The Mouse Action Recognition System (MARS) software pipeline for automated analysis of social behaviors in mice

Abstract

The study of naturalistic social behavior requires quantification of animals interactions. This is generally done through manual annotation--a highly time consuming and tedious process. Recent advances in computer vision enable tracking the pose (posture) of freely-behaving animals. However, automatically and accurately classifying complex social behaviors remains technically challenging. We introduce the Mouse Action Recognition System (MARS), an automated pipeline for pose estimation and behavior quantification in pairs of freely interacting mice. We compare MARSs annotations to human annotations and find that MARSs pose estimation and behavior classification achieve human-level performance. We also release the pose and annotation datasets used to train MARS, to serve as community benchmarks and resources. Finally, we introduce the Behavior Ensemble and Neural Trajectory Observatory (BENTO), a graphical user interface for analysis of multimodal neuroscience datasets. Together, MARS and BENTO provide an end-to-end pipeline for behavior data extraction and analysis, in a package that is user-friendly and easily modifiable.