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  • Title: Maximally informative stimuli and tuning curves for sigmoidal rate-coding neurons and populations.
    Author: McDonnell MD, Stocks NG.
    Journal: Phys Rev Lett; 2008 Aug 01; 101(5):058103. PubMed ID: 18764432.
    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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