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of 154
pro vyhledávání: '"Pandarinath, Chethan"'
Autor:
McCart, Jonathan D., Sedler, Andrew R., Versteeg, Christopher, Mifsud, Domenick, Rigotti-Thompson, Mattia, Pandarinath, Chethan
Recent advances in recording technology have allowed neuroscientists to monitor activity from thousands of neurons simultaneously. Latent variable models are increasingly valuable for distilling these recordings into compact and interpretable represe
Externí odkaz:
http://arxiv.org/abs/2407.21195
The advent of large-scale neural recordings has enabled new methods to discover the computational mechanisms of neural circuits by understanding the rules that govern how their state evolves over time. While these \textit{neural dynamics} cannot be d
Externí odkaz:
http://arxiv.org/abs/2309.06402
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
Latent factor analysis via dynamical systems (LFADS) is an RNN-based variational sequential autoencoder that achieves state-of-the-art performance in denoising high-dimensional neural activity for downstream applications in science and engineering. R
Externí odkaz:
http://arxiv.org/abs/2309.01230
Artificial neural networks that can recover latent dynamics from recorded neural activity may provide a powerful avenue for identifying and interpreting the dynamical motifs underlying biological computation. Given that neural variance alone does not
Externí odkaz:
http://arxiv.org/abs/2212.03771
Autor:
Zhu, Feng, Sedler, Andrew R., Grier, Harrison A., Ahad, Nauman, Davenport, Mark A., Kaufman, Matthew T., Giovannucci, Andrea, Pandarinath, Chethan
Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampli
Externí odkaz:
http://arxiv.org/abs/2111.00070
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
Autor:
Ye, Joel, Pandarinath, Chethan
Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, such as those based on recurrent neural networks (RNNs). However, using r
Externí odkaz:
http://arxiv.org/abs/2108.01210
Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, several methods have been proposed to infer latent dynamic structure from hi
Externí odkaz:
http://arxiv.org/abs/1908.07896
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Akademický článek
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