Dopaminergic neuromodulation of prefrontal cortex activity requires the NMDA receptor coagonist D-serine

Autor: Dallérac, Glenn, Li, Xia, Lecouflet, Pierre, Morisot, Nadege, Sacchi, Silvia, Asselot, Rachel, Pham, Thu Ha, Potier, Brigitte, Watson, David J. G., Schmidt, Staffan, Levasseur, Grégoire, Fossat, Pascal, Besedin, Andrey, Rivet, Jean-Michel, Coyle, Joseph T., Collo, Ginetta, Pollegioni, Loredano, Kehr, Jan, Galante, Micaela, Fone, Kevin C., Gardier, Alain M., Freret, Thomas, Contarino, Angelo, Millan, Mark J., Mothet, Jean-Pierre

Publikováno v: Proceedings of the National Academy of Sciences of the United States of America, 2021 Jun 01. 118(23), 1-9.

Externí odkaz: https://www.jstor.org/stable/27040917

Dopaminergic neuromodulation of prefrontal cortex activity requires the NMDA receptor coagonist D-serine

Autor: Li, Xia, Lecouflet, Pierre, Sacchi, Silvia, Asselot, Rachel, Pham, Thu Ha, Potier, Brigitte, Watson, David J. G., Schmidt, Staffan, Fossat, Pascal, Besedin, Andrey, Rivet, Jean Michel, Coyle, Joseph T., Collo, Ginetta, Pollegioni, Loredano, Kehr, Jan, Galante, Micaela, Fone, Kevin C., Gardier, Alain M., Freret, Thomas, Contarino, Angelo, Millan, Mark J., Mothet, Jean Pierre

Prefrontal control of cognitive functions critically depends upon glutamatergic transmission and N-methyl D-aspartate (NMDA) receptors, the activity of which is regulated by dopamine. Yet whether the NMDA receptor coagonist D-serine is implicated in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=core_ac_uk__::c2e3b72daa9772373cc0d783cf48cd89

Evolutive deep models for online learning on data streams with no storage

Autor: Besedin, Andrey, Blanchart, Pierre, Crucianu, Michel, Ferecatu, Marin

Publikováno v: ECML/PKDD 2017 Workshop on Large-scale Learning from Data Streams in Evolving Environments
ECML/PKDD 2017 Workshop on Large-scale Learning from Data Streams in Evolving Environments, Sep 2017, Skopje, Macedonia

International audience; In recent years Deep Learning based methods gained a growing recognition in many applications and became the state-of-the-art approach in various fields of Machine Learning, such as Object Recognition, Scene Understanding, Nat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::a5750dcdd5ac69f72eb124279936d730
https://cea.hal.science/cea-01832986/document