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Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations
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McDonnell, Mark D. 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). doi:10.1103/PhysRevLett.101.058103 ISSN 0031-9007.
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Official URL: http://dx.doi.org/10.1103/PhysRevLett.101.058103
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 | ||||
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Subjects: | Q Science > QC Physics Q Science > QP Physiology |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > 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 | ||||
Official Date: | 1 August 2008 | ||||
Dates: |
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Volume: | Vol.101 | ||||
Number: | No.5 | ||||
Number of Pages: | 4 | ||||
DOI: | 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) |
Data sourced from Thomson Reuters' Web of Knowledge
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