CEBRA is a library for estimating Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables, primarily for biology and neuroscience datasets.

Which programming language is CEBRA implemented in?

CEBRA is implemented in Python, utilizing self-supervised learning algorithms in PyTorch.

What are the main applications of CEBRA?

CEBRA is used for analyzing high-dimensional biological and neural recordings, including compressing time series data to reveal hidden structures in data variability.

How can I install CEBRA?

CEBRA can be installed using conda, pip, or docker. Please refer to the dedicated Installation Guide on the CEBRA documentation site.

Is CEBRA open source?

Yes, CEBRA is open source software available under the Apache 2.0 license since version 0.4.0.

Can CEBRA integrate with other data analysis libraries?

Yes, CEBRA offers integrations for libraries like scikit-learn and matplotlib, and supports computing embeddings on DeepLabCut outputs.

CEBRA was initially developed by Steffen Schneider, Jin H. Lee, and Mackenzie Mathis. It is currently maintained by Steffen Schneider, Célia Benquet, and Mackenzie Mathis.

Where can I find usage instructions for CEBRA?

Step-by-step usage instructions for CEBRA are available under the Usage tab on the CEBRA documentation site.

How can I contribute to CEBRA's development?

Guidelines for contributing to CEBRA can be found under the Contributing tab on the CEBRA documentation site.

What datasets are compatible with CEBRA?

CEBRA supports a variety of datasets commonly used in biological and neuroscience research, including those from the mouse visual cortex and rat hippocampus.

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Cebra

Name: Cebra
Brand: Cebra
Rating: 5 (1 reviews)
Author: Cebra

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Last updated: August 8, 2024

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What is Cebra?

CEBRA is a library designed to estimate Consistent EmBeddings of high-dimensional Recordings utilizing Auxiliary variables. By leveraging self-supervised learning algorithms implemented with PyTorch, CEBRA supports various datasets predominantly used in biology and neuroscience. This tool is adept at compressing time series data to reveal hidden structures, making it highly compatible for simultaneous behavioural and neural data analysis. CEBRA can be integrated with popular data analysis libraries, features diverse installation options, and is open source under the Apache 2.0 license. It continues to be actively developed, with contributions welcome from the community.

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Cebra's Top Features

Consistent embeddings of high-dimensional recordings

Self-supervised learning algorithms in PyTorch

Integration with popular data analysis libraries

Support for a variety of biology and neuroscience datasets

Multiple installation options (conda, pip, docker)

Open source under Apache 2.0 license

Active development and community contributions

High accuracy and performance in latent space modeling

Comprehensive documentation and usage guides

Support for analyzing both single and multi-session data

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