Structure of activity in multiregion recurrent neural networks

David G Clark, Manuel Beiran

Abstract: The increasing availability of multiregion neural recordings underscores the challenge of understanding neural dynamics across interconnected brain regions. We propose and analyze a multiregion network model leveraging low-rank connectivity for selective activity routing. We develop a dynamical mean-field theory to analyze the model, revealing a competition between signal generation and transmission within regions. Our work provides an analytically tractable approach to multiregion interactions in high-dimensional, nonlinear neural systems, combining an experimentally motivated form of connectivity with recurrent network dynamics. The mean-field formalism offers a perspective on routing, establishes a theoretical foundation for machine-learning tools in neural data analysis, and advances our understanding of multiregion neural circuits.