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Bayesian Brain – Probabilistic Approaches to Neural Coding - MIT Press Scholarship Online
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Bayesian Brain: Probabilistic Approaches to Neural Coding

Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao


A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic functioning of the brain circuit, and by suggesting optimal ways of deciphering experimental data. This book brings together contributions from both experimental and theoretical neuroscientists that examine the brain mechanisms of perception, decision making, and motor control according to the concepts of Bayesian estimation. After an overvi ... More

Keywords: normative predictions, ideal sensory system, prior knowledge, observation, mechanistic interpretation, dynamic functioning, brain circuit, deciphering experimental data, theoretical neuroscientists, brain mechanisms

Bibliographic Information

Print publication date: 2006 Print ISBN-13: 9780262042383
Published to MIT Press Scholarship Online: August 2013 DOI:10.7551/mitpress/9780262042383.001.0001


Affiliations are at time of print publication.

Kenji Doya, editor

Shin Ishii, editor

Alexandre Pouget, editor

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Part I Introduction

1 A Probability Primer

Kenji Doya, and Shin Ishii

PART II Reading Neural Codes

2 Spike Coding

Adrienne Fairhall

5 Bayesian Treatments of Neuroimaging Data

Will Penny, and Karl Friston

PART III Making Sense of the World

6 Population Codes

Pouget Alexandre, and Richard S. Zemel

7 Computing with Population Codes

Peter Latham, and Pouget Alexandre

Part IV Making Decisions and Movements

10 The Speed and Accuracy of a Simple Perceptual Decision: A Mathematical Primer

Michael N. Shadlen, Timothy D. Hanks, Anne K. Churchland, Roozbeh Kiani, and Tianming Yang

12 Optimal Control Theory

Emanuel Todorov

End Matter