Bayesian brain

✍ Scribed by Doya K., Ishii S., Pouget A., Rao R.P.N. (eds.)

• Publisher: MIT
• Year: 2007
• Tongue: English
• Leaves: 341
• Series: Computational Neuroscience
• Category: Library

✦ Synopsis

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. Bayesian Brain 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 overview of the mathematical concepts, including Bayes' theorem, that are basic to understanding the approaches discussed, contributors discuss how Bayesian concepts can be used for interpretation of such neurobiological data as neural spikes and functional brain imaging. Next, contributors examine the modeling of sensory processing, including the neural coding of information about the outside world. Finally, contributors explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation.

✦ Table of Contents

Bayesian Brain......Page 4
Contents......Page 6
Series Foreword......Page 10
Preface......Page 12
I. Introduction......Page 16
1 A Probability Primer......Page 18
II. Reading Neural Codes......Page 30
2 Spike Coding......Page 32
3 Likelihood-Based Approaches to Modeling the Neural Code......Page 68
4 Combining Order Statistics with Bayes Theorem for Millisecond-by-Millisecond Decoding of Spike Trains......Page 86
5 Bayesian Treatments of Neuroimaging Data......Page 108
III. Making Sense of the World......Page 128
6 Population Codes......Page 130
7 Computing with Population Codes......Page 146
8 Efficient Coding of Visual Scenes by Grouping and Segmentation......Page 160
9 Bayesian Models of Sensory Cue Integration......Page 204
IV. Making Decisions and Movements......Page 222
10 The Speed and Accuracy of a Simple Perceptual Decision: A Mathematical Primer......Page 224
11 Neural Models of Bayesian Belief Propagation......Page 254
12 Optimal Control Theory......Page 284
13 Bayesian Statistics and Utility Functions in Sensorimotor Control......Page 314
Contributors......Page 336
Index......Page 339