Evolving dynamical networks with transient cluster activity

TL;DR

This paper investigates evolving dynamical networks with activity-dependent topology, demonstrating their ability to generate robust yet input-sensitive sequences of metastable cluster states.

Contribution

It introduces a model of evolving networks that produce sequential cluster activity and analyzes their robustness and sensitivity properties.

Findings

Networks generate sequences of metastable cluster states.

Sequences are robust against noise and perturbations.

Sequences are sensitive to input information.

Abstract

We study transient sequential dynamics of evolving dynamical networks, i.e., those having active nodes and links and activity-dependent topology. We show that such networks can generate sequences of metastable cluster states where each state is a cyclic sequence of clusters following each other in a certain order. We found the way how the sequences generated by such networks can be robust against background noise, small perturbations of initial conditions, and parameter detuning, and at the same time, can be sensitive to input information.