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Insects constitute the most species-rich radiation of metazoa, a success that is due to the evolution of active flight. Unlike pterosaurs, birds and bats, the wings of insects did not evolve from legs1, but are novel structures that are attached to the body via a biomechanically complex hinge that transforms tiny, high-frequency oscillations of specialized power muscles into the sweeping back-and-forth motion of the wings2. The hinge consists of a system of tiny, hardened structures called sclerites that are interconnected to one another via flexible joints and regulated by the activity of specialized control muscles. Here we imaged the activity of these muscles in a fly using a genetically encoded calcium indicator, while simultaneously tracking the three-dimensional motion of the wings with high-speed cameras. Using machine learning, we created a convolutional neural network3 that accurately predicts wing motion from the activity of the steering muscles, and an encoder–decoder4

United kingdomInternational conference on neural networks volCurran associatesInternational conference on engineeringHow dipteraInternational conferenceNeural networksInsect morphologyArthropod relationshipsNeural information processing systemsKalman filteringJohn wiley