文献类型：期刊文献

中文题名：Dynamics of a Cortical Neural Network Based on a Simple Model

英文题名：Dynamics of a Cortical Neural Network Based on a Simple Model

作者：Qu Jing-Yi[1];Wang Ru-Bin[2]

机构：[1]Civil Aviat Univ, Tianjin Key Lab Adv Signal Proc, Tianjin 300300, Peoples R China;[2]E China Univ Sci & Technol, Inst Cognit Neurodynam, Sch Sci, Shanghai 200237, Peoples R China

年份：2012

卷号：29

期号：8

中文期刊名：Chinese Physics Letters

外文期刊名：CHINESE PHYSICS LETTERS

收录：CSTPCD;;EI(收录号：20220711666106);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000307667100014)】；CSCD:【CSCD2011_2012】；

基金：Supported by Science and Technology Support Plan Topics (2011BAH24B12), Start-up Fund of Civil Aviation University and Fundamental Research Funds for the Central Universities (ZXH2012C004, ZXH2011C006).

语种：英文

中文关键词：anatomy;collective;cortex;

外文关键词：Dynamics - Mammals - Neural networks - Nonlinear equations

摘要：The collective dynamics of a randomly connected neuronal network motivated by the anatomy of a mammalian cortex based on a simple model are studied.This simple model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term.By varying some key parameters,such as the connection weights of neurons,the external current injection and the noise of intensity,this neuronal network will exhibit various collective behaviors.It is demonstrated that the synchronization status of the neuronal network has a strong relationship with the key parameters and the external current has more influence on the spiking of inhibitory neurons than that of excitatory neurons.These results may be instructive in understanding the collective dynamics of a mammalian cortex.
The collective dynamics of a randomly connected neuronal network motivated by the anatomy of a mammalian cortex based on a simple model are studied. This simple model can not only reproduce the rich behaviors of biological neurons but also has only two equations and one nonlinear term. By varying some key parameters, such as the connection weights of neurons, the external current injection and the noise of intensity, this neuronal network will exhibit various collective behaviors. It is demonstrated that the synchronization status of the neuronal network has a strong relationship with the key parameters and the external current has more influence on the spiking of inhibitory neurons than that of excitatory neurons. These results may be instructive in understanding the collective dynamics of a mammalian cortex.