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Synchronization stability in conductance-based neural networks under electromagnetic modulation

Author

Listed:
  • Ye, Zhiqiu
  • Liu, Lu
  • Liu, Yingqi
  • Zeng, Jiapei
  • Xie, Ying
  • Jia, Ya
  • Yang, Lijian

Abstract

Neuronal synchronization plays a crucial role in maintaining brain function and regulating neural rhythms, and its stability is influenced by both synaptic coupling and external regulatory mechanisms. In particular, how electromagnetic induction modulates synchronization stability remains insufficiently understood, especially in conductance-based neural networks, where varying synaptic conductance introduce more complex synchronization dynamics. In this study, we employ a modified Morris-Lecar (mML) neuron model incorporating a flux-dependent feedback mechanism to construct conductance-based neural networks, and systematically analyze the synergistic effects of electromagnetic induction and synaptic conductance on synchronization stability using the master stability function (MSF). The results show that in excitatory networks, electromagnetic modulation exhibits a monotonic enhancement effect on synchronization stability, with higher conductance requiring stronger electromagnetic gain to maintain stability. In contrast, inhibitory networks exhibit a non-monotonic modulation: electromagnetic feedback suppresses synchronization at low conductance levels, but as conductance increases, it initially promotes and then weakens synchronization stability, reflecting a dynamic mechanism based on the balance of interacting currents. Numerical simulations consistent well with the theoretical predictions of MSF across various representative network topologies. These findings reveal the complex coupling mechanism between electromagnetic feedback and synaptic conductance, providing theoretical insight and modeling foundations for understanding synchronization regulation in complex neuronal systems.

Suggested Citation

  • Ye, Zhiqiu & Liu, Lu & Liu, Yingqi & Zeng, Jiapei & Xie, Ying & Jia, Ya & Yang, Lijian, 2025. "Synchronization stability in conductance-based neural networks under electromagnetic modulation," Chaos, Solitons & Fractals, Elsevier, vol. 200(P2).
    • Handle: RePEc:eee:chsofr:v:200:y:2025:i:p2:s0960077925010963
    DOI: 10.1016/j.chaos.2025.117083
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    References listed on IDEAS

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    1. Yu, Dong & Lu, Lulu & Wang, Guowei & Yang, Lijian & Jia, Ya, 2021. "Synchronization mode transition induced by bounded noise in multiple time-delays coupled FitzHugh–Nagumo model," Chaos, Solitons & Fractals, Elsevier, vol. 147(C).
    2. Wu, Yong & Xie, Ying & Huang, Weifang & Jia, Ya & Ye, Zhiqiu & Ding, Qianming, 2025. "Effects of network structure and node dynamics on synchronization with time delays," Chaos, Solitons & Fractals, Elsevier, vol. 196(C).
    3. Ramasamy, Mohanasubha & Devarajan, Subhasri & Kumarasamy, Suresh & Rajagopal, Karthikeyan, 2022. "Effect of higher-order interactions on synchronization of neuron models with electromagnetic induction," Applied Mathematics and Computation, Elsevier, vol. 434(C).
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