The rich variety of behaviours observed in animals arises through the interplay between sensory processing and motor control. To understand these sensorimotor transformations, it is useful to build models that predict not only neural responses to sensory input^1,2,3,4,5 but also how each neuron causally contributes to behaviour^6,7. Here we demonstrate a novel modelling approach to identify a one-to-one mapping between internal units in a deep neural network and real neurons by predicting the behavioural changes that arise from systematic perturbations of more than a dozen neuronal cell types. A key ingredient that we introduce is ‘knockout training’, which involves perturbing the network during training to match the perturbations of the real neurons during behavioural experiments. We apply this approach to model the sensorimotor transformations of Drosophila melanogaster males during a complex, visually guided social behaviour^8,9,10,11. The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels¹², and prior work indicates that each channel encodes a specific visual feature to drive a particular behaviour^13,14. Our model reaches a different conclusion: combinations of visual projection neurons, including those involved in non-social behaviours, drive male interactions with the female, forming a rich population code for behaviour. Overall, our framework consolidates behavioural effects elicited from various neural perturbations into a single, unified model, providing a map from stimulus to neuronal cell type to behaviour, and enabling future incorporation of wiring diagrams of the brain¹⁵ into the model.

在动物中观察到的丰富多样的行为是通过感觉处理和运动控制之间的相互作用而产生的。为了理解这些感觉运动转换，构建模型不仅可以预测神经对感觉输入的反应 ^1,2,3,4,5 ，还可以预测每个神经元如何因果地影响行为 ^6,7 。在这里，我们展示了一种新颖的建模方法，通过预测十多种神经元细胞类型的系统扰动所产生的行为变化，来识别深层神经网络中的内部单元与真实神经元之间的一对一映射。我们引入的一个关键要素是“淘汰训练”，它涉及在训练期间扰动网络以匹配行为实验期间真实神经元的扰动。我们应用这种方法来模拟雄性黑腹果蝇在复杂的、视觉引导的社会行为中的感觉运动转变 ^8,9,10,11 。视叶和中脑交界处的视觉投射神经元形成一组离散通道 ¹² ，之前的工作表明每个通道编码特定的视觉特征来驱动特定的行为 ^13,14 纳入到模型中。模型。