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.
About the Editors
Kenji Doya is Principal Investigator in the Neural Computation Unit in the Initial Research Project at the Okinawa Institute of Science and Technology, Japan.
Shin Ishii is Professor in the Graduate School of Information Science, Nara Institute of Science and Technology, Japan.
Alexandre Pouget is Associate Professor in the Brain and Cognitive Science Department at the University of Rochester and Head of the Laboratory of Computational Cognitive Neuroscience.