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HVCModelCode.txt
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HVCModelCode.txt
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function HVCModelCode
% Script to reproduce figure 5 a-d and extended data figure 10 a-d from
% Okubo et al.
%
% Change this to a .m file (From .txt) to run in MATLAB.
% Code was tested in MATLAB 2014a, 2015b
%
% This file will be posted as supplemental info for Okubo et al, so is
% formatted as one script to reproduce figure 5 a-d and extended data
% figure 10 a-d. More useable code (separated into different .m files),
% including code for the other figures and supplemental movies, can be
% found here: https://github.com/emackev/HVCModelCode
%
% Emily Mackevicius 7/18/2015, based on Hannah Payne's code
% which builds off Ila Fiete's model, with help from Michale Fee and Tatsuo
% Okubo.
%% *Alternating differentiation*
% From subsong through protosyllable stage through splitting,
% to generate figure 5 a-d
% Calls HVCIter to step through one iteration of the model
%% Alternating differentiation: network parameters
% fixed parameters
seed = 9038;
p.seed = seed; % seed random number generator
p.n = 100; % n neurons
p.trainint = 10; % Time interval between inputs
p.nsteps = 100; % time-steps to simulate -- each time-step is 1
% burst duration.
nstepsSubsong = 1000; % time-steps to simulate for subsong stage
p.pin = .01; % probability of external stimulation of at least
% one neuron at any time
k = 10; % number of training neurons
p.trainingInd = 1:k; % index of training neurons
p.beta = .115; % strength of feedforward inhibition
p.alpha = 30; % strength of neural adaptation
p.eta = .025; % learning rate parameter
p.epsilon = .2; % relative strength of heterosynaptic LTD
p.tau = 4; % time constant of adaptation
gammaStart= .01; % strength of recurrent inhibition
gammaSplit =.18; % increased strength of recurrent inhibition to
% induce splitting
wmaxStart = 1; % single synapse hard bound
wmaxSplit = 2; % single synapse hard bound to induce splitting
% (increased to encourage fewer stronger synapses)
mStart = 10; % desired number of synapses per neuron
% (wmax = Wmax/m)
Wmax = mStart*wmaxStart;% soft bound for weights of each neuron
mSplit = Wmax/wmaxSplit;% keep Wmax constant, change m & wmax to induce
% fewer stronger synapses
HowClamped = 10; % give training neurons higher threshold
HowOn = 10; % higher inputs to training neurons
% how many iterations to run before plotting
nIterProto = 500; % end of protosyllable stage
nIterPlotSplit1 = 492; % number of splitting iterations before plotting
% intermediate splitting phase
nIterPlotSplit2 = 2000; % total number of splitting iterations
% parameters that change over development
protosyllableStage = [true(1,nIterProto) false(1,nIterPlotSplit2)];
splittingStage = [false(1,nIterProto) true(1,nIterPlotSplit2)];
gammas(protosyllableStage) = gammaStart;
gammas(splittingStage) = gammaSplit*sigmf(1:nIterPlotSplit2,[1/200 500]);
wmaxs(protosyllableStage) = wmaxStart;
wmaxs(splittingStage) = wmaxSplit;
ms(protosyllableStage) = mStart;
ms(splittingStage) = mSplit;
%Subsong Inputs
rng(seed)
isOnset = rand(1,nstepsSubsong)>.9;
Input =-HowClamped*ones(k, nstepsSubsong); % clamp training neurons
% (effectively giving them higher threshold)
Input(:,isOnset) = HowOn;
bdyn = double(rand(p.n,nstepsSubsong)>=(1-p.pin)); % Random activation
bdyn(1:k,:) = Input;
subsongInput = bdyn;
%Protosyllable inputs
PsylInput = -HowClamped*ones(k, p.nsteps); %clamp training neurons
% (effectively giving them higher threshold)
PsylInput(:,mod(1:p.nsteps,p.trainint)==1) = HowOn; % rhythmic activation
% of training neurons
%Alternating Inputs
AltInput =-HowClamped*ones(k, p.nsteps);
AltInput(1:k/2,mod(1:p.nsteps,2*p.trainint)==1) = HowOn;
AltInput((k/2+1):k,mod(1:p.nsteps,2*p.trainint)==p.trainint+1) = HowOn;
% alternating rhythmic activation of training neurons
%% Alternating differentiation: run simulation
% random initial weights
rng(seed);
w0 = 2*rand(p.n)*Wmax/p.n;
% subsong stage
pSubsong = p;
pSubsong.gamma = gammas(1);
pSubsong.wmax = wmaxs(1);
pSubsong.m = ms(1);
pSubsong.eta = 0;
pSubsong.epsilon = 0;
pSubsong.nsteps = nstepsSubsong;
pSubsong.w = w0;
pSubsong.input = subsongInput;
% Run subsong network
[wSubsong xdynSubsong] = HVCIter(pSubsong);
w = wSubsong;
% learning stages
for t = 1:(nIterProto+nIterPlotSplit2)
p.w = w;
% set parameters that change over development
p.gamma = gammas(t);
p.wmax = wmaxs(t);
p.m = ms(t);
% Construct input
bdyn = double(rand(p.n,p.nsteps)>=(1-p.pin)); % Random activation
bdyn(1:k,:) = protosyllableStage(t)*PsylInput+ ...
splittingStage(t)*AltInput; % drive to seed neurons
p.input = bdyn;
% run one iteration
[w xdyn] = HVCIter(p);
% save certain iterations for plotting later
switch t
case nIterProto;
wProto = w;
xdynProto = xdyn;
case nIterProto + nIterPlotSplit1;
wSplit1 = w;
xdynSplit1 = xdyn;
case nIterProto + nIterPlotSplit2;
wSplit2 = w;
xdynSplit2 = xdyn;
end
end
%% Alternating differentiation: plotting parameters
figure(1)
isEPS = 0;
clf
set(gcf, 'color', ones(1,3));
if isEPS
PlottingParams.msize = 8;
PlottingParams.linewidth = .25;
set(0,'defaultAxesFontName', 'Arial')
set(0,'defaultTextFontName', 'Arial')
PlottingParams.labelFontSize = 7;
set(gcf, 'units','centimeters', 'position', [5 5 13.5 6])
else
PlottingParams.msize = 10;
PlottingParams.linewidth = .25;
PlottingParams.labelFontSize = 7;
end
PlottingParams.SeedColor = [.95 .5 1];
PlottingParams.Syl1Color = [1 0 0];
PlottingParams.Syl2Color = [0 0 1];
PlottingParams.ProtoSylColor = [0 0 0];
PlottingParams.ProtoSylBarColor = [.5 .5 .5];
PlottingParams.SubsongSylColor = [0 0 0];
PlottingParams.SubsongBarColor = [1 1 1];
PlottingParams.numFontSize = 5;
PlottingParams.wplotmin = 0;
PlottingParams.wplotmax = 2; % this should be wmaxSplit
PlottingParams.wprctile = 0; % plot all weights above this percentile.
% If nonzero, ignores wplotmin, wplotmax
PlottingParams.wperneuron = 6; % max outgoing weights plotted
PlottingParams.wperneuronIn = 9; % min incoming weights plotted
PlottingParams.totalPanels = 4;
nplots = 4;
bottom = .1;
height = .45;
scale = .005;
spacing = .75/(2*nplots);
%% Alternating differentiation: plotting subsong
trainingNeuronsSubsong{1}.nIDs = 1:k;
trainingNeuronsSubsong{1}.tind = find(isOnset);
trainingNeuronsSubsong{1}.candLat = 1:2*p.trainint;
trainingNeuronsSubsong{1}.thres = 12; % criteria for participation during
% subsong (thres from testLatSig --
% must fire at consistent latency
% more than 12 times in the bout of
% ~100 syllables to count as
% participating)
PlottingParams.thisPanel = 1;
PlottingParams.Hor = 1;
plotSubsong(wSubsong, xdynSubsong, trainingNeuronsSubsong, PlottingParams)
%% Alternating differentiation: plotting protosylable
trainingNeuronsPsyl{1}.nIDs = 1:k;
trainingNeuronsPsyl{2}.nIDs = 1:k;
trainingNeuronsPsyl{1}.tind = find(mod(1:p.nsteps, p.trainint)==1);
trainingNeuronsPsyl{2}.tind = find(mod(1:p.nsteps, p.trainint)==1);
trainingNeuronsPsyl{1}.candLat = 1:p.trainint;
trainingNeuronsPsyl{2}.candLat = 1:p.trainint;
trainingNeuronsPsyl{1}.thres = 4; % criteria for participation during
% protosyllable stage (thres from
% testLatSig -- must fire at consistent
% latency more than 4 times in the bout
% of 10 syllables to count as
% participating)
trainingNeuronsPsyl{2}.thres = 4;
PlottingParams.thisPanel = 2;
subplot('position', ...
[PlottingParams.thisPanel/nplots-.9/nplots, .6, .9/nplots, .35])
plotHVCnet(wProto,xdynProto,p.trainint,trainingNeuronsPsyl,PlottingParams)
set(gca, 'color', 'none');
PlottingParams.axesPosition = ...
[PlottingParams.thisPanel/nplots-2*spacing, bottom, 40*scale, height];
plotAlternating(wProto, xdynProto, p.trainint, ...
trainingNeuronsPsyl, PlottingParams)
set(gca, 'color', 'none')
%% Alternating differentiation: plotting splitting stages
trainingNeuronsAlt{1}.nIDs = 1:k/2;
trainingNeuronsAlt{2}.nIDs = (k/2+1):k;
trainingNeuronsAlt{1}.tind = find(mod(1:p.nsteps, 2*p.trainint)==1);
trainingNeuronsAlt{2}.tind = find(mod(1:p.nsteps, ...
2*p.trainint)==p.trainint+1);
trainingNeuronsAlt{1}.candLat = 1:p.trainint;
trainingNeuronsAlt{2}.candLat = 1:p.trainint;
trainingNeuronsAlt{1}.thres = 2; % criteria for participation during
% splitting stage (thres from testLatSig
% -- must fire at consistent latency
% more than 2 times in the bout of 5
% syllables (of each type) to count
% as participating)
trainingNeuronsAlt{2}.thres = 2;
PlottingParams.thisPanel = 3;
subplot('position', ...
[PlottingParams.thisPanel/nplots-.9/nplots, .6, .9/nplots, .35])
plotHVCnet(wSplit1,xdynSplit1,p.trainint,trainingNeuronsAlt,PlottingParams)
set(gca, 'color', 'none');
PlottingParams.axesPosition = ...
[PlottingParams.thisPanel/nplots-2*spacing, bottom, 40*scale, height];
plotAlternating(wSplit1, xdynSplit1, p.trainint, ...
trainingNeuronsAlt, PlottingParams)
set(gca, 'color', 'none')
PlottingParams.thisPanel = 4;
subplot('position', ...
[PlottingParams.thisPanel/nplots-.9/nplots, .6, .9/nplots, .35])
plotHVCnet(wSplit2,xdynSplit2,p.trainint,trainingNeuronsAlt,PlottingParams)
set(gca, 'color', 'none');
PlottingParams.axesPosition = ...
[PlottingParams.thisPanel/nplots-2*spacing, bottom, 40*scale, height];
plotAlternating(wSplit2, xdynSplit2, p.trainint, ...
trainingNeuronsAlt, PlottingParams)
set(gca, 'color', 'none')
%% Alternating differentiation: exporting
if isEPS
cd('Z:\Fee_lab\Papers\HVC_differentiation\Figures\EPS_files');
export_fig(1,'Figure5a.eps','-transparent','-eps','-painters');
else
%figure parameters, exporting
figw = 6;
figh = 2;
set(gcf, 'color', [1 1 1],...
'papersize', [figw figh], 'paperposition', [0 0 figw*.9 figh])
% print -dmeta -r150
end
%% *Bout-onset differentiation*
% Code to generate figure 5 j-m, which shows bout onset differentiation
clear all;
%% Bout-onset differentiation: network parameters
% fixed parameters
seed = 1009;
p.seed = seed; % seed random number generator
p.n = 100; % n neurons
p.trainint = 10; % Time interval between inputs
p.nsteps = 500; % time-steps to simulate --
% each time-step is 1 burst duration.
p.pin = .01; % probability of external stimulation
% of at least one neuron at any time
k = 10; % number of training neurons
p.trainingInd = 1:k; % index of training neurons
p.beta = .13; % strength of feedforward inhibition
p.alpha = 30; % strength of neural adaptation
p.eta = .05; % learning rate parameter
p.epsilon = .14; % relative strength of heterosynaptic LTD
p.tau = 4; % time constant of adaptation
gammaStart = .01; % strength of recurrent inhibition
gammaSplit = .04; % increased strength of recurrent inhibition
% to induce splitting
wmaxStart = 1; % single synapse hard bound
wmaxSplit = 2; % single synapse hard bound to induce splitting
% (increased to encourage fewer stronger synapses)
mStart = 5; % desired number of synapses per neuron
% (wmax = Wmax/m)
Wmax = mStart*wmaxStart;% soft bound for weights of each neuron
mSplit = Wmax/wmaxSplit;% keep Wmax constant, change m & wmax
% to induce fewer stronger synapses
HowClamped = 10; % give training neurons higher threshold
HowOn = 10; % higher inputs to bout onset training neurons
HowOnPsylStart = HowOn; % inputs to protosyllable training neurons
HowOnPsylSplit = 1; % decrease input to protosyllable training neurons
% during splitting
% how many iterations to run before plotting
nIterEarly = 5; % early protosyllable stage
nIterProto = 100; % end of protosyllable stage
nIterPlotSplit1 = 30; % number of splitting iterations before plotting
% intermediate splitting phase
nIterPlotSplit2 = 500; % total number of splitting iterations
% parameters that change over development
protosyllableStage = [true(1,nIterProto) false(1,nIterPlotSplit2)];
splittingStage = [false(1,nIterProto) true(1,nIterPlotSplit2)];
gammas(protosyllableStage) = gammaStart;
gammas(splittingStage) = gammaSplit * sigmf(1:nIterPlotSplit2,[1/200 250]);
wmaxs(protosyllableStage) = wmaxStart;
wmaxs(splittingStage) = wmaxSplit;
ms(protosyllableStage) = mStart;
ms(splittingStage) = mSplit;
HowOnPsyl(protosyllableStage) = HowOnPsylStart;
HowOnPsyl(splittingStage) = HowOnPsylSplit;
% params for training inputs
CyclesPerBout = 5;
bOnOffset = 3;
%% Bout-onset differentiation: run simulation
% random initial weights
rng(seed);
w = 2*rand(p.n)*Wmax/p.n;
bOnOffsetVar = [1 randperm(20)]; % variable inter-bout-interval
% learning stages
for t = 1:(nIterProto+nIterPlotSplit2)
p.w = w;
% set parameters that change over development
p.gamma = gammas(t);
p.wmax = wmaxs(t);
p.m = ms(t);
% Construct input
Input = -HowClamped*ones(k, p.nsteps); % clamp training neurons
bOnOffsetVar = [1 randperm(20)]; % variable inter-bout-interval
% initializing
indPsyl = []; indBstart = []; indOff = []; prevPsylEnd = 1;
for i = 1:(p.nsteps/CyclesPerBout/p.trainint)
istart = (i-1)*CyclesPerBout*p.trainint+1+bOnOffsetVar(i)+bOnOffset;
indBstart = [indBstart istart-bOnOffset]; % bout onset times
indPsyl = [indPsyl ...
istart istart+p.trainint istart+2*p.trainint]; % 3psyls/bout
indOff = [indOff ...
prevPsylEnd:(istart-bOnOffset-1)]; % will clamp all neurons
% between bouts
prevPsylEnd = istart+3*p.trainint; % keep track of when bout ends,
% to clamp neurons between bouts
end
indPsyl = indPsyl(indPsyl<=p.nsteps);
indBstart = indBstart(indBstart<=p.nsteps);
Input(1:k/2,indBstart) = HowOn; % input to bout onset neurons
Input((k/2+1):k,indPsyl) = HowOnPsyl(t); % input to psyl neurons
bdyn = double(rand(p.n,p.nsteps)>=(1-p.pin)); % Random activation
bdyn(1:k,:) = Input;
bdyn(:,indOff) = -HowClamped; % clamp all neurons between bouts
p.input = bdyn;
% run one iteration
[w xdyn] = HVCIter(p);
% save certain iterations for plotting later
switch t
case nIterEarly
wEarly = w;
xdynEarly = xdyn;
trainingNeuronsEarly{1}.tind = indBstart+bOnOffset;
trainingNeuronsEarly{2}.tind = ...
setdiff(indPsyl, indBstart+bOnOffset);
case nIterProto;
wProto = w;
xdynProto = xdyn;
trainingNeuronsProto{1}.tind = indBstart+bOnOffset;
trainingNeuronsProto{2}.tind = ...
setdiff(indPsyl, indBstart+bOnOffset);
case nIterProto + nIterPlotSplit1;
wSplit1 = w;
xdynSplit1 = xdyn;
trainingNeuronsSplit1{1}.tind = indBstart+bOnOffset;
trainingNeuronsSplit1{2}.tind = ...
setdiff(indPsyl, indBstart+bOnOffset);
case nIterProto + nIterPlotSplit2;
wSplit2 = w;
xdynSplit2 = xdyn;
trainingNeuronsSplit2{1}.tind = indBstart+bOnOffset;
trainingNeuronsSplit2{2}.tind = ...
setdiff(indPsyl, indBstart+bOnOffset);
end
end
%% Bout-onset differentiation: plotting parameters
figure(2)
isEPS = 0;
clf
set(gcf, 'color', ones(1,3));
if isEPS
PlottingParams.msize = 8; % change to what is best for EPS figure
PlottingParams.linewidth = .25;
set(0,'defaultAxesFontName', 'Arial')
set(0,'defaultTextFontName', 'Arial')
PlottingParams.labelFontSize = 7;
set(gcf, 'units','centimeters', 'position', [5 5 13.5 6])
else
PlottingParams.msize = 10;
PlottingParams.linewidth = .25;
PlottingParams.labelFontSize = 7;
end
PlottingParams.SeedColor = [.95 .5 1];
PlottingParams.Syl1Color = [0 0 1];
PlottingParams.Syl2Color = [1 0 0];
PlottingParams.Syl1BarColor = [0 0 1];
PlottingParams.Syl2BarColor = [1 0 0];
PlottingParams.numFontSize = 5;
PlottingParams.wplotmin = 0;
PlottingParams.wplotmax = 2; % this should be wmaxSplit
PlottingParams.wprctile = 0; % plot all weights above this percentile.
PlottingParams.totalPanels = 4;
PlottingParams.thisPanel = 1;
PlottingParams.sortby = 'weightMatrix';
%% Bout-onset differentiation: plotting early network activity
trainingNeuronsEarly{1}.nIDs = 1:k/2;
trainingNeuronsEarly{2}.nIDs = (k/2+1):k;
trainingNeuronsEarly{1}.candLat = (-bOnOffset+1):p.trainint;
trainingNeuronsEarly{2}.candLat = 1:p.trainint;
trainingNeuronsEarly{1}.thres = 4;
trainingNeuronsEarly{2}.thres = 6;
PlottingParams.thisPanel = 1;
PlottingParams.Hor = 0;
pp1 = PlottingParams;
pp1.Syl1BarColor = [1 1 1];
pp1.Syl2BarColor = [.5 .5 .5];
plotHVCnet_boutOnset(wEarly, xdynEarly, trainingNeuronsEarly, pp1)
PlottingParams.Hor = 1;
%% Bout-onset differentiation: plotting protosyllable
trainingNeuronsProto{1}.nIDs = 1:k/2;
trainingNeuronsProto{2}.nIDs = (k/2+1):k;
trainingNeuronsProto{1}.candLat = (-bOnOffset+1):p.trainint;
trainingNeuronsProto{2}.candLat = 1:p.trainint;
trainingNeuronsProto{1}.thres = 4;
trainingNeuronsProto{2}.thres = 6;
PlottingParams.thisPanel = 2;
plotHVCnet_boutOnset(wProto, xdynProto, ...
trainingNeuronsProto, PlottingParams)
%% Bout-onset differentiation: plotting splitting stages
trainingNeuronsSplit1{1}.nIDs = 1:k/2;
trainingNeuronsSplit1{2}.nIDs = (k/2+1):k;
trainingNeuronsSplit1{1}.candLat = (-bOnOffset+1):p.trainint;
trainingNeuronsSplit1{2}.candLat = 1:p.trainint;
trainingNeuronsSplit1{1}.thres = 4;
trainingNeuronsSplit1{2}.thres = 6;
PlottingParams.thisPanel = 3;
plotHVCnet_boutOnset(wSplit1, xdynSplit1, ...
trainingNeuronsSplit1, PlottingParams)
trainingNeuronsSplit2{1}.nIDs = 1:k/2;
trainingNeuronsSplit2{2}.nIDs = (k/2+1):k;
trainingNeuronsSplit2{1}.candLat = (-bOnOffset+1):p.trainint;
trainingNeuronsSplit2{2}.candLat = 1:p.trainint;
trainingNeuronsSplit2{1}.thres = 4;
trainingNeuronsSplit2{2}.thres = 6;
PlottingParams.thisPanel = 4;
plotHVCnet_boutOnset(wSplit2, xdynSplit2, ...
trainingNeuronsSplit2, PlottingParams)
%% Bout-onset differentiation: exporting
if isEPS
cd('Z:\Fee_lab\Papers\HVC_differentiation\Figures\EPS_files');
export_fig(2,'Figure5h.eps','-transparent','-eps','-painters');
else
figw = 6;
figh = 4;
set(gcf, 'color', [1 1 1],...
'papersize', [figw figh], 'paperposition', [0 0 figw*.9 figh])
%print -dmeta -r150
end
end
%% *HVCIter function*
function [w xdyn] = HVCIter(p)
% Runs one iteration of the simulation. p is a structure of parameters.
% redefining params that are used often outside the for loop
nsteps = p.nsteps;
n = p.n;
m = p.m;
w = p.w;
wmax = p.wmax;
Wmax = wmax*m;
eta = p.eta;
bdyn=p.input;
% initializing variables
xdyn=zeros(n,nsteps);
oldx = zeros(n,1);
oldy = zeros(n,1);
for t = 1:nsteps
% Adaptation
y = oldy + 1/p.tau*(-oldy+oldx);
Aadapt = p.alpha * y; % adaptation
% Net feedforward input.
B = bdyn(:,t); % external input
AE = w*oldx; % excitatory input
AIff = p.beta * sum(oldx); % feed forward inhibition
Anet = AE - AIff - Aadapt + B; % net feed forward input
Anet(Anet < 0) = 0; % rectify
% recurrent inhibition
AIrec = p.gamma * sum(Anet);
% binary output
x = (Anet - AIrec) > 0;
% STDP rule (Fiete et al 2010)
dw_STDP = eta.*(x*(oldx)'-(oldx)*x');
% Hetersynaptic penalty (Fiete et al 2010)
dw_hLTDpre = ...
eta*ones(n,1)*max(0, sum(w+dw_STDP,1)-Wmax); % Weights leaving
% cells (pre)
dw_hLTDpost = ...
eta*max(0, sum(w+dw_STDP,2)-Wmax)*ones(1,n); % Weights onto
% cells (post)
% Update weights
if eta>0
dwtotal = dw_STDP-p.epsilon*(dw_hLTDpre+dw_hLTDpost);
w = w + dwtotal;
w(w > wmax) = wmax; % hard limit on strength of a single synapse
w(w < 0) = 0; % weights cannot be negative
w = w.*(~eye(p.n)); % clamp diagonal
end
oldx = double(x);
oldy = y;
xdyn(:,t)=x;
end
end
%% Plotting functions: *findLatency*
function Latency = findLatency(xsort, trainingNeurons)
% Calculates the mode latency of each neuron
% w: weight matrix
% xdyn: activity of network
% m: duration of one syllable, in timesteps
% trainingNeurons: cell array of structures containing
% neuron and time indices for each syllable type
% PlottingParams: sets linewidth, etc.
nsteps = size(xsort,2);
n = size(xsort,1);
% iterate over candidate latencies
for syli = 1:length(trainingNeurons)
nFired = zeros(n,length(trainingNeurons{syli}.candLat));
for lati = 1:length(trainingNeurons{syli}.candLat);
tInds = zeros(1,nsteps);
tInds(min(nsteps, ...
trainingNeurons{syli}.tind + ...
trainingNeurons{syli}.candLat(lati))-1) = 1;
nFired(:,lati) = ...
sum(bsxfun(@times, xsort, tInds),2); % number of times each
% neuron fired at this
% latency
end
for ni = 1:n
[~, Latency{syli}.mode(ni)] = max(nFired(ni,:));
Latency{syli}.mode(ni) = ...
trainingNeurons{syli}.candLat(Latency{syli}.mode(ni));
Latency{syli}.FireDur(ni) = ...
max(nFired(ni,:))>trainingNeurons{syli}.thres;
end
end
end
%% Plotting functions: *plotAlternating*
function plotAlternating(w, xsort, m, trainingNeurons, PlottingParams)
% Makes network activity plot, called by AlternatingDifferentiation
% w: weight matrix
% xsort: activity of network
% m: duration of one syllable, in timesteps
% trainingNeurons: cell array of structures containing
% neuron and time indices for each syllable type
% PlottingParams: sets linewidth, etc.
Syl1Color = PlottingParams.Syl1Color;
Syl2Color = PlottingParams.Syl2Color;
ProtoSylColor = PlottingParams.ProtoSylColor;
numFontSize = PlottingParams.numFontSize;
labelFontSize = PlottingParams.labelFontSize;
Latency = findLatency(xsort, trainingNeurons);
% plotting the mode latency for each syll type
xplot = zeros(size(w,1),2*m);
for ni = 1:size(w,1)
if Latency{1}.FireDur(ni) & ~isnan(Latency{1}.mode(ni))
xplot(ni,Latency{1}.mode(ni)) = 1;
end
if Latency{2}.FireDur(ni) & ~isnan(Latency{2}.mode(ni))
xplot(ni,Latency{2}.mode(ni)+m) = 1;
end
end
% keep track of which neurons participated in each syllable
FireDur1 = Latency{1}.FireDur;
FireDur2= Latency{2}.FireDur;
% classify neurons as specific or shared
Specific1 = FireDur1&~FireDur2;
Specific2 = FireDur2&~FireDur1;
Shared = (FireDur1&FireDur2);
indshared = find(Shared);
Red = trainingNeurons{1}.nIDs;
Green = trainingNeurons{2}.nIDs;
if issame(Red,Green)
IsProto = zeros(1,length(xplot)); IsProto(Red) = 1;
Red = [];
Green = [];
else
IsProto = zeros(1,length(xplot));
end
IsTrain1 = zeros(1,length(xplot)); IsTrain1(Red) = 1;
IsTrain2 = zeros(1,length(xplot)); IsTrain2(Green) = 1;
if length(Green)==0
tmp= xplot>0;
tmpXplot = tmp(:,1:(size(xplot,2)/2))+tmp(:,(size(xplot,2)/2+1):end);
else
tmpXplot = xplot>0;
end
[~,sortind] = sortrows(tmpXplot);
xplot = xplot(flipud(sortind),:); % from early (top) to late (bottom)
w = w(flipud(sortind),flipud(sortind));
IsTrain1 = IsTrain1(flipud(sortind));
IsTrain2 = IsTrain2(flipud(sortind));
Specific1 = Specific1(flipud(sortind));
Specific2 = Specific2(flipud(sortind));
IsProto = IsProto(flipud(sortind));
% if differentiated, sort shared neurons first, then specific neurons
if length(Green)>0
sharedind = (sum(xplot,2)>=2)&(sum(xplot,2)<8);
else
sharedind = zeros(1,size(xplot,1));
end
rest = find(~sharedind);
xplotall = xplot([find(sharedind); (rest)],:);
IsTrain1 = IsTrain1([find(sharedind); (rest)]);
IsTrain2 = IsTrain2([find(sharedind); (rest)]);
Specific1 = Specific1([find(sharedind); (rest)]);
Specific2 = Specific2([find(sharedind); (rest)]);
IsProto = IsProto([find(sharedind); (rest)]);
% plotting the activity for the two syll types
for i = 1:2
axesPos = PlottingParams.axesPosition;
axesPos(1) = axesPos(1)+(i-1)*axesPos(3)/2;
axesPos(3) = axesPos(3)*.3;
subplot('position', axesPos);
xplot = ...
xplotall(:,(1:(size(xplotall,2)/2))+(i-1)*(size(xplotall,2)/2));
tplot = (1:(size(xplot,2)))*10; % assuming each bin is 10ms
tOffset = 0;
for j=1:size(xplot,2) % for all the time steps
Idx = ...
find(xplot(1:end-1,j)>0); % find the indices of active neurons
if ~isempty(Idx)
for k=1:length(Idx) % for all the active neurons
Color = IsProto(Idx(k))*PlottingParams.ProtoSylColor + ...
Specific1(Idx(k))*PlottingParams.Syl1Color + ...
Specific2(Idx(k))*PlottingParams.Syl2Color;
h = patch(10*([j-1,j,j,j-1]+tOffset),...
[Idx(k)-1,Idx(k)-1,Idx(k),Idx(k)],...
Color,'edgecolor','none');
end
end
end
hold on
% plot line between each syl type
if length(Green)>0
train2 = find(IsTrain2); train2 = train2(1)-1;
plot([0 size(xplot,2)*10], [train2 train2], 'k', ...
'linewidth', PlottingParams.linewidth)
end
% plot line between shared and specific neurons
if length(rest)>0 & sum(sharedind)>0 & length(Green)>0
plot([0 size(xplot,2)*10], [sum(sharedind) sum(sharedind)],...
'k', 'linewidth', PlottingParams.linewidth)
end
% plot colored bars above each syllable
if length(Green) == 0
patch([0 90 90 0],[-4 -4 -2 -2],PlottingParams.ProtoSylBarColor);
text(40,-10,'\alpha','fontsize',7);
elseif i == 1
patch([0 90 90 0],[-4 -4 -2 -2],Syl1Color);
text(40,-10,'\beta','fontsize',7)
else
patch([0 90 90 0],[-4 -4 -2 -2],Syl2Color);
text(40,-10,'\gamma','fontsize',7);
end
% plotting parameters
ylim([-5 size(xplot,1)-1]);
box off
set(gca, 'ydir', 'reverse','tickdir','out',...
'ticklength',[0.015 0.015], 'color', 'none', ...
'xtick', 0:50:100,'fontsize', numFontSize,'tickdir','out');
xlim([-2 100]);
if PlottingParams.thisPanel==1
ylabel('Neuron #', 'fontsize', labelFontSize,'fontname','arial');
set(gca,'ytick',0:20:100,'fontsize',numFontSize)
else
set(gca,'ytick',0:20:100,'yticklabel', {})
end
end
end
%% Plotting functions: *plotHVCnet*
function plotHVCnet(w, xdyn, m, trainingNeurons, PlottingParams)
% Makes network diagram for alternating differentiation
% w: weight matrix
% xdyn: activity of network
% m: duration of one syllable, in timesteps
% trainingNeurons: cell array of structures containing
% neuron and time indices for each training neuron type
% PlottingParams: sets linewidth, etc.
msize = PlottingParams.msize;
linewidth = PlottingParams.linewidth;
Syl1Color = PlottingParams.Syl1Color;
Syl2Color = PlottingParams.Syl2Color;
ProtoSylColor = PlottingParams.ProtoSylColor;
Latency = findLatency(xdyn, trainingNeurons);
% first exclude all neurons that don't fire at a consistent phase
cla; hold on
x = zeros(1,size(w,1));
y = zeros(1,size(w,1));
for ni = 1:size(w,1)
% if it fired during either syll
if Latency{1}.FireDur(ni)|Latency{2}.FireDur(ni)
% if it fired during both sylls
if (Latency{1}.FireDur(ni)&Latency{2}.FireDur(ni))
% if fired during both sylls at same phase
if (Latency{1}.mode(ni)==Latency{2}.mode(ni))
x(ni) = Latency{1}.mode(ni);
else % exclude from plot if different phases for both sylls
x(ni) = NaN;
end
elseif Latency{1}.FireDur(ni) % if it fired during syll 1
x(ni) = Latency{1}.mode(ni);
else % fired during syll 2 only
x(ni) = Latency{2}.mode(ni);
end
else % if it fired during neither syll
x(ni) = NaN;
end
end
indkeep = ~isnan(x);
y = y(indkeep);
w = w(indkeep,indkeep);
xdyn = xdyn(indkeep,:);
x = x(indkeep);
ux = unique(x);
% keep track of training neuron and syl time indices
trainingset1 = trainingNeurons{1}.nIDs;
trainingset2 = trainingNeurons{2}.nIDs;
x(trainingset1) = 1;
x(trainingset2) = 1;
tind1 = (trainingNeurons{1}.tind);
tind2 = (trainingNeurons{2}.tind);
% keep track of which neurons participated in each syllable
FireDur1 = Latency{1}.FireDur(indkeep);
FireDur2= Latency{2}.FireDur(indkeep);
% classify neurons as specific or shared
Specific1 = FireDur1&~FireDur2;
Specific2 = FireDur2&~FireDur1;
Shared = (FireDur1&FireDur2);
indshared = find(Shared);
% calculate the incoming weights from specific neurons of each type, to
% determine sorting in y axis and color
c1 = zeros(1,length(x));
c2 = zeros(1,length(x));
for ni = 1:size(w,1)
tmp = find(xdyn(ni,:));
if sum(w(ni,:))>0
c1(ni) = sum(w(ni,Specific1))/sum(w(ni,:));
c2(ni) = sum(w(ni,Specific2))/sum(w(ni,:));
end
y(ni) = c1(ni)-c2(ni);
end
% for each latency (x), sort along y, with small gap between shared and
% specific neurons
y1 = zeros(1,size(w,1));
for ui = 1:length(ux)
indshared = (x==ux(ui))&Shared;
ind1 = (x==ux(ui))&Specific1;
ind2 = (x==ux(ui))&Specific2;
[~,y1(indshared)] = sort(y(indshared));
tocentershared = 1+(numel(find(indshared))-1)/2;
y1(indshared) = y1(indshared)-tocentershared;
[~,y1(ind1)] = sort(y(ind1));
y1(ind1) = y1(ind1) + (numel(find(indshared)))/2;
[~,y1(ind2)] = sort(y(ind2));
y1(ind2) = y1(ind2) -numel(find(ind2))-1- (numel(find(indshared)))/2;
end
cla; hold on
% keep only feedforward part of weight matrix
wplot = w;
n = size(wplot,1);
for i = 1:n
for j = 1:n
ff = x(i)<x(j);
longrange = abs(x(i)-x(j))>2;
if (~ff) | longrange
wplot(j,i) = 0;
end
end
end
% Color weights white to black between wplotmin and wplotmax
wplot = wplot-PlottingParams.wplotmin;
wplot(wplot<0) = 0;
wplot = wplot/(PlottingParams.wplotmax-PlottingParams.wplotmin);
wplot(wplot<prctile(wplot(:), PlottingParams.wprctile)) = 0;
wplotold = wplot;
n = size(wplot,1);
for i = 1:n
[~,ind] = sort(wplot(:,i), 'descend');
indplot = zeros(n,1);
indplot(ind(1:min(PlottingParams.wperneuron,length(ind)))) = 1;
wplot(~indplot,i) = 0;
end
for i = 1:n
if sum(wplot(i,:)>0)<PlottingParams.wperneuronIn
[~,ind] = sort(wplotold(i,:), 'descend');
wplot(i,ind(1:min(PlottingParams.wperneuron,length(ind)))) = ...
wplotold(i,ind(1:min(PlottingParams.wperneuron,length(ind))));
end
end
% jitter a little in x and y, so it doesn't look like a grid, but
% don't jitter seed neurons
jitter = .1;
Seed0 = randn(1,300);
indJitter = setdiff(1:length(x), union(trainingset1, trainingset2));
x(indJitter)= x(indJitter)+jitter*Seed0(1:length(x(indJitter)));
y1(indJitter) = ...
y1(indJitter)+...
jitter*Seed0((length(x(indJitter))+1):(2*length(x(indJitter))));
% plot w in order from weakest to strongest, so darker lines are on top
js = repmat((1:n)',1,n);
is = repmat((1:n),n,1);
isVec = is(:);
jsVec = js(:);
wVec = wplot(:);
[wSort,indSort] = sort(wVec, 'ascend');
nplotted = zeros(1,n);
for k = 1:length(wSort)
i = isVec(indSort(k));
j = jsVec(indSort(k));
if wplot(j,i)>0
ff = x(i)<=x(j);
longrange = abs(x(i)-x(j))>2;
loopback = (round(x(i))==round(max(x)))&...
(round(x(j))==round(min(x)));
if (ff & ~longrange)%|loopback
C = ones(1,3)-wplot(j,i)*ones(1,3);
plot([x(i), x(j)], [y1(i),y1(j)], ...
'color', C, 'linewidth', linewidth)
end
end
end
% color each neuron based on its relative input from each syllable type
for pli = 1:length(x)
tmpC = c1(pli)'/(max(c1)+eps)*Syl1Color+c2(pli)'/(max(c2)+eps)*Syl2Color;
tmpC = tmpC/(max(tmpC)+eps); % normalize so colors are bright
if Shared(pli)
tmpC = zeros(1,3);
end
if Specific1(pli)
tmpC = Syl1Color;
end
if Specific2(pli)
tmpC = Syl2Color;
end
plot(x(pli),y1(pli), 'marker', '.', 'color', tmpC, 'markersize', msize)
end
% plot training neurons in given colors
if sum(Specific1)>0
plot(x(trainingset1),y1(trainingset1), ...
'.', 'markersize', msize, 'color', Syl1Color)
plot(x(trainingset2),y1(trainingset2), ...
'.', 'markersize', msize, 'color', Syl2Color)
else
plot(x([trainingset1 trainingset2]),y1([trainingset1 trainingset2]),...
'.', 'markersize', msize, 'color', ProtoSylColor)
end
% plot rectangle for syl1 seed neurons
rx = 1-.5;
ry = min(y1(trainingset1))-.5;
rw = 1;
rh = max(y1(trainingset1)) - min(y1(trainingset1))+1;
rectangle('Position', [rx ry rw rh], ...
'FaceColor', 'none',...
'LineStyle', '-', 'LineWidth', .5, ...
'EdgeColor', PlottingParams.SeedColor, ...
'curvature', [.98 .1])
% plot rectangle for syl2 seed neurons
rx = 1-.5;
ry = min(y1(trainingset2))-.5;
rw = 1;
rh = max(y1(trainingset2)) - min(y1(trainingset2))+1;
rectangle('Position', [rx ry rw rh], ...
'FaceColor', 'none',...
'LineStyle', '-', 'LineWidth', .5, ...
'EdgeColor', PlottingParams.SeedColor, ...
'curvature', [.98 .1])
axis tight; axis off;
xlim([-.5 m+.5]);
ylim([min(y1)-1 max(y1)+1])
set(gca, 'color', 'none')
end
%% Plotting functions: *plotHVCnet_boutOnset*
function plotHVCnet_boutOnset(w, xdyn, trainingNeurons, PlottingParams)
% Makes network diagram and raster plots, called by RunHVC_boutOnset_net
% w: weight matrix
% xdyn: activity of network
% m: duration of one syllable, in timesteps
% trainingNeurons: cell array of structures containing
% neuron and time indices for each syllable type
% PlottingParams: sets linewidth, etc. See RunHVC_split
%plotting parameters%
msize = PlottingParams.msize;
linewidth = PlottingParams.linewidth;
Syl1Color = PlottingParams.Syl1Color;
Syl2Color = PlottingParams.Syl2Color;
numFontSize = PlottingParams.numFontSize;
labelFontSize = PlottingParams.labelFontSize;
nplots = PlottingParams.totalPanels;
ploti = PlottingParams.thisPanel;