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Journal of Computational Neuroscience

Erschienen in:

01.08.2014

A coarse-grained framework for spiking neuronal networks: between homogeneity and synchrony

verfasst von: Jiwei Zhang, Douglas Zhou, David Cai, Aaditya V. Rangan

Erschienen in: Journal of Computational Neuroscience | Ausgabe 1/2014

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Abstract

Homogeneously structured networks of neurons driven by noise can exhibit a broad range of dynamic behavior. This dynamic behavior can range from homogeneity to synchrony, and often incorporates brief spurts of collaborative activity which we call multiple-firing-events (MFEs). These multiple-firing-events depend on neither structured architecture nor structured input, and are an emergent property of the system. Although these MFEs likely play a major role in the neuronal avalanches observed in culture and in vivo, the mechanisms underlying these MFEs cannot easily be captured using current population-dynamics models. In this work we introduce a coarse-grained framework which illustrates certain dynamics responsible for the generation of MFEs. By using a new kind of ensemble-average, this coarse-grained framework can not only address the nucleation of MFEs, but can also faithfully capture a broad range of dynamic regimes ranging from homogeneity to synchrony.

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Titel: A coarse-grained framework for spiking neuronal networks: between homogeneity and synchrony
verfasst von: Jiwei Zhang
Douglas Zhou
David Cai
Aaditya V. Rangan
Publikationsdatum: 01.08.2014
Verlag: Springer US
Erschienen in: Journal of Computational Neuroscience / Ausgabe 1/2014
Print ISSN: 0929-5313
Elektronische ISSN: 1573-6873
DOI: https://doi.org/10.1007/s10827-013-0488-y

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