Adaptive recursive algorithm for optimal weighted suprathreshold stochastic resonance

Author:

Xu Liyan1ORCID,Duan Fabing1,Gao Xiao2,Abbott Derek3ORCID,McDonnell Mark D.23

Affiliation:

1. Institute of Complexity Science, Qingdao University, Qingdao 266071, People's Republic of China

2. Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, School of Information Technology and Mathematical Sciences, University of South Australia, Adelaide, South Australia 5095, Australia

3. Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, South Australia 5005, Australia

Abstract

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.

Funder

National Natural Science Foundation of China

the Science and Technology Development Program of Shandong Province

Publisher

The Royal Society

Subject

Multidisciplinary

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