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What is Cebra?

Cebra is a machine learning tool used to compress time series data and reveal hidden structures and variability. It excels in analyzing behavioral neural data and can decode activity in the visual cortex of a mouse brain to reconstruct viewed video.

Cebra can also be applied to rat hippocampus data and 2-photon neuropixels recordings to map the space and uncover complex kinematic features. The tool uses behavioral and neural data in a joint, learnable, and self-supervised manner to produce consistent high-performance latent spaces. It is validated for accuracy and utility across sensory motor tasks and simple and complex behaviors across species.

Cebra also allows single and multi-session datasets to be leveraged for hypothesis testing without the need for labeling. The pre-print paper and software implementation for the algorithm are available on arxiv and Github respectively.

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⭐ Core features & benefits

Cebra offers a variety of features and benefits that make it a top choice for a variety of use cases. These are some of the key features:

  • ✔ī¸ Compress time series data
  • ✔ī¸ Decode activity in visual cortex of mouse brain
  • ✔ī¸ Map space and uncover complex kinematic features
  • ✔ī¸ Produce consistent high-performance latent spaces
  • ✔ī¸ Allow single and multi-session datasets to be leveraged for hypothesis testing

⚙ī¸ Use case ideas for Cebra

  1. Analyzing visual cortex activity in mouse brain to reconstruct viewed video.
  2. Mapping rat hippocampus data and uncovering complex kinematic features.
  3. Producing high-performance latent spaces for sensory motor tasks and behaviors across species.
  4. Leveraging single and multi-session datasets for hypothesis testing without labeling.
  5. Compressing time series data for efficient storage and analysis.

🙋‍♂ī¸ Users who use this tool

Cebra is used by many user groups, including but not limited to some of the following:

Neuroscientists
Researchers in biological sciences

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In summary Cebra is a machine learning tool that analyzes behavioral neural data in the visual cortex of mice and rat hippocampus using 2-photon neuropixels recordings, revealing hidden structures and variability.

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