A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster

Understanding how the brain encodes behaviour is the ultimate goal of neuroscience and the ability to objectively and reproducibly describe and quantify behaviour is a necessary milestone on this path. Recent progresses in machine learning and computational power have pushed the development and adoption of powerful systems leveraging on high-resolution videorecording to track pose and describe behaviour in all four dimensions, however the temporal and spatial resolution of these systems come as a compromise with their throughput and accessibility. Here we describe coccinella, an open source reductionist framework that allows for high-throughput analysis of behaviour using real-time tracking on a distributed mesh of microcomputers followed by resource-lean statistical learning. We show that a reductionist system outperforms state-of-the-art alternatives when exploring the pharmacobehaviour in Drosophila melanogaster.