Probabilistic brains: knowns and unknowns.

Published

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Pouget, A; Beck, JM; Ma, WJ; Latham, PE

Published Date

  • September 2013

Published In

Volume / Issue

  • 16 / 9

Start / End Page

  • 1170 - 1178

PubMed ID

  • 23955561

Pubmed Central ID

  • 23955561

Electronic International Standard Serial Number (EISSN)

  • 1546-1726

Digital Object Identifier (DOI)

  • 10.1038/nn.3495

Language

  • eng

Conference Location

  • United States