2026-04-14T06:33:54Zhttps://recercat.cat/oai/request
oai:recercat.cat:10230/468782025-12-18T01:26:26Zcom_2072_6col_2072_452952
RECERCAT author Sanz Perl, Yonatan author Bocaccio, Hernán author Pérez-Ipiña, Ignacio author Zamberlán, Federico author Piccinini, Juan author Laufs, Helmut author Kringelbach, Morten L. author Deco, Gustavo author Tagliazucchi, Enzo 2021-03-22T08:37:33Z2021-03-22T08:37:33Z2020 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.Authors acknowledge funding from Agencia Nacional De Promocion Cientifica Y Tecnologica (Argentina), Grant No. PICT-2018-03103. © American Physical Society. Published article available at https://doi.org/10.1103/PhysRevLett.125.238101 info:eu-repo/semantics/openAccess Patterns in complex systems Neural encoding Neuroscience Generative embeddings of brain collective dynamics using variational autoencoders info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion