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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.
LFADS – Latent Factor Analysis via Dynamical Systems
David Sussillo, Rafal Jozefowicz, LF Abbott, and Chethan Pandarinath · 2016
Earlier work this paper cites.
Inferring single-trial neural population dynamics using sequential auto-encoders
Chethan Pandarinath, Daniel J O’Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D Stavisky, Jonathan C Kao, Eric M Trautmann, Matthew T Kaufman, Stephen I Ryu, Leigh R Hochberg, et al · 2018
Earlier work this paper cites.
Tune: A research platform for distributed model selection and training
Richard Liaw, Eric Liang, Robert Nishihara, Philipp Moritz, Joseph E Gonzalez, and Ion Stoica · 2018
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Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Mohammad Reza Keshtkaran and Chethan Pandarinath · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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PyTorch Lightning, 3 2019
William Falcon and The PyTorch Lightning team · 2019
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Hydra - A framework for elegantly configuring complex applications
Omry Yadan · 2019
Cited alongside, same era.
The representation of finger movement and force in human motor and premotor cortices
Robert D Flint, Matthew C Tate, Kejun Li, Jessica W Templer, Joshua M Rosenow, Chethan Pandarinath, and Marc W Slutzky · 2020
Cited alongside, same era.
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
Feng Zhu*, Andrew R Sedler*, Harrison A Grier, Nauman Ahad, Mark Davenport, Matthew Kaufman, Andrea Giovannucci, and Chethan Pandarinath · 2021
Cited alongside, same era.
Neural Latents Benchmark’21: evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski, Anqi Wu, Raeed H Chowdhury, Hansem Sohn, Joseph E O’Doherty, Krishna V Shenoy, Matthew T Kaufman, Mark Churchland, et al · 2021
Cited alongside, same era.
A large-scale neural network training framework for generalized estimation of single-trial population dynamics
Mohammad Reza Keshtkaran*, Andrew R Sedler*, Raeed H Chowdhury, Raghav Tandon, Diya Basrai, Sarah L Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E Miller, and Chethan Pandarinath · 2022
A deep learning framework for inference of single-trial neural population activity from calcium imaging with sub-frame temporal resolution
Feng Zhu, Harrison A Grier, Raghav Tandon, Changjia Cai, Anjali Agarwal, Andrea Giovannucci, Matthew T Kaufman, and Chethan Pandarinath · 2022
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Stabilizing brain-computer interfaces through alignment of latent dynamics
Brianna M Karpowicz, Yahia H Ali, Lahiru N Wimalasena, Andrew R Sedler, Mohammad Reza Keshtkaran, Kevin Bodkin, Xuan Ma, Lee E Miller, and Chethan Pandarinath · 2022
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iLQR-VAE: control-based learning of input-driven dynamics with applications to neural data
Marine Schimel, Ta-Chu Kao, Kristopher T Jensen, and Guillaume Hennequin · 2022
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Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis
Taiga Abe, Ian Kinsella, Shreya Saxena, E Kelly Buchanan, Joao Couto, John Briggs, Sian Lee Kitt, Ryan Glassman, John Zhou, Liam Paninski, et al · 2022
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autolfads-tf2
Andrew R Sedler · 2022
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Cited alongside, same era.
Estimating muscle activation from EMG using deep learning-based dynamical systems models
Lahiru N Wimalasena, Jonas F Braun, Mohammad Reza Keshtkaran, David Hofmann, Juan Álvaro Gallego, Cristiano Alessandro, Matthew C Tresch, Lee E Miller, and Chethan Pandarinath · 2022
Cited alongside, same era.
Later among the works it cites.