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Chaos and bursting patterns in two-neuron Hopfield neural network and analog implementation

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  • Li, Fangyuan
  • Chen, Zhuguan
  • Bao, Han
  • Bai, Lianfa
  • Bao, Bocheng

Abstract

To demonstrate and elucidate bursting patterns and their bifurcation mechanisms, a two-neuron Hopfield neural network is proposed in this paper. The proposed non-autonomous model has a time-varying equilibrium point whose stability undergoes continuous evolution in response to changes in stimulation, and exhibits chaotic dynamics, especially the quasi-periodic and periodic bursting patterns. Over a full bursting cycle, the stability evolution of the time-varying equilibrium point triggers Hopf bifurcation and fold bifurcation, leading to the emergence of quasi-periodic or periodic bursting. To elucidate the bifurcation mechanisms, the transitions between the spiking state and the resting state are demonstrated, thereby identifying the Hopf/Hopf quasi-periodic bursting and fold/fold/Hopf periodic bursting. In addition, a simple analog electronic circuit is designed for the physical implementation of the non-autonomous model, and a printed-circuit board based hardware circuit is made to test the experimental results to verify the numerical results.

Suggested Citation

  • Li, Fangyuan & Chen, Zhuguan & Bao, Han & Bai, Lianfa & Bao, Bocheng, 2024. "Chaos and bursting patterns in two-neuron Hopfield neural network and analog implementation," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    • Handle: RePEc:eee:chsofr:v:184:y:2024:i:c:s0960077924005988
    DOI: 10.1016/j.chaos.2024.115046
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