Stochastic resonance with colored noise for neural signal detection

Fabing Duan; François Chapeau-Blondeau; Derek Abbott

doi:10.1371/journal.pone.0091345

Stochastic resonance with colored noise for neural signal detection

PLoS One. 2014 Mar 14;9(3):e91345. doi: 10.1371/journal.pone.0091345. eCollection 2014.

Authors

Fabing Duan¹, François Chapeau-Blondeau², Derek Abbott³

Affiliations

¹ Institute of Complexity Science, Qingdao University, Qingdao, P. R. China.
² Laboratoire d'Ingénierie des Systèmes Automatisés, Université d'Angers, Angers, France.
³ Centre for Biomedical Engineering and School of Electrical & Electronic Engineering, The University of Adelaide, Adelaide, Southern Australia, Australia.

Abstract

We analyze signal detection with nonlinear test statistics in the presence of colored noise. In the limits of small signal and weak noise correlation, the optimal test statistic and its performance are derived under general conditions, especially concerning the type of noise. We also analyze, for a threshold nonlinearity-a key component of a neural model, the conditions for noise-enhanced performance, establishing that colored noise is superior to white noise for detection. For a parallel array of nonlinear elements, approximating neurons, we demonstrate even broader conditions allowing noise-enhanced detection, via a form of suprathreshold stochastic resonance.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Algorithms
Models, Theoretical
Neurons / physiology*
Noise*
Signal Detection, Psychological*
Stochastic Processes*

Grants and funding

This work is sponsored by the NSF of Shandong Province (No. ZR2010FM006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.