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 that predicts the 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 aerodynamic forces. A physics-based simulation incorporating our hinge model generates flight manoeuvres 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 among the most sophisticated and evolutionarily important skeletal structures in the natural world.

昆虫是后生动物中物种最丰富的，其成功归功于主动飞行的进化。与翼龙、鸟类和蝙蝠不同，昆虫的翅膀并不是从腿进化而来的¹，而是一种新颖的结构，通过生物力学上复杂的铰链连接到身体上，该铰链将专门动力肌肉的微小高频振荡转化为扫过的背部运动。机翼²的前后运动。铰链由一种称为骨片的微小硬化结构系统组成，这些结构通过灵活的关节相互连接，并受到专门控制肌肉的活动的调节。在这里，我们使用基因编码的钙指示剂对苍蝇中这些肌肉的活动进行成像，同时用高速摄像机跟踪翅膀的三维运动。利用机器学习，我们创建了一个卷积神经网络³，可以根据转向肌肉的活动准确预测机翼运动，以及一个编码器-解码器⁴，可以预测单个骨片对机翼运动的作用。通过重播动态缩放的机器人苍蝇的机翼运动模式，我们量化了转向肌肉活动对空气动力的影响。结合我们的铰链模型的基于物理的模拟生成的飞行动作与自由飞行的苍蝇非常相似。这种综合的、多学科的方法揭示了昆虫翅膀铰链的机械控制逻辑，可以说是自然界中最复杂和进化上最重要的骨骼结构之一。