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Spatiotemporal patterns and chaotic burst synchronization in a small-world neuronal network

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  • Zheng, Yan Hong
  • Lu, Qi Shao

Abstract

The spatiotemporal patterns and chaotic burst synchronization of a small-world neuronal network are studied in this paper. The synchronization parameter, similarity parameter and order parameter are introduced to investigate the dynamics behaviour of the neurons. Chaotic burst synchronization and nearly complete synchronization can be observed if the link probability and the coupling strength are large enough. It is found that with increasing link probability and the coupling strength chaotic bursts become appreciably synchronous in space and coherent in time, and the maximal spatiotemporal order appears at some particular values of the probability and the coupling strength, respectively. The larger the size of the network, the smaller the probability and the coupling strength are needed for the network to achieve burst synchronization. Moreover, the bursting activity and the spatiotemporal patterns are robust to small noise.

Suggested Citation

  • Zheng, Yan Hong & Lu, Qi Shao, 2008. "Spatiotemporal patterns and chaotic burst synchronization in a small-world neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3719-3728.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:14:p:3719-3728
    DOI: 10.1016/j.physa.2008.02.039
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    References listed on IDEAS

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    1. M. E. J. Newman & D. J. Watts, 1999. "Renormalization Group Analysis of the Small-World Network Model," Working Papers 99-04-029, Santa Fe Institute.
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    4. Wang, Qing Yun & Lu, Qi Shao & Guan Rong Chen,, 2007. "Ordered bursting synchronization and complex wave propagation in a ring neuronal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(2), pages 869-878.
    5. M. E. J. Newman & D. J. Watts, 1999. "Scaling and Percolation in the Small-World Network Model," Working Papers 99-05-034, Santa Fe Institute.
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    Cited by:

    1. Wang, Baoying & Gong, Yubing & Xie, Huijuan & Wang, Qi, 2016. "Optimal autaptic and synaptic delays enhanced synchronization transitions induced by each other in Newman–Watts neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 372-378.
    2. Gong, Yubing & Xie, Yanhang & Lin, Xiu & Hao, Yinghang & Ma, Xiaoguang, 2010. "Ordering chaos and synchronization transitions by chemical delay and coupling on scale-free neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 43(1), pages 96-103.
    3. Parastesh, Fatemeh & Rajagopal, Karthikeyan & Alsaadi, Fawaz E. & Hayat, Tasawar & Pham, V.-T. & Hussain, Iqtadar, 2019. "Birth and death of spiral waves in a network of Hindmarsh–Rose neurons with exponential magnetic flux and excitable media," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 377-384.
    4. Hao, Yinghang & Gong, Yubing & Wang, Li & Ma, Xiaoguang & Yang, Chuanlu, 2011. "Single or multiple synchronization transitions in scale-free neuronal networks with electrical or chemical coupling," Chaos, Solitons & Fractals, Elsevier, vol. 44(4), pages 260-268.
    5. Li, Chengren & Lü, Ling & Sun, Ying & Wang, Ying & Wang, Wenjun & Sun, Ao, 2016. "Parameter identification and synchronization for uncertain network group with different structures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 457(C), pages 624-631.

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