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 .

基于动力系统的潜在因素分析（LFADS）是一种基于RNN的变分时序自编码器，可在科学和工程领域中实现去噪高维神经活动的最先进性能。lfads-torch是LFADS的一个新开源实现，利用动态计算图、简化配置文件和大规模训练，统一了现有的变体，并旨在更易理解、配置和扩展。

lfads-torch：一种通过动力系统进行潜在因子分析的模块化和可扩展实现