Entropic stochastic resonance in a confined asymmetrical bistable system with non-Gaussian noise

2020 ◽  
Vol 63 ◽  
pp. 382-391 ◽  
Author(s):  
Jinzheng Xu ◽  
Xiaoqin Luo
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Bistable System ◽  
Non Gaussian

scholarly journals Stochastic resonance in FHN neural system driven by non-Gaussian noise

2012 ◽  
Vol 61 (13) ◽  
pp. 130502
Author(s):  
Zhang Jing-Jing ◽  
Jin Yan-Fei
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Neural System ◽  
Non Gaussian

STOCHASTIC RESONANCE IN BISTABLE AND EXCITABLE SYSTEMS: EFFECT OF NON-GAUSSIAN NOISES

2003 ◽  
Vol 03 (04) ◽  
pp. L365-L371 ◽  
Author(s):  
M. A. FUENTES ◽  
C. J. TESSONE ◽  
H. S. WIO ◽  
R. TORAL
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Excitable System ◽  
Ratio Curve ◽  
Bistable Systems ◽  
Excitable Systems ◽  
Noise Ratio ◽  
Non Gaussian

We analyze stochastic resonance in systems driven by non-Gaussian noises. For the bistable double well we compare the signal-to-noise ratio resulting from numerical simulations with some quasi-analytical results predicted by a consistent Markovian approximation in the case of a colored non-Gaussian noise. We also study the FitzHugh–Nagumo excitable system in the presence of the same noise. In both systems, we find that, as the noise departs from Gaussian behavior, there is a regime (different for the excitable and the bistable systems) in which there is a notable robustness against noise tuning since the signal-to-noise ratio curve broadens and becomes less sensitive to the actual value of the noise intensity. We also compare our results with some experiments in sensory systems.

CONSTRUCTIVE ACTION OF ADDITIVE NOISE IN OPTIMAL DETECTION

2005 ◽  
Vol 15 (09) ◽  
pp. 2985-2994 ◽  
Author(s):  
FRANÇOIS CHAPEAU-BLONDEAU ◽  
DAVID ROUSSEAU
Keyword(s):  
Information Processing ◽  
White Noise ◽  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Additive Noise ◽  
Optimal Detection ◽  
Processing Conditions ◽  
Noise Optimization ◽  
Additive White Noise ◽  
Non Gaussian

The optimal detection of a signal of known form hidden in additive white noise is examined in the framework of stochastic resonance and noise-aided information processing. Conditions are exhibited where the performance in the optimal detection increases when the level of the additive (non-Gaussian bimodal) noise is raised. On the additive signal–noise mixture, when a threshold quantization is performed prior to the optimal detection, another form of improvement by noise can be obtained, with subthreshold signals and Gaussian noise. Optimization of the quantization threshold shows that even in symmetric detection settings, the optimal threshold can be away from the center of symmetry and in subthreshold configuration of the signals. These properties concerning non-Gaussian noise and nonlinear preprocessing in optimal detection, are meaningful to the current exploration of the various modalities and potentialities of stochastic resonance.

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