ArticleCommunications biology2022
Identifying behavioral structure from deep variational embeddings of animal motion.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 104 papers, 1 of them a synthesis that pooled it.
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Who cites it
104 citing papers in PubMed, 1 synthesis or guideline pooled it, 146 citations in OpenAlex.
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- Toward video-LLM driven workflow for behavioral segmentation and scoring in mice performing a skilled water-reaching task: an evaluation of recent LLM models.Neurophotonics · 2026Article
- Considerations for the selection and phenotyping of mouse models for the study of Alzheimer's disease.STAR protocols · 2026Review
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- Therapeutic targeting of fibrin-microglia interactions ameliorates Alzheimer's disease-related hyperexcitability and brain network dysfunction.bioRxiv : the preprint server for biology · 2026Article
- AVATAR: AI Vision Analysis for Three-dimensional Action in Real-time.Experimental neurobiology · 2026Article
- Behavioral heterogeneity in host seeking and post-feeding suppression among disease vector mosquitoes.Communications biology · 2026Article
- Optimizing Intermediate Representations: A Framework for Low-Cost, High-Accuracy Behavior Quantification.bioRxiv : the preprint server for biology · 2026Article
- Feature library for behavioural characterization of early and late seizures in an experimental model of post-traumatic epilepsy.Journal of neuroscience methods · 2026Article
- Integrating supervised and unsupervised machine learning for behavior segmentation reveals latent frailty signatures and improves aging clocks in isogenic and outbred mice.bioRxiv : the preprint server for biology · 2026Article
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- Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.ArXiv · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- Visual detection of seizures in mice using supervised machine learning.Cell reports methods · 2025Article
- Deep neural networks and stochastic methods for cognitive modeling of rat behavioral dynamics inCognitive neurodynamics · 2025Article
44 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Quantification and detection of the hierarchical organization of behavior is a major challenge in neuroscience. Recent advances in markerless pose estimation enable the visualization of high-dimensional spatiotemporal behavioral dynamics of animal motion. However, robust and reliable technical approaches are needed to uncover underlying structure in these data and to segment behavior into discrete hierarchically organized motifs. Here, we present an unsupervised probabilistic deep learning framework that identifies behavioral structure from deep variational embeddings of animal motion (VAME). By using a mouse model of beta amyloidosis as a use case, we show that VAME not only identifies discrete behavioral motifs, but also captures a hierarchical representation of the motif's usage. The approach allows for the grouping of motifs into communities and the detection of differences in community-specific motif usage of individual mouse cohorts that were undetectable by human visual observation. Thus, we present a robust approach for the segmentation of animal motion that is applicable to a wide range of experimental setups, models and conditions without requiring supervised or a-priori human interference.
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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.