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Statistically optimal perception and learning: from behavior to neural representations

Identifieur interne : 001441 ( Ncbi/Checkpoint ); précédent : 001440; suivant : 001442

Statistically optimal perception and learning: from behavior to neural representations

Auteurs : J Zsef Fiser [États-Unis] ; Pietro Berkes [États-Unis] ; Gerg Orbán [États-Unis, Hongrie] ; Máté Lengyel [Royaume-Uni]

Source :

Trends in cognitive sciences [ 1364-6613 ] ; 2010.

RBID : PMC:2939867

Abstract

Human perception has recently been characterized as statistical inference based on noisy and ambiguous sensory inputs. Moreover, suitable neural representations of uncertainty have been identified that could underlie such probabilistic computations. In this review, we argue that learning an internal model of the sensory environment is another key aspect of the same statistical inference procedure and thus perception and learning need to be treated jointly. We review evidence for statistically optimal learning in humans and animals, and reevaluate possible neural representations of uncertainty based on their potential to support statistically optimal learning. We propose that spontaneous activity can have a functional role in such representations leading to a new, sampling-based, framework of how the cortex represents information and uncertainty.

Url:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2939867

DOI: 10.1016/j.tics.2010.01.003
PubMed: 20153683
PubMed Central: 2939867

Affiliations:

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PMC:2939867

Le document en format XML

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Statistically optimal perception and learning: from behavior to neural representations

Statistically optimal perception and learning: from behavior to neural representations

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Abstract

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