ArticleNature communications2021
B-SOiD, an open-source unsupervised algorithm for identification and fast prediction of behaviors.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 155 papers.
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155 citing papers in PubMed.
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- Vestibular nucleus stimulation for ameliorating locomotor dynamics in a Parkinsonian mouse model.Nature communications · 2026Article
- Decoding the Oxytocinergic and Behavioral Signatures of Milk Ejection.Neuroscience bulletin · 2026Article
- Early-life adversity shapes anomalous spontaneous behavior in mice heterozygous for Cntnap2.Translational psychiatry · 2026Article
- Automated analysis of C. elegans behavior by LabGym: an open-source, AI-powered platform.G3 (Bethesda, Md.) · 2026Article
- 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
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- Autobehaver: An AI-Based Pipeline for Animal Behavior Analysis.bioRxiv : the preprint server for biology · 2026Article
- AVATAR: AI Vision Analysis for Three-dimensional Action in Real-time.Experimental neurobiology · 2026Article
- Feature library for behavioural characterization of early and late seizures in an experimental model of post-traumatic epilepsy.Journal of neuroscience methods · 2026Article
- Optimizing Intermediate Representations: A Framework for Low-Cost, High-Accuracy Behavior Quantification.bioRxiv : the preprint server for biology · 2026Article
- GrimACE: automated, multimodal cage-side assessment of pain and well-being in mice.Lab animal · 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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Abstract
Studying naturalistic animal behavior remains a difficult objective. Recent machine learning advances have enabled limb localization; however, extracting behaviors requires ascertaining the spatiotemporal patterns of these positions. To provide a link from poses to actions and their kinematics, we developed B-SOiD - an open-source, unsupervised algorithm that identifies behavior without user bias. By training a machine classifier on pose pattern statistics clustered using new methods, our approach achieves greatly improved processing speed and the ability to generalize across subjects or labs. Using a frameshift alignment paradigm, B-SOiD overcomes previous temporal resolution barriers. Using only a single, off-the-shelf camera, B-SOiD provides categories of sub-action for trained behaviors and kinematic measures of individual limb trajectories in any animal model. These behavioral and kinematic measures are difficult but critical to obtain, particularly in the study of rodent and other models of pain, OCD, and movement disorders.
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