Can attractor network models account for the statistics of firing during persistent activity in prefrontal cortex?

Published online

Journal Article

Persistent activity observed in neurophysiological experiments in monkeys is thought to be the neuronal correlate of working memory. Over the last decade, network modellers have strived to reproduce the main features of these experiments. In particular, attractor network models have been proposed in which there is a coexistence between a non-selective attractor state with low background activity with selective attractor states in which sub-groups of neurons fire at rates which are higher (but not much higher) than background rates. A recent detailed statistical analysis of the data seems however to challenge such attractor models: the data indicates that firing during persistent activity is highly irregular (with an average CV larger than 1), while models predict a more regular firing process (CV smaller than 1). We discuss here recent proposals that allow to reproduce this feature of the experiments.

Full Text

Duke Authors

Cited Authors

  • Barbieri, F; Brunel, N

Published Date

  • July 2008

Published In

Volume / Issue

  • 2 / 1

Start / End Page

  • 114 - 122

PubMed ID

  • 18982114

Pubmed Central ID

  • 18982114

Electronic International Standard Serial Number (EISSN)

  • 1662-453X

Digital Object Identifier (DOI)

  • 10.3389/neuro.01.003.2008

Language

  • eng

Conference Location

  • Switzerland