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<p>Memristors with locally-active property can mimic excitatory and inhibitory interactions between neurons and are usually utilized to construct artificial synapses. This paper proposes a memristive Chialvo neuron network (MCNN) by connecting two 1D Chialvo neurons by a locally-active memristor. The equilibria of MCNN depend on the neuron parameters exhibiting different numbers and stability. Numerical analyses reveal the rich dynamic behaviors in MCNN. The coupling-strength-relied hidden hyperchaos is investigated. With the co-effects of multiple neuron parameters, there are diverse firing patterns, which are portrayed by the dual-variable Lyapunov exponent spectrum and phase diagrams. Extreme multistability, with different types varying depending on the memristor’s initial conditions, is found in the MCNN as well. Hardware realization successfully reproduces the numerical analysis outcomes of MCNN, which confirms its physical feasibility. In addition, 3DSE-based complexity analysis and NIST tests demonstrate the good performance of MCNN.</p>

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Rich dynamics induced by memristive synapse in Chialvo neuron network

  • 期刊论文

  • 发表日期：

    2025年2月19日

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  • Minghong Qin,
  • Qiang Lai,
  • Luigi Fortuna,
  • Xiao-Wen Zhao

摘要

Memristors with locally-active property can mimic excitatory and inhibitory interactions between neurons and are usually utilized to construct artificial synapses. This paper proposes a memristive Chialvo neuron network (MCNN) by connecting two 1D Chialvo neurons by a locally-active memristor. The equilibria of MCNN depend on the neuron parameters exhibiting different numbers and stability. Numerical analyses reveal the rich dynamic behaviors in MCNN. The coupling-strength-relied hidden hyperchaos is investigated. With the co-effects of multiple neuron parameters, there are diverse firing patterns, which are portrayed by the dual-variable Lyapunov exponent spectrum and phase diagrams. Extreme multistability, with different types varying depending on the memristor’s initial conditions, is found in the MCNN as well. Hardware realization successfully reproduces the numerical analysis outcomes of MCNN, which confirms its physical feasibility. In addition, 3DSE-based complexity analysis and NIST tests demonstrate the good performance of MCNN.