Fly motion vision is tuned to maximize 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 test the hypothesis that evolution co-tunes physics and physiology by aligning an insect’s sensors to its dynamically-significant modes of self-motion. Specifically, we show that the tuning of the blowfly motion vision system maximizes 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 novel robotic systems combining high performance with low power-consumption.