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

ABSTRACT: Measuring animal behavior over long timescales has been traditionally limited to behaviors that are easily measurable with real-time sensors. More complex behaviors have been measured over time, but these approaches are considerably more challenging due to the intensive manual effort required for scoring behaviors. Recent advances in machine learning have introduced automated behavior analysis methods, but these often overlook long-term behavioral patterns and struggle with classification in varying environmental conditions. To address this, we developed a pipeline that enables continuous, parallel recording and acquisition of animal behavior for an indefinite duration. As part of this pipeline, we applied a recent breakthrough self-supervised computer vision model to reduce training bias

SUBMITTER: Perry LJ 

PROVIDER: S-EPMC11326128 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature