The Bayesian Brain: The Role of Uncertainty in Neural Coding and Computation
Trends in Neurosciences, 27 (12):
712-719 (2004)Abstract
To use sensory information efficiently tomake judgments and guide
action in the world, the brain must represent and use information
about uncertainty in its computations for perception and action.
Bayesianmethods 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 nonexistent. A major challenge for neuroscientists is to test
these ideas experimentally, and so determine whether and how neurons
code information about sensory uncertainty.
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