广西师范大学学报（自然科学版） ›› 2024, Vol. 42 ›› Issue (1): 67-78.doi: 10.16088/j.issn.1001-6600.2023032802

• 研究论文 • 上一篇    下一篇

忆阻Morris-Lecar神经网络的同步行为研究

黄微, 韦笃取*   

  1. 广西师范大学 电子与信息工程学院, 广西 桂林 541004
  • 收稿日期:2023-03-28 修回日期:2023-06-26 出版日期:2024-01-25 发布日期:2024-01-19
  • 通讯作者: 韦笃取(1975—),男(壮族),广西贵港人,广西师范大学教授,博士。E-mail:weiduqu@gxnu.edu.cn
  • 基金资助:
    国家自然科学基金(62062014);广西自然科学基金(2021JJA170004)

Synchronization Behavior of Memristor Morris-Lecar Neural Networks

HUANG Wei, WEI Duqu*   

  1. College of Electronic and Information Engineering, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2023-03-28 Revised:2023-06-26 Online:2024-01-25 Published:2024-01-19

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摘要： 本文研究电磁感应系数和膜表面电荷尺寸对忆阻Morris-Lecar(ML)神经网络动力学行为的影响。以电荷尺寸为参数研究忆阻ML神经网络的放电行为,发现增大电荷尺寸会抑制神经元放电频率和幅度,神经元随着时间的推移进入静息状态。以电磁感应系数和电荷尺寸作为变量研究神经网络的同步行为。固定电荷尺寸,增大电磁感应系数,神经网络由混沌变得有序;电磁感应系数是决定神经网络同步的关键因素,当电磁感应系数K>3.2时,能更有效地诱导和增强同步。引入同步因子和标准差分别对网络的同步程度和神经元放电程度进行量化。实验结果表明,电磁感应系数可以有效调节神经网络的同步行为,具有控制神经网络的放电模式和网络同步的作用。

关键词: Morris-Lecar, 神经元, 电磁感应, 表面电荷, 同步放电

Abstract: The effects of electromagnetic induction coefficient and membrane surface charge size on the memristor Morris-Lecar (ML) neural network dynamics are investigated. Using charge size as a parameter to study the discharge behavior of memristor ML neural network, it is found that increasing charge size will inhibit the firing frequency and amplitude of neurons, and the neurons will enter the resting state over time. The synchronization behavior of neural network is studied with electromagnetic induction coefficient and charge size as variables. When the electric charge size is fixed and the electromagnetic induction coefficient is increased, the neural network becomes orderly from chaos. The electromagnetic induction coefficient is the key factor to determine neural network synchronization. When the electromagnetic induction coefficient K>3.2, the synchronization can be induced and enhanced more effectively. Synchronization factor and standard deviation are introduced to quantify the degree of network synchronization and neuron firing. The experimental results show that the electromagnetic induction coefficient can effectively regulate the synchronization behavior of neural network, and has the function of controlling the discharge mode and network synchronization.

Key words: Morris-Lecar, neurons, electromagnetic induction, surface charge, synchronous discharge

中图分类号:  TP183

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