CEBRA
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About
CEBRA is a machine-learning method for compressing time series data to reveal hidden structures in its variability. It's particularly effective with simultaneous behavioural and neural data, capable of decoding visual cortex activity to reconstruct viewed videos. It uses behavioural and neural data in supervised or self-supervised ways to create consistent and high-performance latent spaces. Accuracy is validated across various datasets, tasks, and species. It can map space, uncover complex kinematic features, and decode natural movies from visual cortex.
Platform
Task
Features
• works with calcium and electrophysiology datasets
• handles single and multi-session datasets
• hypothesis-driven and discovery-driven analysis
• decoding neural activity
• reveals hidden structures in data variability
• time series compression
• joint behavioural and neural analysis
• learnable latent embeddings
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