%%% LAYER WEIGHT CONNECTIONS
net=network; % Create a custom neural network

net.numInputs  = 2;            %%% 2 onafhankelijke inputs
net.numLayers  = 2;            %%% 2 hidden layers + output 

net.biasConnect = [1           %%% 1st hidden has bias
	               1];         %%% 1nd hidden has bias

net.inputConnect = [1 0        %%% connect the 1th input with the 1th hidden layer
	                0 1];      %%% connect the 2th input with the 2th hidden layer
                                
                                                    
net.layerConnect = [0 0
	                1 0];      %%% Connect 1st hidden with  2nd hidden layer

net.outputConnect = [0 1];     %%% Connect 2nd hidden layer to network output



%%% LAYER SIZES

net.inputs{1}.size=2*30%2*Nfreq %%%size 1st input:cochlea 2*Nfreq
%net.inputs{1}.range=[  input_range(1,:) ;  input_range(2,:)];
       
net.inputs{2}.size=2*10%2*Nmsc %%%size 2st input: eyepos 2*Nmsc
%net.inputs{2}.range=[  input_range(3,:) ];

net.layers{1}.size=4%Naud;  %%%%% NUMBER OF UNITS FIRST HIDDEN LAYER
net.layers{2}.size=20%Nhidden;

net.outputs{1}.size=10

net

%%

net = network
 

% net.numInputs is the number of input sources, not the number of elements in an input vector (net.inputs{i}.size). 
net.numInputs = 2; % Two input layers
net.numLayers = 2; % two hidden layers


%%%% Input and Layer Weight Connections %%%%
%  layers (vertical)  by inputs (horizontal)
%       biasConnect: [0; 0]
%       inputConnect: [0 0; 0 0]
%       layerConnect: [0 0; 0 0]
%       outputConnect: [0 0]


net.inputConnect = [1 0
	                0 1];

net.layerConnect = [0 0
	                1 0];

net.outputConnect = [0 1];
% net.inputConnect(1,1) = 1;
% net.inputConnect(2,1) = 1;
% net.inputConnect(2,2) = 1;
% 
% 
% 
% or this single line of code: 
% net.inputConnect = [1 0; 1 1; 0 0];


net