The behavioral space of zebrafish locomotion and its neural network analog

  • Girdhar K
  • Gruebele M
  • Chemla Y
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Abstract

How simple is the underlying controlmechanism for the complex locomotion of verte- brates?We explore this question for the swimming behavior of zebrafish larvae. A param- eter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish "eigen- shapes" as coordinates, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative "postural space" that captures >96%of the observed zebrafish locomotion. Viewed in postural space, swim bouts aremanifested as trajectories consisting of cycles of shapes repeated in succes- sion. To classify behavioral patterns quantitatively and to understand behavioral varia- tions among an ensemble of fish, we construct a "behavioral space" usingmulti- dimensional scaling (MDS). Thismethod turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analy- sis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age.With the insight into fish behavior from postural space and behavioral space, we construct a two- channel neural network model for fish locomotion, which produces strikingly similar pos- tural space and behavioral space dynamics compared to real zebrafish.

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Authors

  • Kiran Girdhar

  • Martin Gruebele

  • Yann R. Chemla

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