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- function wT1 = updateW_v5(wT, yT, xT, dT1, etaW, capW, maxW)
%%%%%
% run this for each timestep to update weight mat. for next timestep
% inputs are:
% wT, weight mat for given timestep
% yT, outputs for timestep
% xT, input for timestep
% dT1 is memorized activity of neuron up to and including next timestep
% (ie, updateD must be run before this)
%
% output is:
% wT1, output weights matrix for next timestep
%%%%%
%%% added modulatory term to slow learning...
%%%
%%%
m = size(wT,1); %num out
n = size(wT,2); %num in
wT1 = wT;
for idx = 1:m
for jdx = 1:n
t1 = wT(idx,jdx); %term 1 of w update
t2n = etaW* yT(idx) * xT(jdx);% - (wT(idx,jdx)*yT(idx))); %num of t2
t2d = 1;%dT1(idx) + eps; %denom of t2
t2 = t2n/t2d; %term 2 of w update %not originally rectified
wT1(idx, jdx) = t1 + t2;
end
end
wT1(wT1==0) = eps; %don't kill synapses (but negs okay)
wT1(wT1>maxW) = maxW; %max magnitude of input weight
wT1(wT1< (-1*maxW)) = -1*maxW; %max magnitude of input weight
for idx = 1:m % cap weight to each output
if norm(wT1(idx, :)) > capW %cap!
wT1(idx,:) = wT1(idx,:) ./ (norm(wT1(idx,:))/capW);
end
end
if isnan(sum(wT1(:)))
wT1(isnan(wT1)) = eps;
end
end
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