//// Dynamic Neural Field Model (1D) 
 clear; clf; 
 nn = 100; dx=2*%pi/nn; sig = 2*%pi/10; C=0.5; 

//// ode function
function udot=rnn_ode(t,u)
// odefile for recurrent network
 tau_inv = 1.;      // inverse of membrane time constant
 r=ones(nn,1)./(1+exp(-u)); 
 sum1=w*r*dx;
 udot=tau_inv*(-u+sum1+I_ext);
endfunction

//// Training weight matrix
 for loc=1:nn;  
     i=(1:nn)'; dis=min(abs(i-loc),nn-abs(i-loc)); 
     pat(:,loc)=exp(-(dis*dx).^2/(2*sig^2));
 end
 w=pat*pat'; w=w/w(1,1); w=4*(w-C);
//// Update with localised input 
 tall = []; rall = []; t=(0:10)';
 I_ext=zeros(nn,1); I_ext(nn/2-floor(nn/10):nn/2+floor(nn/10))=1;
 u=ode(zeros(nn,1),0,t,rnn_ode);
 r=ones(nn,size(u,2))./(1+exp(-u)); tall=[tall;t]; rall=[rall,r];
//// Update without input 
 I_ext=zeros(nn,1); t=(10:20)';
 u=ode(u(:,size(u,2)),10,t,rnn_ode);
 r=ones(nn,size(u,2))./(1+exp(-u)); tall=[tall;t]; rall=[rall,r];
//// Plotting results
 surf(tall,1:nn,rall);