文献类型：期刊文献

中文题名：Non-identical Neural Network Synchronization Study Based on an Adaptive Learning Rule of Synapses

英文题名：Non-identical Neural Network Synchronization Study Based on an Adaptive Learning Rule of Synapses

作者：Yan Chuan-Kui[1,2];Wang Ru-Bin[1]

机构：[1]E China Univ Sci & Technol, Inst Cognit Neurodynam, Sch Informat Sci & Engn, Dept Math,Sch Sci, Shanghai 200237, Peoples R China;[2]Hangzhou Normal Univ, Sch Sci, Dept Math, Hangzhou 310036, Zhejiang, Peoples R China

年份：2012

卷号：29

期号：9

中文期刊名：Chinese Physics Letters

外文期刊名：CHINESE PHYSICS LETTERS

收录：CSTPCD;;EI(收录号：20220711654718);Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000309112300006)】；CSCD:【CSCD2011_2012】；

基金：Supported by the National Natural Science Foundation of China under Grant No 10872068, the Fundamental Research Funds for the Central Universities, Youth Cultivating Foundation of Hangzhou Normal University under Grant No 2010QN02, and the Key Program of National Natural Science Foundation of China under Grant No 11232005 (on neurodynamics research and experimental analysis of perceptual cognition and decision making).

语种：英文

中文关键词：neural;identical;app;

外文关键词：Topology - Neural networks

摘要：An adaptive learning rule of synapses is proposed for a general asymmetric non-identical neural network.Its feasibility is proved by the Lasalle principle.Numerical simulation results show that synaptic connection weight can converge to an appropriate strength and the identical network comes to synchronization.Furthermore,by this approach of learning,a non-identical neural population can still reach synchronization.This means that the learning rule has robustness on mismatch parameters.The firing rhythm of the neural population is totally dependent on topological properties,which promotes our understanding of neuron population activities.
An adaptive learning rule of synapses is proposed for a general asymmetric non-identical neural network. Its feasibility is proved by the Lasalle principle. Numerical simulation results show that synaptic connection weight can converge to an appropriate strength and the identical network comes to synchronization. Furthermore, by this approach of learning, a non-identical neural population can still reach synchronization. This means that the learning rule has robustness on mismatch parameters. The firing rhythm of the neural population is totally dependent on topological properties, which promotes our understanding of neuron population activities.