Machine learning reveals the control mechanics of the insect wing hinge.

Insects constitute the most species-rich radiation of metazoa, a success due primarily to the evolution of active flight. Unlike pterosaurs, birds, and bats, the wings of insects did not evolve from legs 1 , but are novel structures attached to the body via a biomechanically complex hinge that transforms the tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings 2 . Due to the minute size and morphological complexity, the basic mechanics of the hinge are poorly understood. The hinge consists of a series of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of a set of specialized steering muscles. In this study, we imaged the activity of these steering muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the 3D motion of the wings with high-speed cameras. Using machine learning approaches, we created a convolutional neural network 3 that accurately predicts wing motion from the activity of the steering muscles, and an autoencoder 4 that predicts the mechanical role of the individual sclerites on wing motion. By replaying patterns of wing motion on a dynamically scaled robotic fly, we quantified the effects of steering muscle activity on the production of aerodynamic forces. A physics-based simulation that incorporates our model of the wing hinge generates flight maneuvers that are remarkably similar to those of free flying flies. This integrative, multi-disciplinary approach reveals the mechanical control logic of the insect wing hinge, arguably the most sophisticated and evolutionarily important skeletal structure in the natural world.