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25 lines
607 B
Matlab
25 lines
607 B
Matlab
function [y,e,r,ytrace]=dim_activation(W,X,y)
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epsilon1=1e-6;
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epsilon2=1e-3;
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iterations=75;
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[n,m]=size(W);
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if nargin<3 || isempty(y)
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y=zeros(n,1,'single'); %initialise prediction neuron outputs
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end
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%set feedback weights equal to feedforward weights normalized by maximum value
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V=bsxfun(@rdivide,abs(W),max(1e-6,max(abs(W),[],2)));
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%V=bsxfun(@rdivide,W,max(1e-6,max(W,[],2)));
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V=V'; %avoid having to take transpose at each iteration
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for t=1:iterations
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x=(X(:,min(t,size(X,2))));
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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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y=max(epsilon1,y).*(W*e);
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if nargout>3, ytrace(:,t)=y; end
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end
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