Please use this identifier to cite or link to this item:
Scopus Web of Science® Altmetric
Type: Journal article
Title: Stochastic resonance with colored noise for neural signal detection
Author: Duan, F.
Chapeau-Blondeau, F.
Abbott, D.
Citation: PLoS One, 2014; 9(3):e91345-
Publisher: Public Library of Science
Issue Date: 2014
ISSN: 1932-6203
Editor: Chacron, M.J.
Statement of
Fabing Duan, François Chapeau-Blondeau, Derek Abbott
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.
Keywords: Neurons
Stochastic Processes
Models, Theoretical
Signal Detection, Psychological
Rights: © 2014 Duan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
DOI: 10.1371/journal.pone.0091345
Published version:
Appears in Collections:Aurora harvest 2
Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_84477.pdfPublished version632.33 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.