Workshop on Methods of Information Theory in Computational NeuroscienceAlice SchwarzeUniversity of Washington
The study of motifs in networks can help researchers uncover links between structure and function of networks in biology, ecology, neuroscience, and many other fields. To connect the study of motifs in networks (which is common, e.g., in biology and the social sciences) with the study of motifs in dynamical processes (which is common in neuroscience), we propose to distinguish between "structure motifs" (i.e., graphlets) in networks and "process motifs" (i.e., structured sets of walks) on networks. Using as examples the covariances and correlations in a multivariate Ornstein--Uhlenbeck process on a network, we demonstrate that the distinction between structure motifs and process motifs makes it possible to gain new, quantitative insights into mechanisms that contribute to important functions of dynamical systems on networks.
