Statistical structure of locomotion and its modulation by odors
Abstract
Most behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly's locomotion can be decomposed into a few locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly.
Data availability
Data has been deposited in Dryad Data Repository and is available at doi:10.5061/dryad.m930f2m.
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Data from: Statistical structure of locomotion and its modulation by odorsDryad Digital Repository, doi:10.5061/dryad.m930f2m.
Article and author information
Author details
Funding
National Institute of Neurological Disorders and Stroke
- Vikas Bhandawat
National Institute on Deafness and Other Communication Disorders
- Vikas Bhandawat
National Science Foundation
- Vikas Bhandawat
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Ronald L Calabrese, Emory University, United States
Version history
- Received: August 21, 2018
- Accepted: January 5, 2019
- Accepted Manuscript published: January 8, 2019 (version 1)
- Version of Record published: February 4, 2019 (version 2)
- Version of Record updated: March 15, 2019 (version 3)
Copyright
© 2019, Tao et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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