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Spike coherence and synchronization on Newman–Watts small-world neuronal networks modulated by spike-timing-dependent plasticity

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  • Yu, Haitao
  • Guo, Xinmeng
  • Wang, Jiang
  • Deng, Bin
  • Wei, Xile

Abstract

The effects of spike-timing-dependent plasticity (STDP) and noise intensity on the temporal and spatial dynamics of Newman–Watts small-world neuronal networks are studied. Numerical results show that, an intermediate intensity of additive noise can optimize the dynamical response of the neural system, where the noise-induced coherence resonance and spiking synchronization occur. The adaptive coupling modulated by STDP can largely depress the temporal coherence and spatial synchrony induced by external noise and random shortcuts. In particular, as the adjusting rate increases, lower noise intensity is needed to maximize the networked synchronization, and more connections are introduced to achieve coherence resonance. Moreover, the small-world topology can significantly affect the dynamics of excitable neuronal networks. It is found that the temporal coherence of neuronal activity reaches peaks for an appropriate number of random shortcuts, while the spiking synchronization is always enhanced as more shortcuts are added into the network.

Suggested Citation

  • Yu, Haitao & Guo, Xinmeng & Wang, Jiang & Deng, Bin & Wei, Xile, 2015. "Spike coherence and synchronization on Newman–Watts small-world neuronal networks modulated by spike-timing-dependent plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 307-317.
    • Handle: RePEc:eee:phsmap:v:419:y:2015:i:c:p:307-317
    DOI: 10.1016/j.physa.2014.10.031
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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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    Cited by:

    1. Yu, Haitao & Guo, Xinmeng & Wang, Jiang & Deng, Bin & Wei, Xile, 2015. "Vibrational resonance in adaptive small-world neuronal networks with spike-timing-dependent plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 436(C), pages 170-179.
    2. Li, Tianyu & Wu, Yong & Yang, Lijian & Zhan, Xuan & Jia, Ya, 2022. "Spike-timing-dependent plasticity enhances chaotic resonance in small-world network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    3. Xie, Huijuan & Gong, Yubing, 2017. "Multiple coherence resonances and synchronization transitions by time delay in adaptive scale-free neuronal networks with spike-timing-dependent plasticity," Chaos, Solitons & Fractals, Elsevier, vol. 94(C), pages 80-85.
    4. Yu, Haitao & Zhang, Lianghao & Guo, Xinmeng & Wang, Jiang & Cao, Yibin & Liu, Jing, 2018. "Effect of inhibitory firing pattern on coherence resonance in random neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1201-1210.
    5. Xie, Huijuan & Gong, Yubing & Wang, Baoying, 2018. "Spike-timing-dependent plasticity optimized coherence resonance and synchronization transitions by autaptic delay in adaptive scale-free neuronal networks," Chaos, Solitons & Fractals, Elsevier, vol. 108(C), pages 1-7.

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