Show Reference: "Statistically optimal perception and learning: from behavior to neural representations"

Statistically optimal perception and learning: from behavior to neural representations Trends in Cognitive Sciences, Vol. 14, No. 3. (12 March 2010), pp. 119-130, doi:10.1016/j.tics.2010.01.003 by József Fiser, Pietro Berkes, Gergo Orbán, Máté Lengyel
    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 re-evaluate 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.
                Copyright 2010 Elsevier Ltd. All rights reserved.},
    author = {Fiser, J\'{o}zsef and Berkes, Pietro and Orb\'{a}n, Gergo and Lengyel, M\'{a}t\'{e}},
    day = {12},
    doi = {10.1016/j.tics.2010.01.003},
    issn = {1879-307X},
    journal = {Trends in Cognitive Sciences},
    keywords = {bayes, model, perception, statistics},
    month = mar,
    number = {3},
    pages = {119--130},
    pmcid = {PMC2939867},
    pmid = {20153683},
    posted-at = {2013-06-25 08:45:42},
    priority = {2},
    title = {Statistically optimal perception and learning: from behavior to neural representations},
    url = {},
    volume = {14},
    year = {2010}

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