Leg-tracking and automated behavioural classification in Drosophila

Much remains unknown about how the nervous system of an animal generates behaviour, and even less is known about the evolution of behaviour. How does evolution alter existing behaviours or invent novel ones? Progress in computational techniques and equipment will allow these broad, complex questions to be explored in great detail. Here we present a method for tracking each leg of a fruit fly behaving spontaneously upon a trackball, in real time. Legs were tracked with infrared-fluorescent dyes invisible to the fly, and compatible with two-photon microscopy and controlled visual stimuli. We developed machine-learning classifiers to identify instances of numerous behavioural features (for example, walking, turning and grooming), thus producing the highest-resolution ethological profiles for individual flies. Detailed analysis of an animal’s posture and gait can provide a rich resource for behavioural studies. Kain et al. apply the technology of real-time motion capture to Drosophila, allowing automatic classification of the behaviours of flies spontaneously roaming on a tracker ball.