文献类型：期刊文献

英文题名：Synchronization study in ring-like and grid-like neuronal networks

作者：Qu, Jingyi[1];Wang, Rubin[1];Du, Ying[1];Cao, Jianting[1]

机构：[1]E China Univ Sci & Technol, Dept Math, Inst Cognit Neurodynam, Sch Sci,Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2012

卷号：6

期号：1

起止页码：21

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000299000300003)】；

基金：The work is supported by National Natural Science Foundation of China (NSFC) (No. 10872068,11002055) and the Fundamental Research Funds for the Central Universities. The authors sincerely thank Kreuz et al. sharing their method and code in their web site and Dr. Braun's sincere help for sharing reference documents to us. The authors also thank the anonymous reviewers for their valuable comments that have led to the present improved version of the original manuscript.

语种：英文

外文关键词：Ring-like and grid-like neuronal network; ISI-distance; Mean field potential; Bifuration diagram; Correlation coefficient

摘要：In this paper, we study the synchronization status of both two gap-junction coupled neurons and neuronal network with two different network connectivity patterns. One of the network connectivity patterns is a ring-like neuronal network, which only considers nearest-neighbor neurons. The other is a grid-like neuronal network, with all nearest neighbor couplings. We show that by varying some key parameters, such as the coupling strength and the external current injection, the neuronal network will exhibit various patterns of firing synchronization. Different types of firing synchronization are diagnosed by means of a mean field potential, a bifurcation diagram, a correlation coefficient and the ISI-distance method. Numerical simulations demonstrate that the synchronization status of multiple neurons is much dependent on the network patters, when the number of neurons is the same. It is also demonstrated that the synchronization status of two coupled neurons is similar with the grid-like neuronal network, but differs radically from that of the ring-like neuronal network. These results may be instructive in understanding synchronization transitions in neuronal systems.