中国物理B ›› 2026, Vol. 35 ›› Issue (6): 60503-060503.doi: 10.1088/1674-1056/ae56e2

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Meminductor synaptic coupling in a heterogeneous HR-FHN neuron network: Model, dynamics, and DSP implementation

Yang Yin(尹扬) and Zhijun Li(李志军)†   

  1. School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
  • 收稿日期:2026-01-15 修回日期:2026-03-20 接受日期:2026-03-25 发布日期:2026-06-05
  • 通讯作者: Zhijun Li E-mail:lizhijun@xtu.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundations of China (Grant No. 62171401) and the Key Research Project of the Hunan Provincial Department of Education (Grant No. 25A0146).

Meminductor synaptic coupling in a heterogeneous HR-FHN neuron network: Model, dynamics, and DSP implementation

Yang Yin(尹扬) and Zhijun Li(李志军)†   

  1. School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
  • Received:2026-01-15 Revised:2026-03-20 Accepted:2026-03-25 Published:2026-06-05
  • Contact: Zhijun Li E-mail:lizhijun@xtu.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundations of China (Grant No. 62171401) and the Key Research Project of the Hunan Provincial Department of Education (Grant No. 25A0146).

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摘要： To functionally emulate the history-dependent plasticity and electromagnetic induction effects inherent in biochemical synapses, this paper proposes a heterogeneous neural network model in which Hindmarsh–Rose (HR) neurons and FitzHugh–Nagumo (FHN) neurons are coupled via a synaptic connection composed of a meminductor in series with a resistor. This architecture explicitly decouples synaptic function: the linear resistor models the instantaneous conductive pathway, while the meminductor implements the history-dependent plastic pathway. The complex firing dynamics of the coupled system are systematically investigated through bifurcation diagrams, Lyapunov exponent spectra, phase portraits, and time-series analysis. The results show that variations in synaptic coupling strength and coupling resistance can induce transitions between diverse firing patterns, including chaotic spiking, periodic bursting, and alternations between periodic and chaotic states. Crucially, the meminductive synapse introduces activity-dependent multistability, manifested as the coexistence of multiple firing patterns determined by its initial internal state. Phase synchronization analysis further demonstrates that adjusting the coupling resistance provides an independent control mechanism that effectively enhances or suppresses synchronous firing between the two heterogeneous neurons, even at a fixed coupling strength. Finally, the physical feasibility of the system is validated through successful digital implementation on a digital signal processor (DSP) platform, as experimental measurements show excellent agreement with numerical simulations. This study establishes the meminductor as a biomimetically grounded element for chemical synapse emulation and provides a dynamically rich, hardware-validated platform for neuromorphic computing and information processing.

关键词: meminductor, heterogeneous neuron network, firing pattern, synchronization, DSP implementation

Abstract: To functionally emulate the history-dependent plasticity and electromagnetic induction effects inherent in biochemical synapses, this paper proposes a heterogeneous neural network model in which Hindmarsh–Rose (HR) neurons and FitzHugh–Nagumo (FHN) neurons are coupled via a synaptic connection composed of a meminductor in series with a resistor. This architecture explicitly decouples synaptic function: the linear resistor models the instantaneous conductive pathway, while the meminductor implements the history-dependent plastic pathway. The complex firing dynamics of the coupled system are systematically investigated through bifurcation diagrams, Lyapunov exponent spectra, phase portraits, and time-series analysis. The results show that variations in synaptic coupling strength and coupling resistance can induce transitions between diverse firing patterns, including chaotic spiking, periodic bursting, and alternations between periodic and chaotic states. Crucially, the meminductive synapse introduces activity-dependent multistability, manifested as the coexistence of multiple firing patterns determined by its initial internal state. Phase synchronization analysis further demonstrates that adjusting the coupling resistance provides an independent control mechanism that effectively enhances or suppresses synchronous firing between the two heterogeneous neurons, even at a fixed coupling strength. Finally, the physical feasibility of the system is validated through successful digital implementation on a digital signal processor (DSP) platform, as experimental measurements show excellent agreement with numerical simulations. This study establishes the meminductor as a biomimetically grounded element for chemical synapse emulation and provides a dynamically rich, hardware-validated platform for neuromorphic computing and information processing.

Key words: meminductor, heterogeneous neuron network, firing pattern, synchronization, DSP implementation

中图分类号:  (Nonlinear dynamics and chaos)

  • 05.45.-a
87.19.lg (Synapses: chemical and electrical (gap junctions)) 87.19.lj (Neuronal network dynamics) 87.19.lm (Synchronization in the nervous system)