In particular, Bayesian inference on large numbers of jointly dependant variables is generally intractable. Bayesian networks are a convenient graphical representation of statistical structures through which one can render inference and learning tractable. They represent groups of dependant variables as nodes, and conditional independencies as links (or lack thereof) between these nodes.
In this presentation, we will show how propagation of activity in interconnected cortical networks with population codes can be interpreted as a propagation of belief in an equivalent bayesian network. From this observation we will propose a new theory for the correspondence between cortical connectivity and modularity, neural response curves in multi-modal brain areas, statistical dependencies in the natural environment and cognitive modularity. As an example we will consider a minimal version of belief propagation were only means and covariance of input variables are propagated and show that it predicts some neural response properties in multi-sensory and sensorimotor areas.