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Neural signal transduction aided by noise in multisynaptic excitatory and inhibitory pathways with saturation

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  • Duan, Fabing
  • Chapeau-Blondeau, François
  • Abbott, Derek

Abstract

We study the stochastic resonance phenomenon in saturating dynamical models of neural signal transduction, at the synaptic stage, wherein the noise in multipathways enhances the processing of neuronal information integrated by excitatory and inhibitory synaptic currents. For an excitatory synaptic pathway, the additive intervention of an inhibitory pathway reduces the stochastic resonance effect. However, as the number of synaptic pathways increases, the signal transduction is greatly improved for parallel multipathways that feature both excitation and inhibition. The obtained results lead us to the realization that the collective property of inhibitory synapses assists neural signal transmission, and a parallel array of neurons can enhance their responses to multiple synaptic currents by adjusting the contributions of excitatory and inhibitory currents.

Suggested Citation

  • Duan, Fabing & Chapeau-Blondeau, François & Abbott, Derek, 2011. "Neural signal transduction aided by noise in multisynaptic excitatory and inhibitory pathways with saturation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(16), pages 2855-2862.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:16:p:2855-2862
    DOI: 10.1016/j.physa.2011.03.039
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    References listed on IDEAS

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    1. Tessone, Claudio J. & Wio, Horacio S., 2007. "Stochastic resonance in an extended FitzHugh–Nagumo system: The role of selective coupling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 374(1), pages 46-54.
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    3. Duan, Fabing & Chapeau-Blondeau, François & Abbott, Derek, 2009. "Input–output gain of collective response in an uncoupled parallel array of saturating dynamical subsystems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1345-1351.
    4. Ueda, Michihito, 2010. "Improvement of signal-to-noise ratio by stochastic resonance in sigmoid function threshold systems, demonstrated using a CMOS inverter," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 1978-1985.
    5. Gong, Yubing & Xie, Yanhang & Hao, Yinghang, 2009. "Coherence resonance induced by non-Gaussian noise in a deterministic Hodgkin–Huxley neuron," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(18), pages 3759-3764.
    6. Li, Jianlong & Xu, Bohou, 2006. "Effects of signal spectrum varying on signal processing by parameter-induced stochastic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 361(1), pages 11-23.
    7. Arun V. Holden, 2004. "Neural coding by correlation?," Nature, Nature, vol. 428(6981), pages 382-382, March.
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    Cited by:

    1. Liu, Jian & Cao, Jie & Wang, Youguo & Hu, Bing, 2019. "Asymmetric stochastic resonance in a bistable system driven by non-Gaussian colored noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 321-336.

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