Statistically optimal perception and learning: from behavior to neural representations
Identifieur interne : 001084 ( Pmc/Curation ); précédent : 001083; suivant : 001085Statistically 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.
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.
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DOI: 10.1016/j.tics.2010.01.003
PubMed: 20153683
PubMed Central: 2939867
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<front><div type="abstract" xml:lang="en"><p id="P1">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.</p>
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National Volen Center for Complex Systems, Brandeis University, Volen 208/MS 013, Waltham, MA 02454, USA</aff>
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Department of Psychology and the Neuroscience Program, Brandeis University, 415 South Street, Waltham, MA 02453, USA</aff>
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Department of Biophysics, Research Institute for Particle and Nuclear Physics, Hungarian Academy of Sciences, Konkoly Thege Miklós út 29–33, H-1121, Budapest, Hungary</aff>
<aff id="A4"><label>4</label>
Computational and Biological Learning Lab, Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1 PZ, United Kingdom</aff>
<author-notes><corresp id="FN1">Corresponding author: Fiser, J. (<email>fiser@brandeis.edu</email>
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<abstract><p id="P1">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.</p>
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