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Matlab

function [W,V]=dim_learn(W,V,y,e,beta)
if nargin<5, beta=0.005; end
%update forward weights
delta=beta.*(y*(e'-1));
W=W.*(1 + delta);
W(W<0)=0;
if nargout>1
%update feedback weights
scale=beta.*heaviside(y-1)*ones(size(e'));
V=V.*(1 + delta+scale);
V(V<0)=0;
end