cebra
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
Science Score: 77.0%
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✓CITATION.cff file
Found CITATION.cff file -
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: arxiv.org -
✓Committers with academic emails
1 of 11 committers (9.1%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (14.7%) to scientific vocabulary
Keywords
Repository
Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
Basic Info
- Host: GitHub
- Owner: AdaptiveMotorControlLab
- License: other
- Language: Python
- Default Branch: main
- Homepage: https://cebra.ai
- Size: 2.22 MB
Statistics
- Stars: 1,023
- Watchers: 32
- Forks: 90
- Open Issues: 10
- Releases: 7
Topics
Metadata Files
README.md
Welcome! 👋
CEBRA is a library for estimating Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables. It contains self-supervised learning algorithms implemented in PyTorch, and has support for a variety of different datasets common in biology and neuroscience.
To receive updates on code releases, please 👀 watch or ⭐️ star this repository!
cebra is a self-supervised method for non-linear clustering that allows for label-informed time series analysis.
It can jointly use behavioral and neural data in a hypothesis- or discovery-driven manner to produce consistent, high-performance latent spaces. While it is not specific to neural and behavioral data, this is the first domain we used the tool in. This application case is to obtain a consistent representation of latent variables driving activity and behavior, improving decoding accuracy of behavioral variables over standard supervised learning, and obtaining embeddings which are robust to domain shifts.
Reference
📄 Publication May 2023: Learnable latent embeddings for joint behavioural and neural analysis. Steffen Schneider, Jin Hwa Lee and Mackenzie Weygandt Mathis. Nature 2023.
📄 Preprint April 2022: Learnable latent embeddings for joint behavioral and neural analysis. Steffen Schneider, Jin Hwa Lee and Mackenzie Weygandt Mathis
License
- Since version 0.4.0, CEBRA is open source software under an Apache 2.0 license.
- Prior versions 0.1.0 to 0.3.1 were released for academic use only (please read the license file).
Owner
- Name: Mathis Lab | Adaptive Motor Control
- Login: AdaptiveMotorControlLab
- Kind: organization
- Email: mackenzie@post.harvard.edu
- Location: Swiss Federal Institute of Technology
- Website: http://mackenziemathislab.org
- Twitter: mwmathislab
- Repositories: 9
- Profile: https://github.com/AdaptiveMotorControlLab
Mechanisms underlying adaptive behavior in intelligent systems
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: >-
Learnable latent embeddings for joint behavioural and
neural analysis
authors:
- given-names: "Steffen"
family-names: "Schneider"
- given-names: "Jin Hwa"
family-names: "Lee"
- given-names: "Mackenzie Weygandt"
family-names: "Mathis"
identifiers:
- type: url
value: 'https://www.nature.com/articles/s41586-023-06031-6'
- type: doi
value: 10.1038/s41586-023-06031-6
repository-code: 'https://github.com/AdaptiveMotorControlLab/CEBRA'
preferred-citation:
title: >-
Learnable latent embeddings for joint behavioural and
neural analysis
authors:
- given-names: "Steffen"
family-names: "Schneider"
- given-names: "Jin Hwa"
family-names: "Lee"
- given-names: "Mackenzie Weygandt"
family-names: "Mathis"
type: "article"
journal: "Nature"
year: 2023
month: 05
doi: 10.1038/s41586-023-06031-6
issn: 1476-4687
url: https://doi.org/10.1038/s41586-023-06031-6
GitHub Events
Total
- Create event: 37
- Commit comment event: 1
- Release event: 4
- Issues event: 47
- Watch event: 106
- Delete event: 37
- Issue comment event: 144
- Push event: 178
- Pull request review comment event: 160
- Pull request review event: 145
- Pull request event: 84
- Fork event: 21
Last Year
- Create event: 37
- Commit comment event: 1
- Release event: 4
- Issues event: 47
- Watch event: 106
- Delete event: 37
- Issue comment event: 144
- Push event: 178
- Pull request review comment event: 160
- Pull request review event: 145
- Pull request event: 84
- Fork event: 21
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Steffen Schneider | s****s@h****m | 59 |
| Mackenzie Mathis | m****s@r****u | 24 |
| Célia Benquet | 3****t | 14 |
| Rodrigo González Laiz | 3****o | 13 |
| Jin Lee | h****e@g****m | 3 |
| Ícaro | i****o@p****e | 3 |
| CEBRA | b****t@c****i | 2 |
| timonmerk | m****5@g****m | 1 |
| Sofia Gilardini | 9****i | 1 |
| Ikko Eltociear Ashimine | e****r@g****m | 1 |
| Guillem Fernández | 6****w | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 59
- Total pull requests: 166
- Average time to close issues: 22 days
- Average time to close pull requests: 15 days
- Total issue authors: 43
- Total pull request authors: 10
- Average comments per issue: 2.24
- Average comments per pull request: 1.67
- Merged pull requests: 143
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 28
- Pull requests: 77
- Average time to close issues: about 1 month
- Average time to close pull requests: 14 days
- Issue authors: 17
- Pull request authors: 5
- Average comments per issue: 1.86
- Average comments per pull request: 1.39
- Merged pull requests: 64
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- stes (9)
- CeliaBenquet (5)
- drsax93 (2)
- researcherofreality (2)
- timonmerk (2)
- YiqiJ (1)
- refox1 (1)
- disadone2 (1)
- icarosadero (1)
- mudphudwang (1)
- snehashis-roy (1)
- gwappa (1)
- melissaxdu (1)
- AlexEMG (1)
- mariakesa (1)
Pull Request Authors
- stes (83)
- MMathisLab (46)
- CeliaBenquet (20)
- gonlairo (15)
- icarosadero (8)
- nastya236 (4)
- timonmerk (2)
- introspective-swallow (2)
- sofiagilardini (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 3
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Total downloads:
- pypi 1,627 last-month
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Total dependent packages: 1
(may contain duplicates) -
Total dependent repositories: 1
(may contain duplicates) - Total versions: 26
- Total maintainers: 2
proxy.golang.org: github.com/adaptivemotorcontrollab/cebra
- Documentation: https://pkg.go.dev/github.com/adaptivemotorcontrollab/cebra#section-documentation
- License: other
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Latest release: v0.5.0
published 10 months ago
Rankings
proxy.golang.org: github.com/AdaptiveMotorControlLab/CEBRA
- Documentation: https://pkg.go.dev/github.com/AdaptiveMotorControlLab/CEBRA#section-documentation
- License: other
-
Latest release: v0.5.0
published 10 months ago
Rankings
pypi.org: cebra
Consistent Embeddings of high-dimensional Recordings using Auxiliary variables
- Homepage: https://github.com/AdaptiveMotorControlLab/CEBRA
- Documentation: https://cebra.readthedocs.io/
- License: Apache Software License
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Latest release: 0.5.0
published 10 months ago