# Prioritized learning of cross-population neural dynamics

## TL;DR

This paper introduces a new method to study interactions between brain regions by learning cross-population dynamics without interference from within-population activity.

## Contribution

The novel contribution is CroP-LDM, a prioritized learning framework that disentangles cross-population dynamics from within-population dynamics.

## Key findings

- CroP-LDM outperforms existing methods in learning cross-population dynamics with low dimensionality.
- The prioritized learning objective is crucial for accurate modeling of cross-regional interactions.
- CroP-LDM can identify interpretable dominant interaction pathways between brain regions.

## Abstract

Objective. Improvements in recording technology for multi-region simultaneous recordings enable the study of interactions among distinct brain regions. However, a major computational challenge in studying cross-regional, or cross-population dynamics in general, is that the cross-population dynamics can be confounded or masked by within-population dynamics. Approach. Here, we propose cross-population prioritized linear dynamical modeling (CroP-LDM) to tackle this challenge. CroP-LDM learns the cross-population dynamics in terms of a set of latent states using a prioritized learning approach, such that they are not confounded by within-population dynamics. Further, CroP-LDM can infer the latent states both causally in time using only past neural activity and non-causally in time, unlike some prior dynamic methods whose inference is non-causal. Main results. First, through comparisons with various LDM methods, we show that the prioritized learning objective in CroP-LDM is key for accurate learning of cross-population dynamics. Second, using multi-regional bilateral motor and premotor cortical recordings during a naturalistic movement task, we demonstrate that CroP-LDM better learns cross-population dynamics compared to recent static and dynamic methods, even when using a low dimensionality. Finally, we demonstrate how CroP-LDM can quantify dominant interaction pathways across brain regions in an interpretable manner. Significance. Overall, these results show that our approach can be a useful framework for addressing challenges associated with modeling dynamics across brain regions.