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Firing patterns transition and desynchronization induced by time delay in neural networks

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  • Huang, Shoufang
  • Zhang, Jiqian
  • Wang, Maosheng
  • Hu, Chin-Kun

Abstract

We used the Hindmarsh–Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

Suggested Citation

  • Huang, Shoufang & Zhang, Jiqian & Wang, Maosheng & Hu, Chin-Kun, 2018. "Firing patterns transition and desynchronization induced by time delay in neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 88-97.
    • Handle: RePEc:eee:phsmap:v:499:y:2018:i:c:p:88-97
    DOI: 10.1016/j.physa.2018.01.039
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    References listed on IDEAS

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    5. Wu, Hao & Jiang, Huijun & Hou, Zhonghuai, 2011. "Spatiotemporal dynamics on small-world neuronal networks: The roles of two types of time-delayed coupling," Chaos, Solitons & Fractals, Elsevier, vol. 44(10), pages 836-844.
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    Cited by:

    1. Huang, Shoufang & Zhang, Jiqian & Hu, Chin-Kun, 2019. "Effects of external stimulations on transition behaviors in neural network with time-delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Sabouri, Amir & Ghasemi, Mahdieh & Mehrabbeik, Mahtab, 2023. "The dynamical analysis of non-uniform neocortical network model in up-down state oscillations," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    3. Shin, Ki-Hong & Baek, Woonhak & Kim, Kyungsik & You, Cheol-Hwan & Chang, Ki-Ho & Lee, Dong-In & Yum, Seong Soo, 2019. "Neural network and regression methods for optimizations between two meteorological factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 778-796.
    4. Djeundam, S.R. Dtchetgnia & Filatrella, G. & Yamapi, R., 2018. "Desynchronization effects of a current-driven noisy Hindmarsh–Rose neural network," Chaos, Solitons & Fractals, Elsevier, vol. 115(C), pages 204-211.

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