Controlling extended criticality via modular connectivity
| Autor: | Philipp Hövel, Nikita Gutjahr, Aline Viol |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
Computer science Phase (waves) FOS: Physical sciences Network topology Topology Metrics epidemic spreading Artificial Intelligence Griffiths phase Biological neural network ddc:530 criticality modular networks Neural correlates of consciousness business.industry 500 Naturwissenschaften und Mathematik::530 Physik::530 Physik Modular design brain networks Nonlinear Sciences - Adaptation and Self-Organizing Systems Expression (mathematics) Computer Science Applications Criticality geodesic entropy FOS: Biological sciences Quantitative Biology - Neurons and Cognition Neurons and Cognition (q-bio.NC) business Adaptation and Self-Organizing Systems (nlin.AO) Information Systems |
| Popis: | Criticality has been conjectured as an integral part of neuronal network dynamics. Operating at a critical threshold requires precise parameter tuning and a corresponding mechanism remains an open question. Recent studies have suggested that topological features observed in brain networks give rise to a Griffiths phase, leading to power-laws in brain activity dynamics and the operational benefits of criticality in an extended parameter region. Motivated by growing evidence of neural correlates of different states of consciousness, we investigate how topological changes affect the expression of a Griffiths phase. We analyze the activity decay in modular networks using a Susceptible-Infected-Susceptible propagation model and find that we can control the extension of the Griffiths phase by altering intra- and intermodular connectivity. We find that by adjusting system parameters, we can counteract changes in critical behavior and maintain a stable critical region despite changes in network topology. Our results give insight into how structural network properties affect the emergence of a Griffiths phase and how its features are linked to established topological network metrics. We discuss how those findings can contribute to understand the observed changes in functional brain networks. Finally, we indicate how our results could be useful in the study of disease spreading. 20 pages, 11 figure (9 in main text, 2 in Appendix) |
| Databáze: | OpenAIRE |
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