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<p>Neuronal networks are capable of demonstrating complex coexisting firing patterns and how to effectively regulate their coexisting firing patterns has always been a challenging task. In this context, a heterogeneous neuron-coupled network is established. This network comprises a Hindmarsh-Rose (HR) neuron and a tri-cell Hopfield Neural Network (HNN) interconnected via three memristive synapses. The complex dynamical behaviors of the heterogeneous neuron-coupled network are explored by means of bifurcation diagrams, Lyapunov exponents, time series and phase portraits. The firing pattern transitions are also revealed as the memristive synaptic coupling strengths vary. Furthermore, coexisting firing patterns, including period-1, period-2, period-4 and chaotic firing, are also observed by manipulating the initial conditions. Especially, a linear augmentation strategy is introduced to target a desired firing pattern. Numerical simulations show that the linear augmentation method can not only select the desired firing patterns, but also promote synchronous firing activities within the heterogeneous neuronal network. The hardware implementation of the proposed network based on STM32 is provided to illustrate its potential application prospects.</p>

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Coexistence and control of firing patterns in a heterogeneous neuron-coupled network by memristive synapses

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Jinyi Wu,
  • Zhijun Li,
  • Yonghong Lan

摘要

Neuronal networks are capable of demonstrating complex coexisting firing patterns and how to effectively regulate their coexisting firing patterns has always been a challenging task. In this context, a heterogeneous neuron-coupled network is established. This network comprises a Hindmarsh-Rose (HR) neuron and a tri-cell Hopfield Neural Network (HNN) interconnected via three memristive synapses. The complex dynamical behaviors of the heterogeneous neuron-coupled network are explored by means of bifurcation diagrams, Lyapunov exponents, time series and phase portraits. The firing pattern transitions are also revealed as the memristive synaptic coupling strengths vary. Furthermore, coexisting firing patterns, including period-1, period-2, period-4 and chaotic firing, are also observed by manipulating the initial conditions. Especially, a linear augmentation strategy is introduced to target a desired firing pattern. Numerical simulations show that the linear augmentation method can not only select the desired firing patterns, but also promote synchronous firing activities within the heterogeneous neuronal network. The hardware implementation of the proposed network based on STM32 is provided to illustrate its potential application prospects.