CEBRA
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About
CEBRA is a machine-learning tool designed to compress time series data, uncovering hidden structures in variability, particularly within behavioral and neural contexts. It excels in decoding activity from the visual cortex of mice while simultaneously utilizing behavioral data. CEBRA provides novel encoding methods that allow for supervised or self-supervised analysis to produce consistent and high-performance latent spaces, useful in decoding neural dynamics. The tool has been validated across various datasets and tasks, supporting both single and multi-session datasets for hypothesis testing. Potential applications include mapping spatial features and revealing complex behaviors in neural data.
Platform
Task
Features
• compresses time series data
• high-accuracy decoding capabilities
• produces consistent latent spaces
• validates across various datasets
• supports supervised and self-supervised learning
• decodes neural activity from behavioral data
• reveals hidden structures in data
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