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36 lines
993 B
Matlab
36 lines
993 B
Matlab
function [y,e,r]=dim_activation(W,x,V,y,iterations)
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[n,m]=size(W);
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[nInputChannels,batchLen]=size(x);
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if nargin<4 || isempty(y)
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y=zeros(n,batchLen,'single'); %initialise prediction neuron outputs
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end
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if nargin<3 || isempty(V)
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%set feedback weights equal to feedforward weights normalized by maximum value
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V=bsxfun(@rdivide,W,max(1e-6,max(W,[],2)));
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end
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V=V'; %avoid having to take transpose at each iteration
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%set parameters
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if nargin<5 || isempty(iterations), iterations=50; end
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epsilon2=1e-2;
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epsilon1=epsilon2/max(sum(V,2));
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%iterate to find steady-state response to input
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%ediff=Inf; ymax=0; eprev=0; ymaxprev=max(y);
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%t=0; while t<iterations && (ediff>0.1 || ymax<0.1), t=t+1;
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for t=1:iterations
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%update responses
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r=V*y;
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e=x./max(epsilon2,r);
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%e=x./(epsilon2+r);
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y=max(epsilon1,y).*(W*e);
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%y=(epsilon1+y).*(W*e);
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%ediff=max(abs(e-eprev));
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%eprev=e;
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%ymax=ymaxprev;
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%ymaxprev=max(y);
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end
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%fprintf(1,'dim complete: t=%i, ediff=%f, ymax=%f\n',t,ediff,ymax)
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