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• W2152614615 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." @default.
• W2152614615 created "2016-06-24" @default.
• W2152614615 creator A5000327505 @default.
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• W2152614615 date "2006-02-06" @default.
• W2152614615 modified "2023-10-16" @default.
• W2152614615 title "Relation between Single Neuron and Population Spiking Statistics and Effects on Network Activity" @default.
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• W2152614615 doi "https://doi.org/10.1103/physrevlett.96.058101" @default.
• W2152614615 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/16486995" @default.
• W2152614615 hasPublicationYear "2006" @default.
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