The Redemption of Noise: Inference with Neural Populations

In 2006, Ma et al. presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and pe...

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Detalles Bibliográficos
Autores principales: Echeveste, Rodrigo, Lengyel, Máté
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Applied Science Publishing 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416224/
https://www.ncbi.nlm.nih.gov/pubmed/30366563
http://dx.doi.org/10.1016/j.tins.2018.09.003
Descripción
Sumario:In 2006, Ma et al. presented an elegant theory for how populations of neurons might represent uncertainty to perform Bayesian inference. Critically, according to this theory, neural variability is no longer a nuisance, but rather a vital part of how the brain encodes probability distributions and performs computations with them.

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