Optimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance

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dc.creatorMcDonnell, Mark D.
dc.creatorStocks, Nigel G.
dc.creatorAbbott, Derek
dc.date2007-04-05
dc.date.accessioned2026-07-07T08:13:01Z
dc.date.available2026-07-07T08:13:01Z
dc.descriptionSuprathreshold stochastic resonance (SSR) is a form of noise enhanced signal transmission that occurs in a parallel array of independently noisy identical threshold nonlinearities, including model neurons. Unlike most forms of stochastic resonance, the output response to suprathreshold random input signals of arbitrary magnitude is improved by the presence of even small amounts of noise. In this paper the information transmission performance of SSR in the limit of a large array size is considered. Using a relationship between Shannon's mutual information and Fisher information, a sufficient condition for optimality, i.e. channel capacity, is derived. It is shown that capacity is achieved when the signal distribution is Jeffrey's prior, as formed from the noise distribution, or when the noise distribution depends on the signal distribution via a cosine relationship. These results provide theoretical verification and justification for previous work in both computational neuroscience and electronics.
dc.descriptionAccepted for publication by Physical Review E, 28 pages of text and references, 5 figures, 2 tables
dc.identifierhttps://arxiv.org/abs/0704.0673
dc.identifierhttp://arxiv.org/abs/0704.0673
dc.identifierPhys. Rev. E 75, 061105 (2007)
dc.identifierdoi:10.1103/PhysRevE.75.061105
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/132658
dc.subjectStatistical Mechanics
dc.subjectNeurons and Cognition
dc.titleOptimal stimulus and noise distributions for information transmission via suprathreshold stochastic resonance
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