A circadian behavioral analysis suite for real-time classification of daily rhythms in complex behaviors.

ABSTRACT: Long-term analysis of animal behavior has been limited by reliance on real-time sensors or manual scoring. Existing machine learning tools can automate analysis but often fail under variable conditions or ignore temporal dynamics. We developed a scalable pipeline for continuous, real-time acquisition and classification of behavior across multiple animals and conditions. At its core is a self-supervised vision model paired with a lightweight classifier that enables robust performance with minimal manual labeling. Our system achieves expert-level performance and can operate indefinitely across diverse recording environments. As a proof-of-concept, we recorded 97 mice over 2 weeks to test whether sex hormones influence circadian behaviors. We discovered sex- and estrogen-dependent rhythms in

SUBMITTER: Perry LJ 

PROVIDER: S-EPMC12146638 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature