Probabilistic brains

Knowns and unknowns

Alexandre Pouget, Jeffrey M. Beck, Wei Ji Ma, Peter E. Latham

Research output: Contribution to journalArticle

Abstract

There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.

Original languageEnglish (US)
Pages (from-to)1170-1178
Number of pages9
JournalNature Neuroscience
Volume16
Issue number9
DOIs
StatePublished - Sep 2013

Fingerprint

Learning
Brain
Cognition
Research Personnel

ASJC Scopus subject areas

  • Neuroscience(all)

Cite this

Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013). Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16(9), 1170-1178. https://doi.org/10.1038/nn.3495

Probabilistic brains : Knowns and unknowns. / Pouget, Alexandre; Beck, Jeffrey M.; Ma, Wei Ji; Latham, Peter E.

In: Nature Neuroscience, Vol. 16, No. 9, 09.2013, p. 1170-1178.

Research output: Contribution to journalArticle

Pouget, A, Beck, JM, Ma, WJ & Latham, PE 2013, 'Probabilistic brains: Knowns and unknowns', Nature Neuroscience, vol. 16, no. 9, pp. 1170-1178. https://doi.org/10.1038/nn.3495
Pouget, Alexandre ; Beck, Jeffrey M. ; Ma, Wei Ji ; Latham, Peter E. / Probabilistic brains : Knowns and unknowns. In: Nature Neuroscience. 2013 ; Vol. 16, No. 9. pp. 1170-1178.
@article{86868b918b06405da4d1633f19868ece,
title = "Probabilistic brains: Knowns and unknowns",
abstract = "There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.",
author = "Alexandre Pouget and Beck, {Jeffrey M.} and Ma, {Wei Ji} and Latham, {Peter E.}",
year = "2013",
month = "9",
doi = "10.1038/nn.3495",
language = "English (US)",
volume = "16",
pages = "1170--1178",
journal = "Nature Neuroscience",
issn = "1097-6256",
publisher = "Nature Publishing Group",
number = "9",

}

TY - JOUR

T1 - Probabilistic brains

T2 - Knowns and unknowns

AU - Pouget, Alexandre

AU - Beck, Jeffrey M.

AU - Ma, Wei Ji

AU - Latham, Peter E.

PY - 2013/9

Y1 - 2013/9

N2 - There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.

AB - There is strong behavioral and physiological evidence that the brain both represents probability distributions and performs probabilistic inference. Computational neuroscientists have started to shed light on how these probabilistic representations and computations might be implemented in neural circuits. One particularly appealing aspect of these theories is their generality: they can be used to model a wide range of tasks, from sensory processing to high-level cognition. To date, however, these theories have only been applied to very simple tasks. Here we discuss the challenges that will emerge as researchers start focusing their efforts on real-life computations, with a focus on probabilistic learning, structural learning and approximate inference.

UR - http://www.scopus.com/inward/record.url?scp=84883460930&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883460930&partnerID=8YFLogxK

U2 - 10.1038/nn.3495

DO - 10.1038/nn.3495

M3 - Article

VL - 16

SP - 1170

EP - 1178

JO - Nature Neuroscience

JF - Nature Neuroscience

SN - 1097-6256

IS - 9

ER -