The Redemption of Noise: Inference with Neural Populations.
Authors
Lengyel, Máté
Change log
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.
Publication Date
2018-11
Online Publication Date
Acceptance Date
2018-09-07
Keywords
Bayesian inference, cortex, neural network, neural variability, perception, uncertainty, Animals, Bayes Theorem, Brain, Humans, Models, Neurological, Nerve Net, Neurons, Probability
Journal Title
Trends Neurosci
Journal ISSN
0166-2236
1878-108X
1878-108X
Volume Title
41
Publisher
Elsevier BV
Sponsorship
Wellcome Trust (095621/Z/11/Z)
ERC Consolidator Grant (726090-COGTOM)