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pro vyhledávání: '"Pei, Felix"'
Autor:
Kapoor, Jaivardhan, Schulz, Auguste, Vetter, Julius, Pei, Felix, Gao, Richard, Macke, Jakob H.
Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While latent variable models can successfully extract low-dimensional embeddings
Externí odkaz:
http://arxiv.org/abs/2407.08751
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. Low-rank rec
Externí odkaz:
http://arxiv.org/abs/2406.16749
Autor:
Bischoff, Sebastian, Darcher, Alana, Deistler, Michael, Gao, Richard, Gerken, Franziska, Gloeckler, Manuel, Haxel, Lisa, Kapoor, Jaivardhan, Lappalainen, Janne K, Macke, Jakob H, Moss, Guy, Pals, Matthijs, Pei, Felix, Rapp, Rachel, Sağtekin, A Erdem, Schröder, Cornelius, Schulz, Auguste, Stefanidi, Zinovia, Toyota, Shoji, Ulmer, Linda, Vetter, Julius
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samples these m
Externí odkaz:
http://arxiv.org/abs/2403.12636
Autor:
Pei, Felix, Ye, Joel, Zoltowski, David, Wu, Anqi, Chowdhury, Raeed H., Sohn, Hansem, O'Doherty, Joseph E., Shenoy, Krishna V., Kaufman, Matthew T., Churchland, Mark, Jazayeri, Mehrdad, Miller, Lee E., Pillow, Jonathan, Park, Il Memming, Dyer, Eva L., Pandarinath, Chethan
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do
Externí odkaz:
http://arxiv.org/abs/2109.04463
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