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Deep attention networks reveal the rules of collective motion in zebrafish

View ORCID ProfileFrancisco J.H. Heras, View ORCID ProfileFrancisco Romero-Ferrero, View ORCID ProfileRobert C. Hinz, View ORCID ProfileGonzalo G. de Polavieja
doi: https://doi.org/10.1101/400747
Francisco J.H. Heras
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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  • ORCID record for Francisco J.H. Heras
  • For correspondence: francisco.heras@neuro.fchampalimaud.org gonzalo.depolavieja@neuro.fchampalimaud.org
Francisco Romero-Ferrero
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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Robert C. Hinz
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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Gonzalo G. de Polavieja
1Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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  • ORCID record for Gonzalo G. de Polavieja
  • For correspondence: francisco.heras@neuro.fchampalimaud.org gonzalo.depolavieja@neuro.fchampalimaud.org
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Abstract

A variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but lack important elements necessary to predict the motion of each individual in the collective. Adding more detail increases predictability but can make models too complex to be insightful. Here we report that deep attention networks can obtain in a data-driven way a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. The model obtains that interactions between two zebrafish, Danio rerio, in a large groups of 60-100, can be approximately be described as repulsive, attractive or as alignment, but only when moving slowly. At high velocities, interactions correspond only to alignment or alignment mixed with repulsion at close distances. The model also shows that each zebrafish decides where to move by aggregating information from the group as a weighted average over neighbours. Weights are higher for neighbours that are close, in a collision path or moving faster in frontal and lateral locations. These weights effectively select 5 relevant neighbours on average, but this number is dynamical, changing between a single neighbour to up to 12, often in less than a second. Our results suggest that each animal in a group decides by dynamically selecting information from the group.

Highlights

  • At 30 days postfertilization, zebrafish, Danio rerio, can move in very cohesive and predictable large groups

  • Deep attention networks obtain a predictive and understadable model of collective motion

  • When moving slowly, interations between pairs of zebrafish have clear components of repulsion, attraction and alignment

  • When moving fast, interactions correspond to alignment and a mixture of alignment and repulsion at close distances

  • Zebrafish turn left or right depending on a weighted average of interaction information with other fish, with weights higher for close fish, those in a collision path or those moving fast in front or to the sides

  • Aggregation is dynamical, oscillating between 1 and 12 neighbouring fish, with 5 on average

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted December 21, 2018.
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Deep attention networks reveal the rules of collective motion in zebrafish
Francisco J.H. Heras, Francisco Romero-Ferrero, Robert C. Hinz, Gonzalo G. de Polavieja
bioRxiv 400747; doi: https://doi.org/10.1101/400747
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Deep attention networks reveal the rules of collective motion in zebrafish
Francisco J.H. Heras, Francisco Romero-Ferrero, Robert C. Hinz, Gonzalo G. de Polavieja
bioRxiv 400747; doi: https://doi.org/10.1101/400747

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