Abstract
In the vertebrate nervous system, sensory stimuli are typically encoded
through the concerted activity of large populations of neurons. Classically,
these patterns of activity have been treated as encoding the value
of the stimulus (e.g., the orientation of a contour), and computation
has been formalized in terms of function approximation. More recently,
there have been several suggestions that neural computation is akin
to a Bayesian inference process, with population activity patterns
representing uncertainty about stimuli in the form of probability
distributions (e.g., the probability density function over the orientation
of a contour). This paper reviews both approaches, with a particular
emphasis on the latter, which we see as a very promising framework
for future modeling and experimental work.
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