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Vibrational resonance in feedforward neuronal network with unreliable synapses

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  • Ming Xue
  • Jiang Wang
  • Bin Deng
  • Xile Wei

Abstract

In this paper, we investigate vibrational resonance in feedforward neuronal network coupled in an all-to-all fashion. In contrast to earlier most work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on signal propagation in this work. It is shown that the neurotransmitter release probability and excitatory synaptic strength largely influence the signal propagation, and better tuning of these synaptic parameters makes the feedforward neuronal network support stable signal propagation. Furthermore, it is found that high-frequency driving plays important roles in causing the response of the feedforward neuronal network to subthreshold low-frequency signal and strengthening the ability of the signal propagation between layers. In particular, the optimal amplitude of high-frequency driving is largely influenced by the stochastic effect of neurotransmitter release and the coupling strength. Finally, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. It is demonstrated that unreliable synaptic coupling is more efficient than the random coupling for the transmission of local input signal. Considering that unreliable synapses are inevitable in neuronal communication, the presented results could have important implications for the weak signal detection and information propagation in neural systems. Copyright EDP Sciences, SIF, Springer-Verlag Berlin Heidelberg 2013

Suggested Citation

  • Ming Xue & Jiang Wang & Bin Deng & Xile Wei, 2013. "Vibrational resonance in feedforward neuronal network with unreliable synapses," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(4), pages 1-9, April.
  • Handle: RePEc:spr:eurphb:v:86:y:2013:i:4:p:1-9:10.1140/epjb/e2013-30782-3
    DOI: 10.1140/epjb/e2013-30782-3
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    Citations

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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. Qin, Ying-Mei & Che, Yan-Qiu & Zhao, Jia, 2018. "Effects of degree distributions on signal propagation in noisy feedforward neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 763-774.
    3. Ge, Mengyan & Lu, Lulu & Xu, Ying & Mamatimin, Rozihajim & Pei, Qiming & Jia, Ya, 2020. "Vibrational mono-/bi-resonance and wave propagation in FitzHugh–Nagumo neural systems under electromagnetic induction," Chaos, Solitons & Fractals, Elsevier, vol. 133(C).
    4. Yao, Zhao & Wang, Chunni, 2022. "Collective behaviors in a multiple functional network with hybrid synapses," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    5. Liu, Huixia & Lu, Lulu & Zhu, Yuan & Wei, Zhouchao & Yi, Ming, 2022. "Stochastic resonance: The response to envelope modulation signal for neural networks with different topologies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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    Keywords

    Statistical and Nonlinear Physics;

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