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White-noise analysis in visual neuroscience

Published online by Cambridge University Press:  02 June 2009

Hiroko M. Sakai ,
Naka Ken-Ichi  and
Michael J. Korenberg
Naka Ken-Ichi
Affiliation:
National Institute for Basic Biology, Okazaki, Japan
Michael J. Korenberg
Affiliation:
Department of Electrical Engineering, Queens University, Kingston, Ontario
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Abstract

In 1827, plant biologist Robert Brown discovered what is known as Brownian motion, a class of chaos. Formal derivative of Brownian motion is Gaussian white-noise. In 1938, Norbert Wiener proposed to use the Gaussian white-noise as an input probe to identify a system by a series of orthogonal functionals known as the Wiener G-functionals.

White-noise analysis is uniquely suited for studying the response dynamics of retinal neurons because (1) white-noise light stimulus is a modulation around a mean luminance, as are the natural photic inputs, and it is a highly efficient input; and (2) the analysis defines the response dynamics and can be extended to spike trains, the final output of the retina. Demonstrated here are typical examples and results from applications of white-noise analysis to a visual system.

Keywords

RetinaPiecewise linearizationStatic nonlinearitySpike generationWiener kernel

Information

Type
Research Article
Information
Visual Neuroscience , Volume 1 , Issue 3 , May 1988 , pp. 287 - 296
Copyright
Copyright © Cambridge University Press 1988

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