Core motifs predict dynamic attractors in combinatorial threshold-linear networks

Combinatorial threshold-linear networks (CTLNs) are a special class of inhibition-dominated TLNs defined from directed graphs. Like more general TLNs, they display a wide variety of nonlinear dynamics including multistability, limit cycles, quasiperiodic attractors, and chaos. In prior work, we have developed a detailed mathematical theory relating stable and unstable fixed points of CTLNs to graph-theoretic properties of the underlying network. Here we find that a special type of fixed points, corresponding to core motifs, are predictive of both static and dynamic attractors. Moreover, the attractors can be found by choosing initial conditions that are small perturbations of these fixed points. This motivates us to hypothesize that dynamic attractors of a network correspond to unstable fixed points supported on core motifs. We tested this hypothesis on a large family of directed graphs of size n = 5, and found remarkable agreement. Furthermore, we discovered that core motifs with similar embeddings give rise to nearly identical attractors. This allowed us to classify attractors based on structurally-defined graph families. Our results suggest that graphical properties of the connectivity can be used to predict a network's complex repertoire of nonlinear dynamics.

Automated summary

Combinatorial threshold-linear networks (CTLNs) are a special class of TLNs defined from directed graphs. In this study, a type of fixed points called core motifs are found to predict both static and dynamic attractors in CTLNs. By choosing initial conditions that are small perturbations of these fixed points, attractors can be found. Furthermore, it is discovered that core motifs with similar embeddings give rise to nearly identical attractors. This study suggests that graphical properties of connectivity can be used to predict a network's complex repertoire of nonlinear dynamics.