Bayesian Brain: Probabilistic Approaches to Neural Coding
Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao
Abstract
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
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 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, they examine the modeling of sensory processing, including the neural coding of information about the outside world, and finally, they explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation.
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 |
