Deep attention networks reveal the rules of collective motion in zebrafish.

ABSTRACT: A variety of simple models has been proposed to understand the collective motion of animals. These models can be insightful but may 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 a model of collective behavior that is simultaneously predictive and insightful thanks to an organization in modules. When using simulated trajectories, the model recovers the ground-truth interaction rule used to generate them, as well as the number of interacting neighbours. For experimental trajectories of large groups of 60-100 zebrafish, Danio rerio, the model obtains that interactions between pairs can approximately

SUBMITTER: Heras FJH 

PROVIDER: S-EPMC6760814 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature