Lang Eva, Stannat Wilhelm
Institut für Mathematik, Technische Universität Berlin, Berlin, 10623, Germany.
Bernstein Center for Computational Neuroscience, Berlin, 10115, Germany.
J Math Neurosci. 2017 Dec;7(1):5. doi: 10.1186/s13408-017-0048-2. Epub 2017 Jul 6.
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.
神经场方程用于描述连续极限下由突触耦合的神经元群体网络中活动的时空演化。它们的启发式推导涉及两个近似步骤。在网络中每个群体都很大的假设下,活动用群体平均值来描述。然后离散网络由一个连续体近似。在本文中,我们明确了这两个近似步骤。扩展布雷斯洛夫和纽比的一个模型,我们用马尔可夫链描述有限群体离散网络中活动的演化。为了确定有限尺寸效应——由于网络中群体的有限尺寸而导致的与平均场极限的偏差——我们分析这个马尔可夫链的涨落并建立一个扩散过程的近似系统。我们表明,作为强连续极限,可以得到一个适定的随机神经场方程,其带有一个考虑了对行波解的有限尺寸效应的噪声项。