Modeling the behavior of multiple subjects using a Cauchy-Schwarz regularized Partitioned Subspace Variational AutoEncoder (CS-PS-VAE)

Daiyao Yi, Shreya Saxena

Abstract: Effectively modeling and quantifying behavior is essential for our understanding of the brain. Modeling behavior across different subjects in a unified manner remains a significant challenge in the field of behavioral quantification, which necessitates partitioning the behavioral data into features that are common across subjects, and others that are distinct to each subject. We build on a semi-supervised approach to partition the subspace adequately known as a Partitioned Subspace Variational AutoEncoder (PS-VAE), and propose a novel regularization based on the Cauchy-Schwarz divergence to model the distinct features across subjects. Our model, called the Cauchy-Schwarz regularized Partitioned Subspace Variational AutoEncoder (CS-PS-VAE), successfully models continuously varying differences in behavior, and models distinct features of the behavioral videos across subjects in an unsupervised manner. This method is also successful at uncovering the relationships between recorded neural data and the ensuing behavior.