Memory-induced mechanism for self-sustaining activity in networks

A. E. Allahverdyan, G. Ver Steeg, and A. Galstyan
Phys. Rev. E 92, 062824 – Published 22 December 2015

Abstract

We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on treelike structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution) and show that it does facilitate the self-sustained activity.

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  • Received 20 April 2015
  • Revised 14 September 2015

DOI:https://doi.org/10.1103/PhysRevE.92.062824

©2015 American Physical Society

Authors & Affiliations

A. E. Allahverdyan1, G. Ver Steeg2, and A. Galstyan2

  • 1Yerevan Physics Institute, Alikhanian Brothers Street 2, Yerevan 375036, Armenia
  • 2USC Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, California 90292, USA

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Issue

Vol. 92, Iss. 6 — December 2015

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