Zobrazeno 1 - 10
of 563
pro vyhledávání: '"Combrisson A"'
Autor:
Etienne Combrisson, Franck Di Rienzo, Anne-Lise Saive, Marcela Perrone-Bertolotti, Juan L. P. Soto, Philippe Kahane, Jean-Philippe Lachaux, Aymeric Guillot, Karim Jerbi
Publikováno v:
Communications Biology, Vol 7, Iss 1, Pp 1-13 (2024)
Abstract Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement plann
Externí odkaz:
https://doaj.org/article/9d2e0ef5a9754d1da3637fecb0e669c5
Autor:
Etienne Combrisson, Ruggero Basanisi, Maelle CM Gueguen, Sylvain Rheims, Philippe Kahane, Julien Bastin, Andrea Brovelli
Publikováno v:
eLife, Vol 12 (2024)
How human prefrontal and insular regions interact while maximizing rewards and minimizing punishments is unknown. Capitalizing on human intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning is be
Externí odkaz:
https://doaj.org/article/ced31d941d7b416caf32f3c1d6302041
Autor:
Combrisson, Etienne1,2,3 (AUTHOR) e.combrisson@gmail.com, Di Rienzo, Franck2 (AUTHOR), Saive, Anne-Lise1,4 (AUTHOR), Perrone-Bertolotti, Marcela5 (AUTHOR), Soto, Juan L. P.6 (AUTHOR), Kahane, Philippe7 (AUTHOR), Lachaux, Jean-Philippe8 (AUTHOR), Guillot, Aymeric2 (AUTHOR), Jerbi, Karim1,9,10 (AUTHOR) Karim.Jerbi.UdeM@gmail.com
Publikováno v:
Communications Biology. 4/27/2024, Vol. 7 Issue 1, p1-13. 13p.
Autor:
Philipp Thölke, Yorguin-Jose Mantilla-Ramos, Hamza Abdelhedi, Charlotte Maschke, Arthur Dehgan, Yann Harel, Anirudha Kemtur, Loubna Mekki Berrada, Myriam Sahraoui, Tammy Young, Antoine Bellemare Pépin, Clara El Khantour, Mathieu Landry, Annalisa Pascarella, Vanessa Hadid, Etienne Combrisson, Jordan O’Byrne, Karim Jerbi
Publikováno v:
NeuroImage, Vol 277, Iss , Pp 120253- (2023)
Machine learning (ML) is increasingly used in cognitive, computational and clinical neuroscience. The reliable and efficient application of ML requires a sound understanding of its subtleties and limitations. Training ML models on datasets with imbal
Externí odkaz:
https://doaj.org/article/5a4ec21c2d304490b2ba04066277b185
Autor:
Combrisson, Etienne, Allegra, Michele, Basanisi, Ruggero, Ince, Robin A.A., Giordano, Bruno L., Bastin, Julien, Brovelli, Andrea
Publikováno v:
In NeuroImage September 2022 258
Autor:
Niso, Guiomar, Krol, Laurens R., Combrisson, Etienne, Dubarry, A. Sophie, Elliott, Madison A., François, Clément, Héjja-Brichard, Yseult, Herbst, Sophie K., Jerbi, Karim, Kovic, Vanja, Lehongre, Katia, Luck, Steven J., Mercier, Manuel, Mosher, John C., Pavlov, Yuri G., Puce, Aina, Schettino, Antonio, Schön, Daniele, Sinnott-Armstrong, Walter, Somon, Bertille, Šoškić, Anđela, Styles, Suzy J., Tibon, Roni, Vilas, Martina G., van Vliet, Marijn, Chaumon, Maximilien
Publikováno v:
In NeuroImage 15 August 2022 257
Akademický článek
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Publikováno v:
Frontiers in Microbiology, Vol 13 (2022)
Externí odkaz:
https://doaj.org/article/b868ea448edf472fa50af6d644ee8734
Autor:
Etienne Combrisson, Michele Allegra, Ruggero Basanisi, Robin A.A. Ince, Bruno L. Giordano, Julien Bastin, Andrea Brovelli
Publikováno v:
NeuroImage, Vol 258, Iss , Pp 119347- (2022)
The reproducibility crisis in neuroimaging and in particular in the case of underpowered studies has introduced doubts on our ability to reproduce, replicate and generalize findings. As a response, we have seen the emergence of suggested guidelines a
Externí odkaz:
https://doaj.org/article/16a3efa927514448b57679c2d039dfcd
Autor:
Guiomar Niso, Laurens R. Krol, Etienne Combrisson, A. Sophie Dubarry, Madison A. Elliott, Clément François, Yseult Héjja-Brichard, Sophie K. Herbst, Karim Jerbi, Vanja Kovic, Katia Lehongre, Steven J. Luck, Manuel Mercier, John C. Mosher, Yuri G. Pavlov, Aina Puce, Antonio Schettino, Daniele Schön, Walter Sinnott-Armstrong, Bertille Somon, Anđela Šoškić, Suzy J. Styles, Roni Tibon, Martina G. Vilas, Marijn van Vliet, Maximilien Chaumon
Publikováno v:
NeuroImage, Vol 257, Iss , Pp 119056- (2022)
Good scientific practice (GSP) refers to both explicit and implicit rules, recommendations, and guidelines that help scientists to produce work that is of the highest quality at any given time, and to efficiently share that work with the community fo
Externí odkaz:
https://doaj.org/article/28f54f8cdd2b42fd93b2e43f4b41e13d