Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/84477
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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 1932-6203 |
Editor: | Chacron, M.J. |
Statement of Responsibility: | 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 Noise Models, Theoretical Signal Detection, Psychological Algorithms |
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: | http://dx.doi.org/10.1371/journal.pone.0091345 |
Appears in Collections: | Aurora harvest 2 Electrical and Electronic Engineering publications |
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hdl_84477.pdf | Published version | 632.33 kB | Adobe PDF | View/Open |
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