Jump to ContentJump to Main Navigation
Sensory Cue Integration$
Users without a subscription are not able to see the full content.

Julia Trommershäuser, Konrad Kording, and Michael S. Landy

Print publication date: 2011

Print ISBN-13: 9780195387247

Published to Oxford Scholarship Online: September 2012

DOI: 10.1093/acprof:oso/9780195387247.001.0001

Show Summary Details
Page of

PRINTED FROM OXFORD SCHOLARSHIP ONLINE (www.oxfordscholarship.com). (c) Copyright Oxford University Press, 2019. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single chapter of a monograph in OSO for personal use (for details see www.oxfordscholarship.com/page/privacy-policy).date: 24 June 2019

A Neural Implementation of Optimal Cue Integration

A Neural Implementation of Optimal Cue Integration

Chapter:
(p.393) CHAPTER 21 A Neural Implementation of Optimal Cue Integration
Source:
Sensory Cue Integration
Author(s):

Wei Ji Ma

Jeff Beck

Alexandre Pouget

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780195387247.003.0021

This chapter lays out a theoretical framework for how optimal cue integration can be implemented by neural populations. The main significance of this framework does not merely lie in understanding multisensory perception in a principled manner, but in the fact that it provides a blueprint for finding neural implementations of other forms of Bayes-optimal computation. Evidence for Bayesian optimality of human behavior has been found in many perceptual tasks, including decision making, visual search, oddity detection, and multiple-trajectory tracking. Probabilistic population coding provides a roadmap for identifying a neural implementation of each of these computations: First the Bayesian model at the behavioral level needs to be worked out, then it needs to be assumed that probability distributions in this model are encoded in neural populations with Poisson-like variability, and finally the neural operations that map onto the desired operations on probability distributions should be identified.

Keywords:   cue integration, neural populations, multisensory perception, Bayesian cue combination, probabilistic population coding

Oxford Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us .