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Wave propagation and synchronization induced by chemical autapse in chain Hindmarsh–Rose neural network

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  • Ge, Mengyan
  • Jia, Ya
  • Xu, Ying
  • Lu, Lulu
  • Wang, Huiwen
  • Zhao, Yunjie

Abstract

In this paper, based on a chain Hindmarsh–Rose (HR) neural network under the action of electromagnetic field, the effects of connection strength between adjacent neurons on the wave propagation are investigated by utilizing numerical simulations. When the connection strength is increased via the decreasing of distance from central neuron, it is found that the firing rates of neurons in chain HR neural network are increased, and the velocity of wave propagation also becomes fast with the increasing of connection strength maximum. The chemical autapse imposed on the central neuron has a great influence on the firing rates of neurons and the wave propagation with different autaptic intensities. The firing rates of neurons are high, and many neurons can stand the excited state by increasing the field coupling strength. However, when the connection strength is decreased via the decreasing of distance from central neuron, the influences of connection strength maximum on the wave propagation are very small. The synchronization factor of the chain HR neural network is investigated by changing the maximum of connection strength, the autaptic intensity, and the field coupling intensity, respectively. It is found that the larger the field coupling strength is, the better the synchronization of neurons in the chain neural network will be, and the firing rates of neurons are high.

Suggested Citation

  • Ge, Mengyan & Jia, Ya & Xu, Ying & Lu, Lulu & Wang, Huiwen & Zhao, Yunjie, 2019. "Wave propagation and synchronization induced by chemical autapse in chain Hindmarsh–Rose neural network," Applied Mathematics and Computation, Elsevier, vol. 352(C), pages 136-145.
    • Handle: RePEc:eee:apmaco:v:352:y:2019:i:c:p:136-145
    DOI: 10.1016/j.amc.2019.01.059
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    11. Lu, Lulu & Ge, Mengyan & Xu, Ying & Jia, Ya, 2019. "Phase synchronization and mode transition induced by multiple time delays and noises in coupled FitzHugh–Nagumo model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    12. Aghababaei, Sajedeh & Balaraman, Sundarambal & Rajagopal, Karthikeyan & Parastesh, Fatemeh & Panahi, Shirin & Jafari, Sajad, 2021. "Effects of autapse on the chimera state in a Hindmarsh-Rose neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    13. Yu, Dong & Wu, Yong & Yang, Lijian & Zhao, Yunjie & Jia, Ya, 2023. "Effect of topology on delay-induced multiple resonances in locally driven systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    14. Shadizadeh, S. Mohadeseh & Nazarimehr, Fahimeh & Jafari, Sajad & Rajagopal, Karthikeyan, 2022. "Investigating different synaptic connections of the Chay neuron model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
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