Fly motion vision maximizes signal energy transfer between mechanical input and sensor output

Insects achieve agile flight using a sensor-rich control architecture whose embodiment eliminates the need for complex computation. For example, their visual systems are tuned to detect the optic flow associated with specific self-motions, but what functional principle does this tuning embed, and how does it facilitate motor control? Here, we tested the hypothesis that evolution cotunes physics and physiology by aligning an insect's sensors to its dynamically important modes of self-motion. Specifically, we show that the spatial tuning of the blowfly motion vision system maximizes the open-loop Hankel singular values, which quantify the flow of signal energy from gust disturbances and control inputs to sensor outputs, jointly optimizing observability and controllability. This evolutionary principle differs from the conventional engineering-design paradigm of optimizing state estimation, with implications for robotic systems combining high performance with minimal actuator usage.