novelty-detection-ncm/Agnotron-model/Clustering Network/updateM_v4.m

function mT1 = updateM_v4(mT, yT, dT1, inhibCap, etaM, maxM)
%%%%%
% run this for each timestep to update weight mat. for next timestep
% inputs are:
%   mT, lateral weight mat for given timestep
%   yT, outputs for timestep
%   dT1 is memorized activity of neuron up to and including next timestep
%       (ie, updateD must be run before this)
%
% output is:
%   mT1, output lateral weights matrix for next timestep
%%%%%
    m = size(mT,1); %num out

    mT1 = zeros(size(mT));
    for idx = 1:m
        for jdx = 1:m
            if idx ~= jdx
                t1 = mT(idx,jdx); %term 1 of m update 

%                 t2n = yT(idx) * (yT(jdx) - (mT(idx,jdx)*yT(idx))); %num of t2
                
                t2n = etaM* yT(idx) * yT(jdx); % - (mT(idx,jdx)*yT(idx))); %num of t2
                t2d = 1;%dT1(idx); %denom of t2

                t2 = t2n/t2d; %term 2 of m update
                
                

                mT1(idx, jdx) = (t1 + t2);
            else
                mT1(idx, jdx) = 0;
            end
        end
    end

    
    mT1(mT1<=0) = eps; % synapses don't die (negs not okay)
    mT1(mT1>maxM) = maxM; %max weight for single synapse
    
    for idx = 1:m
        for jdx = 1:m
            if idx == jdx %change here for upper triang
                mT1(idx, jdx) = 0; %BUT DIAG STILL 0 
            end
        end
        if inhibCap > 0 %if input cap is 0, no cap 
            mT1(idx,:) = mT1(idx,:) ./ (norm(mT1(idx,:))/inhibCap); %normalize inhib to each
        end
    end
    
    
    if isnan(sum(mT1(:)))
        mT1(isnan(mT1)) = 0;
    end
end