Fast population coding (Huys et al. 2007)

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Accession:93394
"Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. ..."
Reference:
1 . Huys QJ, Zemel RS, Natarajan R, Dayan P (2007) Fast population coding. Neural Comput 19:404-41 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Methods;
Implementer(s):
% GETSTIM.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 generates the stimuli. 
%
% Copyright Quentin Huys 2006



[dt1,dt2] = meshgrid([1:T]*delta,[1:T]*delta);
dt = abs(dt1-dt2);
meanstim = zeros(1,T);

if rw ==0;	Cstim = lambda*exp(-dt.^2/tau^2);
elseif rw==1;	Cstim = lambda.^dt*tau^2/(1-lambda^2);
end

stim = mvnrnd(zeros(T,1),Cstim,infsamples);