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

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dc.creatorMcDonnell, Mark D.
dc.creatorStocks, Nigel G.
dc.date2008-02-12
dc.date2008-07-04
dc.date.accessioned2026-07-07T09:54:04Z
dc.date.available2026-07-07T09:54:04Z
dc.descriptionA 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.
dc.descriptionAccepted by Physical Review Letters. This revision updates figures and text
dc.identifierhttps://arxiv.org/abs/0802.1570
dc.identifierhttp://arxiv.org/abs/0802.1570
dc.identifierPhysical Review Letters 101, 058103, 2008
dc.identifierdoi:10.1103/PhysRevLett.101.058103
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/166182
dc.subjectNeurons and Cognition
dc.titleMaximally Informative Stimuli and Tuning Curves for Sigmoidal Rate-Coding Neurons and Populations
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