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.

Bayesian Brain: Probabilistic Approaches to Neural Coding (Computational Neuroscience) PDF

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