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Collapse of ordered spatial pattern in neuronal network

Author

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  • Song, Xinlin
  • Wang, Chunni
  • Ma, Jun
  • Ren, Guodong

Abstract

Spatiotemporal systems can emerge some regular spatial patterns due to self organization or under external periodical pacing while external attack or intrinsic collapse can destroy the regularity in the spatial system. For an example, the electrical activities of neurons in nervous system show regular spatial distribution under appropriate coupling and connection. It is believed that distinct regularity could be induced in the media by appropriate forcing or feedback, while a diffusive collapse induced by continuous destruction can cause breakdown of the media. In this paper, the collapse of ordered spatial distribution is investigated in a regular network of neurons (Morris–Lecar, Hindmarsh–Rose) in two-dimensional array. A stable target wave is developed regular spatial distribution emerges by imposing appropriate external forcing with diversity, or generating heterogeneity (parameter diversity in space). The diffusive invasion could be produced by continuous parameter collapse or switch in local area, e.g, the diffusive poisoning in ion channels of potassium in Morris–Lecar neurons causes breakdown in conductance of channels. It is found that target wave-dominated regularity can be suppressed when the collapsed area is diffused in random. Statistical correlation functions for sampled nodes (neurons) are defined to detect the collapse of ordered state by series analysis.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:phsmap:v:451:y:2016:i:c:p:95-112
    DOI: 10.1016/j.physa.2016.01.049
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    References listed on IDEAS

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    1. Gu, Hua-Guang & Jia, Bing & Li, Yu-Ye & Chen, Guan-Rong, 2013. "White noise-induced spiral waves and multiple spatial coherence resonances in a neuronal network with type I excitability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1361-1374.
    2. Lu, Jianquan & Cao, Jinde, 2007. "Synchronization-based approach for parameters identification in delayed chaotic neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(2), pages 672-682.
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    Cited by:

    1. 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.
    2. Xie, Qingshuang & Wang, Tonghuan & Zeng, Chunhua & Dong, Xiaohui & Guan, Lin, 2018. "Predicting fluctuations-caused regime shifts in a time delayed dynamics of an invading species," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 69-83.
    3. Liu, Yong & Ren, Guodong & Zhou, Ping & Hayat, Tasawar & Ma, Jun, 2019. "Synchronization in networks of initially independent dynamical systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 370-380.
    4. Guo, Shengli & Xu, Ying & Wang, Chunni & Jin, Wuyin & Hobiny, Aatef & Ma, Jun, 2017. "Collective response, synapse coupling and field coupling in neuronal network," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 120-127.
    5. Xu, Ying & Jia, Ya & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir, 2017. "Synchronization between neurons coupled by memristor," Chaos, Solitons & Fractals, Elsevier, vol. 104(C), pages 435-442.
    6. Qin, Huixin & Wang, Chunni & Cai, Ning & An, Xinlei & Alzahrani, Faris, 2018. "Field coupling-induced pattern formation in two-layer neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 141-152.
    7. Shengli Guo & Jun Tang & Jun Ma & Chunni Wang, 2017. "Autaptic Modulation of Electrical Activity in a Network of Neuron-Coupled Astrocyte," Complexity, Hindawi, vol. 2017, pages 1-13, June.

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