神经场方程行波解的有限尺寸效应。

IF 2.3 4区 医学 Q1 Neuroscience
Journal of Mathematical Neuroscience Pub Date : 2017-12-01 Epub Date: 2017-07-06 DOI:10.1186/s13408-017-0048-2
Eva Lang, Wilhelm Stannat
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引用次数: 6

摘要

神经场方程用于描述连续体极限下突触耦合神经元群体网络活动的时空演化。它们的启发式推导包括两个近似步骤。在假设网络中的每个群体都很大的情况下,活动用群体平均值来描述。然后用连续体来近似离散网络。在本文中,我们明确了这两个近似步骤。扩展了Bressloff和Newby的模型,用马尔可夫链描述了有限种群离散网络中活动的演化。为了确定有限大小的效应-由于网络中种群的有限大小而偏离平均场极限-我们分析了该马尔可夫链的波动并建立了一个扩散过程的近似系统。我们证明了一个考虑行波解的有限尺寸效应的随机神经场方程是作为强连续极限的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finite-Size Effects on Traveling Wave Solutions to Neural Field Equations.

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.

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来源期刊
Journal of Mathematical Neuroscience
Journal of Mathematical Neuroscience Neuroscience-Neuroscience (miscellaneous)
自引率
0.00%
发文量
0
审稿时长
13 weeks
期刊介绍: The Journal of Mathematical Neuroscience (JMN) publishes research articles on the mathematical modeling and analysis of all areas of neuroscience, i.e., the study of the nervous system and its dysfunctions. The focus is on using mathematics as the primary tool for elucidating the fundamental mechanisms responsible for experimentally observed behaviours in neuroscience at all relevant scales, from the molecular world to that of cognition. The aim is to publish work that uses advanced mathematical techniques to illuminate these questions. It publishes full length original papers, rapid communications and review articles. Papers that combine theoretical results supported by convincing numerical experiments are especially encouraged. Papers that introduce and help develop those new pieces of mathematical theory which are likely to be relevant to future studies of the nervous system in general and the human brain in particular are also welcome.
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