Accurate identification of communication between multiple interacting neural populations

Abstract

Neural recording technologies now enable simultaneous recording of population activity across many brain regions, motivating the development of data-driven models of inter-regional communication. However, existing models can struggle to disentangle the influences that drive recorded population activity, leading to inaccurate portraits of communication. Here, we introduce Multi-Region Latent Factor Analysis via Dynamical Systems (MR-LFADS), a sequential variational autoencoder designed to disentangle inter-regional communication, inputs from unobserved regions, and local neural population dynamics. We show that MR-LFADS outperforms existing approaches at identifying communication across dozens of simulations of task-trained multi-region networks. When applied to large-scale electrophysiology, MR-LFADS predicts brain-wide effects of circuit perturbations that were held out during model fitting. These validations on synthetic and real neural data position MR-LFADS as a promising tool for discovering principles of brain-wide information processing.