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- We were commenting TABLE statement from hh.mod as TABLE statement was not supported in mod2c. - NMODL on CoreNEURON side supports TABLE statements also on GPU side. - Hence, we can remove the CMake toggling logic for TABLE. - Also update the references for tests in ringtest and testcorenrn repos fixes #1764
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,125 @@ | ||
| TITLE hh.mod squid sodium, potassium, and leak channels | ||
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| COMMENT | ||
| This is the original Hodgkin-Huxley treatment for the set of sodium, | ||
| potassium, and leakage channels found in the squid giant axon membrane. | ||
| ("A quantitative description of membrane current and its application | ||
| conduction and excitation in nerve" J.Physiol. (Lond.) 117:500-544 (1952).) | ||
| Membrane voltage is in absolute mV and has been reversed in polarity | ||
| from the original HH convention and shifted to reflect a resting potential | ||
| of -65 mV. | ||
| Remember to set celsius=6.3 (or whatever) in your HOC file. | ||
| See squid.hoc for an example of a simulation using this model. | ||
| SW Jaslove 6 March, 1992 | ||
| ENDCOMMENT | ||
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| UNITS { | ||
| (mA) = (milliamp) | ||
| (mV) = (millivolt) | ||
| (S) = (siemens) | ||
| } | ||
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| ? interface | ||
| NEURON { | ||
| SUFFIX hh | ||
| USEION na READ ena WRITE ina | ||
| USEION k READ ek WRITE ik | ||
| NONSPECIFIC_CURRENT il | ||
| RANGE gnabar, gkbar, gl, el, gna, gk | ||
| :GLOBAL minf, hinf, ninf, mtau, htau, ntau | ||
| RANGE minf, hinf, ninf, mtau, htau, ntau | ||
| THREADSAFE : assigned GLOBALs will be per thread | ||
| } | ||
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| PARAMETER { | ||
| gnabar = .12 (S/cm2) <0,1e9> | ||
| gkbar = .036 (S/cm2) <0,1e9> | ||
| gl = .0003 (S/cm2) <0,1e9> | ||
| el = -54.3 (mV) | ||
| } | ||
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| STATE { | ||
| m h n | ||
| } | ||
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| ASSIGNED { | ||
| v (mV) | ||
| celsius (degC) | ||
| ena (mV) | ||
| ek (mV) | ||
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| gna (S/cm2) | ||
| gk (S/cm2) | ||
| ina (mA/cm2) | ||
| ik (mA/cm2) | ||
| il (mA/cm2) | ||
| minf hinf ninf | ||
| mtau (ms) htau (ms) ntau (ms) | ||
| } | ||
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| ? currents | ||
| BREAKPOINT { | ||
| SOLVE states METHOD cnexp | ||
| gna = gnabar*m*m*m*h | ||
| ina = gna*(v - ena) | ||
| gk = gkbar*n*n*n*n | ||
| ik = gk*(v - ek) | ||
| il = gl*(v - el) | ||
| } | ||
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| INITIAL { | ||
| rates(v) | ||
| m = minf | ||
| h = hinf | ||
| n = ninf | ||
| } | ||
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| ? states | ||
| DERIVATIVE states { | ||
| rates(v) | ||
| m' = (minf-m)/mtau | ||
| h' = (hinf-h)/htau | ||
| n' = (ninf-n)/ntau | ||
| } | ||
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| :LOCAL q10 | ||
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| ? rates | ||
| PROCEDURE rates(v(mV)) { :Computes rate and other constants at current v. | ||
| :Call once from HOC to initialize inf at resting v. | ||
| LOCAL alpha, beta, sum, q10 | ||
| TABLE minf, mtau, hinf, htau, ninf, ntau DEPEND celsius FROM -100 TO 100 WITH 200 | ||
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| UNITSOFF | ||
| q10 = 3^((celsius - 6.3)/10) | ||
| :"m" sodium activation system | ||
| alpha = .1 * vtrap(-(v+40),10) | ||
| beta = 4 * exp(-(v+65)/18) | ||
| sum = alpha + beta | ||
| mtau = 1/(q10*sum) | ||
| minf = alpha/sum | ||
| :"h" sodium inactivation system | ||
| alpha = .07 * exp(-(v+65)/20) | ||
| beta = 1 / (exp(-(v+35)/10) + 1) | ||
| sum = alpha + beta | ||
| htau = 1/(q10*sum) | ||
| hinf = alpha/sum | ||
| :"n" potassium activation system | ||
| alpha = .01*vtrap(-(v+55),10) | ||
| beta = .125*exp(-(v+65)/80) | ||
| sum = alpha + beta | ||
| ntau = 1/(q10*sum) | ||
| ninf = alpha/sum | ||
| } | ||
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| FUNCTION vtrap(x,y) { :Traps for 0 in denominator of rate eqns. | ||
| if (fabs(x/y) < 1e-6) { | ||
| vtrap = y*(1 - x/y/2) | ||
| }else{ | ||
| vtrap = x/(exp(x/y) - 1) | ||
| } | ||
| } | ||
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| UNITSON |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,84 @@ | ||
| // NOTE: this assumes neuronapi.h is on your CPLUS_INCLUDE_PATH | ||
| #include "neuronapi.h" | ||
| #include <dlfcn.h> | ||
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| #include <array> | ||
| #include <cassert> | ||
| #include <cstdlib> | ||
| #include <cstring> | ||
| #include <iostream> | ||
| #include <fstream> | ||
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| #include "./test_common.h" | ||
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| using std::cout; | ||
| using std::endl; | ||
| using std::ofstream; | ||
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| constexpr std::array<double, 3> EXPECTED_V{ | ||
| -0x1.04p+6, | ||
| -0x1.b254ad82e20edp+5, | ||
| -0x1.24a52af1ab463p+6, | ||
| }; | ||
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| extern "C" void modl_reg(){/* No modl_reg */}; | ||
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| int main(void) { | ||
| static std::array<const char*, 4> argv = {"hh_sim", "-nogui", "-nopython", nullptr}; | ||
| nrn_init(3, argv.data()); | ||
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| // load the stdrun library | ||
| char* temp_str = strdup("stdrun.hoc"); | ||
| nrn_str_push(&temp_str); | ||
| nrn_function_call(nrn_symbol("load_file"), 1); | ||
| nrn_double_pop(); | ||
| free(temp_str); | ||
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| // topology | ||
| Section* soma = nrn_section_new("soma"); | ||
| nrn_nseg_set(soma, 3); | ||
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| // define soma morphology with two 3d points | ||
| nrn_section_push(soma); | ||
| for (double x: {0, 0, 0, 10}) { | ||
| nrn_double_push(x); | ||
| } | ||
| nrn_function_call(nrn_symbol("pt3dadd"), 4); | ||
| nrn_double_pop(); // pt3dadd returns a number | ||
| for (double x: {10, 0, 0, 10}) { | ||
| nrn_double_push(x); | ||
| } | ||
| nrn_function_call(nrn_symbol("pt3dadd"), 4); | ||
| nrn_double_pop(); // pt3dadd returns a number | ||
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| // ion channels | ||
| nrn_mechanism_insert(soma, nrn_symbol("hh")); | ||
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| // current clamp at soma(0.5) | ||
| nrn_double_push(0.5); | ||
| Object* iclamp = nrn_object_new(nrn_symbol("IClamp"), 1); | ||
| nrn_property_set(iclamp, "amp", 0.3); | ||
| nrn_property_set(iclamp, "del", 1); | ||
| nrn_property_set(iclamp, "dur", 0.1); | ||
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| // setup recording | ||
| Object* v = nrn_object_new(nrn_symbol("Vector"), 0); | ||
| nrn_rangevar_push(nrn_symbol("v"), soma, 0.5); | ||
| nrn_double_push(5.); | ||
| nrn_method_call(v, nrn_method_symbol(v, "record"), 2); | ||
| nrn_object_unref(nrn_object_pop()); // record returns the vector | ||
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| // finitialize(-65) | ||
| nrn_double_push(-65); | ||
| nrn_function_call(nrn_symbol("finitialize"), 1); | ||
| nrn_double_pop(); | ||
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| // continuerun(10) | ||
| nrn_double_push(10.5); | ||
| nrn_function_call(nrn_symbol("continuerun"), 1); | ||
| nrn_double_pop(); | ||
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| if (!approximate(EXPECTED_V, v)) { | ||
| return 1; | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,94 @@ | ||
| // NOTE: this assumes neuronapi.h is on your CPLUS_INCLUDE_PATH | ||
| #include <cstdlib> | ||
| #include <cstring> | ||
| #include <iostream> | ||
| #include <fstream> | ||
| #include <dlfcn.h> | ||
| #include "neuronapi.h" | ||
| #include "./test_common.h" | ||
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| using std::cout; | ||
| using std::endl; | ||
| using std::ofstream; | ||
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| constexpr std::array<double, 6> EXPECTED_V{ | ||
| -0x1.04p+6, | ||
| -0x1.085a63d029bc3p+6, | ||
| -0x1.112a5e95eb67cp+6, | ||
| -0x1.1795abaec26c1p+6, | ||
| -0x1.0422351f3f9dcp+6, | ||
| -0x1.03e5317ac368cp+6, | ||
| }; | ||
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| extern "C" void modl_reg(){/* No modl_reg */}; | ||
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| int main(void) { | ||
| static std::array<const char*, 4> argv = {"netcon", "-nogui", "-nopython", nullptr}; | ||
| nrn_init(3, argv.data()); | ||
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| // load the stdrun library | ||
| char* temp_str = strdup("stdrun.hoc"); | ||
| nrn_str_push(&temp_str); | ||
| nrn_function_call(nrn_symbol("load_file"), 1); | ||
| nrn_double_pop(); | ||
| free(temp_str); | ||
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| // topology | ||
| auto soma = nrn_section_new("soma"); | ||
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| // ion channels | ||
| nrn_mechanism_insert(soma, nrn_symbol("hh")); | ||
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| // NetStim | ||
| auto ns = nrn_object_new(nrn_symbol("NetStim"), 0); | ||
| nrn_property_set(ns, "start", 5); | ||
| nrn_property_set(ns, "noise", 1); | ||
| nrn_property_set(ns, "interval", 5); | ||
| nrn_property_set(ns, "number", 10); | ||
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| nrn_double_push(1); | ||
| nrn_double_push(2); | ||
| nrn_double_push(3); | ||
| nrn_method_call(ns, nrn_method_symbol(ns, "noiseFromRandom123"), 3); | ||
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| // syn = h.ExpSyn(soma(0.5)) | ||
| nrn_double_push(0.5); | ||
| auto syn = nrn_object_new(nrn_symbol("ExpSyn"), 1); | ||
| nrn_property_set(syn, "tau", 3); // 3 ms timeconstant | ||
| nrn_property_set(syn, "e", 0); // 0 mV reversal potential (excitatory synapse) | ||
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| // nc = h.NetCon(ns, syn) | ||
| nrn_object_push(ns); | ||
| nrn_object_push(syn); | ||
| auto nc = nrn_object_new(nrn_symbol("NetCon"), 2); | ||
| nrn_property_array_set(nc, "weight", 0, 0.5); | ||
| nrn_property_set(nc, "delay", 0); | ||
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| auto vec = nrn_object_new(nrn_symbol("Vector"), 0); | ||
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| // nc.record(vec) | ||
| nrn_object_push(vec); | ||
| nrn_method_call(nc, nrn_method_symbol(nc, "record"), 1); | ||
| // TODO: record probably put something on the stack that should be removed | ||
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| // setup recording | ||
| Object* v = nrn_object_new(nrn_symbol("Vector"), 0); | ||
| nrn_rangevar_push(nrn_symbol("v"), soma, 0.5); | ||
| nrn_double_push(20); | ||
| nrn_method_call(v, nrn_method_symbol(v, "record"), 2); | ||
| nrn_object_unref(nrn_object_pop()); // record returns the vector | ||
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| // finitialize(-65) | ||
| nrn_double_push(-65); | ||
| nrn_function_call(nrn_symbol("finitialize"), 1); | ||
| nrn_double_pop(); | ||
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| // continuerun(100) | ||
| nrn_double_push(100.5); | ||
| nrn_function_call(nrn_symbol("continuerun"), 1); | ||
| nrn_double_pop(); | ||
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| if (!approximate(EXPECTED_V, v)) { | ||
| return 1; | ||
| } | ||
| } |
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