Scientific article

Probabilistic brains: knowns and unknowns

Published inNature neuroscience, vol. 16, no. 9, p. 1170-1178
Publication date2013

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.

  • Animals
  • Brain/cytology/physiology
  • Humans
  • Learning
  • Models, Neurological
  • Neurons/physiology
  • Perception
  • Probability
  • Sensation
Citation (ISO format)
POUGET, Alexandre et al. Probabilistic brains: knowns and unknowns. In: Nature neuroscience, 2013, vol. 16, n° 9, p. 1170–1178. doi: 10.1038/nn.3495
Main files (1)
Article (Published version)
ISSN of the journal1097-6256

Technical informations

Creation12/05/2013 5:10:00 PM
First validation12/05/2013 5:10:00 PM
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