McDonnell, Mark D., 1975- and Stocks, Nigel G.. (2008) Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations. Physical Review Letters, Vol.101 (No.5). ISSN 0031-9007

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Abstract

A general method for deriving maximally informative sigmoidal tuning curves for neural systems with small normalized variability is presented. The optimal tuning curve is a nonlinear function of the cumulative distribution function of the stimulus and depends on the mean-variance relationship of the neural system. The derivation is based on a known relationship between Shannon's mutual information and Fisher information, and the optimality of Jeffrey's prior. It relies on the existence of closed-form solutions to the converse problem of optimizing the stimulus distribution for a given tuning curve. It is shown that maximum mutual information corresponds to constant Fisher information only if the stimulus is uniformly distributed. As an example, the case of sub-Poisson binomial firing statistics is analyzed in detail.

Item Type:	Journal Article
Subjects:	Q Science > QC Physics Q Science > QP Physiology
Divisions:	Faculty of Science > Engineering
Library of Congress Subject Headings (LCSH):	Neural networks (Computer science), Neural networks (Neurobiology), Neural transmission, Neurons, Neural circuitry
Journal or Publication Title:	Physical Review Letters
Publisher:	American Physical Society
ISSN:	0031-9007
Date:	1 August 2008
Volume:	Vol.101
Number:	No.5
Number of Pages:	4
Identification Number:	10.1103/PhysRevLett.101.058103
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
Funder:	Australian Research Council (ARC), Engineering and Physical Sciences Research Council (EPSRC)
Grant number:	DP0770747 (ARC), EP/C523334/1 (EPSRC)
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URI:	http://wrap.warwick.ac.uk/id/eprint/29533

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