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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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