CEBRA is an advanced machine learning tool that combines behavioral and neural data to create accurate and consistent latent spaces. It enables researchers to map behavioral actions to neural activity, gaining insights into neural dynamics during adaptive behaviors and uncovering underlying correlates of behavior. The tool generates neural latent embeddings for hypothesis testing and discovery-driven analysis, validated across diverse datasets and species. CEBRA works with single or multi-session datasets without labels, efficiently mapping complex kinematic features and providing rapid, high-accuracy decoding of natural movies from the visual cortex. Its open-source code on GitHub and pre-print on arxiv.org encourage collaboration and transparency. For neuroscientists, CEBRA proves invaluable in analyzing and decoding behavioral and neural data, revealing essential neural representations shaping behaviors across various species.
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