Shared Representation Discovery for Multi-Subject Neural Data Analysis

Abstract

Neural recordings from different individuals vary substantially even when behavior is broadly shared. The sampled neurons differ, and the same behavior occurs at different times. Standard cross-subject analyses rely on matched time points or anatomical correspondence, which excludes many datasets. We recently introduced Shared Representation Discovery (ShaReD), which identifies neural-behavioral relationships conserved across subjects by learning a shared behavioral projection together with subject-specific neural projections. Here we develop and benchmark this method using synthetic, primate, and rat data. On synthetic data, ShaReD recovers common structure across noise levels, sample sizes, and subject counts, and separates components confined to different groups of subjects. In non-human primate motor cortex, ShaReD identifies kinematic representations that generalize across individuals and across reaching tasks with different movement statistics. In rats navigating a spatial alternation task, ShaReD isolates behavior-aligned directions within the CA1-to-PFC communication subspace. ShaReD thus extends multi-subject analysis to datasets in which comparable behaviors occur without cross-subject temporal correspondence.