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<p>This paper investigates the coupling mechanisms between distinct brain regions by proposing a brain-inspired memristor-coupled Hopfield neural network (HNN) system with tunable neural activation gradients. A novel memristor model is introduced to emulate the synaptic interactions between subnetworks. The system’s dynamical behavior is systematically analyzed through bifurcation diagrams, 2D dynamical distribution maps, and Lyapunov exponent (LE) spectra. Furthermore, spectral entropy (SE) is used to quantify the complexity of the network dynamics. The results reveal a rich set of brain-like chaotic behaviors, including complex bifurcation evolutions, transient chaos, and initial-induced coexisting attractors. These findings highlight the pivotal role of memristors in enabling flexible and complex dynamical regulation within brain-inspired coupling mechanisms. In addition, the proposed system is implemented via analog circuit design and successfully deployed on a DSP platform, demonstrating its potential for neuromorphic computing and real-time hardware applications. This study offers theoretical foundations and practical guidance for the design of neuromorphic systems and the analysis of nonlinear dynamics in coupled neural circuits.</p>

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Rich dynamical behaviors and circuit implementation of brain-inspired memristor-coupled neural network

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Tao Ma,
  • Jun Mou,
  • Wanzhong Chen

摘要

This paper investigates the coupling mechanisms between distinct brain regions by proposing a brain-inspired memristor-coupled Hopfield neural network (HNN) system with tunable neural activation gradients. A novel memristor model is introduced to emulate the synaptic interactions between subnetworks. The system’s dynamical behavior is systematically analyzed through bifurcation diagrams, 2D dynamical distribution maps, and Lyapunov exponent (LE) spectra. Furthermore, spectral entropy (SE) is used to quantify the complexity of the network dynamics. The results reveal a rich set of brain-like chaotic behaviors, including complex bifurcation evolutions, transient chaos, and initial-induced coexisting attractors. These findings highlight the pivotal role of memristors in enabling flexible and complex dynamical regulation within brain-inspired coupling mechanisms. In addition, the proposed system is implemented via analog circuit design and successfully deployed on a DSP platform, demonstrating its potential for neuromorphic computing and real-time hardware applications. This study offers theoretical foundations and practical guidance for the design of neuromorphic systems and the analysis of nonlinear dynamics in coupled neural circuits.