# From eye to AI: studying rodent social behavior in the era of machine Learning

arXiv: https://arxiv.org/abs/2508.04255 · HTML: https://mlsanity.com/paper/2508.04255/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 136 references that are in the map.
- First posted on arXiv 6 Aug 2025.

## Builds on (most cited first, 16 of 16)

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- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
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- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Learnable latent embeddings for joint behavioral and neural analysis](https://mlsanity.com/paper/2204.00673/) (2022)
- [Deep learning tools for the measurement of animal behavior in neuroscience](https://mlsanity.com/paper/1909.13868/) (2019)
- [The Multi-Agent Behavior Dataset: Mouse Dyadic Social Interactions](https://mlsanity.com/paper/2104.02710/) (2021)
- [Task Programming: Learning Data Efficient Behavior Representations](https://mlsanity.com/paper/2011.13917/) (2020)
- [Mapping the stereotyped behaviour of freely-moving fruit flies](https://mlsanity.com/paper/1310.4249/) (2013)
- [AmadeusGPT: a natural language interface for interactive animal behavioral analysis](https://mlsanity.com/paper/2307.04858/) (2023)
- [Predictability and hierarchy in Drosophila behavior](https://mlsanity.com/paper/1605.03626/) (2016)
- [Learning Behavior Representations Through Multi-Timescale Bootstrapping](https://mlsanity.com/paper/2206.07041/) (2022)
- [Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis](https://mlsanity.com/paper/2303.08811/) (2023)
- [ToxTrac: a fast and robust software for tracking organisms](https://mlsanity.com/paper/1706.02577/) (2017)
- [idtracker.ai: Tracking all individuals in large collectives of unmarked animals](https://mlsanity.com/paper/1803.04351/) (2018)
- [Generalized Glauber dynamics for inference in biology](https://mlsanity.com/paper/2208.03483/) (2022)

## Abstract (arXiv)

The study of rodent social behavior has shifted in the last years from relying on direct human observation to more nuanced approaches integrating computational methods in artificial intelligence (AI) and machine learning. While conventional approaches introduce bias and can fail to capture the complexity of rodent social interactions, modern approaches bridging computer vision, ethology and neuroscience provide more multifaceted insights into behavior which are particularly relevant to social neuroscience. Despite these benefits, the integration of AI into social behavior research also poses several challenges. Here we discuss the main steps involved and the tools available for analyzing rodent social behavior, examining their advantages and limitations. Additionally, we suggest practical solutions to address common hurdles, aiming to guide young researchers in adopting these methods and to stimulate further discussion among experts regarding the evolving requirements of these tools in scientific applications.