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Leg-tracking and automated behavioural classification in Drosophila

Author

Listed:
  • Jamey Kain

    (Rowland Institute, Harvard University)

  • Chris Stokes

    (Rowland Institute, Harvard University)

  • Quentin Gaudry

    (Harvard Medical School)

  • Xiangzhi Song

    (Rowland Institute, Harvard University
    College of Chemistry and Chemical Engineering, Central South University)

  • James Foley

    (Rowland Institute, Harvard University)

  • Rachel Wilson

    (Harvard Medical School)

  • Benjamin de Bivort

    (Rowland Institute, Harvard University
    Center for Brain Science, Harvard University
    Harvard University)

Abstract

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.

Suggested Citation

  • Jamey Kain & Chris Stokes & Quentin Gaudry & Xiangzhi Song & James Foley & Rachel Wilson & Benjamin de Bivort, 2013. "Leg-tracking and automated behavioural classification in Drosophila," Nature Communications, Nature, vol. 4(1), pages 1-8, October.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms2908
    DOI: 10.1038/ncomms2908
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