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Noise-induced coexisting firing patterns in hybrid-synaptic interacting networks

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  • Wang, Xinyi
  • Zhang, Xiyun
  • Zheng, Muhua
  • Xu, Leijun
  • Xu, Kesheng

Abstract

Variability in synaptic conductance has been evidenced by neuroscientists, but the precise mechanisms whereby it contributes to coexisting firing activities are subtle and remain elusive. We present an excitatory–inhibitory balanced neural network of nodes with hybrid synaptic coupling to investigate the dynamic role of synaptic noise intensity and excitatory weight in the appearance of coexisting firing patterns. This neural network can produce two forms of coexisting firing patterns: time-varying and parameter-dependent multistability under different combinations of synaptic noise intensity and excitatory weight. Synaptic noise intensity and excitatory weight are significant but different in producing two forms of coexisting firing states. Synaptic noise is beneficial for the emergence of various firing patterns and their firing transitions, and excitatory weight can change the synaptic noise intensity range of coexisting firing states. Our results shed light on the emergence of multistability in neuronal networks with hybrid interactions and peak synaptic variability.

Suggested Citation

  • Wang, Xinyi & Zhang, Xiyun & Zheng, Muhua & Xu, Leijun & Xu, Kesheng, 2023. "Noise-induced coexisting firing patterns in hybrid-synaptic interacting networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    • Handle: RePEc:eee:phsmap:v:615:y:2023:i:c:s0378437123001462
    DOI: 10.1016/j.physa.2023.128591
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    References listed on IDEAS

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