Thu, 21 Aug 2008 07:07:22 BST CiteULike: i-stevenson's library [133 articles] http://www.citeulike.org/user/i-stevenson/article/93508 CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/i-stevenson/article/93508 <i>Trends Neurosci, Vol. 27, No. 12. (December 2004), pp. 712-719.</i><br /><br />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. The Bayesian brain: the role of uncertainty in neural coding and computation. DC Knill A Pouget doi:10.1016/j.tins.2004.10.007 Trends Neurosci, Vol. 27, No. 12. (December 2004), pp. 712-719. 2005-02-11T21:30:41-00:00 2004 Trends Neurosci 0166-2236 27 12 712 719 bayesian population-codes