Network resilience in the presence of non-equilibrium dynamics

TL;DR

This paper investigates how non-equilibrium dynamics in nodes influence the resilience of complex neuronal networks, revealing that more nodes with such dynamics can delay transitions and enhance resilience, with effects varying by network topology.

Contribution

It introduces the role of non-equilibrium node dynamics in network resilience, highlighting their impact across different network topologies and transition types.

Findings

Higher proportion of non-equilibrium nodes delays network transitions.

Non-equilibrium dynamics increase network resilience against environmental stress.

Predictability of transitions decreases with more disordered network topology.

Abstract

Many complex networks are known to exhibit sudden transitions between alternative steady states with contrasting properties. Such a sudden transition demonstrates a network's resilience, which is the ability of a system to persist in the face of perturbations. Most of the research on network resilience has focused on the transition from one equilibrium state to an alternative equilibrium state. Although the presence of non-equilibrium dynamics in some nodes may advance or delay sudden transitions in networks and give early warning signals of an impending collapse, it has not been studied much in the context of network resilience. Here we bridge this gap by studying a neuronal network model with diverse topologies, in which non-equilibrium dynamics may appear in the network even before the transition to a resting state from an active state in response to environmental stress deteriorating their external conditions. We find that the percentage of uncoupled nodes exhibiting non-equilibrium dynamics plays a vital role in determining the network's transition type. We show that a higher proportion of nodes with non-equilibrium dynamics can delay the tipping and increase networks' resilience against environmental stress, irrespective of their topology. Further, predictability of an upcoming transition weakens, as the network topology moves from regular to disordered.