Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (1): 67-78.doi: 10.16088/j.issn.1001-6600.2023032802

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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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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

CLC Number:  TP183
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