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Sensory Cue Integration$
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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

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A Neural Implementation of Optimal Cue Integration

A Neural Implementation of Optimal Cue Integration

(p.393) CHAPTER 21 A Neural Implementation of Optimal Cue Integration
Sensory Cue Integration

Wei Ji Ma

Jeff Beck

Alexandre Pouget

Oxford University Press

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

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