Many networks are important because they are substrates for dynamical systems,
and their pattern of functional connectivity can itself be dynamic -- they can
functionally reorganize, even if their underlying anatomical structure remains
fixed. However, the recent rapid progress in discovering the community
structure of networks has overwhelmingly focused on that constant anatomical
connectivity. In this paper, we lay out the problem of discovering
functional communities, and describe an approach to doing so. This method
combines recent work on measuring information sharing across stochastic
networks with an existing and successful community-discovery algorithm for
weighted networks. We illustrate it with an application to a large biophysical
model of the transition from beta to gamma rhythms in the hippocampus.