Inference and computation with
population codes
Alex Pouget Peter Dayan Rich Zemel
Annual Review of Neuroscience 26, 381-410.
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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