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Phys Rev Lett. 2006 Feb 10;96(5):058101. Epub 2006 Feb 6.

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

Author information

1
Center for Neural Science, New York University, 4 Washington Place, New York, New York 10003, USA.

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.

PMID:
16486995
DOI:
10.1103/PhysRevLett.96.058101
[Indexed for MEDLINE]

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