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Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales

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  • Joshua M Mueller
  • Primoz Ravbar
  • Julie H Simpson
  • Jean M Carlson

Abstract

Mathematical modeling of behavioral sequences yields insight into the rules and mechanisms underlying sequence generation. Grooming in Drosophila melanogaster is characterized by repeated execution of distinct, stereotyped actions in variable order. Experiments demonstrate that, following stimulation by an irritant, grooming progresses gradually from an early phase dominated by anterior cleaning to a later phase with increased walking and posterior cleaning. We also observe that, at an intermediate temporal scale, there is a strong relationship between the amount of time spent performing body-directed grooming actions and leg-directed actions. We then develop a series of data-driven Markov models that isolate and identify the behavioral features governing transitions between individual grooming bouts. We identify action order as the primary driver of probabilistic, but non-random, syntax structure, as has previously been identified. Subsequent models incorporate grooming bout duration, which also contributes significantly to sequence structure. Our results show that, surprisingly, the syntactic rules underlying probabilistic grooming transitions possess action duration-dependent structure, suggesting that sensory input-independent mechanisms guide grooming behavior at short time scales. Finally, the inclusion of a simple rule that modifies grooming transition probabilities over time yields a generative model that recapitulates the key features of observed grooming sequences at several time scales. These discoveries suggest that sensory input guides action selection by modulating internally generated dynamics. Additionally, the discovery of these principles governing grooming in D. melanogaster demonstrates the utility of incorporating temporal information when characterizing the syntax of behavioral sequences.Author summary: Analysis of temporally rich behavioral sequences provides a quantitative description of the rules underlying their generation. Drosophila melanogaster grooming behavior consists of many complex sequences involving repetitions of well-characterized actions. In this paper, we leverage advances in machine vision to automatically annotate over 40 hours of video data of flies covered in dust and develop mathematical models that reveal the existence of syntax in D. melanogaster grooming. We find that sequence organization depends on grooming action identity, as has been well-established, and, more surprisingly, grooming action duration. The discovery of duration-dependent action selection leads us to conclude that, although sensory input informs grooming decisions on long time scales, internal dynamics also guide individual transitions between grooming actions. Therefore, incorporating action duration into our models allows us to uncover multi-scale temporal dynamics that suggest the existence of neural circuits dedicated to partially sensory-independent decision-making. Our approach highlights the importance of incorporating temporal information into sequential models, as doing so reveals the relative contributions of sensory input and internal dynamics to behavioral sequence generation.

Suggested Citation

  • Joshua M Mueller & Primoz Ravbar & Julie H Simpson & Jean M Carlson, 2019. "Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-25, June.
  • Handle: RePEc:plo:pcbi00:1007105
    DOI: 10.1371/journal.pcbi.1007105
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    References listed on IDEAS

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    1. Maximiliano L. Suster & Michael Bate, 2002. "Embryonic assembly of a central pattern generator without sensory input," Nature, Nature, vol. 416(6877), pages 174-178, March.
    2. Kentaro Katahira & Kenta Suzuki & Kazuo Okanoya & Masato Okada, 2011. "Complex Sequencing Rules of Birdsong Can be Explained by Simple Hidden Markov Processes," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-9, September.
    3. Michael A. Long & Dezhe Z. Jin & Michale S. Fee, 2010. "Support for a synaptic chain model of neuronal sequence generation," Nature, Nature, vol. 468(7322), pages 394-399, November.
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