Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/87166
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Type: Journal article
Title: Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations
Author: McDonnell, M.
Stocks, N.
Citation: Physical Review Letters, 2008; 101(5):058103-1-058103-4
Publisher: American Physical Society
Issue Date: 2008
ISSN: 0031-9007
1079-7114
Statement of
Responsibility: 
Mark D. McDonnell and Nigel G. Stocks
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.
Keywords: Neurons
Poisson Distribution
Synaptic Transmission
Action Potentials
Models, Neurological
Rights: © 2008 American Physical Society
DOI: 10.1103/PhysRevLett.101.058103
Grant ID: http://purl.org/au-research/grants/arc/DP0770747
Published version: http://dx.doi.org/10.1103/PhysRevLett.101.058103
Appears in Collections:Aurora harvest 7
Electrical and Electronic Engineering publications

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