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Coexisting multiple firing behaviors of fractional-order memristor-coupled HR neuron considering synaptic crosstalk and its ARM-based implementation

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  • Ding, Dawei
  • Chen, Xiaoyu
  • Yang, Zongli
  • Hu, Yongbing
  • Wang, Mouyuan
  • Zhang, Hongwei
  • Zhang, Xu

Abstract

The crosstalk between synapses has a far-reaching impact on the specificity of synaptic communication in human brain. Therefore, the influence of synaptic crosstalk on the dynamic behaviors of neural network cannot be ignored. In this paper, we investigate the dynamics of fractional-order (FO) memristor-coupled Hindmarsh-Rose (HR) neuron model considering synaptic crosstalk. Firstly, the equilibrium points and stability are investigated via qualitative analysis, from which it can be obtained that the number and stability of equilibrium points have diversity for different crosstalk strength parameters. Secondly, the global coexistence of multiple firing patterns is reflected by phase diagrams, Lyapunov exponent spectrums and bifurcation diagrams. The local attraction basins reveal the multi-stability phenomenon related to the initial value. The parameter related firing behaviors are described by the two-parameter bifurcation diagram. In particular, the model shows the offset boosting control method for a single variable. Then, spectral entropy (SE) and C0 complexity chaos diagram are used to observe the change of system complexity when two parameters change simultaneously. Finally, the digital implementations based on ARM are given to verify the consistency with the numerical simulation results.

Suggested Citation

  • Ding, Dawei & Chen, Xiaoyu & Yang, Zongli & Hu, Yongbing & Wang, Mouyuan & Zhang, Hongwei & Zhang, Xu, 2022. "Coexisting multiple firing behaviors of fractional-order memristor-coupled HR neuron considering synaptic crosstalk and its ARM-based implementation," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:chsofr:v:158:y:2022:i:c:s0960077922002247
    DOI: 10.1016/j.chaos.2022.112014
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    References listed on IDEAS

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    5. Yu, Fei & Shen, Hui & Zhang, Zinan & Huang, Yuanyuan & Cai, Shuo & Du, Sichun, 2021. "Dynamics analysis, hardware implementation and engineering applications of novel multi-style attractors in a neural network under electromagnetic radiation," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    6. Zhang, Sen & Zheng, Jiahao & Wang, Xiaoping & Zeng, Zhigang, 2021. "A novel no-equilibrium HR neuron model with hidden homogeneous extreme multistability," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
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    Cited by:

    1. Ding, Dawei & Xu, Xinyue & Yang, Zongli & Zhang, Hongwei & Zhu, Haifei & Liu, Tao, 2024. "Extreme multistability of fractional-order hyperchaotic system based on dual memristors and its implementation," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    2. Zhang, Shaohua & Zhang, Hongli & Wang, Cong & Lin, Hairong, 2024. "Bionic modeling and dynamics analysis of heterogeneous brain regions connected by memristive synaptic crosstalk," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    3. Chen, Mo & Xue, Wanqi & Luo, Xuefeng & Zhang, Yunzhen & Wu, Huagan, 2023. "Effects of coupling memristors on synchronization of two identical memristive Chua's systems," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    4. Li, Zhijun & Chen, Kaijie, 2023. "Neuromorphic behaviors in a neuron circuit based on current-controlled Chua Corsage Memristor," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    5. Hu, Jingting & Bao, Han & Xu, Quan & Chen, Mo & Bao, Bocheng, 2024. "Synchronization generations and transitions in two map-based neurons coupled with locally active memristor," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    6. Lin, Hairong & Wang, Chunhua & Sun, Jingru & Zhang, Xin & Sun, Yichuang & Iu, Herbert H.C., 2023. "Memristor-coupled asymmetric neural networks: Bionic modeling, chaotic dynamics analysis and encryption application," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).

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