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Title

Probabilistic brains: knowns and unknowns

Authors
Beck, Jeffrey M
Ma, Wei Ji
Latham, Peter E
Published in Nature Neuroscience. 2013, vol. 16, no. 9, p. 1170-8
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.
Keywords AnimalsBrain/cytology/physiologyHumansLearningModels, NeurologicalNeurons/physiologyPerceptionProbabilitySensation
Identifiers
PMID: 23955561
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Article (Published version) (666 Kb) - document accessible for UNIGE members only Limited access to UNIGE
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Research group Groupe Alexandre Pouget (938)
Citation
(ISO format)
POUGET, Alexandre et al. Probabilistic brains: knowns and unknowns. In: Nature Neuroscience, 2013, vol. 16, n° 9, p. 1170-8. https://archive-ouverte.unige.ch/unige:33855

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Deposited on : 2014-01-29

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