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AdaptiveMotorControlLab

CEBRA

Learnable latent embeddings for joint behavioral and neural analysis - Official implementation of CEBRA

Model DevelopmentInterpretabilityPython
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创建于 2022-04-04 · 更新于 2026-10-16 · 今日第 3953 名
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AdaptiveMotorControlLab/CEBRA 排名第 3,953,有 1,126 个星标。

同类别比较

AdaptiveMotorControlLab/CEBRA 排名第 3,953,此处有 8 个同类条目;autonomousvision/navsim 排名第 3,952。

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关键参数与规格
关键参数与规格
参数值
语言Python
类别Model Development
子类别Interpretability
星标数1,126
分叉数102
贡献者11
创建日期2022-04-04
更新日期2026-10-16
1 天星标数2
7 天星标数4
排名3,953
上次排名5,979
排名变化2,026
7 天星标排名6,225
上次 7 天星标排名8,285
7 天星标排名变化2,060
总星标排名10,178
上次总星标排名10,172
总星标排名变化-6
国家Switzerland
README 摘录

📚Documentation | 💡DEMOS | 🛠️ Installation | 🌎 Home Page | 🚨 News | 🪲 Reporting Issues 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 patented 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. References 📄 **Publication April 2025**: Time-series attribution maps with regularized contrastive learning. Steffen Schneider, Rodrigo González Laiz, Anastasiia Filipova, Markus Frey, Mackenzie Weygandt Mathis. 📄 **Publication April 2025**: Self-supervised contrastive learning performs non-linear system identification Rodrigo González Laiz*, Tobias Schmidt*, Steffen Schneider. 📄 **Publication May 2023**: Learnable latent embeddings for joint behavioural and neural analysis. Steffen Schneider*, Jin Hwa Lee* and Mackenzie Weygandt Mathis. 📄 **Preprint April 2022**: Learnable latent embeddings for joint behavioral and neural analysis. Steffen

Schneider*, Jin Hwa Lee* and Mackenzie Weygandt Mathis Patent Information Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings. Steffen Schneider* & Mackenzie Weygandt Mathis*. Please contact the TTO office at EPFL for licensing. 0, CEBRA is open source software under an Apache 2. 1 were released for academic use only (please read the license file).

常见问题

记录了多少星标和分叉?

AdaptiveMotorControlLab/CEBRA 有 1,126 个星标和 102 个分叉。

仓库排名是多少?

AdaptiveMotorControlLab/CEBRA 排名第 3,953。

记录的语言和类别是什么?

AdaptiveMotorControlLab/CEBRA 有 1,126 个星标,语言为 Python,类别为 Model Development。

哪个仓库排名最接近?

AdaptiveMotorControlLab/CEBRA 与 autonomousvision/navsim 比较,后者排名第 3,952,有 1,130 个星标。

README

📚Documentation | 💡DEMOS | 🛠️ Installation | 🌎 Home Page | 🚨 News | 🪲 Reporting Issues

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 patented 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.

References

Patent Information

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).
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