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Chaotic dynamics and synchronization of multi-region neural network based on locally active memristor

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  • Wang, Ertong
  • Hu, Bin
  • Guan, Zhi-Hong

Abstract

The influence of neural synapses on the collaboration of different brain regions is an urgent need for current research. In this paper, a multi-region neural network (MRNN) is proposed using multistable locally-active memristor (MLAM). A new memristor is first designed with multistable, non-volatile, and locally-active. Then, the memristor is modeled as a neural synapse connecting two different regions to construct the MRNN, which is a multistable locally-active memristive Hopfield neural network. The neural network exhibits rich chaotic dynamics, and the dynamic coupling strength of the synapse is analyzed using bifurcation, phase diagrams, and two-parameter chaotic maps. The neural network also demonstrates self-boosting of attractors driven by the parameters of synapse. The effect of memristive parameters on the self-boosting of the attractor is revealed by describing the phase diagram and the basin of attraction. In order to explore the collective behavior of the proposed network, controllers are further designed to realize the state synchronization cross multiple brain regions.

Suggested Citation

  • Wang, Ertong & Hu, Bin & Guan, Zhi-Hong, 2025. "Chaotic dynamics and synchronization of multi-region neural network based on locally active memristor," Chaos, Solitons & Fractals, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:chsofr:v:197:y:2025:i:c:s0960077925004503
    DOI: 10.1016/j.chaos.2025.116437
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    References listed on IDEAS

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    1. Ying, Jiajie & Liang, Yan & Wang, Junlan & Dong, Yujiao & Wang, Guangyi & Gu, Meiyuan, 2021. "A tristable locally-active memristor and its complex dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    2. Marco Fuscà & Felix Siebenhühner & Sheng H. Wang & Vladislav Myrov & Gabriele Arnulfo & Lino Nobili & J. Matias Palva & Satu Palva, 2023. "Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    3. 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).
    4. Yao, Zhao & Sun, Kehui & Wang, Huihai, 2024. "Collective behaviors of fractional-order FithzHugh–Nagumo network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 639(C).
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