@Article{Knill2004,
  author   = {David C Knill and Alexandre Pouget},
  journal  = {Trends Neurosci},
  title    = {The {B}ayesian brain: {T}he role of uncertainty in neural coding and computation},
  year     = {2004},
  number   = {12},
  pages    = {712-9},
  volume   = {27},
  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.},
  doi      = {10.1016/j.tins.2004.10.007},
  keywords = {Animals, Bayes Theorem, Biological, Brain, Humans, Models, Nerve Net, Neurons, Perception, 15541511},
}

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