Predicting the long-term collective behaviour of fish pairs with deep learning

Vaios Papaspyros, Ramón Escobedo, Alexandre Alahi, Guy Theraulaz, Clément Sire, Francesco Mondada

Mar 2024 - Journal of the Royal Society Interface

Abstract

Modern computing has enhanced our understanding of how social interactions shape collective behaviour in animal societies. Although analytical models dominate in studying collective behaviour, this study introduces a deep learning model to assess social interactions in the fish species Hemigrammus rhodostomus. We compare the results of our deep learning approach with experiments and with the results of a state-of-the-art analytical model. To that end, we propose a systematic methodology to assess the faithfulness of a collective motion model, exploiting a set of stringent individual and collective spatio-temporal observables. We demonstrate that machine learning (ML) models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasizes the need for consistent validation across different timescales, and identifies key design aspects that enable our deep learning approach to capture both short- and long-term dynamics. We also show that our approach can be extended to larger groups without any retraining, and to other fish species, while retaining the same architecture of the deep learning network. Finally, we discuss the added value of ML in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.

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Reference:

@article{papaspyros2024predicting,
  title={Predicting the long-term collective behaviour of fish pairs with deep learning},
  author={Papaspyros, Vaios and Escobedo, Ram{\'o}n and Alahi, Alexandre and Theraulaz, Guy and Sire, Cl{\'e}ment and Mondada, Francesco},
  journal={Journal of the Royal Society Interface},
  volume={21},
  number={212},
  pages={20230630},
  year={2024},
  publisher={The Royal Society}
}