Czanner, Gabriela, Eden, Uri T. and Brown, E. N. (Emery N.) (2008) A signal-to-noise ratio estimator for generalized linear model systems. In: World Congress on Engineering 2008, Imperial College London, England, Jul 02-04, 2008. Published in: Lecture Notes in Engineering and Computer Science (Proceedings of the World Congress on Engineering 2008), Vol.2171 pp. 1063-1069.

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Abstract

The signal-to-noise ratio (SNR) is a commonly used measure of system fidelity estimated as the ratio of the variance of a signal to the variance of the noise. Although widely used in analyses of physical systems, this estimator is not appropriate for point process models of neural systems or other non-Gaussian and/or non-additive signal and noise systems. We show that the extension of the standard estimator to the class of generalized linear models (GLM) yields a new SNR estimator that is ratio of two estimated prediction errors. Each prediction error estimate is an approximate chi-squared random variable whose expected value is given by its number of degrees of freedom. This allows us to compute a new bias-corrected SNR estimator. We illustrate its application in a study of simulated neural spike trains from a point process model in which the signal is task-specific modulation across multiple trials of a neurophysiological experiment. The new estimator characterizes the SNR of a neural system in terms commonly used for physical systems. It can be further extended to analyze any system in which modulation of the system's response by distinct signal components can be expressed as separate components of a likelihood function.

Item Type:	Conference Item (UNSPECIFIED)
Subjects:	Q Science > QA Mathematics T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Linear models (Statistics), Point processes, Information theory, Statistical communication theory, Signal processing, Analysis of variance
Journal or Publication Title:	Lecture Notes in Engineering and Computer Science (Proceedings of the World Congress on Engineering 2008)
Publisher:	International Association of Engineers
ISSN:	978-988-17012-3-7
Date:	2008
Volume:	Vol.2171
Page Range:	pp. 1063-1069
Status:	Not Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access
Title of Event:	World Congress on Engineering 2008
Type of Event:	Other
Location of Event:	Imperial College London, England
Date(s) of Event:	Jul 02-04, 2008
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URI:	http://wrap.warwick.ac.uk/id/eprint/43460

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