Perspective: network-guided pattern formation of neural dynamics

TL;DR

This paper introduces a new perspective on neural dynamics by analyzing how brain network architecture influences self-organized activity patterns, emphasizing deviations from regular graphs and the role of topological features.

Contribution

It proposes a novel framework for understanding neural activity patterns based on deviations from regular graphs and the influence of topological features like hubs and modules.

Findings

Network architecture constrains neural activity to specific collective states.

Topological features such as hubs and modules shape activity patterns.

Numerical simulations demonstrate the impact of network structure on dynamics.

Abstract

The understanding of neural activity patterns is fundamentally linked to an understanding of how the brain's network architecture shapes dynamical processes. Established approaches rely mostly on deviations of a given network from certain classes of random graphs. Hypotheses about the supposed role of prominent topological features (for instance, the roles of modularity, network motifs, or hierarchical network organization) are derived from these deviations. An alternative strategy could be to study deviations of network architectures from regular graphs (rings, lattices) and consider the implications of such deviations for self-organized dynamic patterns on the network. Following this strategy, we draw on the theory of spatiotemporal pattern formation and propose a novel perspective for analyzing dynamics on networks, by evaluating how the self-organized dynamics are confined by network architecture to a small set of permissible collective states. In particular, we discuss the role of prominent topological features of brain connectivity, such as hubs, modules and hierarchy, in shaping activity patterns. We illustrate the notion of network-guided pattern formation with numerical simulations and outline how it can facilitate the understanding of neural dynamics.