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Stochastic Neural Field Theory

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Encyclopedia of Computational Neuroscience

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Definition

One of the major challenges in neuroscience is to determine how noise that is present at the molecular and cellular levels affects dynamics and information processing at the macroscopic level of synaptically coupled neuronal populations. Often noise is incorporated into deterministic neural network models using extrinsic noise sources. An alternative approach is to assume that noise arises intrinsically as a collective population effect, which has led to a master equation formulation of stochastic neural networks. Stochastic neural fields are obtained by taking a continuum limit of a stochastic neural network with spatially structured synaptic weights.

Detailed Description

The spike trains of individual cortical neurons in vivo tend to be very noisy, having interspike interval (ISI) distributions that are close to Poisson (Softky and Koch 1993). The main source of intrinsic fluctuations at the single-cell level is channel noise, which arises from the variability in the...

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Author information

Authors and Affiliations

  1. Department of Mathematics, University of Utah, Salt Lake City, UT, USA

    Prof. Paul Bressloff

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  1. Prof. Paul Bressloff
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Correspondence to Paul Bressloff .

Editor information

Editors and Affiliations

  1. Atlanta, Georgia, USA

    Dieter Jaeger

  2. Department of Biomedical Engineering, Florida International University, Miami, Florida, USA

    Ranu Jung

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Bressloff, P. (2013). Stochastic Neural Field Theory. In: Jaeger, D., Jung, R. (eds) Encyclopedia of Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7320-6_77-3

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  • DOI: https://doi.org/10.1007/978-1-4614-7320-6_77-3

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  • Online ISBN: 978-1-4614-7320-6

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