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dc.contributor.author
Perl, Yonatan Sanz
dc.contributor.author
Bocaccio, Hernán
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Pérez Ipiña, Ignacio
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Zamberlán, Federico
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Piccinini, Juan Ignacio
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Laufs, Helmut
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Kringelbach, Morten
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Deco, Gustavo
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Tagliazucchi, Enzo Rodolfo
dc.date.available
2021-11-04T16:15:06Z
dc.date.issued
2020-12
dc.identifier.citation
Perl, Yonatan Sanz; Bocaccio, Hernán; Pérez Ipiña, Ignacio; Zamberlán, Federico; Piccinini, Juan Ignacio; et al.; Generative Embeddings of Brain Collective Dynamics Using Variational Autoencoders; American Physical Society; Physical Review Letters; 125; 23; 12-2020; 1-6
dc.identifier.issn
0031-9007
dc.identifier.uri
http://hdl.handle.net/11336/146026
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.format
application/pdf
dc.language.iso
eng
dc.publisher
American Physical Society
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Autoencoders
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Dynamics
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Consciousness
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Machine Learning
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Otras Ciencias Físicas
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Ciencias Físicas
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CIENCIAS NATURALES Y EXACTAS
dc.title
Generative Embeddings of Brain Collective Dynamics Using Variational Autoencoders
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2021-09-07T18:21:57Z
dc.journal.volume
125
dc.journal.number
23
dc.journal.pagination
1-6
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Perl, Yonatan Sanz. Universidad de San Andrés; Argentina. Universitat Pompeu Fabra; España. Universidad de Buenos Aires; Argentina
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Fil: Bocaccio, Hernán. Universidad de Buenos Aires; Argentina
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Fil: Pérez Ipiña, Ignacio. Universidad de Buenos Aires; Argentina
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Fil: Zamberlán, Federico. Universidad de Buenos Aires; Argentina
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Fil: Piccinini, Juan Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
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Fil: Laufs, Helmut. Christian-Albrechts-University Kiel; Alemania
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Fil: Kringelbach, Morten. University of Oxford; Reino Unido
dc.description.fil
Fil: Deco, Gustavo. Universitat Pompeu Fabra; España
dc.description.fil
Fil: Tagliazucchi, Enzo Rodolfo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
dc.journal.title
Physical Review Letters
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1103/PhysRevLett.125.238101
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.125.238101
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