M-ary suprathreshold stochastic resonance in multilevel threshold systems with signal-dependent noise

2017 ◽  
Vol 479 ◽  
pp. 48-56 ◽  
Author(s):  
Chaojun Cheng ◽  
Bingchang Zhou ◽  
Xiao Gao ◽  
Mark D. McDonnell
Keyword(s):  
Stochastic Resonance ◽  
Suprathreshold Stochastic Resonance

Enhancing threshold neural network via suprathreshold stochastic resonance for pattern classification

2021 ◽  
pp. 127387
Author(s):  
Xiaojie Liu ◽  
Lingling Duan ◽  
Fabing Duan ◽  
François Chapeau-Blondeau ◽  
Derek Abbott
Keyword(s):  
Neural Network ◽  
Stochastic Resonance ◽  
Pattern Classification ◽  
Suprathreshold Stochastic Resonance

scholarly journals Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

2017 ◽  
Vol 4 (9) ◽  
pp. 160889 ◽  
Author(s):  
Liyan Xu ◽  
Fabing Duan ◽  
Xiao Gao ◽  
Derek Abbott ◽  
Mark D. McDonnell
Keyword(s):  
Stochastic Resonance ◽  
Recursive Algorithm ◽  
Signal Strength ◽  
Distinct Form ◽  
Mean Square ◽  
Least Mean Square ◽  
Complete Knowledge ◽  
The Mean ◽  
Suprathreshold Stochastic Resonance ◽  
Over Time

Suprathreshold stochastic resonance (SSR) is a distinct form of stochastic resonance, which occurs in multilevel parallel threshold arrays with no requirements on signal strength. In the generic SSR model, an optimal weighted decoding scheme shows its superiority in minimizing the mean square error (MSE). In this study, we extend the proposed optimal weighted decoding scheme to more general input characteristics by combining a Kalman filter and a least mean square (LMS) recursive algorithm, wherein the weighted coefficients can be adaptively adjusted so as to minimize the MSE without complete knowledge of input statistics. We demonstrate that the optimal weighted decoding scheme based on the Kalman–LMS recursive algorithm is able to robustly decode the outputs from the system in which SSR is observed, even for complex situations where the signal and noise vary over time.

Suprathreshold stochastic resonance: decoding

2009 ◽  
pp. 167-232
Author(s):  
Mark D. McDonnell ◽  
Nigel G. Stocks ◽  
Charles E. M. Pearce ◽  
Derek Abbott
Keyword(s):  
Stochastic Resonance ◽  
Suprathreshold Stochastic Resonance
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