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• W2056936200 startingPage "e1003301" @default.
• W2056936200 abstract "Firing-rate models provide an attractive approach for studying large neural networks because they can be simulated rapidly and are amenable to mathematical analysis. Traditional firing-rate models assume a simple form in which the dynamics are governed by a single time constant. These models fail to replicate certain dynamic features of populations of spiking neurons, especially those involving synchronization. We present a complex-valued firing-rate model derived from an eigenfunction expansion of the Fokker-Planck equation and apply it to the linear, quadratic and exponential integrate-and-fire models. Despite being almost as simple as a traditional firing-rate description, this model can reproduce firing-rate dynamics due to partial synchronization of the action potentials in a spiking model, and it successfully predicts the transition to spike synchronization in networks of coupled excitatory and inhibitory neurons." @default.
• W2056936200 created "2016-06-24" @default.
• W2056936200 creator A5028433393 @default.
• W2056936200 creator A5037127074 @default.
• W2056936200 creator A5055798083 @default.
• W2056936200 date "2013-10-31" @default.
• W2056936200 modified "2023-10-18" @default.
• W2056936200 title "A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks" @default.
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• W2056936200 doi "https://doi.org/10.1371/journal.pcbi.1003301" @default.
• W2056936200 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3814717" @default.
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• W2056936200 hasPublicationYear "2013" @default.
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