Repository logo
 

The Redemption of Noise: Inference with Neural Populations.

Accepted version
Peer-reviewed

Repository URI

https://www.repository.cam.ac.uk/handle/1810/285367

Repository DOI

https://doi.org/10.17863/CAM.27620
Thumbnail Image

Files

Accepted version (1.42 MB)

Type

Article

Change log

Authors

Echeveste, Rodrigo  ORCID logo  https://orcid.org/0000-0002-6155-8679

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

Volume Title

41

Publisher

Elsevier

Publisher DOI

https://doi.org/10.1016/j.tins.2018.09.003

Rights and licensing

undefined
Except where otherwised noted, this item's license is described as http://www.rioxx.net/licenses/all-rights-reserved
Sponsorship
Wellcome Trust (095621/Z/11/Z)
ERC Consolidator Grant (726090-COGTOM)

Collections

University of Cambridge Research Outputs (Articles and Conferences)