The Redemption of Noise: Inference with Neural Populations.
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Publication Date
2018-11Journal Title
Trends Neurosci
ISSN
0166-2236
Publisher
Elsevier BV
Volume
41
Issue
11
Pages
767-770
Language
eng
Type
Article
Physical Medium
Print
Metadata
Show full item recordCitation
Echeveste, R., & Lengyel, M. (2018). The Redemption of Noise: Inference with Neural Populations.. Trends Neurosci, 41 (11), 767-770. https://doi.org/10.1016/j.tins.2018.09.003
Abstract
In 2006, Ma et al. (Nat. Neurosci. 1006;9:1432-1438) 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.
Keywords
Brain, Nerve Net, Neurons, Animals, Humans, Probability, Bayes Theorem, Models, Neurological
Sponsorship
ERC Consolidator Grant (726090-COGTOM)
Funder references
Wellcome Trust (095621/Z/11/Z)
Identifiers
External DOI: https://doi.org/10.1016/j.tins.2018.09.003
This record's URL: https://www.repository.cam.ac.uk/handle/1810/285367
Rights
Licence:
http://www.rioxx.net/licenses/all-rights-reserved
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