Aperiodic stochastic resonance in neural information processing with Gaussian colored noise

Kang, Yanmei and Liu, Ruonan and Mao, Xuerong (2020) Aperiodic stochastic resonance in neural information processing with Gaussian colored noise. Cognitive Neurodynamics. ISSN 1871-4099 (In Press)

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Abstract

The aim of this paper is to explore the phenomenon of aperiodic stochastic resonance in neural systems with colored noise. For nonlinear dynamical systems driven by Gaussian colored noise, we prove that the stochastic sample trajectory can converge to the corresponding deterministic trajectory as noise intensity tends to zero in mean square, under global and local Lipschitz conditions, respectively. Then, following forbidden interval theorem we predict the phenomenon of aperiodic stochastic resonance in bistable and excitable neural systems. Two neuron models are further used to verify the theoretical prediction. Moreover, we disclose the phenomenon of aperiodic stochastic resonance induced by correlation time and this finding suggests that adjusting noise correlation might be a biologically more plausible mechanism in neural signal processing.

ORCID iDs

Kang, Yanmei, Liu, Ruonan and Mao, Xuerong ORCID logoORCID: https://orcid.org/0000-0002-6768-9864;
  • Item type:Article
    ID code:73869
    Dates:
    Date
    Event
    1 September 2020
    Published
    1 September 2020
    Accepted
    Keywords:Ornstein-Ulenbeck process, local Lipschitz condition, aperiodic stochastic resonance, mutual information, Mathematics, Cognitive Neuroscience, Mathematics(all)
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:16 Sep 2020 12:49
    Last modified:18 Jan 2023 11:09
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/73869
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