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34 lines
916 B
Matlab
34 lines
916 B
Matlab
function [y,e,W,V,U]=dim_activation(W,x,y,iterations,V,beta,U)
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[n,m]=size(W);
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psi=1;%5000; %need to learn using e-(1/psi)
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epsilon1=0.0001; %>0.001 this becomes significant compared to y and hence
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%produces sustained responses and more general suppression
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epsilon2=100*epsilon1*psi;%this determines scaling of initial transient response
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if nargin<3 || isempty(y), y=zeros(n,1,'single'); end
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if nargin<4, iterations=25; end
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if nargin<5,
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%set feedback weights equal to feedforward weights normalized by maximum value
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%V=W./(1e-9+(max(W')'*ones(1,m)));
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V=bsxfun(@rdivide,W,(1e-9+max(W,[],2)));
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U=V;
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end
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x=min(1,x);
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%x=tanh(pi.*x);
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for i=1:iterations
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%update responses
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e=x./(epsilon2+(V'*y));
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y=(epsilon1+y).*(W*e);
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%perform learning at every step - if required
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if nargout>2
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[W,V]=dim_learn(W,V,y,e,beta);
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end
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if nargout>4
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U=dim_learn_feedback(U,y,x,beta);
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end
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end
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