We aim to look at intelligent systems and their environments jointly to understand stimulus-evoked neural computation and behavior. One way to increase our understanding of stimulus-evoked neural computation is to use and build models of neural circuits and compare their representations to data measured in biological neural circuits. In particular, deep convolutional neural networks (DNNs) for image classification are compelling models of neural computation in the mammalian visual system. But they lack a one-to-one mapping of artificial to biological neurons. To remedy this lack of the actual circuitry in DNNs, we ask how we can incorporate knowledge of the connectivity of neural circuits into models of neural computation? We investigate this question together with Dr. Srini Turaga and other scientists from the HHMI Janelia Research Campus. To do so, we develop connectome-constrained models of the Drosophila visual system, which learn to recognize e.g. movement in naturalistic movie sequences.