Relation between single neuron and population spiking statistics and effects on network activity

Hideyuki Cĝteau, Alex D. Reyes

Research output: Contribution to journalArticlepeer-review

Abstract

To simplify theoretical analyses of neural networks, individual neurons are often modeled as Poisson processes. An implicit assumption is that even if the spiking activity of each neuron is non-Poissonian, the composite activity obtained by summing many spike trains limits to a Poisson process. Here, we show analytically and through simulations that this assumption is invalid. Moreover, we show with Fokker-Planck equations that the behavior of feedforward networks is reproduced accurately only if the tendency of neurons to fire periodically is incorporated by using colored noise whose autocorrelation has a negative component.

Original languageEnglish (US)
Article number058101
JournalPhysical Review Letters
Volume96
Issue number5
DOIs
StatePublished - 2006

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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