Learning Interpretable Switching Dynamics in Shared Neural-Behavioral Latent Space
Yongxu Zhang, Josue Ortega Caro, Rachel Oren, Michael J. Higley, Jessica A. Cardin, Anne K. Churchland, Shreya Saxena
This paper introduces SSLD, a framework that learns shared neural-behavioral latent dynamics and private modality-specific variables, showing it accurately reconstructs signals and isolates behaviorally relevant information across multiple datasets.
bioRxiv · Oct 9, 2026 · PreprintNeuroscience