Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Chau, Geeling"'
Autor:
Chau, Geeling, Wang, Christopher, Talukder, Sabera, Subramaniam, Vighnesh, Soedarmadji, Saraswati, Yue, Yisong, Katz, Boris, Barbu, Andrei
We present a self-supervised framework that learns population-level codes for arbitrary ensembles of neural recordings at scale. We address two key challenges in scaling models with neural time-series data: sparse and variable electrode distribution
Externí odkaz:
http://arxiv.org/abs/2406.03044
Autor:
Chau, Geeling, An, Yujin, Iqbal, Ahamed Raffey, Chung, Soon-Jo, Yue, Yisong, Talukder, Sabera
A major goal in neuroscience is to discover neural data representations that generalize. This goal is challenged by variability along recording sessions (e.g. environment), subjects (e.g. varying neural structures), and sensors (e.g. sensor noise), a
Externí odkaz:
http://arxiv.org/abs/2402.18546
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Griggs, Whitney S., Norman, Sumner L., Deffieux, Thomas, Segura, Florian, Osmanski, Bruno-Félix, Chau, Geeling, Christopoulos, Vasileios, Liu, Charles, Tanter, Mickael, Shapiro, Mikhail G., Andersen, Richard A.
Publikováno v:
Nature Neuroscience; January 2024, Vol. 27 Issue: 1 p196-207, 12p
Publikováno v:
ArXiv [ArXiv] 2024 Oct 09. Date of Electronic Publication: 2024 Oct 09.
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2021 Nov; Vol. 2021, pp. 6679-6682.