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The European Physical Journal Special Topics

, Volume 223, Issue 9, pp 1787–1803 | Cite as

Exploratory activity and habituation of Drosophila in confined domains

  • B. Soibam
  • L. Chen
  • G. W. Roman
  • G. H. Gunaratne
Regular Article
Part of the following topical collections:
  1. Soft Matter in Confinement: Systems from Biology to Physics

Abstract

Animals use locomotion to find food, shelter, and escape routes as well as to locate predators, competitors, and mates. Thus, locomotion is related to many behavioral traits, and can be used to characterize these more complex facets of behavior. Exploratory behaviors are random and need to be assessed through stochastic analysis. By comparing ensembles of trajectories from Drosophila and a model animal, we identify a pair of principles that govern the stochastic motion of a specific species. The first depends on local cues and quantify directional persistence, i.e., the propensity of an animal to maintain direction; the second, its attraction to walls, is relevant for exploration in confined arenas. Statistical properties of exploratory activity in several types of arenas can be computed from these principles. A pair of spiral arenas are designed to demonstrate that centrophobicity, or fear of the center of an arena, is not a fundamental feature of exploration. xxxx We provide evidence to show that the decay in an animal’s activity following its introduction into a novel arena is correlated to its familiarity with the arena. We define two measures, coverage and habituation, to quantify familiarity. It is found that the relationship between activity and coverage is independent of the arena size. Finally, we use an analysis of exploration of mutant species to infer that in Drosophila, habituation relies on visual cues.

Keywords

Arena European Physical Journal Special Topic Central Zone Radial Distribution Function Stochastic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© EDP Sciences and Springer 2014

Authors and Affiliations

  • B. Soibam
    • 1
  • L. Chen
    • 2
  • G. W. Roman
    • 3
  • G. H. Gunaratne
    • 4
  1. 1.Department of Biology and BiochemistryUniversity of HoustonHoustonUSA
  2. 2.Department of PhysicsUniversity of HoustonHoustonUSA
  3. 3.Department of Biology and Biochemistry and the Biology of Behavior InstituteUniversity of HoustonHoustonUSA
  4. 4.Department of PhysicsUniversity of HoustonHoustonUSA

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