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 FitzHugh–Nagumo neural system driven by correlated non-Gaussian noise and Gaussian noise

2017 ◽  
Vol 31 (32) ◽  
pp. 1750264 ◽  
Author(s):  
Yong-Feng Guo ◽  
Bei Xi ◽  
Fang Wei ◽  
Jian-Guo Tan
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Neural System ◽  
Integral Approach ◽  
Stationary Probability Distribution ◽  
Non Gaussian ◽  
Two State Model

In this paper, the phenomenon of stochastic resonance in FitzHugh–Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.

Inverse stochastic resonance in Hodgkin–Huxley neural system driven by Gaussian and non-Gaussian colored noises

2020 ◽  
Vol 100 (1) ◽  
pp. 877-889 ◽  
Author(s):  
Lulu Lu ◽  
Ya Jia ◽  
Mengyan Ge ◽  
Ying Xu ◽  
Anbang Li
Keyword(s):  
Stochastic Resonance ◽  
Neural System ◽  
Non Gaussian

Stochastic Kinetics for a FitzHugh–Nagumo Neural System with Time Delay Driven by Non-Gaussian Noise and a Multiplicative Periodic Signal

2019 ◽  
Vol 18 (04) ◽  
pp. 1950027 ◽  
Author(s):  
Kang-Kang Wang ◽  
De-Cai Zong ◽  
Hui Ye ◽  
Ya-Jun Wang
Keyword(s):  
Time Delay ◽  
Probability Density ◽  
Gaussian Noise ◽  
Correlation Time ◽  
Neural System ◽  
Noise Correlation ◽  
Stable States ◽  
The Stability ◽  
Non Gaussian ◽  
System With Time Delay

In the present paper, the stability and the phenomena of stochastic resonance (SR) for a FitzHugh–Nagumo (FHN) system with time delay driven by a multiplicative non-Gaussian noise and an additive Gaussian white noise are investigated. By using the fast descent method, unified colored noise approximation and the two-state theory for the SR, the expressions for the stationary probability density function (SPDF) and the signal-to-noise ratio (SNR) are obtained. The research results show that the two noise intensities and time delay can always decrease the probability density at the two stable states and impair the stability of the neural system; while the noise correlation time [Formula: see text] can increase the probability density around both stable states and consolidate the stability of the neural system. Furthermore, the other noise correlation time [Formula: see text] can increase the probability at the resting state, but reduce that around the excited state. With respect to the SNR, it is discovered that the two noise strengths can both weaken the SR effect, while time delay [Formula: see text] and the departure parameter [Formula: see text] will always amplify the SR phenomenon. Moreover, the noise correlation time [Formula: see text] can motivate the SR effect, but not alter the peak value of the SNR. What’s most interesting is that the other noise correlation time [Formula: see text] can not only stimulate the SR phenomenon, but also results in the occurrence of two resonant peaks, whose heights are simultaneously improved because of the action of [Formula: see text].

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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