Dynamical robustness of neuronal networks with higher-order (non-pairwise) interactions

Investigate the dynamical robustness of neuronal networks modeled with higher-order interactions beyond pairwise coupling (e.g., group interactions represented via simplicial complexes or hypergraphs), including whether aging transitions occur and how their characteristics differ from pairwise networks. Establish conditions under which global oscillations persist or collapse when a fraction of neuronal units becomes inactive, and determine how higher-order coupling mechanisms influence the critical inactivation thresholds for aging transitions.

Background

Most existing studies on dynamical robustness and aging transitions have assumed dyadic (pairwise) interactions among oscillators. However, many real systems—including ecological, neuronal, and social networks—exhibit group interactions among three or more units, which can be modeled by higher-order network formalisms such as hypergraphs or simplicial complexes.

The authors emphasize that while dynamical robustness has been examined in neuronal systems with pairwise couplings, the role of higher-order interactions in determining robustness and aging transitions in neuronal networks remains unexplored. Addressing this gap is important given the established relevance of higher-order dependencies in brain dynamics and neural computation.