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Bayesian BrainProbabilistic Approaches to Neural Coding$
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Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao

Print publication date: 2006

Print ISBN-13: 9780262042383

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262042383.001.0001

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PRINTED FROM MIT PRESS SCHOLARSHIP ONLINE (www.mitpress.universitypressscholarship.com). (c) Copyright The MIT Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in MITSO for personal use.date: 28 February 2021

Population Codes

Population Codes

Chapter:
(p.114) (p.115) 6 Population Codes
Source:
Bayesian Brain
Author(s):

Pouget Alexandre

Richard S. Zemel

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262042383.003.0006

Population codes encode many variables in the brain such as the orientation of visual contours. This chapter begins by reviewing the standard approach to coding and decoding using orientation selectivity as a case study. It describes how the populations of neurons encode information about single variables and how this information can be decoded from the population activity. The chapter also discusses the computations for extracting approximations, such as the population vector method, the maximum likelihood estimator, and Bayesian estimates, and finally, discusses uncertainty or probability distribution over the encoded stimulus and their computational advantages or disadvantages.

Keywords:   population codes, visual contours, coding, decoding, orientation selectivity, neurons, population vector method, maximum likelihood estimator, Bayesian estimates

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