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CITATION.cff
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cff-version: 1.2.0
title: >-
lfads-torch: A modular and extensible implementation of
latent factor analysis via dynamical systems
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Andrew R
family-names: Sedler
email: [email protected]
affiliation: Emory University
orcid: 'https://orcid.org/0000-0001-9480-0698'
- given-names: Chethan
family-names: Pandarinath
email: [email protected]
affiliation: Emory University
orcid: 'https://orcid.org/0000-0003-1241-1432'
identifiers:
- type: other
value: 'https://arxiv.org/abs/2309.01230'
description: arXiv preprint
repository-code: 'https://github.com/arsedler9/lfads-torch'
abstract: >-
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.
keywords:
- deep learning
- neuroscience
- dynamical systems
license: Apache-2.0