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Chaos shapes transient synchrony activities and switchings in the excitatory-inhibitory networks

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Abstract

Chaotic dynamics have been evidenced in experimental data and numerical simulations ranging from the most straightforward individual neurons to the more complex neural groups. Nevertheless, whether chaotic behavior significantly contributes to the emergence of phase shift and spontaneous switchings between transient synchrony activities is subtle and remains elusive. We investigated the emergence of spontaneous transient synchrony activities and self-induced switchings in an excitatory-inhibitory network. We demonstrate that the dynamic nature of isolated neurons plays an essential role in the emergence of chaotic behavior, thereby affecting spontaneous transient synchrony and types of phase shift. We also show that the neural network of the cells with chaotic characteristics after synaptic interactions exhibits a broader range of excitatory transient synchronous dynamics. Our results offer several possibilities for explaining sensory perception and brain disease observed in experiments.

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Funding

This work was supported by National Natural Science Foundation of China (Grants Nos. 12165016, 12005079 and 12305043), the funding for the Scientific Research Startup of Jiangsu University, China (Grant Nos.  4111190017 and 4111710001), the Natural Science Foundation of Jiangsu Province, China (Grant No. BK20220511). M. Zheng. appreciates the support from the Jiangsu Specially-Appointed Professor Program.

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  1. School of Physics and Electronic Engineering, Jiangsu University, Zhenjiang, 213012, Jiangsu, China

    Gaobiao Zhu, Muhua Zheng & Kesheng Xu

  2. Teaching Department, Jiangsu University Jingjiang College, Zhenjiang, 212028, Jiangsu, China

    Yan Zhang

  3. School of Mathematical Sciences, Jiangsu University, Zhenjiang, 213012, Jiangsu, China

    Jiao Wu

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  1. Gaobiao Zhu
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Zhu, G., Zhang, Y., Wu, J. et al. Chaos shapes transient synchrony activities and switchings in the excitatory-inhibitory networks. Nonlinear Dyn 112, 7555–7570 (2024). https://doi.org/10.1007/s11071-024-09471-5

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