72 citations · 173 across the 9 of their papers we have counts for
9 papers
Diffusion-Based Generation of Neural Activity from Disentangled Latent Codes
Jonathan D. McCart, Andrew R. Sedler, Christopher Versteeg +3
Recent advances in recording technology have allowed neuroscientists to monitor activity from thousands of neurons simultaneously. Latent variable models are increasingly valuable…
Expressive dynamics models with nonlinear injective readouts enable reliable recovery of latent features from neural activity
Christopher Versteeg, Andrew R. Sedler, Jonathan D. McCart +1
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 sta…
lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
Andrew R. Sedler, Chethan Pandarinath
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 neu…
Expressive architectures enhance interpretability of dynamics-based neural population models
Andrew R. Sedler, Christopher Versteeg, Chethan Pandarinath
Artificial neural networks that can recover latent dynamics from recorded neural activity may provide a powerful avenue for identifying and interpreting the dynamical motifs underl…
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
Feng Zhu, Andrew R. Sedler, Harrison A. Grier +5
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 s…
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski +13
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this…
Representation learning for neural population activity with Neural Data Transformers
Joel Ye, Chethan Pandarinath
Neural population activity is theorized to reflect an underlying dynamical structure. This structure can be accurately captured using state space models with explicit dynamics, suc…
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Mohammad Reza Keshtkaran, Chethan Pandarinath
Continuing advances in neural interfaces have enabled simultaneous monitoring of spiking activity from hundreds to thousands of neurons. To interpret these large-scale data, severa…
LFADS - Latent Factor Analysis via Dynamical Systems
David Sussillo, Rafal Jozefowicz, L. F. Abbott +1
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data sho…