Central complex representations of self-movement are sufficient to compute wind direction in flight.

Flying flies can determine ambient wind direction in flight, but what neural representations might support this behavior are unclear. Ambient wind acting on a flying fly creates distinct patterns of airflow and optic flow. Here, we used two-photon imaging to characterize encoding of these two variables across columnar inputs to the fly navigation center, called PFNs. We find tuning for airflow direction and speed across many PFN types but only optic flow direction tuning in limited types. We do not observe tuning to optic flow speed. We build and validate an encoding model that enables simulation of PFN representations during real and simulated flight maneuvers. We show that these representations are sufficient to decode ambient wind direction both theoretically and using a simple feedforward ANN. Our work shows how a compact multisensory representation of self-motion could be used to infer a property of the external world that cannot be directly measured by a single sensory system.