Topic: movemes

Understanding the hierarchical organization of natural behavior is a fundamental objective in neuroscience. Despite the availability of unsupervised methods for behavioral analysis, hierarchical variants are still lacking. Here, we introduce hBehaveMAE, a Masked Autoencoder framework designed to elucidate the hierarchical nature of motion capture data in an unsupervised fashion. Our hierarchical framework offers interpretability, with lower encoder levels capturing fine-grained movements, while higher discerning complex actions. To validate the effectiveness of hBehaveMAE, we present results f