A reductionist paradigm for high-throughput behavioural fingerprinting in Drosophila melanogaster .
| Autor: | Jones H; Department of Life Sciences, Imperial College London, London, United Kingdom., Willis JA; Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom., Firth LC; Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom., Giachello CNG; Syngenta, Jealott's Hill International Research Centre, Bracknell, United Kingdom., Gilestro GF; Department of Life Sciences, Imperial College London, London, United Kingdom. |
|---|---|
| Jazyk: | angličtina |
| Zdroj: | ELife [Elife] 2023 Nov 08; Vol. 12. Date of Electronic Publication: 2023 Nov 08. |
| DOI: | 10.7554/eLife.86695 |
| Abstrakt: | 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 . Competing Interests: HJ, JW, LF, CG, GG No competing interests declared (© 2023, Jones et al.) |
| Databáze: | MEDLINE |
| Externí odkaz: |
|