An unsupervised method for quantifying the behavior of paired animals.

TitleAn unsupervised method for quantifying the behavior of paired animals.
Publication TypeJournal Article
Year of Publication2017
AuthorsKlibaite, U, Berman, GJ, Cande, J, Stern, DL, Shaevitz, JW
JournalPhys Biol
Volume14
Issue1
Pagination015006
Date Published2017 Feb 16
ISSN1478-3975
KeywordsAnimals, Behavior, Animal, Drosophila melanogaster, Female, Machine Learning, Male, Pair Bond, Sexual Behavior, Animal, Video Recording
Abstract

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.

DOI10.1088/1478-3975/aa5c50
Alternate JournalPhys Biol
PubMed ID28140374
PubMed Central IDPMC5414632
Grant ListP50 GM071508 / GM / NIGMS NIH HHS / United States
R01 GM098090 / GM / NIGMS NIH HHS / United States
T32 HG003284 / HG / NHGRI NIH HHS / United States