【作者】 许一凡； 韩芳； 王直杰；

【Author】 Yifan Xu;Fang Han;Zhijie Wang;College of Information Science and Technology,Donghua University;Engineering Research Center of Digital Textile & Apparel Technology,Ministry of Education,Donghua University;

【机构】 东华大学信息科学与技术学院； 数字化纺织服装技术教育部工程研究中心；

【摘要】 嵌合态与中继同步现象对于理解大脑不同区域协同工作以及信息传递机制非常重要.本文构建混合突触耦合的三层神经元网络研究其嵌合态和中继同步的规律,其中网络节点由Hindmarsh-Rose神经元表示,网络连接采用层内电耦合、层间化学突触耦合的连接方式.我们发现在层内层间耦合强度的双参数域中出现了多种层内层间同步模式,诸如嵌合态+中继同步、嵌合态+层间不同步等,深入探究其规律发现耦合作用增强使网络层与层之间出现了中继同步现象,中继同步使神经元集群内的节点状态发生变化,诱发了诸如移动嵌合态、弱嵌合态和脉冲嵌合态等多样的嵌合态类型.然后考虑真实神经网络中的拓扑结构的随机性,将网络拓扑变为ER随机网络后发现中继同步会使网络更容易趋向于同步状态.我们的研究有望加深对神经元网络复杂动力学与同步现象的理解,也为构建人工神经系统提供依据.更多还原

【Abstract】 The phenomena of chimera states and relay synchronization play a crucial role in understanding the collaborative operation and information transmission mechanism across different regions of the brain.In order to further explore the mechanistic role of relay synchronization in chimera states and other states,this study investigates various relay synchronization scenarios within a three-layer neuronal network with hybrid synaptic coupling,where the network nodes are represented by Hindmarsh-Rose neurons coupled by intra-layer electrical synapses and inter-layer chemical synapses.Within the parameter space of intra-and inter-layer coupling strengths,diverse intra-layer and inter-layer synchronization patterns emerge,including ‘chimera state plus relay synchronization’,’chimera state plus inter-layer asynchronization’ etc..When increasing the coupling effect,relay synchronization between network layers occurs,and relay synchronization changes the state of the nodes in the neuron clusters,thereby induce diverse types of chimera states such as traveling chimera states and weak chimera states.Finally,considering the randomness of the topology in the network,it is observed that relay synchronization makes it the whole network tends to synchronize with the increase of clustering degree.This research is expected to deepen the understanding of the complex dynamics and synchronization of neural networks,and provides a basis for the construction of artificial neural systems.更多还原