ArticleNature methods2022
SLEAP: A deep learning system for multi-animal pose tracking.
Article in Nature methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 404 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
404 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Systematic review of artificial intelligence use in behavioral analysis of invertebrate and larval model organisms: methods, applications and future recommendations.Frontiers in artificial intelligence · 2026Pooled it
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- Anesthesia unconsciousness recruits dynamic parabrachial activity within a distributed brain-wide ensemble.bioRxiv : the preprint server for biology · 2026Article
- Amygdalostriatal transition zone neurons encode sustained cue responses to guide defensive behaviors.Neuron · 2026Article
- Noise-induced hearing loss delays auditory learning and reduces the influence of acoustic evidence on choice.bioRxiv : the preprint server for biology · 2026Article
- Multi-Camera Analysis of Simulated Porcine Recovery States Based on Three-Dimensional Pose Using Synthetic Data.Journal of imaging · 2026Article
- ABEL: an active-learning behavior estimation and labeling platform.bioRxiv : the preprint server for biology · 2026Article
- Spontaneous oxycodone withdrawal alters behavior and oligodendrocyte-related gene expression in mice.Pharmacology, biochemistry, and behavior · 2026Article
- Born This Way: Individuality Is Seeded Before Birth and Robust to Ecological Stress.Ecology letters · 2026Article
- Cross-species identification of conserved and divergent locomotor kinematic strategies using AutoGaitA.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- Immersive Social Interaction Assay (ISIA) for Studying Voluntary and Long-Term Social Behavior in Mice.Bio-protocol · 2026Article
- Artificial Intelligence-Based Decoding of Animal Micro-Expressions: A Review of Methodological Advances and Translational Applications.Animals : an open access journal from MDPI · 2026Review
344 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
- Erratum issued
Authors and funding
21 authors.
Funding
Abstract
The desire to understand how the brain generates and patterns behavior has driven rapid methodological innovation in tools to quantify natural animal behavior. While advances in deep learning and computer vision have enabled markerless pose estimation in individual animals, extending these to multiple animals presents unique challenges for studies of social behaviors or animals in their natural environments. Here we present Social LEAP Estimates Animal Poses (SLEAP), a machine learning system for multi-animal pose tracking. This system enables versatile workflows for data labeling, model training and inference on previously unseen data. SLEAP features an accessible graphical user interface, a standardized data model, a reproducible configuration system, over 30 model architectures, two approaches to part grouping and two approaches to identity tracking. We applied SLEAP to seven datasets across flies, bees, mice and gerbils to systematically evaluate each approach and architecture, and we compare it with other existing approaches. SLEAP achieves greater accuracy and speeds of more than 800 frames per second, with latencies of less than 3.5 ms at full 1,024 × 1,024 image resolution. This makes SLEAP usable for real-time applications, which we demonstrate by controlling the behavior of one animal on the basis of the tracking and detection of social interactions with another animal.
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Registered trials
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.