High-dimensional dynamics in low-dimensional networks

Abstract

Many networks in nature and applications have an approximate low-rank structure in the sense that their connectivity structure is dominated by a few dimensions. It is natural to expect that dynamics on such networks would also be low-dimensional. Indeed, theoretical results show that low-rank networks produce low-dimensional dynamics whenever the network is isolated from external perturbations or input. However, networks in nature are rarely isolated. Here, we study the dimensionality of dynamics in recurrent networks with low-dimensional structure driven by high-dimensional inputs or perturbations. We find that dynamics in such networks can be high- or low-dimensional and we derive mathematical conditions on the network structure under which linearized dynamics are high-dimensional. In many low-rank networks, dynamics are suppressed in directions aligned with the network's low-rank structure, a phenomenon we term ``low-rank suppression.'' We show that several low-rank network structures arising in nature satisfy the conditions for generating high-dimensional dynamics and low-rank suppression. Our results clarify important, but counterintuitive relationships between a recurrent network's connectivity structure and the structure of its response to external input.