Generative embeddings of brain collective dynamics using variational autoencoders
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- dc.contributor.author Sanz Perl, Yonatan
- dc.contributor.author Bocaccio, Hernán
- dc.contributor.author Pérez-Ipiña, Ignacio
- dc.contributor.author Zamberlán, Federico
- dc.contributor.author Piccinini, Juan
- dc.contributor.author Laufs, Helmut
- dc.contributor.author Kringelbach, Morten L.
- dc.contributor.author Deco, Gustavo
- dc.contributor.author Tagliazucchi, Enzo
- dc.date.accessioned 2021-03-22T08:37:33Z
- dc.date.available 2021-03-22T08:37:33Z
- dc.date.issued 2020
- dc.description.abstract We consider the problem of encoding pairwise correlations between coupled dynamical systems in a low-dimensional latent space based on few distinct observations. We use variational autoencoders (VAEs) to embed temporal correlations between coupled nonlinear oscillators that model brain states in the wake-sleep cycle into a two-dimensional manifold. Training a VAE with samples generated using two different parameter combinations results in an embedding that encodes the repertoire of collective dynamics, as well as the topology of the underlying connectivity network. We first follow this approach to infer the trajectory of brain states measured from wakefulness to deep sleep from the two end points of this trajectory; then, we show that the same architecture was capable of representing the pairwise correlations of generic Landau-Stuart oscillators coupled by complex network topology.
- dc.description.sponsorship Authors acknowledge funding from Agencia Nacional De Promocion Cientifica Y Tecnologica (Argentina), Grant No. PICT-2018-03103.
- dc.format.mimetype application/pdf
- dc.identifier.citation Perl YS, Bocaccio H, Pérez-Ipiña I, Zamberlán F, Piccinini J, Laufs H, Kringelbach M, Deco G, Tagliazucchi E. Generative embeddings of brain collective dynamics using variational autoencoders. Phys Rev Lett. 2020 Dec 4;125(23):238101. DOI: 10.1103/PhysRevLett.125.238101
- dc.identifier.doi http://dx.doi.org/10.1103/PhysRevLett.125.238101
- dc.identifier.issn 0031-9007
- dc.identifier.uri http://hdl.handle.net/10230/46878
- dc.language.iso eng
- dc.publisher American Physical Society
- dc.relation.ispartof Phys Rev Lett. 2020 Dec 4;125(23):238101
- dc.rights © American Physical Society. Published article available at https://doi.org/10.1103/PhysRevLett.125.238101
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.subject.keyword Patterns in complex systems
- dc.subject.keyword Neural encoding
- dc.subject.keyword Neuroscience
- dc.title Generative embeddings of brain collective dynamics using variational autoencoders
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion