Cebra

ABOUT Cebra

Introducing Cebra, a remarkable machine learning tool in the research category, specifically designed for joint behavioural and neural data analysis. Utilizing non-linear techniques, Cebra creates consistent and high-performance latent spaces, ensuring reliability and efficiency.

Among the key features of Cebra are Neural Latent Embeddings, which enable hypothesis testing and discovery-driven analysis. The tool’s efficacy has been validated on a variety of datasets such as calcium and electrophysiology, sensory and motor tasks, and simple or complex behaviours across species.

Cebra offers versatility as it can be employed on single or multi-session datasets and does not require labels. High-accuracy decoding is another hallmark of Cebra, providing rapid decoding of natural movies from the visual cortex. Interested users can easily access the tool’s code on GitHub and read the pre-print on arxiv.org.

Researchers and neuroscientists can utilize Cebra to analyze and decode behavioural and neural data, revealing the underlying neural representations at play. It empowers them to map and uncover complex kinematic features, essential in neuroscience research. By producing consistent latent spaces across various data types and experiments, Cebra proves to be an invaluable AI tool for researching adaptive behaviours and advancing the understanding of neural mechanisms.

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