Revealing dynamics, communities and criticality from data
File(s)
Published version
Author(s)
Eroglu, Deniz
Tanzi, Matteo
van Strien, Sebastian
Pereira, Tiago
Type
Journal Article
Abstract
Complex systems such as ecological communities and neuron networks are essential parts of our everyday lives. These systems are composed of units which interact through intricate networks. The ability to predict sudden changes in the dynamics of these networks, known as critical transitions, from data is important to avert disastrous consequences of major disruptions. Predicting such changes is a major challenge as it requires forecasting the behaviour for parameter ranges for which no data on the system is available. We address this issue for networks with weak individual interactions and chaotic local dynamics. We do this by building a model network, termed an {}, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behaviour of such networks can be decomposed in terms of an emergent deterministic component and a {} term. Traditionally, such fluctuations are filtered out. However, as we show, they are key to accessing the interaction structure. { We illustrate this approach on synthetic time-series of realistic neuronal interaction networks of the cat cerebral cortex and on experimental multivariate data of optoelectronic oscillators. } We reconstruct the community structure by analysing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range.
Date Issued
2020-06-01
Date Acceptance
2020-04-06
Citation
Physical Review X, 2020, 10
ISSN
2160-3308
Publisher
American Physical Society
Journal / Book Title
Physical Review X
Volume
10
Copyright Statement
© 2020 The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/). Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.
License URL
Sponsor
Commission of the European Communities
Identifier
https://journals.aps.org/prx/abstract/10.1103/PhysRevX.10.021047
Grant Number
339523
Subjects
Science & Technology
Physical Sciences
Physics, Multidisciplinary
Physics
BRAIN NETWORKS
SYNCHRONIZATION
CONNECTIVITY
ORGANIZATION
MOTION
0201 Astronomical and Space Sciences
0204 Condensed Matter Physics
0206 Quantum Physics
Publication Status
Published
Article Number
021047
Date Publish Online
2020-06-01