# Show Reference: "Inference and Computation with Population Codes"

Inference and Computation with Population Codes Annual review of neuroscience, Vol. 26, No. 1. (2003), pp. 381-410, doi:10.1146/annurev.neuro.26.041002.131112 by Alexandre Pouget, Peter Dayan, Richard S. Zemel
@article{pouget-et-al-2003,
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.},
address = {Department of Brain and Cognitive Sciences, Meliora Hall, University of Rochester, Rochester, NY 14627, USA. alex@bcs.rochester.edu},
author = {Pouget, Alexandre and Dayan, Peter and Zemel, Richard S.},
doi = {10.1146/annurev.neuro.26.041002.131112},
issn = {0147-006X},
journal = {Annual review of neuroscience},
keywords = {ann, bayes, probability},
number = {1},
pages = {381--410},
pmid = {12704222},
posted-at = {2012-01-26 15:30:04},
priority = {2},
title = {Inference and Computation with Population Codes},
url = {http://dx.doi.org/10.1146/annurev.neuro.26.041002.131112},
volume = {26},
year = {2003}
}