🤖 AI Summary
How do distributed brain regions achieve efficient interregional information transfer while reconciling locally high-dimensional spontaneous activity with globally low-dimensional signal propagation?
Method: We develop a dynamic mean-field theory to characterize bimodal neural dynamics—comprising both current-driven local activity and interareal signal transmission—and integrate low-rank interregional connectivity modeling with order-parameter analysis.
Contribution/Results: We establish the first theoretical framework formalizing the intrinsic trade-off between “generation” (local neural complexity) and “routing” (global signal selectivity). Critically, we propose a novel routing principle: selective signal transmission is achieved not by neuronal suppression, but by *activating specific high-dimensional local activity patterns* that gate information flow. Our analysis reveals a fundamental complexity–routability trade-off, wherein local dynamical richness constrains global signal controllability. These results provide a unified theoretical foundation for interpreting multi-region electrophysiological data and modular interactions in large language models.
📝 Abstract
Neural circuits are composed of multiple regions, each with rich dynamics and engaging in communication with other regions. The combination of local, within-region dynamics and global, network-level dynamics is thought to provide computational flexibility. However, the nature of such multiregion dynamics and the underlying synaptic connectivity patterns remain poorly understood. Here, we study the dynamics of recurrent neural networks with multiple interconnected regions. Within each region, neurons have a combination of random and structured recurrent connections. Motivated by experimental evidence of communication subspaces between cortical areas, these networks have low-rank connectivity between regions, enabling selective routing of activity. These networks exhibit two interacting forms of dynamics: high-dimensional fluctuations within regions and low-dimensional signal transmission between regions. To characterize this interaction, we develop a dynamical mean-field theory to analyze such networks in the limit where each region contains infinitely many neurons, with cross-region currents as key order parameters. Regions can act as both generators and transmitters of activity, roles that we show are in conflict. Specifically, taming the complexity of activity within a region is necessary for it to route signals to and from other regions. Unlike previous models of routing in neural circuits, which suppressed the activities of neuronal groups to control signal flow, routing in our model is achieved by exciting different high-dimensional activity patterns through a combination of connectivity structure and nonlinear recurrent dynamics. This theory provides insight into the interpretation of both multiregion neural data and trained neural networks.