An unsupervised method for quantifying the behavior of paired animals

Ugne Klibaite, Gordon J. Berman, Jessica Cande, David L. Stern, Joshua W. Shaevitz

Research output: Contribution to journalArticlepeer-review

36 Scopus citations


Behaviors involving the interaction of multiple individuals are complex and frequently crucial for an animal's survival. These interactions, ranging across sensory modalities, length scales, and time scales, are often subtle and difficult to characterize. Contextual effects on the frequency of behaviors become even more difficult to quantify when physical interaction between animals interferes with conventional data analysis, e.g. due to visual occlusion. We introduce a method for quantifying behavior in fruit fly interaction that combines high-throughput video acquisition and tracking of individuals with recent unsupervised methods for capturing an animal's entire behavioral repertoire. We find behavioral differences between solitary flies and those paired with an individual of the opposite sex, identifying specific behaviors that are affected by social and spatial context. Our pipeline allows for a comprehensive description of the interaction between two individuals using unsupervised machine learning methods, and will be used to answer questions about the depth of complexity and variance in fruit fly courtship.

Original languageEnglish (US)
Article number015006
JournalPhysical Biology
Issue number1
StatePublished - Feb 16 2017

All Science Journal Classification (ASJC) codes

  • Molecular Biology
  • Biophysics
  • Structural Biology
  • Cell Biology


  • Drosophila melanogaster
  • animal behavior
  • computer vision
  • courtship
  • segmentation
  • social behavior


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