Motion-based depth estimation in Drosophila

Inferring three-dimensional structure from two-dimensional visual input requires a second perspective, either in time or in space. In flying animals, the first of these options lends itself as an ideal source for constructing internal representations of the environment, since their continuous self-motion automatically produces an optic flow. How neural circuits transform this signal into estimates of object distance, and what form such representations take, remains unknown. We address this problem in the visual system of the fruit fly. By immersing flies in a virtual environment while recording population-level calcium activity of motion-sensitive neurons, we extract an optic flow-based map of space. This map contains accurate estimates of object distance computed through spatial integration of elementary motion detector signals. Using electrophysiological recordings, we identify postsynaptic wide-field neurons as a cellular substrate for this operation. In behavioural experiments, we show that motion vision is essential for depth perception; without access to visual motion information, the free flight trajectories of motion-blind flies inevitably end in collisions. Our experiments link population-level neural activity to behaviourally relevant representations of environmental structure and demonstrate that motion vision is essential for navigation in three-dimensional space.