Optimal population coding by noisy spiking neurons.

ABSTRACT: In retina and in cortical slice the collective response of spiking neural populations is well described by "maximum-entropy" models in which only pairs of neurons interact. We asked, how should such interactions be organized to maximize the amount of information represented in population responses? To this end, we extended the linear-nonlinear-Poisson model of single neural response to include pairwise interactions, yielding a stimulus-dependent, pairwise maximum-entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete--stimulus decorrelation, error correction, and independent encoding. These functions reflecte

SUBMITTER: Tkacik G 

PROVIDER: S-EPMC2922524 | biostudies-literature | 2010 Aug

REPOSITORIES: biostudies-literature