% GETINF.M
%
% This code is released in conjunction with the paper
%
% Huys QJM, Zemel RS, Natarajan R and Dayan P (2006): Fast population
% coding Neural Computation
%
% and can be downloaded from
%
% http://www.gatsby.ucl.ac.uk/~qhuys/code.html
%
% This script is the workhorse. It is called by MAIN.M and infers the posterior
% distribution p(s_T\xi). If app=1 in PARAM.M, it also infers the approximate
% posteriors with metronomic spikes.
%
% Copyright Quentin Huys 2006
%.........................compute true p(s_Tphi_0:T)...............................
minspkt = min(round(spiketime/delta)); % find the minimum spike time. Start
% inference there, as don't want to do acausal
% inference.
P(:,1:minspkt)=1/ds; M(1:minspkt) =0; % uniform distribution before observe spikes
ts=1;
for t = min(round(spiketime/delta)):T
if ~isempty(ts);tsold = ts;end
ts = max(find(round(spiketime/delta)==t));
% posterior is different depending on whether there is a spike at time t
if ~isempty(ts);
[V(t),M(t),weight,P(:,t)] = psinf(spikeid(1:ts),spiketime(1:ts),sigma,lambda,tau,rw,s);
elseif isempty(ts);
[V(t),M(t),weight,P(:,t)] = pspred(spikeid(1:tsold),spiketime(1:tsold),t*delta,sigma,lambda,tau,rw,s);
end
end
P = P./repmat(sum(P),ds,1); % renormalize for the plots;
