https://github.com/adaptivemotorcontrollab/cebra

Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA
https://github.com/adaptivemotorcontrollab/cebra

contrastive-learning machine-learning neuroscience-methods pytorch

Last synced: about 1 month ago
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Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA

• Host: GitHub
• URL: https://github.com/adaptivemotorcontrollab/cebra
• Owner: AdaptiveMotorControlLab
• License: other
• Created: 2022-04-04T11:12:47.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-10T21:49:24.000Z (2 months ago)
• Last Synced: 2024-10-11T04:40:26.995Z (about 1 month ago)
• Topics: contrastive-learning, machine-learning, neuroscience-methods, pytorch
• Language: Python
• Homepage: https://cebra.ai
• Size: 546 KB
• Stars: 902
• Watchers: 34
• Forks: 75
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE.md
  • Code of conduct: CODE_OF_CONDUCT.md
  • Citation: CITATION.cff

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README

        








[📚Documentation](https://cebra.ai/docs/) |

[💡DEMOS](https://cebra.ai/docs/demos.html) |

[🛠️ Installation](https://cebra.ai/docs/installation.html) |

[🌎 Home Page](https://www.cebra.ai) |

[🚨 News](https://cebra.ai/docs/index.html) |

[🪲 Reporting Issues](https://github.com/AdaptiveMotorControlLab/CEBRA)

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# Welcome! 👋

**CEBRA** is a library for estimating **C**onsistent **E**m**B**eddings of high-dimensional **R**ecordings using **A**uxiliary 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.](https://doi.org/10.1038/s41586-023-06031-6)

  Steffen Schneider*, Jin Hwa Lee* and Mackenzie Weygandt Mathis. Nature 2023.

- 📄 **Preprint April 2022**:

  [Learnable latent embeddings for joint behavioral and neural analysis.](https://arxiv.org/abs/2204.00673)

  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).