首页|Numerical solution of the stochastic neural field equation with applications to working memory
Numerical solution of the stochastic neural field equation with applications to working memory
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
Elsevier
The main goal of the present work is to investigate the effect of noise in some neural fields, used to simulate working memory processes. The underlying mathematical model is a stochastic integro-differential equation. In order to approximate this equation we apply a numerical scheme which uses the Galerkin method for the space discretization. In this way we obtain a system of stochastic differential equations, which are then approximated in two different ways, using the Euler-Maruyama and the Ito-Taylor methods. We apply this numerical scheme to explain how a population of cortical neurons may encode in its firing pattern simultaneously the nature and time of sequential stimulus events. Numerical examples are presented and their results are discussed. (c) 2022 Elsevier B.V. All rights reserved.
Stochastic neural field equationGalerkin methodOne-and multi-bump solutionsLATERAL-INHIBITIONTRAVELING-WAVESMODELTIMESTABILITYNETWORKSBUMPS
Lima, P. M.、Erlhagen, W.、Kulikova, M., V、Kulikov, G. Yu
NSTL
Elsevier
