On the maximization of information flow between spiking neurons.

Published

Journal Article

A feedforward spiking network represents a nonlinear transformation that maps a set of input spikes to a set of output spikes. This mapping transforms the joint probability distribution of incoming spikes into a joint distribution of output spikes. We present an algorithm for synaptic adaptation that aims to maximize the entropy of this output distribution, thereby creating a model for the joint distribution of the incoming point processes. The learning rule that is derived depends on the precise pre- and postsynaptic spike timings. When trained on correlated spike trains, the network learns to extract independent spike trains, thereby uncovering the underlying statistical structure and creating a more efficient representation of the incoming spike trains.

Full Text

Duke Authors

Cited Authors

  • Parra, LC; Beck, JM; Bell, AJ

Published Date

  • November 2009

Published In

Volume / Issue

  • 21 / 11

Start / End Page

  • 2991 - 3009

PubMed ID

  • 19635018

Pubmed Central ID

  • 19635018

International Standard Serial Number (ISSN)

  • 0899-7667

Digital Object Identifier (DOI)

  • 10.1162/neco.2009.04-06-184

Language

  • eng

Conference Location

  • United States