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arXiv · 0802.1570

Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations

Mark D. McDonnell·Nigel G. Stocks
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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.

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Mark D. McDonnell, Nigel G. Stocks. 2008-07-04. Maximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations. https://doi.org/10.1103/physrevlett.101.058103

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