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

Rodrigo Echeveste
, Máté Lengyel^*

^*Corresponding author for this work

University of Cambridge

Research output: Contribution to journal › Short survey › peer-review

Abstract (may include machine translation)

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.

Original language	English
Pages (from-to)	767-770
Number of pages	4
Journal	Trends in Neurosciences
Volume	41
Issue number	11
DOIs	https://doi.org/10.1016/j.tins.2018.09.003
State	Published - Nov 2018

Keywords

Bayesian inference
cortex
neural network
neural variability
perception
uncertainty

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10.1016/j.tins.2018.09.003License: CC BY

Lengyel-Mate3_2018Publisher version, 709 KBLicense: CC BY

Cite this

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title = "The Redemption of Noise: Inference with Neural Populations",

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",

author = "Rodrigo Echeveste and M{\'a}t{\'e} Lengyel",

note = "Publisher Copyright: {\textcopyright} 2018 The Authors",

year = "2018",

month = nov,

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AU - Lengyel, Máté

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N2 - 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.

AB - 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.

KW - Bayesian inference

KW - cortex

KW - neural network

KW - neural variability

KW - perception

KW - uncertainty

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DO - 10.1016/j.tins.2018.09.003

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The Redemption of Noise: Inference with Neural Populations

Abstract (may include machine translation)

Keywords

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COGTOM: Cognitive Tomography of Mental Representations

Cite this

The Redemption of Noise: Inference with Neural Populations

Abstract (may include machine translation)

Keywords

Access to Document

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Projects

COGTOM: Cognitive Tomography of Mental Representations

Cite this