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

Author:

Jones Hannah1ORCID,Willis Jenny A2,Firth Lucy C2,Giachello Carlo NG2ORCID,Gilestro Giorgio F1ORCID

Affiliation:

1. Department of Life Sciences, Imperial College London

2. Syngenta, Jealott’s Hill International Research Centre

Abstract

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 technological progresses in machine learning and computational power have boosted the development and adoption of systems leveraging on high-resolution video recording to track an animal pose and describe behaviour in all four dimensions. However, the high temporal and spatial resolution that these systems offer must come as a compromise with their throughput and accessibility. Here, we describe coccinella, an open-source reductionist framework combining high-throughput analysis of behaviour using real-time tracking on a distributed mesh of microcomputers (ethoscopes) with resource-lean statistical learning (HCTSA/Catch22). Coccinella is a reductionist system, yet outperforms state-of-the-art alternatives when exploring the pharmacobehaviour in Drosophila melanogaster.

Funder

Biotechnology and Biological Sciences Research Council

Publisher

eLife Sciences Publications, Ltd

Subject

General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

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