Show Reference: "The Bayesian brain: the role of uncertainty in neural coding and computation"

The Bayesian brain: the role of uncertainty in neural coding and computation. Trends in neurosciences, Vol. 27, No. 12. (December 2004), pp. 712-719, doi:10.1016/j.tins.2004.10.007 by David C. Knill, Alexandre Pouget
@article{knill-and-pouget-2004,
    abstract = {To use sensory information efficiently to make judgments and guide action in the world, the brain must represent and use information about uncertainty in its computations for perception and action. Bayesian methods have proven successful in building computational theories for perception and sensorimotor control, and psychophysics is providing a growing body of evidence that human perceptual computations are "Bayes' optimal". This leads to the "Bayesian coding hypothesis": that the brain represents sensory information probabilistically, in the form of probability distributions. Several computational schemes have recently been proposed for how this might be achieved in populations of neurons. Neurophysiological data on the hypothesis, however, is almost non-existent. A major challenge for neuroscientists is to test these ideas experimentally, and so determine whether and how neurons code information about sensory uncertainty.},
    address = {Center for Visual Science and the Department of Brain and Cognitive Science, University of Rochester, NY 14627, USA. knill@cvs.rochester.edu},
    author = {Knill, David C. and Pouget, Alexandre},
    doi = {10.1016/j.tins.2004.10.007},
    issn = {0166-2236},
    journal = {Trends in neurosciences},
    keywords = {ann, bayes, math, model, population-coding, probability},
    month = dec,
    number = {12},
    pages = {712--719},
    pmid = {15541511},
    posted-at = {2012-05-04 17:08:54},
    priority = {3},
    title = {The {B}ayesian brain: the role of uncertainty in neural coding and computation.},
    url = {http://dx.doi.org/10.1016/j.tins.2004.10.007},
    volume = {27},
    year = {2004}
}

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Bayesian models have been used to model natural cognition.

Bayesian information processing does not represent and manipulate unitary variables but PDFs over variables.

According to Knill and Pouget, being an optimal Bayesian observer only means to take into account the uncertainty of the available information (in the system—that's after lossy transformation from physical stimuli to neural representations).