The ability to precisely measure the connectivity of large networks of neurons, even entire brains, has rapidly grown due to advances in microscopy and image processing. The resulting connectivity diagrams, or "connectomes," promise detailed knowledge that can be used to inform models of brain signaling and, ultimately, the biological basis of intelligent behavior. This project will develop theoretical methods for the analysis of these datasets. As the scale of connectomes grow, such methods will be increasingly important. This project will accelerate the development of approaches that are capable of scaling to large brains and that are robust to the heterogeneity and complexity present in real nervous systems, while also facilitating the recruitment and training of interdisciplinary scientists with strong analytical skills to work with connectome datasets and build new models. There is a relative lack of techniques for exploiting connectomic data for hypothesis generation beyond manual examination of individual connections between previously identified neurons with hypothesized functions. Given the orders of magnitude difference in scale between previously available and more recently released connectome datasets (∼300 neurons in C. elegans vs. ∼140,000 in the adult Drosophila brain, for instance), moving from manual approaches to statistical descriptions and analyses whole-brain connectivity is critical. This project's research involves two approaches: analysis of whole-