Topic: masked autoencoder

Connor Lane will lead a journal club on the recent BrainLM preprint, a foundation model for fMRI trained using self-supervised masked autoencoder training. Preprint: https://www.biorxiv.org/content/10.1101/2023.09.12.557460v1 Tweeprint: https://twitter.com/david_van_dijk/status/1702336882301112631?t=Q2-U92-BpJUBh9C35iUbUA&s=19

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