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<a href="/docs"> <i class="fas fa-book"></i>
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<a href="https://cebra.ai/docs/" target="_blank"> <i class="fas fa-book"></i>
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Documentation
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<p>CEBRA is a machine-learning method that can be used to compress time series in a way that reveals otherwise hidden structures in the variability of the data. It excels on behavioural and neural data recorded simultaneously. We have shown it can be used and it can decode activity from the visual cortex of the mouse brain to reconstruct a viewed video, to decode trajectories from the sensoirmot cortex of primates, and for decoding position during navigation. For this and other demos, see below!</p>
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<p>CEBRA is a machine-learning method that can be used to
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compress time series in a way that reveals otherwise hidden
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structures in the variability of the data. It excels on
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behavioural and neural data recorded simultaneously.
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We have shown it can be used to decode the activity from the
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visual cortex of the mouse brain to reconstruct a viewed video,
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to decode trajectories from the sensoirmotor cortex of primates,
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and for decoding position during navigation. For these use cases
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and other demos see our <a href="https://cebra.ai/docs/" style="color: #6235E0;">Documentation</a>.</p>
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<a href="https://arxiv.org/abs/2204.00673" target="_blank" class="btn btn-link" style="color: #6235E0;"><i class="fas fa-file-alt"></i> Preprint</a>
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<div class="paper-title">Time-series attribution maps with regularized contrastive learning</div>
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<p>Steffen Schneider, Rodrigo González Laiz, Anastasiia Filipova, Markus Frey, Mackenzie Weygandt Mathis. AISTATS 2025</p>
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<p>An extension of CEBRA that provides attribution maps for time-series data using regularized contrastive learning.</p>
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<a href="https://openreview.net/forum?id=aGrCXoTB4P" target="_blank" class="btn btn-link" style="color: #6235E0;"><i class="fas fa-external-link-alt"></i> Read Paper</a>
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<a href="https://arxiv.org/abs/2502.12977" target="_blank" class="btn btn-link" style="color: #6235E0;"><i class="fas fa-file-alt"></i> Preprint</a>
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<a href="https://sslneurips23.github.io/paper_pdfs/paper_80.pdf" target="_blank" class="btn btn-link" style="color: #6235E0;"><i class="fas fa-file-pdf"></i> NeurIPS-W 2023 Version</a>
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<div class="paper-title">Patent Pending</div>
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<p>Please note EPFL has filed a patent titled "Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings" (<a href="https://patents.google.com/patent/WO2023143843A1" target="_blank" style="color: #6235E0;">https://patents.google.com/patent/WO2023143843A1</a>) so if this does not work for your non-academic use case, please contact the TTO Office at EPFL.</p>
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<p>Please note EPFL has filed a patent titled <a href="https://patents.google.com/patent/WO2023143843A1" target="_blank" style="color: #6235E0;">"Dimensionality reduction of time-series data, and systems and devices that use the resultant embeddings"</a> so if this does not work for your non-academic use case, please contact the Tech Transfer Office at EPFL.</p>
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You can find our official implementation of the CEBRA algorithm on GitHub:
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<a href="https://github.com/AdaptiveMotorControlLab/CEBRA" target="blank_">Watch and Star the repository</a> to
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be notified of future updates and releases.
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You can also <a href="https://twitter.com/cebraAI" target="blank_">follow us on Twitter</a> or subscribe to our
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<a href="https://groups.google.com/g/cebra-info" target="blank_">mailing list</a> for updates on the project.
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You can also <a href="https://twitter.com/cebraAI" target="blank_">follow us on Twitter</a> for updates on the project.
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<p>If you are interested in collaborations, please contact us via
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<small class="code">
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@inproceedings{schneider2025timeseries,<br/>
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&nbsp;&nbsp;title={Time-series attribution maps with regularized contrastive learning},<br/>
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&nbsp;&nbsp;author={Steffen Schneider and Rodrigo Gonz{\'a}lez Laiz and Anastasiia Filippova and Markus Frey and Mackenzie W Mathis},<br/>
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&nbsp;&nbsp;booktitle={The 28th International Conference on Artificial Intelligence and Statistics},<br/>
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&nbsp;&nbsp;year={2025},<br/>
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&nbsp;&nbsp;url={https://openreview.net/forum?id=aGrCXoTB4P}<br/>
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}
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