821 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Institut du CerveauFR17 papers
- Centre National de la Recherche ScientifiqueFR13 papers
- InsermFR12 papers
- Sorbonne UniversitéFR12 papers
- Pitié-Salpêtrière HospitalFR8 papers
- Institut national de recherche en sciences et technologies du numériqueFR7 papers
- Assistance Publique – Hôpitaux de ParisFR5 papers
- Institut des Sciences MoléculairesFR4 papers
- Centre de Recherche des CordeliersFR3 papers
- Université Pierre-et-Marie-CurieFR3 papers
- Santa Fe InstituteUS2 papers
- Sorbonne Paris CitéFR2 papers
5 papers · 1 filter
Improving J-divergence of brain connectivity states by graph Laplacian denoising
Tiziana Cattai, Gaetano Scarano, Marie-Constance Corsi +3
Functional connectivity (FC) can be represented as a network, and is frequently used to better understand the neural underpinnings of complex tasks such as motor imagery (MI) detec…
Geometry-Aware Hamiltonian Variational Auto-Encoder
Clément Chadebec, Clément Mantoux, Stéphanie Allassonnière
Variational auto-encoders (VAEs) have proven to be a well suited tool for performing dimensionality reduction by extracting latent variables lying in a potentially much smaller dim…
BCI learning induces core-periphery reorganization in M/EEG multiplex brain networks
Marie-Constance Corsi, Mario Chavez, Denis Schwartz +6
Brain-computer interfaces (BCIs) constitute a promising tool for communication and control. However, mastering non-invasive closed-loop systems remains a learned skill that is diff…
Inferring epistasis from genomic data with comparable mutation and outcrossing rate
Hong-Li Zeng, Eugenio Mauri, Vito Dichio +3
We consider a population evolving due to mutation, selection and recombination, where selection includes single-locus terms (additive fitness) and two-loci terms (pairwise epistati…
Gaussian Graphical Model exploration and selection in high dimension low sample size setting
Thomas Lartigue, Simona Bottani, Stephanie Baron +3
Gaussian Graphical Models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM i…