The Redemption of Noise: Inference with Neural Populations

Rodrigo Echeveste; Máté Lengyel

doi:10.1016/j.tins.2018.09.003

The Redemption of Noise: Inference with Neural Populations

Trends Neurosci. 2018 Nov;41(11):767-770. doi: 10.1016/j.tins.2018.09.003.

Authors

Rodrigo Echeveste¹, Máté Lengyel²

Affiliations

¹ Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK.
² Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK; Department of Cognitive Science, Central European University, Budapest, Hungary. Electronic address: m.lengyel@eng.cam.ac.uc.uk.

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: Bayesian inference; cortex; neural network; neural variability; perception; uncertainty.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Animals
Bayes Theorem
Brain / physiology*
Humans
Models, Neurological*
Nerve Net / physiology*
Neurons / physiology*
Probability

Grants and funding

Wellcome Trust/United Kingdom