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<p>In this paper, a novel discrete memristor is designed to connect Chialvo models to construct a two-neuron coupled system and a two-dimensional lattice neural network. Synchronization and spatiotemporal dynamics are analyzed in the neural network. The system exhibits various synchronization behaviors, including phase synchronization and complete synchronization, under different coupling gains. In addition, target waves are generated in the neural network due to the heterogeneity caused by different external stimuli, and spiral waves are induced in different ways, including by adding an obstacle and changing the coupling gain. Because of the potential harm caused by spiral waves to biological systems, we also present a novel approach to suppressing spiral waves by introducing local heterogeneity caused by coupling gain diversity. The results show that the constructed neural network can realize information transmission and has good robustness. Moreover, the spiral waves are suppressed by the proposed method and the spatiotemporal patterns become more regular, which has significant research value.</p>

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Spatiotemporal dynamics and synchronization in a memristive Chialvo neural network

  • 期刊论文

  • 发表日期：

    2025年1月25日

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  • Huihai Wang,
  • Hanqi Chen,
  • Kehui Sun,
  • Wanting Zhu,
  • Zhao Yao

摘要

In this paper, a novel discrete memristor is designed to connect Chialvo models to construct a two-neuron coupled system and a two-dimensional lattice neural network. Synchronization and spatiotemporal dynamics are analyzed in the neural network. The system exhibits various synchronization behaviors, including phase synchronization and complete synchronization, under different coupling gains. In addition, target waves are generated in the neural network due to the heterogeneity caused by different external stimuli, and spiral waves are induced in different ways, including by adding an obstacle and changing the coupling gain. Because of the potential harm caused by spiral waves to biological systems, we also present a novel approach to suppressing spiral waves by introducing local heterogeneity caused by coupling gain diversity. The results show that the constructed neural network can realize information transmission and has good robustness. Moreover, the spiral waves are suppressed by the proposed method and the spatiotemporal patterns become more regular, which has significant research value.