Unsupervised mouse behavior analysis: A data-driven study of mice interactions
Automatic analysis of rodent behavior has been receiving growing attention in recent years since rodents have been the reference species for many neuroscientific studies, with the social interaction being among the subjects of the most important ones. Systems that are employed in these studies are mainly based on tracking of mice and activity classification through supervised learning methods, trained on datasets manually annotated by experts. In this paper, we introduce a completely unsupervised way of analysing tracking data for the automatic identification of social and non-social behaviors using models capable of spotting regularities in the data. In particular, a mean-covariance Restricted Boltzmann Machine is employed to abstract higher-level behavioral configurations of mice interacting in an arena for a long time.