The Redemption of Noise: Inference with Neural Populations.
Accepted version
Peer-reviewed
Repository URI
Repository DOI
Change log
Authors
Echeveste, Rodrigo https://orcid.org/0000-0002-6155-8679
Lengyel, Máté
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.
Description
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
Conference Name
Journal ISSN
0166-2236
1878-108X
1878-108X
Volume Title
41
Publisher
Elsevier BV
Publisher DOI
Sponsorship
Wellcome Trust (095621/Z/11/Z)
ERC Consolidator Grant (726090-COGTOM)