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Multi-channels coupling-induced pattern transition in a tri-layer neuronal network

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Listed:
  • Wu, Fuqiang
  • Wang, Ya
  • Ma, Jun
  • Jin, Wuyin
  • Hobiny, Aatef

Abstract

Neurons in nerve system show complex electrical behaviors due to complex connection types and diversity in excitability. A tri-layer network is constructed to investigate the signal propagation and pattern formation by selecting different coupling channels between layers. Each layer is set as different states, and the local kinetics is described by Hindmarsh–Rose neuron model. By changing the number of coupling channels between layers and the state of the first layer, the collective behaviors of each layer and synchronization pattern of network are investigated. A statistical factor of synchronization on each layer is calculated. It is found that quiescent state in the second layer can be excited and disordered state in the third layer is suppressed when the first layer is controlled by a pacemaker, and the developed state is dependent on the number of coupling channels. Furthermore, the collapse in the first layer can cause breakdown of other layers in the network, and the mechanism is that disordered state in the third layer is enhanced when sampled signals from the collapsed layer can impose continuous disturbance on the next layer.

Suggested Citation

  • Wu, Fuqiang & Wang, Ya & Ma, Jun & Jin, Wuyin & Hobiny, Aatef, 2018. "Multi-channels coupling-induced pattern transition in a tri-layer neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 54-68.
    • Handle: RePEc:eee:phsmap:v:493:y:2018:i:c:p:54-68
    DOI: 10.1016/j.physa.2017.10.041
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    References listed on IDEAS

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    1. Song, Xinlin & Wang, Chunni & Ma, Jun & Ren, Guodong, 2016. "Collapse of ordered spatial pattern in neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 95-112.
    2. Ma, Jun & Xu, Ying & Wang, Chunni & Jin, Wuyin, 2016. "Pattern selection and self-organization induced by random boundary initial values in a neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 586-594.
    3. Yilmaz, Ergin & Baysal, Veli & Ozer, Mahmut & Perc, Matjaž, 2016. "Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 538-546.
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    Cited by:

    1. Ouannas, Adel & Batiha, Iqbal M. & Bekiros, Stelios & Liu, Jinping & Jahanshahi, Hadi & Aly, Ayman A. & Alghtani, Abdulaziz H., 2021. "Synchronization of the glycolysis reaction-diffusion model via linear control law," LSE Research Online Documents on Economics 112776, London School of Economics and Political Science, LSE Library.
    2. Wu, Fuqiang & Zhou, Ping & Alsaedi, Ahmed & Hayat, Tasawar & Ma, Jun, 2018. "Synchronization dependence on initial setting of chaotic systems without equilibria," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 124-132.
    3. Rostami, Zahra & Rajagopal, Karthikeyan & Khalaf, Abdul Jalil M. & Jafari, Sajad & Perc, Matjaž & Slavinec, Mitja, 2018. "Wavefront-obstacle interactions and the initiation of reentry in excitable media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1162-1173.
    4. Yao, Zhao & Zhou, Ping & Alsaedi, Ahmed & Ma, Jun, 2020. "Energy flow-guided synchronization between chaotic circuits," Applied Mathematics and Computation, Elsevier, vol. 374(C).

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