Long timescale dynamics in freely behaving rats

Natural behavior is composed of rich postural dynamics that contain stereotyped movements performed by the animal. These behaviors span multiple timescales and are performed in a structured manner during spontaneous behavior. Thus, a quantitative understanding of behavioral dynamics is crucial to help unravel the latent physiological states driving behavior. Here, we extract postural information from videos of freely moving rats in an arena using markerless tracking tools. Using these postural time series’ we create a low-dimensional behavioral state space using unsupervised methods that characterizes stereotypic behavioral bouts. We find long, non-Markovian timescales of predictability across novel and familiar trials of light and dark conditions in the arena. These behavioral sequences are found to be arranged in hierarchical clusters, similar to previous results in fruit flies. These results support hierarchical organization of behavior as a general principle across species for generating long timescale dynamics.