...) +#simConfig.analysis['plotCSD'] = {} + + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +#sim.analysis.plotCSD() + diff --git a/examples/dipoleRecording/mod/IC.mod b/examples/dipoleRecording/mod/IC.mod new file mode 100644 index 000000000..b489e1b7d --- /dev/null +++ b/examples/dipoleRecording/mod/IC.mod @@ -0,0 +1,88 @@ +TITLE Ca-dependent potassium current +: +: Ca++ dependent K+ current IC responsible for +: action potentials AHP's +: Differential equations +: +: Model of Yamada, Koch & Adams, in: Methods in Neuronal Modeling, +: Ed. by Koch & Segev, MIT press, 1989. +: +: This current models the "fast" IK[Ca]: +: - potassium current +: - activated by intracellular calcium +: - VOLTAGE DEPENDENT +: +: Written by Alain Destexhe, Salk Institute, Sept 18, 1992 +: +: should be considered 'BK' - fast, big conductance + +NEURON { + SUFFIX ikc + USEION k READ ek WRITE ik + USEION ca READ cai + RANGE gkbar, ik + RANGE m_inf, tau_m + RANGE taumin + GLOBAL ascale,bscale,vfctr +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + v (mV) + celsius (degC) + ek (mV) + cai (mM) + gkbar = .003 (mho/cm2) : taken from + taumin = 0.1 + ascale = 250.0 + bscale = 0.1 + vfctr = 24.0 +} + +STATE { + m +} + +INITIAL { + evaluate_fct(v,cai) + m = m_inf +} + +ASSIGNED { + ik (mA/cm2) + m_inf + tau_m (ms) +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gkbar * m * (v - ek) +} + +DERIVATIVE states { + evaluate_fct(v,cai) + m' = (m_inf - m) / tau_m +} + +UNITSOFF +PROCEDURE evaluate_fct(v(mV),cai(mM)) { LOCAL a,b,tadj +: +: activation kinetics of Yamada et al were at 22 deg. C +: transformation to 36 deg assuming Q10=3 +: + tadj = 3 ^ ((celsius-22.0)/10) + + a = ascale * cai * exp(v/vfctr) + b = bscale * exp(-v/vfctr) + + tau_m = 1.0 / (a + b) / tadj + if(tau_m < taumin){ tau_m = taumin } + m_inf = a / (a + b) +} +UNITSON diff --git a/examples/dipoleRecording/mod/cadad.mod b/examples/dipoleRecording/mod/cadad.mod new file mode 100644 index 000000000..46466753c --- /dev/null +++ b/examples/dipoleRecording/mod/cadad.mod @@ -0,0 +1,74 @@ +: $Id: cadad.mod,v 1.4 2002/11/08 15:42:37 billl Exp $ +TITLE Fast mechanism for submembranal Ca++ concentration (cai) +: +: Takes into account: +: +: - increase of cai due to calcium currents +: - extrusion of calcium with a simple first order equation +: +: This mechanism is compatible with the calcium pump "cad" and has the +: same name and parameters; however the parameters specific to the pump +: are dummy here. +: +: Parameters: +: +: - depth: depth of the shell just beneath the membran (in um) +: - cainf: equilibrium concentration of calcium (2e-4 mM) +: - taur: time constant of calcium extrusion (must be fast) +: - kt,kd: dummy parameters +: +: Written by Alain Destexhe, Salk Institute, 1995 +: + +INDEPENDENT {t FROM 0 TO 1 WITH 1 (ms)} + +NEURON { + SUFFIX cadad + USEION ca READ ica, cai WRITE cai + RANGE depth,kt,kd,cainf,taur +} + +UNITS { + (molar) = (1/liter) : moles do not appear in units + (mM) = (millimolar) + (um) = (micron) + (mA) = (milliamp) + (msM) = (ms mM) +} + +CONSTANT { + FARADAY = 96489 (coul) : moles do not appear in units + : FARADAY = 96.489 (k-coul) : moles do not appear in units +} + +PARAMETER { + depth = 1 (um) : depth of shell + taur = 5 (ms) : rate of calcium removal + cainf = 2.4e-4 (mM) + kt = 0 (mM/ms) : dummy + kd = 0 (mM) : dummy +} + +STATE { + cai (mM) +} + +INITIAL { + cai = cainf +} + +ASSIGNED { + ica (mA/cm2) + drive_channel (mM/ms) +} + +BREAKPOINT { + SOLVE state METHOD cnexp +} + +DERIVATIVE state { + drive_channel = - (10000) * ica / (2 * FARADAY * depth) + if (drive_channel <= 0.) { drive_channel = 0. } : cannot pump inward + cai' = drive_channel + (cainf-cai)/taur +} + diff --git a/examples/dipoleRecording/mod/cagk.mod b/examples/dipoleRecording/mod/cagk.mod new file mode 100644 index 000000000..662d43764 --- /dev/null +++ b/examples/dipoleRecording/mod/cagk.mod @@ -0,0 +1,87 @@ +TITLE CaGk +: Calcium activated K channel. +: Modified from Moczydlowski and Latorre (1983) J. Gen. Physiol. 82 + +UNITS { + (molar) = (1/liter) +} + +UNITS { + (mV) = (millivolt) + (mA) = (milliamp) + (mM) = (millimolar) +} + + +NEURON { + SUFFIX cagk + USEION ca READ cai + USEION k READ ek WRITE ik + RANGE gbar,gkca,ik + RANGE oinf, tau +} + +UNITS { + FARADAY = (faraday) (kilocoulombs) + R = 8.313424 (joule/degC) +} + +PARAMETER { + celsius (degC) + v (mV) + gbar=.01 (mho/cm2) : Maximum Permeability + cai (mM) + ek (mV) + + d1 = .84 + d2 = 1. + k1 = .48e-3 (mM) + k2 = .13e-6 (mM) + abar = .28 (/ms) + bbar = .48 (/ms) + st=1 (1) +} + +ASSIGNED { + ik (mA/cm2) + oinf + tau (ms) + gkca (mho/cm2) +} + +INITIAL { + rate(v,cai) + o=oinf +} + +STATE { o } : fraction of open channels + +BREAKPOINT { + SOLVE state METHOD cnexp + gkca = gbar*o^st + ik = gkca*(v - ek) +} + +DERIVATIVE state { : exact when v held constant; integrates over dt step + rate(v, cai) + o' = (oinf - o)/tau +} + +FUNCTION alp(v (mV), c (mM)) (1/ms) { :callable from hoc + alp = c*abar/(c + exp1(k1,d1,v)) +} + +FUNCTION bet(v (mV), c (mM)) (1/ms) { :callable from hoc + bet = bbar/(1 + c/exp1(k2,d2,v)) +} + +FUNCTION exp1(k (mM), d, v (mV)) (mM) { :callable from hoc + exp1 = k*exp(-2*d*FARADAY*v/R/(273.15 + celsius)) +} + +PROCEDURE rate(v (mV), c (mM)) { :callable from hoc + LOCAL a + a = alp(v,c) + tau = 1/(a + bet(v, c)) + oinf = a*tau +} diff --git a/examples/dipoleRecording/mod/cal_mig.mod b/examples/dipoleRecording/mod/cal_mig.mod new file mode 100644 index 000000000..9069e8643 --- /dev/null +++ b/examples/dipoleRecording/mod/cal_mig.mod @@ -0,0 +1,131 @@ +TITLE L-calcium channel +: L-type calcium channel with [Ca]i inactivation +: from Jaffe, D. B., Ross, W. N., Lisman, J. E., Laser-Ross, N., Miyakawa, H., and Johnston, D. A. A model for dendritic Ca2 +: accumulation in hippocampal pyramidal neurons based on fluorescence imaging measurements. J. Neurophysiol. 71:1O65-1077 1994. +: conduction density estimate of 50-200 pS/mu2; 0.0025 S/cm2 (5-20 channels of 10 each) +: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 +: hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). +: adapted from http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=3263&file=\ca3_db\cal2.mod +: this version from https://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=148094&file=\kv72-R213QW-mutations\cal2.mod +: Miceli F, Soldovieri MV, Ambrosino P, Barrese V, Migliore M, Cilio MR, Taglialatela M (2013) Genotype-phenotype +: correlations in neonatal epilepsies caused by mutations in the voltage sensor of Kv7.2 potassium channel subunits. PNAS 110:4386-4391 + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + + FARADAY = 96520 (coul) + R = 8.3134 (joule/degC) + KTOMV = .0853 (mV/degC) +} + +PARAMETER { + v (mV) + celsius (degC) + gcalbar=.003 (mho/cm2) + ki=.001 (mM) + cai = 50.e-6 (mM) + cao = 2 (mM) + q10 = 5 + mmin=0.2 + tfa = 1 + a0m =0.1 + zetam = 2 + vhalfm = 4 + gmm=0.1 + USEGHK=1 + erev = 100 +} + + +NEURON { + SUFFIX cal + USEION ca READ cai,cao WRITE ica + RANGE gcalbar,cai, ica, gcal, ggk + RANGE minf,tau + GLOBAL USEGHK +} + +STATE { + m +} + +ASSIGNED { + ica (mA/cm2) + gcal (mho/cm2) + minf + tau (ms) + ggk +} + +INITIAL { + rate(v) + m = minf +} + +BREAKPOINT { + SOLVE state METHOD cnexp + gcal = gcalbar*m*m*h2(cai) + if (USEGHK == 1) { + ggk=ghk(v,cai,cao) + } else { + ggk=v-erev + } + ica = gcal*ggk +} + +FUNCTION h2(cai(mM)) { + h2 = ki/(ki+cai) +} + + +FUNCTION ghk(v(mV), ci(mM), co(mM)) (mV) { + LOCAL nu,f + f = KTF(celsius)/2 + nu = v/f + ghk=-f*(1. - (ci/co)*exp(nu))*efun(nu) +} + +FUNCTION KTF(celsius (DegC)) (mV) { + KTF = ((25./293.15)*(celsius + 273.15)) +} + + +FUNCTION efun(z) { + if (fabs(z) < 1e-4) { + efun = 1 - z/2 + }else{ + efun = z/(exp(z) - 1) + } +} + +FUNCTION alp(v(mV)) (1/ms) { + alp = 15.69*(-1.0*v+81.5)/(exp((-1.0*v+81.5)/10.0)-1.0) +} + +FUNCTION bet(v(mV)) (1/ms) { + bet = 0.29*exp(-v/10.86) +} + +FUNCTION alpmt(v(mV)) { + alpmt = exp(0.0378*zetam*(v-vhalfm)) +} + +FUNCTION betmt(v(mV)) { + betmt = exp(0.0378*zetam*gmm*(v-vhalfm)) +} + +DERIVATIVE state { + rate(v) + m' = (minf - m)/tau +} + +PROCEDURE rate(v (mV)) { :callable from hoc + LOCAL a, b, qt + qt=q10^((celsius-25)/10) + a = alp(v) + b = 1/((a + bet(v))) + minf = a*b + tau = betmt(v)/(qt*a0m*(1+alpmt(v))) + if (tau (alpha,beta) +: +: Following this model, the activation fct will be half-activated at +: a concentration of Cai = (beta/alpha)^(1/n) = cac (parameter) +: +: The mod file is here written for the case n=2 (2 binding sites) +: --------------------------------------------- +: +: Kinetics based on: Partridge & Swandulla, TINS 11: 69-72, 1988. +: +: This current has the following properties: +: - inward current (non specific for cations Na, K, Ca, ...) +: - activated by intracellular calcium +: - NOT voltage dependent +: +: A minimal value for the time constant has been added +: +: Ref: Destexhe et al., J. Neurophysiology 72: 803-818, 1994. +: See also: http://www.cnl.salk.edu/~alain , http://cns.fmed.ulaval.ca +: + +: Updated by Kiki Sidiropoulou (2010) so that dADP has slow inactivation kinetics and it +: is activated after 5 spikes + +: Updated by Sam Neymotin (2016) to avoid using n ion and get rid of 'mystart' rule; also +: make sure that INITIAL block assigns currents + +NEURON { + SUFFIX ican + NONSPECIFIC_CURRENT i + USEION ca READ cai + USEION na WRITE ina + RANGE gbar, m_inf, tau_m + GLOBAL beta, cac, taumin +} + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + +PARAMETER { + v (mV) + celsius = 36 (degC) + erev = -20 (mV) : reversal potential + cai (mM) : initial [Ca]i + gbar = 0.0001 (mho/cm2) + beta = 0.0001 (1/ms) : backward rate constant + cac = 0.0004 (mM) + : middle point of activation fct, for ip3 as somacar, for current injection + taumin = 0.1 (ms) : minimal value of time constant +} + +STATE { + m +} + +ASSIGNED { + i (mA/cm2) + ina (mA/cm2) + m_inf + tau_m (ms) + tadj + g (mho/cm2) +} + +PROCEDURE iassign () { + g = gbar * m * m + i = g * (v - erev) + ina = 0.7 * i +} + +BREAKPOINT { + SOLVE states METHOD cnexp + iassign() +} + +DERIVATIVE states { + evaluate_fct(v,cai) + m' = (m_inf - m) / tau_m +} + +UNITSOFF +INITIAL { + : activation kinetics are assumed to be at 22 deg. C + : Q10 is assumed to be 3 + tadj = 3.0 ^ ((celsius-22.0)/10) + evaluate_fct(v,cai) + m = m_inf + iassign() +} + +PROCEDURE evaluate_fct(v(mV),cai(mM)) { LOCAL alpha2 + alpha2 = beta * (cai/cac)^2 + tau_m = 1 / (alpha2 + beta) / tadj + m_inf = alpha2 / (alpha2 + beta) + if(tau_m < taumin) { tau_m = taumin } : min value of time constant +} +UNITSON diff --git a/examples/dipoleRecording/mod/kBK.mod b/examples/dipoleRecording/mod/kBK.mod new file mode 100644 index 000000000..353571e71 --- /dev/null +++ b/examples/dipoleRecording/mod/kBK.mod @@ -0,0 +1,108 @@ +: from https://senselab.med.yale.edu/ModelDB/ShowModel.cshtml?model=168148&file=/stadler2014_layerV/kBK.mod +TITLE large-conductance calcium-activated potassium channel (BK) + :Mechanism according to Gong et al 2001 and Womack&Khodakakhah 2002, + :adapted for Layer V cells on the basis of Benhassine&Berger 2005. + :NB: concentrations in mM + +NEURON { + SUFFIX kBK + USEION k READ ek WRITE ik + USEION ca READ cai + RANGE gpeak, gkact, caPh, caPk, caPmax, caPmin + RANGE caVhh, CaVhk, caVhmax, caVhmin, k, tau + GLOBAL pinfmin : cutoff - if pinf < pinfmin, set to 0.; by default cutoff not used (pinfmin==0) +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (molar) = (1/liter) + (mM) = (millimolar) +} + + +PARAMETER { + :maximum conductance (Benhassine 05) + gpeak = 268e-4 (mho/cm2) <0, 1e9> + + : Calcium dependence of opening probability (Gong 2001) + caPh = 2e-3 (mM) : conc. with half maximum open probaility + caPk = 1 : Steepness of calcium dependence curve + caPmax = 1 : max and + caPmin = 0 : min open probability + + : Calcium dependence of Vh shift (Womack 2002) + caVhh = 2e-3 (mM) : Conc. for half of the Vh shift + caVhk = -0.94208 : Steepness of the Vh-calcium dependence curve + caVhmax = 155.67 (mV) : max and + caVhmin = -46.08 (mV) : min Vh + + : Voltage dependence of open probability (Gong 2001) + : must not be zero + k = 17 (mV) + + : Timeconstant of channel kinetics + : no data for a description of a calcium&voltage dependence + : some points (room temp) in Behassine 05 & Womack 02 + tau = 1 (ms) <1e-12, 1e9> + scale = 100 : scaling to incorporate higher ca conc near ca channels + + pinfmin = 0.0 : cutoff for pinf - less than that set pinf to 0.0 + +} + + +ASSIGNED { + v (mV) + ek (mV) + ik (mA/cm2) + cai (mM) + caiScaled (mM) + pinf (1) +} + + +STATE { + p +} + +BREAKPOINT { + SOLVE states METHOD cnexp + ik = gpeak*p* (v - ek) +} + +DERIVATIVE states { + rate(v, cai) + p' = (pinf - p)/tau +} + +INITIAL { + rate(v, cai) + p = pinf +} + +PROCEDURE rate(v(mV), ca(mM)) { + caiScaled = ca*scale + pinf = P0ca(caiScaled) / ( 1 + exp( (Vhca(caiScaled)-v)/k ) ) + if(pinf < pinfmin) { pinf = 0.0 } +} + +FUNCTION P0ca(ca(mM)) (1) { + + if (ca < 1E-18) { :check for division by zero + P0ca = caPmin + } else { + P0ca = caPmin + ( (caPmax - caPmin) / ( 1 + (caPh/ca)^caPk )) + } +} + +FUNCTION Vhca(ca(mM)) (mV) { + + if (ca < 1E-18) { :check for division by zero + Vhca = caVhmax + } else { + Vhca = caVhmin + ( (caVhmax - caVhmin ) / ( 1 + ((caVhh/ca)^caVhk)) ) + } +} + diff --git a/examples/dipoleRecording/mod/kap_BS.mod b/examples/dipoleRecording/mod/kap_BS.mod new file mode 100644 index 000000000..501817d5b --- /dev/null +++ b/examples/dipoleRecording/mod/kap_BS.mod @@ -0,0 +1,124 @@ +TITLE K-A channel from Klee Ficker and Heinemann +: modified to account for Dax A Current --- M.Migliore Jun 1997 +: modified to be used with cvode M.Migliore 2001 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter, removing "ka" from parameter names, reformatting, setting sh = 0 (was 24 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) +} + +PARAMETER { + v (mV) + celsius (degC) + ek + + sh = 0 + gbar = 0.008 (mho/cm2) + vhalfn = 11 (mV) + vhalfl = -56 (mV) + a0l = 0.05 (/ms) + a0n = 0.05 (/ms) + zetan = -1.5 (1) + zetal = 3 (1) + gmn = 0.55 (1) + gml = 1 (1) + lmin = 5 (mS) + nmin = 0.4 (mS) + pw = -1 (1) + tq = -40 + qq = 5 + q10 = 5 + qtl = 1 +} + + +NEURON { + SUFFIX kap + USEION k READ ek WRITE ik + RANGE gbar, g, sh, tq, vhalfn, vhalfl, ik : +: GLOBAL ninf,linf,taul,taun,lmin +} + +STATE { + n + l +} + +ASSIGNED { + ik (mA/cm2) + ninf + linf + taul + taun + g +} + +INITIAL { + rates(v) + n=ninf + l=linf +} + + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*n*l + ik = g*(v-ek) +} + + +FUNCTION alpn(v(mV)) { + LOCAL zeta + zeta=zetan+pw/(1+exp((v-tq-sh)/qq)) + alpn = exp(1.e-3*zeta*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betn(v(mV)) { + LOCAL zeta + zeta=zetan+pw/(1+exp((v-tq-sh)/qq)) + betn = exp(1.e-3*zeta*gmn*(v-vhalfn-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION alpl(v(mV)) { + alpl = exp(1.e-3*zetal*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +FUNCTION betl(v(mV)) { + betl = exp(1.e-3*zetal*gml*(v-vhalfl-sh)*9.648e4/(8.315*(273.16+celsius))) +} + +DERIVATIVE states { : exact when v held constant; integrates over dt step + rates(v) + n' = (ninf - n) / taun + l' = (linf - l) / taul +} + +PROCEDURE rates(v (mV)) { :callable from hoc + LOCAL a,qt + qt = q10^((celsius-24)/10) + + a = alpn(v) + ninf = 1/(1 + a) + taun = betn(v)/(qt*a0n*(1+a)) + if (taun +#include +#include /* contains LONG_MAX */ +#include +#include +#include +#include + +#if !defined(t) + #define _pval pval +#endif + +typedef struct LISTVEC { + int isz; + Object* pL; + double** pv; + unsigned int* plen; + unsigned int* pbuflen; +} ListVec; + +typedef struct BVEC { + int size; + int bufsize; + short *x; + Object* o; +} bvec; + +#define BYTEHEADER int _II__; char *_IN__; char _OUT__[16]; int BYTESWAP_FLAG=0; +#define BYTESWAP(_X__,_TYPE__) \ + if (BYTESWAP_FLAG == 1) { \ + _IN__ = (char *) &(_X__); \ + for (_II__=0;_II__ (Y) ? (X) : (Y)) + +//square root of 2 * PI +#define SQRT2PI 2.5066282746310002416 +//ln(2), base e log of 2 +#define LG2 0.69314718055994530941723212145818 +#define VRRY 200 +#define ISVEC(_OB__) (strncmp(hoc_object_name(_OB__),"Vector",6)==0) +#define dmaxuint 4294967295. // for 32 bits + +// Andre Fentons cast designations +typedef unsigned char ui1; /* one byte unsigned integer */ +typedef char si1; /* one byte signed integer */ +typedef unsigned short ui2; /* two byte unsigned integer */ +typedef short si2; /* two byte signed integer */ +typedef unsigned int ui4; /* four byte unsigned integer */ +typedef int si4; /* four byte signed integer */ +typedef float sf4; /* four byte signed floating point number */ +typedef double sf8; /* eight byte signed floating point number */ + +extern double ERR,GET,SET,OK,NOP,ALL,NEG,POS,CHK,NOZ,GTH,GTE,LTH,LTE,EQU; +extern double EQV,EQW,EQX,NEQ,SEQ,RXP,IBE,EBI,IBI,EBE; + +extern double *vector_newsize(); +extern unsigned int dcrsz; +extern double *dcr; +extern double *dcrset(int); +extern unsigned int scrsz; +extern unsigned int *scr; +extern unsigned int *scrset(int); +extern unsigned int iscrsz; +extern int *iscr; +extern int *iscrset(int); +extern double BVBASE; +extern double* hoc_pgetarg(); +extern void hoc_notify_iv(); +extern double hoc_call_func(Symbol*, int narg); +extern FILE* hoc_obj_file_arg(int narg); +extern Object** hoc_objgetarg(); +char *gargstr(); +char** hoc_pgargstr(); +extern void vector_resize(); +extern int vector_instance_px(); +extern void* vector_arg(); +extern double* vector_vec(); +extern int vector_buffer_size(void*); +extern double hoc_epsilon; +extern int stoprun; +extern void set_seed(); +extern void dshuffle(double* x,int nx); +extern void mcell_ran4_init(u_int32_t); +extern double mcell_ran4(u_int32_t *idx1, double *x, unsigned int n, double range); +extern int nrn_mlh_gsort(); +extern int ivoc_list_count(Object*); +extern Object* ivoc_list_item(Object*, int); +extern int list_vector_px2(); +extern int hoc_is_double_arg(int narg); +extern int hoc_is_str_arg(int narg); +extern int hoc_is_object_arg(int narg); +extern int hoc_is_pdouble_arg(int narg); +extern Symbol *hoc_get_symbol(char *); +extern Symbol *hoc_lookup(const char*); +extern Point_process* ob2pntproc(Object*); + +extern char* hoc_object_name(Object*); +extern int cmpdfn(); +extern int openvec(int, double **); +int list_vector_px(); +double *list_vector_resize(); +static void hxe() { hoc_execerror("",0); } +extern void FreeListVec(ListVec** pp); +extern ListVec* AllocListVec(Object* p); +extern ListVec* AllocILV(Object*, int, double *); +void FillListVec(ListVec* p,double dval); +void ListVecResize(ListVec* p,int newsz); +extern short *nrn_artcell_qindex_; +extern double nrn_event_queue_stats(double*); +extern void clear_event_queue(); + +static double sc[6]; +static FILE* testout; + +//* in vecst.mod +extern int** getint2D(int rows,int cols); +extern void freeint2D(int*** ppp,int rows); +extern double** getdouble2D(int rows,int cols); +extern void freedouble2D(double*** ppp,int rows); +extern double ismono1 (double *x, int n, int flag); + +//* in stats.mod +double kcorfast(double* input1, double* input2, double* i1d , double* i2d,int n,double* ps); +double Rktau (double* x, double* y, int n); // R version +double kcorfast (double* input1, double* input2, double* i1d , double* i2d,int n,double* ps); diff --git a/examples/dipoleRecording/mod/nax_BS.mod b/examples/dipoleRecording/mod/nax_BS.mod new file mode 100644 index 000000000..b9b4cfd7b --- /dev/null +++ b/examples/dipoleRecording/mod/nax_BS.mod @@ -0,0 +1,115 @@ +TITLE nax +: Na current for axon. No slow inact. +: M.Migliore Jul. 1997 +: added sh to account for higher threshold M.Migliore, Apr.2002 +: thread-safe 2010-05-31 Ben Suter +: 2010-11-07 Ben Suter reformatting, renaming thegna to g, setting sh = 0 (was 8 mV) +: +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: +: Copyright 2011, Benjamin Suter (for changes only) +: Used in model of corticospinal neuron BS0284 and published as: +: "Intrinsic electrophysiology of mouse corticospinal neurons: a characteristic set of features embodied in a realistic computational model" +: by Benjamin Suter, Michele Migliore, and Gordon Shepherd +: Submitted September 2011 +: ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + + +NEURON { + SUFFIX nax + USEION na READ ena WRITE ina + RANGE gbar, ina, sh +: GLOBAL minf, hinf, mtau, htau,thinf, qinf, Rb, Rg, qg +} + +PARAMETER { + v (mV) + celsius (degC) + ena (mV) : must be explicitly def. in hoc + + sh = 0 (mV) + gbar = 0.010 (mho/cm2) + + tha = -30 (mV) : v 1/2 for act + qa = 7.2 (mV) : act slope (4.5) + Ra = 0.4 (/ms) : open (v) + Rb = 0.124 (/ms) : close (v) + + thi1 = -45 (mV) : v 1/2 for inact + thi2 = -45 (mV) : v 1/2 for inact + qd = 1.5 (mV) : inact tau slope + qg = 1.5 (mV) + mmin = 0.02 + hmin = 0.5 + q10 = 2 + Rg = 0.01 (/ms) : inact recov (v) + Rd = 0.03 (/ms) : inact (v) + + thinf = -50 (mV) : inact inf slope + qinf = 4 (mV) : inact inf slope +} + + +UNITS { + (mA) = (milliamp) + (mV) = (millivolt) + (pS) = (picosiemens) + (um) = (micron) +} + +ASSIGNED { + ina (mA/cm2) + g (mho/cm2) + minf + hinf + mtau (ms) + htau (ms) +} + +STATE { m h} + +BREAKPOINT { + SOLVE states METHOD cnexp + g = gbar*m*m*m*h + ina = g * (v - ena) +} + +INITIAL { + trates(v,sh) + m = minf + h = hinf +} + +DERIVATIVE states { + trates(v,sh) + m' = (minf-m)/mtau + h' = (hinf-h)/htau +} + +PROCEDURE trates(vm,sh2) { + LOCAL a, b, qt + qt = q10^((celsius-24)/10) + + a = trap0(vm,tha+sh2,Ra,qa) + b = trap0(-vm,-tha-sh2,Rb,qa) + mtau = 1/(a+b)/qt + if (mtau 1e-6) { + trap0 = a * (v - th) / (1 - exp(-(v - th)/q)) + } else { + trap0 = a * q + } +} diff --git a/examples/dipoleRecording/mod/savedist.mod b/examples/dipoleRecording/mod/savedist.mod new file mode 100644 index 000000000..d4168565c --- /dev/null +++ b/examples/dipoleRecording/mod/savedist.mod @@ -0,0 +1,14 @@ +TITLE Mech to store distance from origin as workaround for multisplit non-uniform densities +: 2011-09-18 Ben Suter, initial version per email from Michael Hines +: 2016-11-31 Ernie Forzano, changed suffix dist to savedist because import3d() and this mod file +: were referencing dist and causing compile error. +: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: + +NEURON { + SUFFIX savedist + RANGE x +} + +PARAMETER { + x = 0 (micron) +} diff --git a/examples/dipoleRecording/net_dipole.py b/examples/dipoleRecording/net_dipole.py new file mode 100644 index 000000000..84c0ac28c --- /dev/null +++ b/examples/dipoleRecording/net_dipole.py @@ -0,0 +1,72 @@ +from netpyne import specs, sim + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + +netParams.sizeX = 200 # x-dimension (horizontal length) size in um +netParams.sizeY = 1000 # y-dimension (vertical height or cortical depth) size in um +netParams.sizeZ = 20 # z-dimension (horizontal length) size in um +netParams.propVelocity = 100.0 # propagation velocity (um/ms) +netParams.probLengthConst = 150.0 # length constant for conn probability (um) + +## Population parameters +netParams.popParams['E2'] = {'cellType': 'E', 'numCells': 20, 'yRange': [100,300], 'cellModel': 'HH'} +netParams.popParams['I2'] = {'cellType': 'I', 'numCells': 20, 'yRange': [100,300], 'cellModel': 'HH'} +netParams.popParams['E4'] = {'cellType': 'E', 'numCells': 20, 'yRange': [300,600], 'cellModel': 'HH'} +netParams.popParams['I4'] = {'cellType': 'I', 'numCells': 20, 'yRange': [300,600], 'cellModel': 'HH'} +netParams.popParams['E5'] = {'cellType': 'E', 'numCells': 20, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'} +netParams.popParams['I5'] = {'cellType': 'I', 'numCells': 20, 'ynormRange': [0.6,1.0], 'cellModel': 'HH'} + +## Cell property rules +netParams.loadCellParamsRule(label='CellRule', fileName='IT2_reduced_cellParams.json') +netParams.cellParams['CellRule']['conds'] = {'cellType': ['E','I']} + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 0.8, 'tau2': 5.3, 'e': 0} # NMDA synaptic mechanism +netParams.synMechParams['inh'] = {'mod': 'Exp2Syn', 'tau1': 0.6, 'tau2': 8.5, 'e': -75} # GABA synaptic mechanism + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 40, 'noise': 0.3} +netParams.stimTargetParams['bkg->all'] = {'source': 'bkg', 'conds': {'cellType': ['E','I']}, 'weight': 10.0, 'sec': 'soma', 'delay': 'max(1, normal(5,2))', 'synMech': 'exc'} + +## Cell connectivity rules +netParams.connParams['E->all'] = { + 'preConds': {'cellType': 'E'}, 'postConds': {'y': [100,1000]}, # E -> all (100-1000 um) + 'probability': 0.1 , # probability of connection + 'weight': '5.0*post_ynorm', # synaptic weight + 'delay': 'dist_3D/propVelocity', # transmission delay (ms) + 'synMech': 'exc'} # synaptic mechanism + +netParams.connParams['I->E'] = { + 'preConds': {'cellType': 'I'}, 'postConds': {'pop': ['E2','E4','E5']}, # I -> E + 'probability': '0.4*exp(-dist_3D/probLengthConst)', # probability of connection + 'weight': 1.0, # synaptic weight + 'delay': 'dist_3D/propVelocity', # transmission delay (ms) + 'synMech': 'inh'} # synaptic mechanism + + +# Simulation configuration +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration +simConfig.duration = 1.0*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.1 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordStep = 1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'net_lfp' # Set file output name + +#simConfig.recordLFP = [[-15, y, 1.0*netParams.sizeZ] for y in range(int(netParams.sizeY/5.0), int(netParams.sizeY), int(netParams.sizeY/5.0))] +#simConfig.saveLFPPops = ['I2', 'E4'] +simConfig.savePickle = True + +simConfig.recordDipole = True +simConfig.saveDipoleCells = ['all'] +simConfig.saveDipolePops = ['E2', 'I2'] + +#simConfig.analysis['plotRaster'] = {'orderBy': 'y', 'orderInverse': True, 'saveFig':True, 'figSize': (9,3)} # Plot a raster +#simConfig.analysis['plotLFP'] = {'pop': 'I2', 'includeAxon': False, 'figSize': (6,10), 'timeRange': [100,3000], 'saveFig': True} +#simConfig.analysis['getCSD'] = {'spacing_um': 200, 'timeRange': [100,3000], 'vaknin': True} +#simConfig.analysis['plotLFP'] = {'includeAxon': False, 'figSize': (6,10), 'timeRange':[100,900], 'minFreq': 10, 'maxFreq':60, 'norm':1, 'plots': ['spectrogram'], 'showFig': True} +#simConfig.analysis['plotCSD'] = True #{'timeRange':[100,200]} + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +#sim.analysis.plotCSD(timeRange=[100,3000]) diff --git a/examples/intervalSaveWeights/README b/examples/intervalSaveWeights/README.md similarity index 90% rename from examples/intervalSaveWeights/README rename to examples/intervalSaveWeights/README.md index 416fbf52a..077d9199f 100644 --- a/examples/intervalSaveWeights/README +++ b/examples/intervalSaveWeights/README.md @@ -3,7 +3,7 @@ This example is based on the multiscale model of mouse primary motor cortex (M1) example. In the init.py function it shows code on how to use the interval saving function and save the synWeights periodically. The key differences are the use of intervalRun and effectively defining two intervals, one for saving weights and one -saving the data. Note also, the use of fileGather instead of gather. +saving the data. Developed using NetPyNE (www.neurosimlab.org/netpyne) diff --git a/examples/intervalSaveWeights/init.py b/examples/intervalSaveWeights/init.py index 372fa757e..728337146 100644 --- a/examples/intervalSaveWeights/init.py +++ b/examples/intervalSaveWeights/init.py @@ -20,7 +20,7 @@ ### This is an example function run at an interval during the simulation ### This function save weights everytime it runs and will save simulation data ### at a different interval defined by cfg.intervalRun and cfg.saveInterval. -def saveWeights(t): +def saveWeights(simTime): # if a list for weights is not initialized make one if not hasattr(sim, 'allWeights'): @@ -33,8 +33,8 @@ def saveWeights(t): # if the sim time matches the saveInterval then save data # NOTE: intervalRun must divide evenly into saveInterval (saveInterval % intervalRun == 0) - if (round(t, 4) % cfg.saveInterval == 0): - sim.intervalSave(t) + if (round(simTime, 4) % cfg.saveInterval == 0): + sim.intervalSave(simTime) print("Starting sim ...") @@ -45,9 +45,8 @@ def saveWeights(t): # if you just want to save the data and not the wieghts you can use the sim.intervalSave function instead of saveWeights sim.runSimWithIntervalFunc(cfg.intervalRun, saveWeights) -# we run fileGather() instead of gather -sim.fileGather() +sim.gatherData() sim.analyze() - +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. sim.checkOutput('M1detailed') diff --git a/examples/intervalSaving/init.py b/examples/intervalSaving/init.py index 47f503461..c8f0b0258 100644 --- a/examples/intervalSaving/init.py +++ b/examples/intervalSaving/init.py @@ -23,9 +23,8 @@ # saveInterval defines how often the data is saved sim.runSimWithIntervalFunc(cfg.saveInterval, sim.intervalSave) -# we run fileGather() instead of gather -sim.fileGather() +sim.gatherData() sim.analyze() - +# Check the model output: sim.checkOutput is used for testing purposes. Please comment out the following line if you are exploring the example. sim.checkOutput('M1detailed') diff --git a/examples/rxd_net/netParams.py b/examples/rxd_net/netParams.py index 5f8360a52..0047a7642 100644 --- a/examples/rxd_net/netParams.py +++ b/examples/rxd_net/netParams.py @@ -82,9 +82,9 @@ ### regions regions = {} -regions['cyt'] = {'cells': 'all', 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}} -regions['er'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}} -regions['cyt_er_membrane'] = {'cells': 'all', 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}} +regions['cyt'] = {'cells': ['all'], 'secs': 'all', 'nrn_region': 'i', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fc'], 'surface_fraction': 1}}} +regions['er'] = {'cells': ['all'], 'secs': 'all', 'geometry': {'class': 'FractionalVolume', 'args': {'volume_fraction': constants['fe']}}} +regions['cyt_er_membrane'] = {'cells': ['all'], 'secs': 'all', 'geometry': {'class': 'ScalableBorder', 'args': {'scale': 1, 'on_cell_surface': False}}} margin = 20 # extracellular volume additional margin x, y, z = [0-margin, 100+margin], [-500-margin, 0+margin], [0-margin, 100+margin] @@ -114,4 +114,4 @@ netParams.rxdParams['multicompartmentReactions'] = mcReactions ### rates -netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate)}} +netParams.rxdParams['rates'] = {'ip3rg': {'species': h_gate, 'rate': '(1. / (1 + 1000. * ca[cyt] / (0.3)) - %s) / ip3rtau'%(h_gate), 'regions' : ['cyt_er_membrane', 'cyt']}} diff --git a/examples/saving/cfg.py b/examples/saving/cfg.py index 46c7caa56..84c75dc0f 100644 --- a/examples/saving/cfg.py +++ b/examples/saving/cfg.py @@ -22,6 +22,7 @@ cfg.createPyStruct = 1 # create Python structure (simulator-independent) when instantiating network cfg.verbose = False # show detailed messages cfg.saveCellSecs = False # avoid saving detailed info about each cell's morphology and mechanisms +cfg.simLabel = 'output' # Recording cfg.recordTraces = {'Vsoma':{'sec':'soma','loc':0.5,'var':'v'}} diff --git a/examples/saving/init.py b/examples/saving/init.py index 1c65cbf85..eb037a4f8 100644 --- a/examples/saving/init.py +++ b/examples/saving/init.py @@ -34,5 +34,5 @@ sim.gatherData() sim.saveData(include=['net'], filename='out_netInstanceCompact') -# check model output +# Check the model output: sim.checkOutput is used for testing purposes. 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"cellType": "RS", + "numCells": 30, + "pop": "RSpop" + } + } + } + }, + "netpyne_changeset": "", + "netpyne_version": "1.0.0.2" +} \ No newline at end of file diff --git a/examples/sonata_300_cells/init.py b/examples/sonata_300_cells/init.py index f5f1ebe8e..2efca60f3 100644 --- a/examples/sonata_300_cells/init.py +++ b/examples/sonata_300_cells/init.py @@ -27,13 +27,13 @@ saveSpikesToBMTK = 0 plotSpikesUsingBMTK = 0 -# Improt SONATA model and instantiate in netpyne +# Import SONATA model and instantiate in NetPyNE if importSonataModel: sonataImporter = sonataImport.SONATAImporter() sonataImporter.importNet(sonataConfigFile, replaceAxon=True, setdLNseg=True) -# code to save network to json so can read from NetPyNE-UI +# Code to save network to .json so can read from NetPyNE-UI if saveForGUI: sim.cfg.saveJson = True #for k,v in sim.net.params.popParams.items(): @@ -59,7 +59,7 @@ sim.saveData(filename='sonata_300cells') -# save json with psection +# Save .json with psection if saveJsonPsection: import json data = {} @@ -84,7 +84,7 @@ json.dump(data, f) -# save json with psection +# Save .json with psection if saveJsonConns: import json data = {} @@ -113,7 +113,7 @@ json.dump(data, f) -# run simulation and plot raster+traces +# Run simulation and plot raster+traces if runPlot: sim.cfg.recordTraces = {'V_soma':{'sec':'soma_0','loc':0.5,'var':'v'}} sim.cfg.recordCells = range(9) diff --git a/examples/subConn/subConn_CellularPops.py b/examples/subConn/subConn_CellularPops.py new file mode 100644 index 000000000..005665e55 --- /dev/null +++ b/examples/subConn/subConn_CellularPops.py @@ -0,0 +1,115 @@ +from netpyne import specs, sim +import matplotlib.pyplot as plt +import numpy as np + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + + +## Cell types +PYRcell = {'secs': {}} + +PYRcell['secs']['soma'] = {'geom': {}, 'mechs': {}} +PYRcell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'Ra': 123.0} +PYRcell['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} + +PYRcell['secs']['dend1'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend1']['geom'] = {'diam': 1.0, 'L': 250.0, 'Ra': 150.0, 'nseg': 5, 'cm': 1} +PYRcell['secs']['dend1']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend1']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend2'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend2']['geom'] = {'diam': 0.8, 'L': 200.0, 'Ra': 150.0, 'nseg': 5, 'cm': 1} +PYRcell['secs']['dend2']['topol'] = {'parentSec': 'dend1', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend2']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend3'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend3']['geom'] = {'diam': 0.5, 'L': 150.0, 'Ra': 150.0, 'nseg': 5, 'cm': 1} +PYRcell['secs']['dend3']['topol'] = {'parentSec': 'dend2', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend3']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +netParams.cellParams['PYR'] = PYRcell + +netParams.defineCellShapes = True # sets 3d geometry aligned along the y-axis + +## Population parameters +Ns = 100 +Nm = 20 +netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': Ns} +netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': Nm} + + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 1.0, 'tau2': 5.0, 'e': 0} # excitatory synaptic mechanism + + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 1, 'noise': 0.5} +netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'} + + +## Cell connectivity rules +netParams.connParams['S->M'] = {'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'probability': 0.5, # probability of connection + 'weight': 0.0001, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'sec': 'dend1', # section to connect to + 'loc': 0.5, # location of synapse + 'synMech': 'exc'} # target synaptic mechanism + + +# Simulation options +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration + +simConfig.duration = 1*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.025 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'},'V_dend1':{'sec':'dend1','loc':0.5,'var':'v'}} # Dict with traces to record +simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'subconn_without' # Set file output name +simConfig.savePickle = False # Save params, network and sim output to pickle file +simConfig.saveDat = True + +watchneuron = Ns # first neuron in population M (where S impinges on) + +simConfig.analysis['plotTraces'] = {'include': [watchneuron], 'saveFig': True} # Plot recorded traces for this list of cells +simConfig.analysis['plotShape'] = {'includePre': ['S'],'includePost': [watchneuron],'cvar':'numSyns','dist':0.7, 'saveFig': True} + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t1 = sim.allSimData['t'] +Vsoma1 = sim.allSimData['V_soma']['cell_%d' %watchneuron] +Vdend1 = sim.allSimData['V_dend1']['cell_%d' %watchneuron] + +################################################# +## Adds a Subcellular synaptic redistribution +################################################# +gridY = [x for x in range(0, 470, 10)] +gridValues = [np.exp(-((x-270)/50)**2) for x in range(0, 470, 10)] + +fig, ax= plt.subplots() +ax.plot(gridY,gridValues) +plt.xlabel('Distance') +plt.ylabel('Density') +fig.savefig('density.jpg') + +netParams.subConnParams['S->M'] = { + 'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'sec': ['dend1','dend2'], # probability of connection + 'density': {'type':'1Dmap','gridY':gridY,'gridValues':gridValues,'fixedSomaY':0} + } + +simConfig.filename = 'subconn_with' # Set file output name + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t2 = sim.allSimData['t'] +Vsoma2 = sim.allSimData['V_soma']['cell_%d' %watchneuron] +Vdend2 = sim.allSimData['V_dend1']['cell_%d' %watchneuron] + +fig, ax= plt.subplots() +ax.plot(t1,Vdend1) +ax.plot(t2,Vdend2) +plt.xlabel('Time') +plt.ylabel('Vdend') +fig.savefig('comparison.jpg') diff --git a/examples/subConn/subConn_DistanceBased.py b/examples/subConn/subConn_DistanceBased.py new file mode 100644 index 000000000..b0913e0f1 --- /dev/null +++ b/examples/subConn/subConn_DistanceBased.py @@ -0,0 +1,123 @@ +from netpyne import specs, sim +import matplotlib.pyplot as plt +import numpy as np + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + + +## Cell types +PYRcell = {'secs': {}} + +PYRcell['secs']['soma'] = {'geom': {}, 'mechs': {}} +PYRcell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'Ra': 123.0} +PYRcell['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} + +PYRcell['secs']['dend0'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend0']['geom'] = {'diam': 1.0, 'L': 300.0, 'Ra': 150.0, 'nseg': 9, 'cm': 1} +PYRcell['secs']['dend0']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend0']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend1'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend1']['geom'] = {'diam': 1.0, 'L': 100.0, 'Ra': 150.0, 'nseg': 9, 'cm': 1} +PYRcell['secs']['dend1']['topol'] = {'parentSec': 'dend0', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend1']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend2'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend2']['geom'] = {'diam': 1.0, 'L': 100.0, 'Ra': 150.0, 'nseg': 9, 'cm': 1} +PYRcell['secs']['dend2']['topol'] = {'parentSec': 'dend0', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend2']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend3'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend3']['geom'] = {'diam': 1.0, 'L': 100.0, 'Ra': 150.0, 'nseg': 9, 'cm': 1} +PYRcell['secs']['dend3']['topol'] = {'parentSec': 'dend0', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend3']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + + +netParams.cellParams['PYR'] = PYRcell + +#netParams.defineCellShapes = True # sets 3d geometry aligned along the y-axis + +## Population parameters +Ns = 100 +Nm = 10 +netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': Ns} +netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': Nm} + + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 1.0, 'tau2': 5.0, 'e': 0} # excitatory synaptic mechanism + + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 1, 'noise': 0.5} +netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'} + + +## Cell connectivity rules +netParams.connParams['S->M'] = {'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'probability': 0.5, # probability of connection + 'weight': 0.0001, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'sec': 'dend0', # section to connect to + 'loc': 0.5, # location of synapse + 'synMech': 'exc'} # target synaptic mechanism + + +# Simulation options +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration + +simConfig.duration = 1*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.025 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'},'V_dend0':{'sec':'dend0','loc':0.5,'var':'v'}} # Dict with traces to record +simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'subconn_without' # Set file output name +simConfig.savePickle = False # Save params, network and sim output to pickle file +simConfig.saveDat = True + +watchneuron = Ns # first neuron in population M (where S impinges on) + +simConfig.analysis['plotTraces'] = {'include': [watchneuron], 'saveFig': True} # Plot recorded traces for this list of cells +simConfig.analysis['plotShape'] = {'includePre': ['S'],'includePost': [watchneuron],'cvar':'numSyns','dist':0.7, 'saveFig': True} + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t1 = sim.allSimData['t'] +Vsoma1 = sim.allSimData['V_soma']['cell_%d' %watchneuron] +Vdend1 = sim.allSimData['V_dend0']['cell_%d' %watchneuron] + +################################################# +## Adds a Subcellular synaptic redistribution +################################################# + +netParams.subConnParams['S->M'] = { + 'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'sec': ['dend3'], # probability of connection + 'density': {'type':'distance','ref_sec':'soma','ref_seg':0.5, 'target_distance': 360} + } + +simConfig.filename = 'subconn_with' # Set file output name + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t2 = sim.allSimData['t'] +Vsoma2 = sim.allSimData['V_soma']['cell_%d' %watchneuron] +Vdend2 = sim.allSimData['V_dend0']['cell_%d' %watchneuron] + + +fig, ax= plt.subplots() +ax.plot(t1,Vdend1) +ax.plot(t2,Vdend2) +plt.xlabel('Time') +plt.ylabel('Vdend') +fig.savefig('comparison.jpg') + +diff = [] +for i in range(len(t1)): + diff.append(Vdend1[i] - Vdend2[i]) +fig, ax= plt.subplots() +ax.plot(t1,diff) +plt.xlabel('Time') +plt.ylabel('Difference') +fig.savefig('difference.jpg') diff --git a/examples/subConn/subConn_DistanceBasedCartesian.py b/examples/subConn/subConn_DistanceBasedCartesian.py new file mode 100644 index 000000000..89d0e7222 --- /dev/null +++ b/examples/subConn/subConn_DistanceBasedCartesian.py @@ -0,0 +1,118 @@ +from netpyne import specs, sim +import matplotlib.pyplot as plt +import numpy as np + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + + +## Cell types +PYRcell = {'secs': {}} + +PYRcell['secs']['soma'] = {'geom': {}, 'mechs': {}} +PYRcell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'Ra': 123.0, 'pt3d': [[0,0,0,20],[0,20,0,20]]} +PYRcell['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} + +PYRcell['secs']['dend1'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend1']['geom'] = {'diam': 1.0, 'L': 200.0, 'Ra': 150.0, 'nseg': 15, 'cm': 1, 'pt3d': [[0,20,0,1],[0,220,0,1]]} +PYRcell['secs']['dend1']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend1']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend2'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend2']['geom'] = {'diam': 0.8, 'L': 200.0, 'Ra': 150.0, 'nseg': 15, 'cm': 1, 'pt3d': [[0,220,0,1],[100,393.205,0,1]]} +PYRcell['secs']['dend2']['topol'] = {'parentSec': 'dend1', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend2']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend3'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend3']['geom'] = {'diam': 0.5, 'L': 200.0, 'Ra': 150.0, 'nseg': 15, 'cm': 1, 'pt3d': [[0,220,0,1],[-173.205,320,0,1]]} +PYRcell['secs']['dend3']['topol'] = {'parentSec': 'dend1', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend3']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +netParams.cellParams['PYR'] = PYRcell + +#netParams.defineCellShapes = True # sets 3d geometry aligned along the y-axis + +## Population parameters +Ns = 100 +Nm = 1 +netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': Ns} +netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': Nm} + + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 1.0, 'tau2': 5.0, 'e': 0} # excitatory synaptic mechanism + + +# Stimulation parameters +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 1, 'noise': 0.5} +netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'} + + +## Cell connectivity rules +netParams.connParams['S->M'] = {'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'probability': 0.5, # probability of connection + 'weight': 0.0001, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'sec': 'dend1', # section to connect to + 'loc': 0.5, # location of synapse + 'synMech': 'exc'} # target synaptic mechanism + + +# Simulation options +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration + +simConfig.duration = 1*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.025 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'},'V_dend1':{'sec':'dend1','loc':0.5,'var':'v'}} # Dict with traces to record +simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'subconn_without' # Set file output name +simConfig.savePickle = False # Save params, network and sim output to pickle file +simConfig.saveDat = True + +watchneuron = Ns # first neuron in population M (where S impinges on) + +simConfig.analysis['plotTraces'] = {'include': [watchneuron], 'saveFig': True} # Plot recorded traces for this list of cells +simConfig.analysis['plotShape'] = {'includePre': ['S'],'includePost': [watchneuron],'cvar':'numSyns','dist':0.7, 'saveFig': True} + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t1 = sim.allSimData['t'] +Vsoma1 = sim.allSimData['V_soma']['cell_%d' %watchneuron] +Vdend1 = sim.allSimData['V_dend1']['cell_%d' %watchneuron] + +################################################# +## Adds a Subcellular synaptic redistribution +################################################# + +netParams.subConnParams['S->M'] = { + 'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'sec': ['dend1','dend3'], # probability of connection +# 'density': {'type':'distance','ref_sec':'soma','ref_seg':0.5, 'target_distance': 310, 'coord': 'topol'} + 'density': {'type':'distance','ref_sec':'soma','ref_seg':0.5, 'target_distance': 310, 'coord': 'cartesian'} + } + +simConfig.filename = 'subconn_with' # Set file output name + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t2 = sim.allSimData['t'] +Vsoma2 = sim.allSimData['V_soma']['cell_%d' %watchneuron] +Vdend2 = sim.allSimData['V_dend1']['cell_%d' %watchneuron] + + +fig, ax= plt.subplots() +ax.plot(t1,Vdend1) +ax.plot(t2,Vdend2) +plt.xlabel('Time') +plt.ylabel('Vdend') +fig.savefig('comparison.jpg') + +diff = [] +for i in range(len(t1)): + diff.append(Vdend1[i] - Vdend2[i]) +fig, ax= plt.subplots() +ax.plot(t1,diff) +plt.xlabel('Time') +plt.ylabel('Difference') +fig.savefig('difference.jpg') diff --git a/examples/subConn/subConn_IncomingNetStims.py b/examples/subConn/subConn_IncomingNetStims.py new file mode 100644 index 000000000..59a4b2b09 --- /dev/null +++ b/examples/subConn/subConn_IncomingNetStims.py @@ -0,0 +1,141 @@ +from netpyne import specs, sim +import matplotlib.pyplot as plt +import numpy as np + +# Network parameters +netParams = specs.NetParams() # object of class NetParams to store the network parameters + + +## Cell types +PYRcell = {'secs': {}} + +PYRcell['secs']['soma'] = {'geom': {}, 'mechs': {}} +PYRcell['secs']['soma']['geom'] = {'diam': 20, 'L': 20, 'Ra': 123.0} +PYRcell['secs']['soma']['mechs']['hh'] = {'gnabar': 0.12, 'gkbar': 0.036, 'gl': 0.003, 'el': -70} + +PYRcell['secs']['dend1'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend1']['geom'] = {'diam': 1.0, 'L': 250.0, 'Ra': 150.0, 'nseg': 5, 'cm': 1} +PYRcell['secs']['dend1']['topol'] = {'parentSec': 'soma', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend1']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend2'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend2']['geom'] = {'diam': 0.8, 'L': 200.0, 'Ra': 150.0, 'nseg': 5, 'cm': 1} +PYRcell['secs']['dend2']['topol'] = {'parentSec': 'dend1', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend2']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +PYRcell['secs']['dend3'] = {'geom': {}, 'topol': {}, 'mechs': {}} +PYRcell['secs']['dend3']['geom'] = {'diam': 0.5, 'L': 150.0, 'Ra': 150.0, 'nseg': 5, 'cm': 1} +PYRcell['secs']['dend3']['topol'] = {'parentSec': 'dend2', 'parentX': 1.0, 'childX': 0} +PYRcell['secs']['dend3']['mechs']['pas'] = {'g': 0.0000357, 'e': -70} + +netParams.cellParams['PYR'] = PYRcell + +netParams.defineCellShapes = True # sets 3d geometry aligned along the y-axis + +## Population parameters +Ns = 100 +Nm = 20 +netParams.popParams['S'] = {'cellType': 'PYR', 'numCells': Ns} +netParams.popParams['M'] = {'cellType': 'PYR', 'numCells': Nm} +netParams.popParams['NetStim_pop'] = {'cellModel': 'NetStim', 'rate': 20, 'noise': 0.5, 'numCells': 100} # NetsStim + +## Synaptic mechanism parameters +netParams.synMechParams['exc'] = {'mod': 'Exp2Syn', 'tau1': 1.0, 'tau2': 5.0, 'e': 0} # excitatory synaptic mechanism + + +# Stimulation parameters (background to all cells) +netParams.stimSourceParams['bkg'] = {'type': 'NetStim', 'rate': 1, 'noise': 0.5} +netParams.stimTargetParams['bkg->PYR'] = {'source': 'bkg', 'conds': {'cellType': 'PYR'}, 'weight': 0.01, 'delay': 5, 'synMech': 'exc'} + + +## Cell connectivity rules +netParams.connParams['S->M'] = {'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'probability': 0.5, # probability of connection + 'weight': 0.0001, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'sec': 'dend1', # section to connect to + 'loc': 0.5, # location of synapse + 'synMech': 'exc'} # target synaptic mechanism + +# Population S is driven by an additional stream of netstims, here as a population +netParams.connParams['NetStim->S'] = {'preConds': {'pop': 'NetStim_pop'}, 'postConds': {'pop': 'S'}, # NetStimPop -> S + 'probability': 0.5, # probability of connection + 'weight': 0.0001, # synaptic weight + 'delay': 5, # transmission delay (ms) + 'sec': 'dend1', # section to connect to + 'loc': 0.5, # location of synapse + 'synMech': 'exc'} # target synaptic mechanism + +# Simulation options +simConfig = specs.SimConfig() # object of class SimConfig to store simulation configuration + +simConfig.duration = 1*1e3 # Duration of the simulation, in ms +simConfig.dt = 0.025 # Internal integration timestep to use +simConfig.verbose = False # Show detailed messages +simConfig.recordTraces = {'V_soma':{'sec':'soma','loc':0.5,'var':'v'},'V_dend1':{'sec':'dend1','loc':0.5,'var':'v'}} # Dict with traces to record +simConfig.recordStep = 0.1 # Step size in ms to save data (eg. V traces, LFP, etc) +simConfig.filename = 'subconn_without' # Set file output name +simConfig.savePickle = False # Save params, network and sim output to pickle file +simConfig.saveDat = True + +watchneuronS = 0 # first neuron in population S (where NetStim_pop impinges on) +watchneuronM = Ns # first neuron in population M (where S impinges on) + +simConfig.analysis['plotTraces'] = {'include': [watchneuronS,watchneuronM], 'saveFig': True} # Plot recorded traces for this list of cells +simConfig.analysis['plotShape'] = {'includePre': ['all'],'includePost': [watchneuronS],'cvar':'numSyns','dist':0.7, 'saveFig': True} + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t1 = sim.allSimData['t'] +Vsoma1_S = sim.allSimData['V_soma']['cell_%d' %watchneuronS] +Vdend1_S = sim.allSimData['V_dend1']['cell_%d' %watchneuronS] +Vsoma1_M = sim.allSimData['V_soma']['cell_%d' %watchneuronM] +Vdend1_M = sim.allSimData['V_dend1']['cell_%d' %watchneuronM] + +################################################# +## Adds a Subcellular synaptic redistribution +################################################# +gridY = [x for x in range(0, 470, 10)] +gridValues = [np.exp(-((x-270)/50)**2) for x in range(0, 470, 10)] + +fig, ax= plt.subplots() +ax.plot(gridY,gridValues) +plt.xlabel('Distance') +plt.ylabel('Density') +fig.savefig('density.jpg') + +netParams.subConnParams['S->M'] = { + 'preConds': {'pop': 'S'}, 'postConds': {'pop': 'M'}, # S -> M + 'sec': ['dend1','dend2'], # probability of connection + 'density': {'type':'1Dmap','gridY':gridY,'gridValues':gridValues,'fixedSomaY':0} + } + +netParams.subConnParams['NetStim->S'] = { + 'preConds': {'pop': 'NetStim_pop'}, 'postConds': {'pop': 'S'}, # NetStim -> S + 'sec': ['dend1','dend2'], # probability of connection + 'density': {'type':'1Dmap','gridY':gridY,'gridValues':gridValues,'fixedSomaY':0} + } + +simConfig.filename = 'subconn_with' # Set file output name + +# Create network and run simulation +sim.createSimulateAnalyze(netParams = netParams, simConfig = simConfig) +t2 = sim.allSimData['t'] +Vsoma2_S = sim.allSimData['V_soma']['cell_%d' %watchneuronS] +Vdend2_S = sim.allSimData['V_dend1']['cell_%d' %watchneuronS] +Vsoma2_M = sim.allSimData['V_soma']['cell_%d' %watchneuronM] +Vdend2_M = sim.allSimData['V_dend1']['cell_%d' %watchneuronM] + +fig, ax= plt.subplots() +ax.plot(t1,Vdend1_S) +ax.plot(t2,Vdend2_S) +plt.xlabel('Time') +plt.ylabel('Vdend neuron S') +fig.savefig('comparison_S.jpg') + +fig, ax= plt.subplots() +ax.plot(t1,Vdend1_M) +ax.plot(t2,Vdend2_M) +plt.xlabel('Time') +plt.ylabel('Vdend neuron M') +fig.savefig('comparison_M.jpg') diff --git a/netpyne/__init__.py b/netpyne/__init__.py index 01c4ae778..0471a25f7 100644 --- a/netpyne/__init__.py +++ b/netpyne/__init__.py @@ -4,7 +4,7 @@ NetPyNE consists of a number of sub-packages and modules. """ -__version__ = '1.0.0.2' +__version__ = '1.0.1' import os, sys display = os.getenv('DISPLAY') nogui = (sys.argv.count('-nogui')>0) @@ -24,6 +24,7 @@ from netpyne import conversion from netpyne import metadata from netpyne import network +from netpyne import plotting from netpyne import sim from netpyne import specs from netpyne import support diff --git a/netpyne/analysis/__init__.py b/netpyne/analysis/__init__.py index 71eaf3dd6..a58b906ce 100644 --- a/netpyne/analysis/__init__.py +++ b/netpyne/analysis/__init__.py @@ -1,79 +1,50 @@ """ -Package for analysis and plotting +Package for analysis """ -from __future__ import unicode_literals -from __future__ import print_function -from __future__ import division -from __future__ import absolute_import +import netpyne -from future import standard_library -standard_library.install_aliases() -import warnings -warnings.filterwarnings("ignore") - -# ------------------------------------------------------------------------------------------------------------------- -## Wrapper to run analysis functions in simConfig -# ------------------------------------------------------------------------------------------------------------------- -from .wrapper import plotData - -# ------------------------------------------------------------------------------------------------------------------- # Import utils methods -# ------------------------------------------------------------------------------------------------------------------- -from .utils import exception, _showFigure, _saveFigData, getCellsInclude, getCellsIncludeTags, _roundFigures, \ - _smooth1d, syncMeasure, invertDictMapping, checkAvailablePlots +from .tools import getInclude, plotData, saveData, loadData +from .utils import exception, _showFigure, _saveFigData, getCellsInclude, getCellsIncludeTags, _roundFigures, _smooth1d, syncMeasure, invertDictMapping, checkAvailablePlots - -# ------------------------------------------------------------------------------------------------------------------- # Import connectivity-related functions -# ------------------------------------------------------------------------------------------------------------------- -from .network import plotConn, _plotConnCalculateFromSim, _plotConnCalculateFromFile, plot2Dnet, plotShape, calculateDisynaptic, \ - plot2Dfiring - +from .network import plotConn, _plotConnCalculateFromSim, _plotConnCalculateFromFile, plot2Dnet, calculateDisynaptic, plot2Dfiring +from ..plotting import plotShape -# ------------------------------------------------------------------------------------------------------------------- # Import spike-related functions -# ------------------------------------------------------------------------------------------------------------------- -from .spikes import calculateRate, plotRates, plotSyncs, plotRaster, plotSpikeHist, plotSpikeStats, \ - plotRatePSD, plotRateSpectrogram, popAvgRates, plotfI, calculatefI +from .spikes import prepareSpikeData, prepareRaster, prepareSpikeHist, popAvgRates +from .spikes_orig import calculateRate, plotRates, plotSyncs, plotSpikeStats, plotRatePSD, plotRateSpectrogram, popAvgRates, plotfI, calculatefI - -# ------------------------------------------------------------------------------------------------------------------- # Import traces-related functions -# ------------------------------------------------------------------------------------------------------------------- +#from .traces import prepareTraces +#from ..plotting import plotTraces from .traces import plotTraces, plotEPSPAmp - -# ------------------------------------------------------------------------------------------------------------------- # Import LFP-related functions -# ------------------------------------------------------------------------------------------------------------------- -from .lfp import plotLFP - +from .lfp import prepareLFP +from .lfp import preparePSD +from .lfp import prepareSpectrogram -# ------------------------------------------------------------------------------------------------------------------- # Import information theory-related functions -# ------------------------------------------------------------------------------------------------------------------- -from .info import nTE, granger, plotGranger +from .info import nTE, granger - -# ------------------------------------------------------------------------------------------------------------------- # Import RxD-related functions -# ------------------------------------------------------------------------------------------------------------------- from .rxd import plotRxDConcentration - -# ------------------------------------------------------------------------------------------------------------------- # Import interactive functions functions -# ------------------------------------------------------------------------------------------------------------------- - try: - from .interactive import iplotDipole, iplotDipoleSpectrogram, iplotDipolePSD, iplotRaster, iplotSpikeHist, iplotRatePSD, iplotTraces, iplotLFP, iplotConn, iplotRxDConcentration, iplot2Dnet, iplotSpikeStats, iplotGranger + from .interactive import iplotDipole, iplotDipoleSpectrogram, iplotDipolePSD, iplotRaster, iplotSpikeHist, iplotRatePSD, iplotTraces, iplotLFP, iplotConn, iplotRxDConcentration, iplot2Dnet, iplotSpikeStats except: print('Warning: could not import interactive plotting functions; make sure the "bokeh" package is installed.') - -# ------------------------------------------------------------------------------------------------------------------- # Import CSD-related functions -# ------------------------------------------------------------------------------------------------------------------- from .csd import getCSD, plotCSD + +# Import dipole-related functions +from .dipole import plotDipole, plotEEG + +# Import mapped functions (for legacy purposes) +from .mapping import plotLFP, plotSpikeHist, plotRaster + diff --git a/netpyne/analysis/csd.py b/netpyne/analysis/csd.py index 64e7c0782..351e5257c 100644 --- a/netpyne/analysis/csd.py +++ b/netpyne/analysis/csd.py @@ -183,9 +183,9 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin """ - # DEFAULT -- CONDITION 1 : LFP DATA COMES FROM SIMULATION + ### DEFAULT -- CONDITION 1 : LFP DATA COMES FROM SIMULATION ### - # if no inputs are given, get LFP data from simulation + # Get LFP data from simulation if LFP_input_data is None and LFP_input_file is None: try: @@ -208,17 +208,23 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin # Sampling rate of data recording during the simulation if sampr is None: # divide by 1000.0 to turn denominator from units of ms to s - sampr = 1.0/(sim.cfg.recordStep/1000.0) + sampr = 1.0/(dt/1000.0) # sim.cfg.recordStep --> == dt - # Spacing between electrodes --> convert from micron to mm + # Spacing between electrodes (in microns) if spacing_um is None: spacing_um = sim.cfg.recordLFP[1][1] - sim.cfg.recordLFP[0][1] + ## This retrieves: + # lfp_data (as an array) + # dt --> recording time step (in ms) + # sampr --> sampling rate of data recording (in Hz) + # spacing_um --> spacing btwn electrodes (in um) - # CONDITION 2 : ARBITRARY LFP DATA - # Note: need to expand capability to include a list of multiple files + + + ### CONDITION 2 : LFP DATA FROM SIM .JSON FILE ### # Note: need to expand capability to include a list of multiple files - # load sim data from a JSON file + # load sim data from JSON file elif LFP_input_data is None and '.json' in LFP_input_file: data = {} @@ -232,7 +238,7 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin # extract LFP data (only works in the 1 input file scenario; expand capability for multiple files) for key in data.keys: - lfp_data_list = data[key]['simData']['LFP'] + lfp_data_list = data[key]['simData']['LFP'] # cast LFP data as Numpy array lfp_data = np.array(lfp_data_list) @@ -260,13 +266,22 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin else: csd_data[i]['dt'] = dt + ## This retrieves: + # lfp_data (as a list) + # dt --> recording time step (in ms) + # sampr --> sampling rate of data recording (in Hz) + # spacing_um --> spacing btwn electrodes (in um) + - # FOR LIST OF LFP DATA WITHOUT ANY .JSON INPUT FILE + ### CONDITION 3 : ARBITRARY LFP DATA ### # NOTE: for condition 3 --> need to also retrieve the dt, sampr, and spacing_um !! # get lfp_data and cast as numpy array - elif len(LFP_input_data) > 0 and LFP_input_file is None: - lfp_data = np.array(LFP_input_data) + elif len(LFP_input_data) > 0 and LFP_input_file is None: + lfp_data = np.array(LFP_input_data) + + + #################################### # Convert spacing from microns to mm spacing_mm = spacing_um/1000 @@ -279,6 +294,7 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin ax = 1 else: ax = 0 + print('ax = ' + str(ax)) # Vaknin correction if vaknin: @@ -290,7 +306,6 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin # now each column (or row) is an electrode -- take CSD along electrodes CSD_data = -np.diff(datband, n=2, axis=ax)/spacing_mm**2 - # noBandpass trial @@ -307,7 +322,7 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin if norm: removemean(datband_noBandpass, ax=ax) - CSD_data_noBandpass = -np.diff(datband_noBandpass,n=2,axis=ax)/spacing_mm**2 + CSD_data_noBandpass = -np.diff(datband_noBandpass,n=2,axis=ax)/spacing_mm**2 # Add CSD and other param values to sim.allSimData for later access @@ -326,14 +341,14 @@ def getCSD(LFP_input_data=None, LFP_input_file=None, sampr=None, dt=None, spacin if getAllData is True: return lfp_data, CSD_data, sampr, spacing_um, dt if getAllData is False: - return CSD_data + return CSD_data # PLOTTING CSD @exception -def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sampr=None, stim_start_time=None, spacing_um=None, ymax=None, dt=None, hlines=False, layer_lines=False, layer_bounds=None, smooth=None, fontSize=12, figSize=(10,10),dpi=200, saveFig=True, showFig=True): +def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sampr=None, stim_start_time=None, spacing_um=None, ymax=None, dt=None, hlines=False, layerLines=False, layerBounds=None, smooth=True, fontSize=12, figSize=(8,8),dpi=200, saveFig=True, showFig=True): """ Function to plot CSD values extracted from simulated LFP data @@ -347,10 +362,11 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa LFP data provided by user (mV). Each element of the list/array must be a list/array containing LFP data for an electrode. **Default:** ``None`` pulls the data from the current NetPyNE sim object. + overlay : str - Option to include other data overlaid on CSD color map plot. - **Default:** ``None`` - **Options:** ``'CSD_raw'``, ``'CSD_bandpassed'``, ``'LFP'`` + Option to include LFP data overlaid on CSD color map plot. + **Default:** ``None`` provides no overlay + OPTIONS are 'LFP' or 'CSD' timeRange : list Time range to plot [start, stop]. @@ -381,11 +397,11 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa Option to include horizontal lines on plot to indicate electrode positions. **Default:** ``False`` - layer_lines : bool + layerLines : bool Whether to plot horizontal lines over CSD plot at layer boundaries. **Default:** ``False`` - layer_bounds : dict + layerBounds : dict Dictionary containing layer labels as keys, and layer boundaries as values, e.g. {'L1':100, 'L2': 160, 'L3': 950, 'L4': 1250, 'L5A': 1334, 'L5B': 1550, 'L6': 2000} **Default:** ``None`` @@ -406,34 +422,49 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa if CSD_data is None: from .. import sim - #lfp_data, CSD_data, sampr, spacing_um, dt = getCSD(sampr=sampr, spacing_um=spacing_um, dt=dt, getAllData=True) + LFP_data, CSD_data, sampr, spacing_um, dt = getCSD(getAllData=True) #getCSD(sampr=sampr, spacing_um=spacing_um, dt=dt, getAllData=True) - sim_data_categories = sim.allSimData.keys() + if timeRange is None: + timeRange = [0,sim.cfg.duration] + + tt = np.arange(timeRange[0], timeRange[1], dt) + + ymax = sim.cfg.recordLFP[-1][1] + spacing_um + + LFP_data = np.array(LFP_data)[int(timeRange[0]/dt):int(timeRange[1]/dt),:] + CSD_data = CSD_data[:,int(timeRange[0]/dt):int(timeRange[1]/dt)] + + CSD_data_noBandpass = sim.allSimData['CSD']['CSD_data_noBandpass'][:,int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep)] + #CSD_data_noBandpass = CSD_data_noBandpass[:,int(timeRange[0]/dt):int(timeRange[1]/dt)] + + + ### The problem with this setup is that it defaults to whatever was saved in .pkl !! + # sim_data_categories = sim.allSimData.keys() - if 'CSD' in sim_data_categories: + # if 'CSD' in sim_data_categories: - if timeRange is None: - timeRange = [0,sim.cfg.duration] + # if timeRange is None: + # timeRange = [0,sim.cfg.duration] - dt = sim.cfg.recordStep - tt = np.arange(timeRange[0],timeRange[1],dt) + # dt = sim.cfg.recordStep + # tt = np.arange(timeRange[0],timeRange[1],dt) - spacing_um = sim.allSimData['CSD']['spacing_um'] - spacing_mm = spacing_um/1000 + # spacing_um = sim.allSimData['CSD']['spacing_um'] + # #spacing_mm = spacing_um/1000 - ymax = sim.cfg.recordLFP[-1][1] + spacing_um + # ymax = sim.cfg.recordLFP[-1][1] + spacing_um - # get LFP data - LFP_data = np.array(sim.allSimData['LFP'])[int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep),:] + # # get LFP data + # LFP_data = np.array(sim.allSimData['LFP'])[int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep),:] - # get CSD data - CSD_data = sim.allSimData['CSD']['CSD_data'][:,int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep)] + # # get CSD data + # CSD_data = sim.allSimData['CSD']['CSD_data'][:,int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep)] - # noBandpass trial - CSD_data_noBandpass = sim.allSimData['CSD']['CSD_data_noBandpass'][:,int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep)] + # # noBandpass trial + # CSD_data_noBandpass = sim.allSimData['CSD']['CSD_data_noBandpass'][:,int(timeRange[0]/sim.cfg.recordStep):int(timeRange[1]/sim.cfg.recordStep)] - else: - raise Exception('No CSD data in sim.') + # else: + # raise Exception('No CSD data in sim.') # CONDITION 2 : ARBITRARY CSD DATA @@ -460,7 +491,7 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa tt = np.arange(timeRange[0], timeRange[1], dt) LFP_data = np.array(LFP_input_data)[int(timeRange[0]/dt):int(timeRange[1]/dt),:] - + # PLOTTING X = np.arange(timeRange[0], timeRange[1], dt) @@ -484,7 +515,7 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa # create plots w/ common axis labels and tick marks axs = [] numplots = 1 - gs_outer = matplotlib.gridspec.GridSpec(2, 2, figure=fig, wspace=0.4, hspace=0.2, height_ratios=[20, 1]) + gs_outer = matplotlib.gridspec.GridSpec(1,1)#(2, 2, figure=fig)#, wspace=0.4, hspace=0.2, height_ratios=[20, 1]) for i in range(numplots): axs.append(plt.Subplot(fig,gs_outer[i*2:i*2+2])) @@ -493,42 +524,36 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa axs[i].tick_params(axis='y', which='major', labelsize=fontSize) axs[i].tick_params(axis='x', which='major', labelsize=fontSize) - # plot interpolated CSD color map - if smooth: - Z = scipy.ndimage.filters.gaussian_filter(Z, smooth, mode='nearest') + ## plot interpolated CSD color map + #if smooth: + # Z = scipy.ndimage.filters.gaussian_filter(Z, sigma = 5, mode='nearest')#smooth, mode='nearest') spline=axs[0].imshow(Z, extent=extent_xy, interpolation='none', aspect='auto', origin='upper', cmap='jet_r', alpha=0.9) axs[0].set_ylabel('Contact depth (um)', fontsize=fontSize) - # set Title of plot & overlay data (CSD_raw, CSD_bandpassed, or LFP) - if overlay is 'CSD_raw' or overlay is 'CSD_bandpassed' or overlay is 'LFP': + + # OVERLAY DATA ('LFP', 'CSD', or None) & Set title of plot + if overlay is None: + print('No data being overlaid') + axs[0].set_title('Current Source Density (CSD)', fontsize=fontSize) + + elif overlay is 'CSD' or overlay is 'LFP': nrow = LFP_data.shape[1] gs_inner = matplotlib.gridspec.GridSpecFromSubplotSpec(nrow, 1, subplot_spec=gs_outer[0:2], wspace=0.0, hspace=0.0) subaxs = [] - # go down grid and add data from each channel - if overlay == 'CSD_raw': - axs[0].set_title('CSD with raw CSD time series overlay', fontsize=fontSize) - for chan in range(nrow): - subaxs.append(plt.Subplot(fig, gs_inner[chan], frameon=False)) - fig.add_subplot(subaxs[chan]) - subaxs[chan].margins(0.0,0.01) - subaxs[chan].get_xaxis().set_visible(False) - subaxs[chan].get_yaxis().set_visible(False) - subaxs[chan].plot(X, CSD_data_noBandpass[chan,:], color='red', linewidth=0.4) - - elif overlay == 'CSD_bandpassed': - axs[0].set_title('CSD with Bandpassed CSD time series overlay', fontsize=fontSize) + if overlay == 'CSD': + axs[0].set_title('CSD with time series overlay', fontsize=fontSize) for chan in range(nrow): subaxs.append(plt.Subplot(fig, gs_inner[chan], frameon=False)) fig.add_subplot(subaxs[chan]) subaxs[chan].margins(0.0,0.01) subaxs[chan].get_xaxis().set_visible(False) subaxs[chan].get_yaxis().set_visible(False) - subaxs[chan].plot(X, CSD_data[chan,:], color='blue', linewidth=0.3) + subaxs[chan].plot(X, CSD_data[chan,:], color='green', linewidth=0.3) # 'blue' elif overlay == 'LFP': - axs[0].set_title('CSD with LFP overlay', fontsize=14) + axs[0].set_title('CSD with LFP overlay', fontsize=fontSize) for chan in range(nrow): subaxs.append(plt.Subplot(fig, gs_inner[chan], frameon=False)) fig.add_subplot(subaxs[chan]) @@ -538,7 +563,7 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa subaxs[chan].plot(X, LFP_data[:,chan], color='gray', linewidth=0.3) else: - print('No data being overlaid') + print('Invalid option specified for overlay argument -- no data overlaid') axs[0].set_title('Current Source Density (CSD)', fontsize=fontSize) @@ -546,22 +571,22 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa # add horizontal lines at electrode locations if hlines: for i in range(len(sim.cfg.recordLFP)): - axs[0].hlines(sim.cfg.recordLFP[i][1], xmin, xmax, colors='black', linestyles='dashed') + axs[0].hlines(sim.cfg.recordLFP[i][1], xmin, xmax, colors='pink', linewidth=1, linestyles='dashed') - if layer_lines: - if layer_bounds is None: - print('No layer boundaries given') + if layerLines: + if layerBounds is None: + print('No layer boundaries given -- will not overlay layer boundaries on CSD plot') else: layerKeys = [] - for i in layer_bounds.keys(): - axs[0].hlines(layer_bounds[i], xmin, xmax, colors='black', linewidth=1, linestyles='dotted') - layerKeys.append(i) + for i in layerBounds.keys(): + axs[0].hlines(layerBounds[i], xmin, xmax, colors='black', linewidth=1, linestyles='dotted') + layerKeys.append(i) # makes a list with names of each layer, as specified in layerBounds dict argument - for n in range(len(layerKeys)): - if n == 0: - axs[0].text(xmax+5, layer_bounds[layerKeys[n]]/2, layerKeys[n], color='black', fontsize=fontSize) - else: - axs[0].text(xmax+5, (layer_bounds[layerKeys[n]] + layer_bounds[layerKeys[n-1]])/2, layerKeys[n], color='black', fontsize=fontSize) + for n in range(len(layerKeys)): # label the horizontal layer lines with the proper layer label + if n == 0: + axs[0].text(xmax+5, layerBounds[layerKeys[n]]/2, layerKeys[n], color='black', fontsize=fontSize) + else: + axs[0].text(xmax+5, (layerBounds[layerKeys[n]] + layerBounds[layerKeys[n-1]])/2, layerKeys[n], color='black', fontsize=fontSize, verticalalignment='center') # set vertical line at stimulus onset if type(stim_start_time) is int or type(stim_start_time) is float: @@ -580,5 +605,5 @@ def plotCSD(CSD_data=None, LFP_input_data=None, overlay=None, timeRange=None, sa plt.savefig('CSD_fig.png', dpi=dpi) # display figure - if showFig is True: + if showFig: plt.show() diff --git a/netpyne/analysis/dipole.py b/netpyne/analysis/dipole.py new file mode 100644 index 000000000..c2249d293 --- /dev/null +++ b/netpyne/analysis/dipole.py @@ -0,0 +1,207 @@ +""" +Module for analyzing and plotting LFP-related results + +""" + +from __future__ import print_function +from __future__ import division +from __future__ import unicode_literals +from __future__ import absolute_import + +from builtins import range +from builtins import round +from builtins import str +try: + basestring +except NameError: + basestring = str + +from future import standard_library +standard_library.install_aliases() +from netpyne import __gui__ + +if __gui__: + import matplotlib.pyplot as plt + from matplotlib import mlab +import numpy as np +from numbers import Number + + + +def plotDipole(showCell=None, showPop=None, timeRange=None, dpi=300, figSize=(6,6), showFig=True, saveFig=True): + from .. import sim + + if showCell: + p = sim.allSimData['dipoleCells'][showCell] + elif showPop: + p = sim.allSimData['dipolePops'][showPop] + else: + p = sim.allSimData['dipoleSum'] + + p = p/1000.0 # convert from nA to mA + + if timeRange is None: + timeRange = [0, sim.cfg.duration] + + timeSteps = [int(timeRange[0]/sim.cfg.recordStep), int(timeRange[1]/sim.cfg.recordStep)] + + # current dipole moment + plt.figure(figsize=figSize) + plt.plot(np.arange(timeRange[0], timeRange[1], sim.cfg.recordStep), np.array(p)[timeSteps[0]:timeSteps[1]]) + #plt.legend([r'$P_x$ (mA um)', r'$P_y$ (mA um)', r'$P_z$ (mA um)']) + plt.legend([r'$P_x$', r'$P_y$', r'$P_z$']) + plt.ylabel(r'$\mathbf{P}(t)$ (mA $\mu$m)') + plt.xlabel('$t$ (ms)') + ax=plt.gca() + ax.grid(False) + + # save figure + if saveFig: + if isinstance(saveFig, basestring): + filename = saveFig + else: + filename = sim.cfg.filename + '_dipole.png' + try: + plt.savefig(filename, dpi=dpi) + except: + plt.savefig('dipole_fig.png', dpi=dpi) + + + # display figure + if showFig is True: + plt.show() + + +def plotEEG(showCell=None, showPop=None, timeRange=None, dipole_location='parietal_lobe', dpi=300, figSize=(19,10), showFig=True, saveFig=True): + from .. import sim + + from lfpykit.eegmegcalc import NYHeadModel + + nyhead = NYHeadModel() + + #dipole_location = 'parietal_lobe' # predefined location from NYHead class + nyhead.set_dipole_pos(dipole_location) + M = nyhead.get_transformation_matrix() + + if showCell: + p = sim.allSimData['dipoleCells'][showCell] + elif showPop: + p = sim.allSimData['dipolePops'][showPop] + else: + p = sim.allSimData['dipoleSum'] + + if timeRange is None: + timeRange = [0, sim.cfg.duration] + + timeSteps = [int(timeRange[0]/sim.cfg.recordStep), int(timeRange[1]/sim.cfg.recordStep)] + + p = np.array(p).T + + p=p[:, timeSteps[0]:timeSteps[1]] + + # We rotate current dipole moment to be oriented along the normal vector of cortex + p = nyhead.rotate_dipole_to_surface_normal(p) + eeg = M @ p * 1E9 # [mV] -> [pV] unit conversion + + # plot EEG daa + x_lim = [-100, 100] + y_lim = [-130, 100] + z_lim = [-160, 120] + + t = np.arange(timeRange[0], timeRange[1], sim.cfg.recordStep) + + plt.close("all") + fig = plt.figure(figsize=[19, 10]) + fig.subplots_adjust(top=0.96, bottom=0.05, hspace=0.17, wspace=0.3, left=0.1, right=0.99) + ax1 = fig.add_subplot(245, aspect=1, xlabel="x (mm)", ylabel='y (mm)', xlim=x_lim, ylim=y_lim) + ax2 = fig.add_subplot(246, aspect=1, xlabel="x (mm)", ylabel='z (mm)', xlim=x_lim, ylim=z_lim) + ax3 = fig.add_subplot(247, aspect=1, xlabel="y (mm)", ylabel='z (mm)', xlim=y_lim, ylim=z_lim) + ax_eeg = fig.add_subplot(244, xlabel="Time (ms)", ylabel='pV', title='EEG at all electrodes') + + ax_cdm = fig.add_subplot(248, xlabel="Time (ms)", ylabel='nA$\cdot \mu$m', + title='Current dipole moment') + dist, closest_elec_idx = nyhead.find_closest_electrode() + print("Closest electrode to dipole: {:1.2f} mm".format(dist)) + + max_elec_idx = np.argmax(np.std(eeg, axis=1)) + time_idx = np.argmax(np.abs(eeg[max_elec_idx])) + max_eeg = np.max(np.abs(eeg[:, time_idx])) + max_eeg_idx = np.argmax(np.abs(eeg[:, time_idx])) + + max_eeg_pos = nyhead.elecs[:3, max_eeg_idx] + fig.text(0.01, 0.25, "Cortex", va='center', rotation=90, fontsize=22) + fig.text(0.03, 0.25, + "Dipole pos: {:1.1f}, {:1.1f}, {:1.1f}\nDipole moment: {:1.2f} {:1.2f} {:1.2f}".format( + nyhead.dipole_pos[0], nyhead.dipole_pos[1], nyhead.dipole_pos[2], + p[0, time_idx], p[1, time_idx], p[2, time_idx] + ), va='center', rotation=90, fontsize=14) + + fig.text(0.01, 0.75, "EEG", va='center', rotation=90, fontsize=22) + fig.text(0.03, 0.75, "Max: {:1.2f} pV at idx {}\n({:1.1f}, {:1.1f} {:1.1f})".format( + max_eeg, max_eeg_idx, max_eeg_pos[0], max_eeg_pos[1], max_eeg_pos[2]), va='center', + rotation=90, fontsize=14) + + ax7 = fig.add_subplot(241, aspect=1, xlabel="x (mm)", ylabel='y (mm)', + xlim=x_lim, ylim=y_lim) + ax8 = fig.add_subplot(242, aspect=1, xlabel="x (mm)", ylabel='z (mm)', + xlim=x_lim, ylim=z_lim) + ax9 = fig.add_subplot(243, aspect=1, xlabel="y (mm)", ylabel='z (mm)', + xlim=y_lim, ylim=z_lim) + + ax_cdm.plot(t, p[2, :], 'k') + [ax_eeg.plot(t, eeg[idx, :], c='gray') for idx in range(eeg.shape[0])] + ax_eeg.plot(t, eeg[closest_elec_idx, :], c='green', lw=2) + + vmax = np.max(np.abs(eeg[:, time_idx])) + v_range = vmax + cmap = lambda v: plt.cm.bwr((v + vmax) / (2*vmax)) + + threshold = 2 + + xz_plane_idxs = np.where(np.abs(nyhead.cortex[1, :] - nyhead.dipole_pos[1]) < threshold)[0] + xy_plane_idxs = np.where(np.abs(nyhead.cortex[2, :] - nyhead.dipole_pos[2]) < threshold)[0] + yz_plane_idxs = np.where(np.abs(nyhead.cortex[0, :] - nyhead.dipole_pos[0]) < threshold)[0] + + ax1.scatter(nyhead.cortex[0, xy_plane_idxs], nyhead.cortex[1, xy_plane_idxs], s=5) + ax2.scatter(nyhead.cortex[0, xz_plane_idxs], nyhead.cortex[2, xz_plane_idxs], s=5) + ax3.scatter(nyhead.cortex[1, yz_plane_idxs], nyhead.cortex[2, yz_plane_idxs], s=5) + + for idx in range(eeg.shape[0]): + c = cmap(eeg[idx, time_idx]) + ax7.plot(nyhead.elecs[0, idx], nyhead.elecs[1, idx], 'o', ms=10, c=c, + zorder=nyhead.elecs[2, idx]) + ax8.plot(nyhead.elecs[0, idx], nyhead.elecs[2, idx], 'o', ms=10, c=c, + zorder=nyhead.elecs[1, idx]) + ax9.plot(nyhead.elecs[1, idx], nyhead.elecs[2, idx], 'o', ms=10, c=c, + zorder=-nyhead.elecs[0, idx]) + + img = ax3.imshow([[], []], origin="lower", vmin=-vmax, + vmax=vmax, cmap=plt.cm.bwr) + plt.colorbar(img, ax=ax9, shrink=0.5) + + ax1.plot(nyhead.dipole_pos[0], nyhead.dipole_pos[1], '*', ms=12, color='orange', zorder=1000) + ax2.plot(nyhead.dipole_pos[0], nyhead.dipole_pos[2], '*', ms=12, color='orange', zorder=1000) + ax3.plot(nyhead.dipole_pos[1], nyhead.dipole_pos[2], '*', ms=12, color='orange', zorder=1000) + + ax7.plot(nyhead.dipole_pos[0], nyhead.dipole_pos[1], '*', ms=15, color='orange', zorder=1000) + ax8.plot(nyhead.dipole_pos[0], nyhead.dipole_pos[2], '*', ms=15, color='orange', zorder=1000) + ax9.plot(nyhead.dipole_pos[1], nyhead.dipole_pos[2], '*', ms=15, color='orange', zorder=1000) + + + # save figure + if saveFig: + if isinstance(saveFig, basestring): + filename = saveFig + else: + filename = sim.cfg.filename + '_EEG.png' + try: + plt.savefig(filename, dpi=dpi) + except: + plt.savefig('EEG_fig.png', dpi=dpi) + + + # display figure + if showFig is True: + plt.show() + + #import IPython as ipy; ipy.embed() \ No newline at end of file diff --git a/netpyne/analysis/info.py b/netpyne/analysis/info.py index 43325cb20..6d7bc4b0b 100644 --- a/netpyne/analysis/info.py +++ b/netpyne/analysis/info.py @@ -30,7 +30,17 @@ @exception def nTE(cells1=[], cells2=[], spks1=None, spks2=None, timeRange=None, binSize=20, numShuffle=30): """ - Function for/to + Function that calculates the Normalized Transfer Entropy (nTE) between two spike train signals. + + Transfer entropy is a model-free statistic that is able to measure + the time-directed transfer of information between stochastic variables + and therefore provides an asymmetric method to measure information transfer. + In simple words, the nTE represents the fraction of information in X explained + by its own past which is not explained by the past of Y. + + Kale, P. et al (2018, July). Normalized Transfer Entropy as a Tool to Identify Multisource + Functional Epileptic Networks IEEE Engineering in Medicine and Biology Society (EMBC) + https://doi.org/10.1109/embc.2018.8512532 Parameters ---------- @@ -181,7 +191,15 @@ def nTE(cells1=[], cells2=[], spks1=None, spks2=None, timeRange=None, binSize=20 @exception def plotGranger(cells1=None, cells2=None, spks1=None, spks2=None, label1=None, label2=None, timeRange=None, binSize=5, testGranger=False, plotFig=True, saveData=None, saveFig=None, showFig=True): """ - Function to plot the Granger Causality between two groups of cells + Function that calculates Granger Causality between two spike train signals. + + The Granger causality test is a statistical hypothesis test for determining + whether one time series is useful in forecasting another. G-causality is based + on the simple idea that causes both precede and help predict their effects. + + Seth, A. K., Barrett, A. B., & Barnett, L. (2015). Granger Causality Analysis in + Neuroscience and Neuroimaging. Journal of Neuroscience, 35(8), 3293–3297. + https://doi.org/10.1523/jneurosci.4399-14.2015 Parameters ---------- diff --git a/netpyne/analysis/lfp.py b/netpyne/analysis/lfp.py index 96ceacab5..918c59974 100644 --- a/netpyne/analysis/lfp.py +++ b/netpyne/analysis/lfp.py @@ -1,5 +1,5 @@ """ -Module for analyzing and plotting LFP-related results +Module for analyzing LFP-related results """ @@ -26,208 +26,58 @@ import numpy as np from numbers import Number from .utils import colorList, exception, _saveFigData, _showFigure, _smooth1d +from ..support.scalebar import add_scalebar -# ------------------------------------------------------------------------------------------------------------------- -## Plot LFP (time-resolved, power spectral density, time-frequency and 3D locations) -# ------------------------------------------------------------------------------------------------------------------- @exception -def plotLFP(timeRange=None, electrodes=['avg', 'all'], plots=['timeSeries', 'PSD', 'spectrogram', 'locations'], inputLFP=None, NFFT=256, noverlap=128, nperseg=256, minFreq=1, maxFreq=100, stepFreq=1, smooth=0, separation=1.0, includeAxon=True, logx=False, logy=False, normSignal=False, normPSD=False, normSpec=False, filtFreq=False, filtOrder=3, detrend=False, transformMethod='morlet', maxPlots=8, overlay=False, colors=None, figSize=(8, 8), fontSize=14, lineWidth=1.5, dpi=200, saveData=None, saveFig=None, showFig=True): - """ - Function for plotting local field potentials (LFP) - - Parameters - ---------- - timeRange : list [start, stop] - Time range to plot. - **Default:** - ``None`` plots entire time range - **Options:** ``