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Welcome to CEBRA’s documentation!#

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.

Please support the development of CEBRA by starring and/or watching the project on Github!

Note

CEBRA is under active development and the API might include breaking changes between versions. If you use CEBRA in your work, we recommend to double check your current version. For writing reproducible analysis and experiment code, we recommend to use Docker.

Installation and Setup#

Please see the dedicated Installation Guide for information on installation options using conda, pip and docker.

Have fun! 😁

Usage#

Please head over to the Usage tab to find step-by-step instructions to use CEBRA on your data. For example use cases, see the Demos tab.

Integrations#

CEBRA can be directly integrated with existing libraries commonly used in data analysis. The cebra.integrations module is getting actively extended. Right now, we offer integrations for scikit-learn-like usage of CEBRA, a package making use of matplotlib to plot the CEBRA model results, as well as the possibility to compute CEBRA embeddings on DeepLabCut outputs directly.

Licensing#

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 see the full license file on Github for further information.

Contributing#

Please refer to the Contributing tab to find our guidelines on contributions.

Code contributors#

The CEBRA code was originally developed by Steffen Schneider, Jin H. Lee, and Mackenzie Mathis (up to internal version 0.0.2). As of March 2023, it is being actively extended and maintained by Steffen Schneider, Célia Benquet, and Mackenzie Mathis.

References#

@article{schneider2023cebra,
  author = {Schneider, Steffen and Lee, Jin H and Mathis, Mackenzie W},
  title = {Learnable latent embeddings for joint behavioural and neural analysis},
  journal = {Nature},
  doi = {https://doi.org/10.1038/s41586-023-06031-6},
  year = {2023},
}

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