Open AccessAperiodic stochastic resonance in neural information processing with Gaussian colored noise
Author(s) -
Yanmei Kang,
Ruonan Liu,
Xuerong Mao
Publication year - 2020
Publication title -
cognitive neurodynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 41
eISSN - 1871-4099
pISSN - 1871-4080
DOI - 10.1007/s11571-020-09632-3
Subject(s) - aperiodic graph , stochastic resonance , colors of noise , bistability , gaussian noise , noise (video) , statistical physics , gaussian , artificial neural network , computer science , stochastic process , trajectory , mathematics , algorithm , physics , artificial intelligence , quantum mechanics , statistics , noise reduction , combinatorics , image (mathematics)
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.