Mapping model units to visual neurons reveals population code for social behaviour

• 成果类型: Article
• 署名作者: Cowley, Benjamin R.; Calhoun, Adam J.; Rangarajan, Nivedita; Ireland, Elise; Turner, Maxwell H.; Pillow, Jonathan W.; Murthy, Mala
• 署名单位: Princeton University; Cold Spring Harbor Laboratory; Stanford University
• 刊物名称: Nature
• ISSN/ISSBN: 0028-4953
• DOI: 10.1038/s41586-024-07451-8
• 发表日期: 2024-05-30
• 页码: 1100-+
• 关键词: detecting neurons; sensory neurons; drosophila; normalization; circuits

摘要

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(1-5) 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(8-11). The visual projection neurons at the interface between the optic lobe and central brain form a set of discrete channels(12), 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 brain(15) into the model.