Dynamical behaviors in discrete memristor-coupled small-world neuronal networks  

作　　者：鲁婕妤 谢小华 卢亚平 吴亚联 李春来 马铭磷 Jieyu Lu;Xiaohua Xie;Yaping Lu;Yalian Wu;Chunlai Li;Minglin Ma(School of Automation and Electronic Information,Xiangtan University,Xiangtan 411105,China;School of Computer Science School of Cyberspace Science,Xiangtan University,Xiangtan 411105,China)

机构地区：[1]School of Automation and Electronic Information,Xiangtan University,Xiangtan 411105,China [2]School of Computer Science School of Cyberspace Science,Xiangtan University,Xiangtan 411105,China

出　　处：《Chinese Physics B》2024年第4期729-734,共6页中国物理B（英文版）

基　　金：Project supported by the Key Projects of Hunan Provincial Department of Education (Grant No.23A0133);the Natural Science Foundation of Hunan Province (Grant No.2022JJ30572);the National Natural Science Foundations of China (Grant No.62171401)。

摘　　要：The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

关 键 词：small-world networks Rulkov neurons MEMRISTOR SYNCHRONIZATION 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TN60[自动化与计算机技术—控制科学与工程]