  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. Recently introduced variants and extensions continue
to demonstrate the applicability of the architecture to a wide variety of
problems in neuroscience. Since the development of the original implementation
of LFADS, new technologies have emerged that use dynamic computation graphs,
minimize boilerplate code, compose model configuration files, and simplify
large-scale training. Building on these modern Python libraries, we introduce
lfads-torch -- a new open-source implementation of LFADS that unifies existing
variants and is designed to be easier to understand, configure, and extend.
Documentation, source code, and issue tracking are available at
https://github.com/arsedler9/lfads-torch .
