中国物理B ›› 2023, Vol. 32 ›› Issue (5): 58701-058701.doi: 10.1088/1674-1056/acb9f7

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Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

Ming-Lin Ma(马铭磷)1,†, Xiao-Hua Xie(谢小华)1, Yang Yang(杨阳)1, Zhi-Jun Li(李志军)1, and Yi-Chuang Sun(孙义闯)2   

  1. 1 School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;
    2 School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK
  • 收稿日期:2022-11-18 修回日期:2023-01-14 接受日期:2023-02-08 出版日期:2023-04-21 发布日期:2023-05-05
  • 通讯作者: Ming-Lin Ma E-mail:minglin_ma@xtu.edu.cn
  • 基金资助:
    Project supported by the Natural Science Foundation of Hunan Province, China (Grant Nos. 2022JJ30572, 2022JJ30160, and 2021JJ30671), the National Natural Science Foundations of China (Grant No. 62171401), and the Key Project of Science and Technology of Shunde District (Grant No. 2130218002544).

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

Ming-Lin Ma(马铭磷)1,†, Xiao-Hua Xie(谢小华)1, Yang Yang(杨阳)1, Zhi-Jun Li(李志军)1, and Yi-Chuang Sun(孙义闯)2   

  1. 1 School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;
    2 School of Engineering and Technology, University of Hertfordshire, Hatfield AL10 9AB, UK
  • Received:2022-11-18 Revised:2023-01-14 Accepted:2023-02-08 Online:2023-04-21 Published:2023-05-05
  • Contact: Ming-Lin Ma E-mail:minglin_ma@xtu.edu.cn
  • Supported by:
    Project supported by the Natural Science Foundation of Hunan Province, China (Grant Nos. 2022JJ30572, 2022JJ30160, and 2021JJ30671), the National Natural Science Foundations of China (Grant No. 62171401), and the Key Project of Science and Technology of Shunde District (Grant No. 2130218002544).

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摘要： At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility, and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use an LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

关键词: locally active discrete memristor (LADM), Rulkov, synchronization coexistence

Abstract: At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility, and local activity, locally active discrete memristors (LADMs) are also suitable for simulating synapses. In this paper, we use an LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

Key words: locally active discrete memristor (LADM), Rulkov, synchronization coexistence

中图分类号:  (Models of single neurons and networks)

  • 87.19.ll
87.19.lj (Neuronal network dynamics) 05.45.Xt (Synchronization; coupled oscillators)