Bayesian Brain: Probabilistic Approaches to Neural Coding

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Kenji Doya
MIT Press, 2007 - Medical - 326 pages
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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.

 

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Contents

1 A Probability Primer
3
Part II Reading Neural Codes
15
2 Spike Coding
17
3 LikelihoodBased Approaches to Modeling the Neural Code
53
4 Combining Order Statistics with Bayes Theorem for MillisecondbyMillisecond Decoding of Spike Trains
71
5 Bayesian Treatments of Neuroimaging Data
93
Part III Making Sense of the World
113
6 Population Codes
115
9 Bayesian Models of Sensory Cue Integration
189
Part IV Making Decisions and Movements
207
A Mathematical Primer
209
11 Neural Models of Bayesian Belief Propagation
239
12 Optimal Control Theory
269
13 Bayesian Statistics and Utility Functions in Sensorimotor Control
299
Contributors
321
Index
324

7 Computing with Population Codes
131
8 Efficient Coding of Visual Scenes by Grouping and Segmentation
145

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About the author (2007)

Shin Ishii is Professor in the Graduate School of Information Science, Nara Institute of Science and Technology, Japan.

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