Generative Embeddings of Brain Collective Dynamics Using Variational Autoencoders

Yonatan Sanz Perl, Hernán Bocaccio, Ignacio Pérez-Ipiña, Federico Zamberlán, Juan Piccinini, Helmut Laufs, Morten Kringelbach, Gustavo Deco, Enzo Tagliazucchi

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

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.

Original languageEnglish
Article number238101
JournalPhysical Review Letters
Volume125
Issue number23
DOIs
StatePublished - 2 Dec 2020
Externally publishedYes

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