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Journal of Computational Neuroscience

Erschienen in:

01.04.2010

Multiplicatively interacting point processes and applications to neural modeling

verfasst von: Stefano Cardanobile, Stefan Rotter

Erschienen in: Journal of Computational Neuroscience | Ausgabe 2/2010

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Abstract

We introduce a nonlinear modification of the classical Hawkes process allowing inhibitory couplings between units without restrictions. The resulting system of interacting point processes provides a useful mathematical model for recurrent networks of spiking neurons described as Wiener cascades with exponential transfer function. The expected rates of all neurons in the network are approximated by a first-order differential system. We study the stability of the solutions of this equation, and use the new formalism to implement a winner-takes-all network that operates robustly for a wide range of parameters. Finally, we discuss relations with the generalised linear model that is widely used for the analysis of spike trains.

Vorheriger Artikel Pseudo-Lyapunov exponents and predictability of Hodgkin-Huxley neuronal network dynamics

Nächster Artikel Nonlinear cross-frequency interactions in primary auditory cortex spectrotemporal receptive fields: a Wiener–Volterra analysis

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Titel: Multiplicatively interacting point processes and applications to neural modeling
verfasst von: Stefano Cardanobile
Stefan Rotter
Publikationsdatum: 01.04.2010
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 2/2010
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-009-0204-0

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