2023/09/03 by Andrew R. Sedler, Sedler, Andrew R., Chethan Pandarinath +1 · 4 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Physics and Python Applications #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Bioinformatics #Neurons and Cognition (q-bio.NC) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2309.01230
openalex publication_date 2023/09/03 · openalex created_date 2023/09/08 · openalex updated_date 2026/07/28
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 .