Table of Contents
ISRN Biomathematics
Volume 2012, Article ID 785791, 19 pages
Review Article

Bayesian Models of Brain and Behaviour

Wellcome Trust Centre for Neuroimaging, University College, London WC1N 3BG, UK

Received 5 August 2012; Accepted 24 September 2012

Academic Editors: C. Brown, E. V. Ignatieva, E. Pereda, L. Pezard, and A. Riva

Copyright © 2012 William Penny. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper presents a review of Bayesian models of brain and behaviour. We first review the basic principles of Bayesian inference. This is followed by descriptions of sampling and variational methods for approximate inference, and forward and backward recursions in time for inference in dynamical models. The review of behavioural models covers work in visual processing, sensory integration, sensorimotor integration, and collective decision making. The review of brain models covers a range of spatial scales from synapses to neurons and population codes, but with an emphasis on models of cortical hierarchies. We describe a simple hierarchical model which provides a mathematical framework relating constructs in Bayesian inference to those in neural computation. We close by reviewing recent theoretical developments in Bayesian inference for planning and control.