文献类型：期刊文献

英文题名：Asymmetric neural network synchronization and dynamics based on an adaptive learning rule of synapses

作者：Yan, Chuankui[1,2]; Wang, Rubin[2]

机构：[1] Department of Mathematics, School of Science, Hangzhou Normal University, Hangzhou, China; [2] Institute for Cognitive Neurodynamics, School of Information Science and Engineering, Department of Mathematics, East China University of Science and Technology, Shanghai, China

年份：2014

卷号：125

起止页码：41

外文期刊名：Neurocomputing

收录：EI(收录号：20134817027912)

语种：英文

外文关键词：Bifurcation (mathematics) - Neural networks

摘要：An adaptive learning rule of synapses was proposed for a general asymmetric neural network. Its feasibility was proved by the Lasalle principle. Numerical simulation results show that synaptic connection weight can converge to an appropriate strength and the network comes to synchronization. Furthermore, ISI (inter-spike interval) of synchronization orbit in neural network has a typical period doubling bifurcation. It is a further improvement compared with bifurcation of the traditional single neuron model, which promotes our understanding of neuron population activities. ? 2013 Elsevier B.V.