function layer = STDPtrain(layer,i,t)
% Modify synaptic strengths of neurons in layer i using spike timing
% dependent plasticity (STDP)
% Ignores inhibitory connections
delta = 10;
smax = 2;
firings = layer{i}.firings;
tf = t-delta;
if ~isempty(firings)
fired = firings((firings(:,1)==tf),2);
else
fired = [];
end;
for j=1:layer{i}.rows*layer{i}.columns
if (ismember(j,fired))
% Neuron j fired at time tf
for k=1:length(layer)
if (~isempty(layer{i}.S{k}) && ~isempty(layer{k}.firings))
% Modify connections from layer k to neuron j
firings2 = layer{k}.firings;
% To be of interest, spike must arrive from layer k
% within delta ms of time of j's firing
d = layer{i}.delay{k};
arr = firings2(:,1)+d(j,firings2(:,2))';
% Incoming spikes before neuron j fired
pre = firings2(arr<tf & arr>(tf-delta),2);
% Incoming spikes after neuron j fired
post = firings2(arr>tf & arr<(tf+delta),2);
% Layer k's influence on neuron j
s = layer{i}.S{k}(j,:);
% Pick out connection strengths > 0
cons = s>0;
% Pick out the delays to firing
ds = zeros(1,length(s));
lf = length(firings2(:,2));
x = firings2(:,2)';
y = abs(tf-arr');
for z=1:lf
% Use the shortest delay, but ignore spikes before window of
% interest
if (ds(x(z))==0 || (y(z)>0 && y(z)<ds(x(z))))
ds(x(z)) = y(z);
end
end
% Increase strengths for incoming spikes before j fired
s(pre) = cons(pre).*(s(pre)+(smax-abs(s(pre)))*0.68.*((delta-ds(pre))/delta));
% Reduce strengths for incoming spikes after j fired
s(post) = cons(post).*(s(post)-(smax-abs(s(post)))*0.68.*((delta-ds(post))/delta));
layer{i}.S{k}(j,:) = s;
end
end
end
end |