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<p>When a memristor-dominated external electromagnetic-radiation channel is introduced into the Chialvo neuron, the neuronal dynamics can be regulated in a flexible and orderly manner. In particular, the piecewise-linear memductance function provides a direct route for editing neuronal dynamics in phase space. With appropriate slope modulation and the interval length, the system can determine the spatial arrangement and basin partition of coexisting attractors. Based on this mechanism, the order and distance of attractors are edited, and the editing strategy is further extended to multidimensional state-space organization. The influence of an external electric field is also investigated, showing its ability to induce transitions in neuronal firing activities. Furthermore, a chaos-embedded reservoir model is developed, which exhibits strong sequence-prediction performance for periodic, chaotic, and real-world time series. The results indicate that relatively regular tasks are better served by moderately rich dynamics, whereas more complex chaotic tasks benefit from a richer nonlinear response space.</p>

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Memristor-dominated dynamics editing of chialvo neuron and its application in reservoir computing

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Yixuan Liu,
  • Chunbiao Li,
  • Tengfei Lei,
  • Fei Jiang,
  • Xiaolong Qi

摘要

When a memristor-dominated external electromagnetic-radiation channel is introduced into the Chialvo neuron, the neuronal dynamics can be regulated in a flexible and orderly manner. In particular, the piecewise-linear memductance function provides a direct route for editing neuronal dynamics in phase space. With appropriate slope modulation and the interval length, the system can determine the spatial arrangement and basin partition of coexisting attractors. Based on this mechanism, the order and distance of attractors are edited, and the editing strategy is further extended to multidimensional state-space organization. The influence of an external electric field is also investigated, showing its ability to induce transitions in neuronal firing activities. Furthermore, a chaos-embedded reservoir model is developed, which exhibits strong sequence-prediction performance for periodic, chaotic, and real-world time series. The results indicate that relatively regular tasks are better served by moderately rich dynamics, whereas more complex chaotic tasks benefit from a richer nonlinear response space.