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

arXiv: https://arxiv.org/abs/2302.06839 · HTML: https://mlsanity.com/paper/2302.06839/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 47 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models](https://mlsanity.com/paper/1805.12114/) (2018)
- [Human Trajectory Forecasting in Crowds: A Deep Learning Perspective](https://mlsanity.com/paper/2007.03639/) (2020)
- [How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model](https://mlsanity.com/paper/1805.11371/) (2018)
- [Automatic Calibration of Artificial Neural Networks for Zebrafish Collective Behaviours using a Quality Diversity Algorithm](https://mlsanity.com/paper/1907.09209/) (2019)
- [FastTrack: an open-source software for tracking varying numbers of deformable objects](https://mlsanity.com/paper/2011.06837/) (2020)

## Built on by (most cited first, 3 of 3)

- [Quantifying the biomimicry gap in biohybrid robot-fish pairs](https://mlsanity.com/paper/2308.08978/) (2023)
- [Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives](https://mlsanity.com/paper/2511.08436/) (2025)
- [A Deep Reinforcement Learning Framework for Closed-loop Guidance of Fish Schools via Virtual Agents](https://mlsanity.com/paper/2603.28200/) (2026)

## Abstract (arXiv)

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 to experiments and to 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 models of social interactions can directly compete with their analytical counterparts in reproducing subtle experimental observables. Moreover, this work emphasises 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 machine learning in the context of the study of collective motion in animal groups and its potential as a complementary approach to analytical models.