文献类型：期刊文献

英文题名：Stochastic model and neural coding of large-scale neuronal population with variable coupling strength

作者：Wang, Rubin[1,2]; Jiao, Xianfa[2,3]

机构：[1]E China Univ Sci & Technol, Sch Informat Sci & Engn, Inst Brain Informat Proc & Cognit Neurodynam, Shanghai 200237, Peoples R China;[2]Donghua Univ, Coll Informat Sci & Technol, Shanghai 200051, Peoples R China;[3]Hefei Univ Technol, Sch Sci, Hefei 230009, Peoples R China

年份：2006

卷号：69

期号：7-9

起止页码：778

外文期刊名：NEUROCOMPUTING

收录：;EI(收录号：2006089712034);WOS:【SCI-EXPANDED(收录号:WOS:000235797000017)】；

语种：英文

外文关键词：neuronal population; variable coupling strength; evolution model; average number density; the effect of learning

摘要：Taking into account the variability of coupling strength with increasing time, we present the nonlinear stochastic dynamical model of neuronal population, where the average number density is introduced as a distributed coding pattern of neuronal population. In the absence of external stimulus, numerical simulations indicate that the synchronized activity of neuronal population increases the coupling strength among neuronal oscillators; the coding pattern of the average number density is related to coupling configuration among neural oscillators. These studies also show that the variability of the coupling strength displays a slow learning process in the weak noise, but the coupling strength exhibits transient process in the strong noise. Numerical simulations confirm that the higher the coupling level is, the larger the synchronization of neuronal population is, and the stronger the coupling strength is. (c) 2005 Elsevier B.V. All rights reserved.