Probabilistic brains: Knowns and unknowns

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

Research output: Contribution to journalReview article

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

ASJC Scopus subject areas

  • Neuroscience(all)

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