CEBRA was initially developed by Mackenzie Mathis and Steffen Schneider (2021+), who are co-inventors on the patent application WO2023143843. Jin Hwa Lee contributed significantly to our first paper:
Schneider, S., Lee, J.H., & Mathis, M.W. Learnable latent embeddings for joint behavioural and neural analysis. Nature 617, 360–368 (2023)
CEBRA is actively developed by Mackenzie Mathis and Steffen Schneider and their labs.
It is a publicly available tool that has benefited from contributions and suggestions from many individuals: CEBRA/graphs/contributors.
- Steffen Schneider, Rodrigo González Laiz, Markus Frey, Mackenzie W. Mathis Identifiable attribution maps using regularized contrastive learning. NeurIPS 4th Workshop on Self-Supervised Learning: Theory and Practice (2023)
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Steffen Schneider, Rodrigo González Laiz, Anastasiia Filippova, Markus Frey, Mackenzie W. Mathis Time-series attribution maps with regularized contrastive learning. AISTATS (2025).
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Rodrigo González Laiz, Tobias Schmidt, Steffen Schneider**: Self-supervised contrastive learning performs non-linear system identification ICLR (2025). Adds the
cebra.dynamicsmodule and introduces improvements tocebra.criterions. For advanced features on dynamics learning, the full reference implementation for the paper is available at https://github.com/dynamical-inference/dcl.