Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations

Abstract Neural field equations are used to describe the spatio-temporal evolution of the activity in a network of synaptically coupled populations of neurons in the continuum limit. Their heuristic derivation involves two approximation steps. Under the assumption that each population in the network...

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Main Authors: Eva Lang, Wilhelm Stannat
Format: Article
Language:English
Published: SpringerOpen 2017-07-01
Series:Journal of Mathematical Neuroscience
Online Access:http://link.springer.com/article/10.1186/s13408-017-0048-2
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spelling doaj-405e2a9e9a0d4bc09130025de2206ae42020-11-24T21:10:31ZengSpringerOpenJournal of Mathematical Neuroscience2190-85672017-07-017113510.1186/s13408-017-0048-2Finite-Size Effects on Traveling Wave Solutions to Neural Field EquationsEva Lang0Wilhelm Stannat1Institut für Mathematik, Technische Universität BerlinInstitut für Mathematik, Technische Universität BerlinAbstract Neural field equations are used to describe the spatio-temporal evolution of the activity in a network of synaptically coupled populations of neurons in the continuum limit. Their heuristic derivation involves two approximation steps. Under the assumption that each population in the network is large, the activity is described in terms of a population average. The discrete network is then approximated by a continuum. In this article we make the two approximation steps explicit. Extending a model by Bressloff and Newby, we describe the evolution of the activity in a discrete network of finite populations by a Markov chain. In order to determine finite-size effects—deviations from the mean-field limit due to the finite size of the populations in the network—we analyze the fluctuations of this Markov chain and set up an approximating system of diffusion processes. We show that a well-posed stochastic neural field equation with a noise term accounting for finite-size effects on traveling wave solutions is obtained as the strong continuum limit.http://link.springer.com/article/10.1186/s13408-017-0048-2
collection DOAJ
language English
format Article
sources DOAJ
author Eva Lang
Wilhelm Stannat
spellingShingle Eva Lang
Wilhelm Stannat
Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations
Journal of Mathematical Neuroscience
author_facet Eva Lang
Wilhelm Stannat
author_sort Eva Lang
title Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations
title_short Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations
title_full Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations
title_fullStr Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations
title_full_unstemmed Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations
title_sort finite-size effects on traveling wave solutions to neural field equations
publisher SpringerOpen
series Journal of Mathematical Neuroscience
issn 2190-8567
publishDate 2017-07-01
description Abstract Neural field equations are used to describe the spatio-temporal evolution of the activity in a network of synaptically coupled populations of neurons in the continuum limit. Their heuristic derivation involves two approximation steps. Under the assumption that each population in the network is large, the activity is described in terms of a population average. The discrete network is then approximated by a continuum. In this article we make the two approximation steps explicit. Extending a model by Bressloff and Newby, we describe the evolution of the activity in a discrete network of finite populations by a Markov chain. In order to determine finite-size effects—deviations from the mean-field limit due to the finite size of the populations in the network—we analyze the fluctuations of this Markov chain and set up an approximating system of diffusion processes. We show that a well-posed stochastic neural field equation with a noise term accounting for finite-size effects on traveling wave solutions is obtained as the strong continuum limit.
url http://link.springer.com/article/10.1186/s13408-017-0048-2
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