Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment.

ABSTRACT: It has recently been shown that networks of spiking neurons with noise can emulate simple forms of probabilistic inference through "neural sampling", i.e., by treating spikes as samples from a probability distribution of network states that is encoded in the network. Deficiencies of the existing model are its reliance on single neurons for sampling from each random variable, and the resulting limitation in representing quickly varying probabilistic information. We show that both deficiencies can be overcome by moving to a biologically more realistic encoding of each salient random variable through the stochastic firing activity of an ensemble of neurons. The resulting model demonstrates that networks of spiking neurons with noise can easily track and carry out basic computational operation

SUBMITTER: Legenstein R 

PROVIDER: S-EPMC4207607 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature