# Predictability and hierarchy in Drosophila behavior

arXiv: https://arxiv.org/abs/1605.03626 · HTML: https://mlsanity.com/paper/1605.03626/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2016.

## Built on by (most cited first, 5 of 5)

- [Representation learning for neural population activity with Neural Data Transformers](https://mlsanity.com/paper/2108.01210/) (2021)
- [A benchmark for computational analysis of animal behavior, using animal-borne tags](https://mlsanity.com/paper/2305.10740/) (2023)
- [Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes](https://mlsanity.com/paper/2109.12434/) (2021)
- [Bubblewrap: Online tiling and real-time flow prediction on neural manifolds](https://mlsanity.com/paper/2108.13941/) (2021)
- [From eye to AI: studying rodent social behavior in the era of machine Learning](https://mlsanity.com/paper/2508.04255/) (2025)

## Abstract (arXiv)

Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent amongst the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested sub-clusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.