Stochastic Resonance. From Suprathreshold Stochastic Resonance to Stochastic Signal Quantization, by M.D. McDonnell, N.G. Stocks, C.E.M. Pearce and D. Abbott

Signal quantization in the presence of independent, identically distributed, large amplitude threshold noise is examined. It has previously been shown that when all quantization thresholds are set to the same value, this situation exhibits a form of stochastic resonance known as suprathreshold stochastic resonance. This means the optimal quantizer performance occurs for a small input signal-to-noise ratio. Here we examine the performance of this stochastic quantization in terms of both mutual information and mean square error distortion. It is also shown that for low input signal-to-noise ratios that the case of all thresholds being identical provides the optimal mean square error distortion performance for the given noise conditions.

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Enhancing threshold neural network via suprathreshold stochastic resonance for pattern classification

Physics Letters A ◽

10.1016/j.physleta.2021.127387 ◽

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

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Preprocessor based on suprathreshold stochastic resonance for improved bearing estimation in shallow ocean

OCEANS 2009 ◽

10.23919/oceans.2009.5422099 ◽

2009 ◽

Author(s):

V. N. Hari ◽

G. V. Anand ◽

A. B. Premkumar ◽

A. S. Madhukumar

Keyword(s):

Stochastic Resonance ◽

Bearing Estimation ◽

Suprathreshold Stochastic Resonance

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Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

Royal Society Open Science ◽

10.1098/rsos.160889 ◽

2017 ◽

Vol 4 (9) ◽

pp. 160889 ◽

Cited By ~ 5

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.

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