Revealing Dynamics, Communities, and Criticality From Data

dc.contributor.author Eroğlu, Deniz
dc.contributor.author Eroğlu, Deniz
dc.contributor.author Tanzi, Matteo
dc.contributor.author van Strien, Sebastian
dc.contributor.author Pereira, Tiago
dc.contributor.other Molecular Biology and Genetics
dc.date.accessioned 2020-06-18T09:15:19Z
dc.date.available 2020-06-18T09:15:19Z
dc.date.issued 2020
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Biyoinformatik ve Genetik Bölümü en_US
dc.description.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 behavior for parameter ranges for which no data on the system are 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 effective network, consisting of the underlying local dynamics and a statistical description of their interactions. We show that behavior of such networks can be decomposed in terms of an emergent deterministic component and a fluctuation 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 analyzing the stochastic fluctuations generated by the network and predict critical transitions for coupling parameters outside the observed range. en_US
dc.description.sponsorship Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP European Research Council (ERC) Turkiye Bilimsel ve Teknolojik Araştırma Kurumu (TUBITAK) Serrapilheira Institute en_US
dc.identifier.citationcount 14
dc.identifier.doi 10.1103/PhysRevX.10.021047 en_US
dc.identifier.issn 2160-3308 en_US
dc.identifier.issn 2160-3308
dc.identifier.issue 2 en_US
dc.identifier.scopus 2-s2.0-85089914079 en_US
dc.identifier.scopusquality Q1
dc.identifier.uri https://hdl.handle.net/20.500.12469/2927
dc.identifier.uri https://doi.org/10.1103/PhysRevX.10.021047
dc.identifier.volume 10 en_US
dc.identifier.wos WOS:000537193700001 en_US
dc.identifier.wosquality Q1
dc.institutionauthor Eroğlu, Deniz en_US
dc.language.iso en en_US
dc.publisher Amer Physical Soc en_US
dc.relation.journal Physical Review X en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 21
dc.subject Brain networks en_US
dc.subject Synchronization en_US
dc.subject Connectivity en_US
dc.subject Organization en_US
dc.subject Motion en_US
dc.title Revealing Dynamics, Communities, and Criticality From Data en_US
dc.type Article en_US
dc.wos.citedbyCount 21
dspace.entity.type Publication
relation.isAuthorOfPublication 5bae555f-a8aa-4b95-bcfe-54cc47812e13
relation.isAuthorOfPublication.latestForDiscovery 5bae555f-a8aa-4b95-bcfe-54cc47812e13
relation.isOrgUnitOfPublication 71ce8622-7449-4a6a-8fad-44d881416546
relation.isOrgUnitOfPublication.latestForDiscovery 71ce8622-7449-4a6a-8fad-44d881416546

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