lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems Article Swipe
Andrew R. Sedler
,
Chethan Pandarinath
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.01230
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2309.01230
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 .
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2309.01230
- https://arxiv.org/pdf/2309.01230
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386501787
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386501787Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2309.01230Digital Object Identifier
- Title
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lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systemsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-09-03Full publication date if available
- Authors
-
Andrew R. Sedler, Chethan PandarinathList of authors in order
- Landing page
-
https://arxiv.org/abs/2309.01230Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2309.01230Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2309.01230Direct OA link when available
- Concepts
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Computer science, Python (programming language), Modular design, Source code, Factor (programming language), Documentation, Programming language, Java, Artificial intelligence, Software engineering, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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