ArticlePLoS computational biology2019
Deep attention networks reveal the rules of collective motion in zebrafish.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Experience-dependent modulation of collective behavior in larval zebrafish.Nature communications · 2026Article
- Inverse Statistics of Active Matter Trajectories to Distinguish Interaction Kernel Anisotropy from Emergent Correlations.Bulletin of mathematical biology · 2026Article
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- Distinct distributed neural dynamics predict pallium-dependent social approach.Nature communications · 2026Article
- Identification of neural crest and melanoma cancer cell invasion and migration genes using high-throughput screening and deep attention networks.Developmental dynamics : an official publication of the American Association of Anatomists · 2026Article
- Development of neural circuits for social motion perception in schooling fish.Current biology : CB · 2024Article
- A rigorous and versatile statistical test for correlations between stationary time series.PLoS biology · 2024Article
- Perception of motion salience shapes the emergence of collective motions.Nature communications · 2024Article
- Collective order and group structure of shoaling fish subject to differing risk-level treatments with a sympatric predator.Royal Society open science · 2024Article
- Selective social interactions and speed-induced leadership in schooling fish.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Dynamical order and many-body correlations in zebrafish show that three is a crowd.Nature communications · 2024Article
- Predicting the long-term collective behaviour of fish pairs with deep learning.Journal of the Royal Society, Interface · 2024Article
- Applying collective motion models to study discordant individual behaviours within a school of fish.Royal Society open science · 2023Article
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- A study of transfer of information in animal collectives using deep learning tools.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Article
- Entropy and Fractal Techniques for Monitoring Fish Behaviour and Welfare in Aquacultural Precision Fish Farming-A Review.Entropy (Basel, Switzerland) · 2023Review
- Visual processing and collective motion-related decision-making in desert locusts.Proceedings. Biological sciences · 2023Article
- Social behavioral profiling by unsupervised deep learning reveals a stimulative effect of dopamine D3 agonists on zebrafish sociality.Cell reports methods · 2023Article
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Authors and funding
4 authors.
Funding
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 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. The network also extracts that the number of interacting individuals is dynamical and typically in the range 8-22, with 1-10 more important ones. Our results suggest that each animal decides by dynamically selecting information from the collective.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.