Interpreting and comparing neural activity across systems by geometric deep learning

Abstract

The MARBLE method addresses a critical challenge in neural population recordings: inferring expressive and interpretable latent representations that are comparable across experiments and animals. It achieves this by explicitly leveraging the low-dimensional structure of neural states through geometric deep learning to learn the dynamical flow fields in neural activity.