EdgeDetection/dim_activation_conv_recurrent.m

function [Y,E,R,Ytrace,Etrace,Rtrace]=dim_activation_conv_recurrent(w,X,Y,iterations,v,downsample)
% w = a cell array of size {N,M}, where N is the number of distinct neuron types
%     and M is the number of input channels. Each element {i,j} of the cell
%     array is a 2-dimensional matrix (a convolution mask) specifying the
%     synaptic weights for neuron type i's RF in input channel j.
% X = a cell array of size {M}. Each element {j} of the cell array is a
%     2-dimensional matrix specifying the bottom-up input to that channel j of
%     the current processing stage (external inputs targetting the error
%     nodes). If inputs arrive from more than one source, then each source
%     provides a different cell in the array. Input values that change over
%     time can be presented using a 3-d matrix, where the 3rd dimension is time.
% Y = a cell array of size {N}. Each element {i} of the cell array is a
%     2-dimensional matrix specifying prediction node activations for type i
%     neurons.
% R = a cell array of size {M}. Each element {j} of the cell array is a
%     2-dimensional matrix specifying the reconstruction of the input channel j.
%     These values are also the top-down feedback that should modulate
%     activity in preceeding processing stages of a hierarchy.
% E = a cell array of size {M}. Each element {j} of the cell array is a
%     2-dimensional matrix specifying the error in the reconstruction of the
%     input channel j.
% downsample = an N element vector of integer values which defines which
%     prediction node values are to be set to zero, and hence, be ignored. This
%     is a method of reducing the density of prediction nodes across the image
%     (ideally, rather than performing convolution at every pixel location and
%     then setting some values to zero, it would be possible to only calulate
%     the filter outputs at certain locations). A value of 1 means keep all
%     values, 2 means keep every alternate value, 3 means keep every third
%     value, and so on. Default is 1 for each type of neuron.  
% iterations = the number of iterations of the DIM algorithm to perform. Default
%     is 50.

[a,b,z]=size(X{1});
[nMasks,nChannels]=size(w);
nInputChannels=length(X);
[c,d]=size(w{1,1});

%set parameters
GP=global_parameters;
epsilon1=1e-5;
epsilon2=1e-3;

disp('dim_activation_conv_recurrent');
disp(['  epsilon1=',num2str(epsilon1),' epsilon2=',num2str(epsilon2)]);

if nargin<3 || isempty(Y), %initialise prediction neuron outputs to zero
  for i=1:nMasks
    Y{i}=zeros(a,b,'single');
  end  
end

if nargin<4 || isempty(iterations), iterations=GP.iterations; end

if nargin<5 || isempty(v)
  %set feedback weights equal to feedforward weights normalized by maximum value
  for i=1:nMasks
    %calc normalisation values by taking into account all weights contributing to
    %each RF type
    maxVal=0;
    for j=1:nChannels
      maxVal=max(maxVal,max(max(w{i,j})));
    end

    %apply normalisation to calculate feedback weight values.
    for j=1:nChannels
      v{i,j}=w{i,j}./(1e-9+maxVal);
    end
  end
end

if nargin<6 || isempty(downsample), downsample=ones(1,nMasks); end
if length(downsample)<nMasks, %allow use to specify same value for multiple neuron types
  downsample(length(downsample)+1:nMasks)=downsample(length(downsample));
end

%try to speed things up
if exist('convnfft')==2 && (max(c,d)>=50-max(a,b) || (min(c,d)>10 && min(a,b)>10))
  conv_fft=1;%use fft version of convolution for large images and/or masks.  FF
  %weights are rotated versions, so that convnfft can be used to apply the
  %filtering
  for j=1:nChannels
    for i=1:nMasks
      w{i,j}=rot90(w{i,j},2);
    end
  end
else
  conv_fft=0;%use standard conv2/filter2 function for smaller images and/or masks, or if faster functions are not installed
end

%initialise error node activations
for j=1:nChannels
  E{j}=zeros(a,b,'single');
end


%iterate DIM equations to determine neural responses
fprintf(1,'dim_conv(%i): ',conv_fft);
for t=1:iterations
  fprintf(1,'.%i.',t);
    
  %copy previous output values to inputs, to provide recurrent input
  for i=1:nMasks
    X{nInputChannels+i}=Y{i};
  end
  
  %update error-detecting neuron responses
  for j=1:nChannels
    %calc predictive reconstruction of the input
    R{j}=zeros(a,b,'single');%reset reconstruction
    for i=1:nMasks
      %sum reconstruction over each RF type
      if ~(isempty(v{i,j}) || iszero(v{i,j})) %skip empty filters: they don't add anything
        if conv_fft==1
          R{j}=R{j}+convnfft(Y{i},v{i,j},'same');
        else
          R{j}=R{j}+conv2(Y{i},v{i,j},'same');
        end
      end
    end
    %calc error between reconstruction and actual input: inputs values are clipped
    %at 1 and temporally changing values are used (if these are provided)
    E{j}=min(1,X{j}(:,:,min(t,size(X{j},3))))./(epsilon2+R{j});
    if nargout>4
      Etrace{j}(:,:,t)=E{j};%record response over time
    end
    if nargout>5
      Rtrace{j}(:,:,t)=R{j};%record response over time
    end
  end

  %update prediction neuron outputs
  for i=1:nMasks
    input=single(0);
    for j=1:nChannels
      %sum inputs to prediction neurons from each channel
      if ~(isempty(w{i,j}) || iszero(w{i,j})) %skip empty filters: they don't add anything
        if conv_fft==1
          input=input+convnfft(E{j},w{i,j},'same');
        else
          input=input+filter2(w{i,j},E{j},'same');
        end
      end
    end
    Y{i}=(epsilon1+Y{i}).*input;  %modulate prediction neuron response by current input
    Y{i}=max(0,Y{i}); %ensure no negative values creep in!
    for ds=1:downsample(i)-1
      Y{i}(ds:downsample(i):a,:)=0;
      Y{i}(:,ds:downsample(i):b)=0;
    end
    if nargout>3
      Ytrace{i}(:,:,t)=Y{i};%record response over time
    end
  end
end
disp(' ');