CEBRA
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About
CEBRA is a cutting-edge machine-learning tool designed to compress time series data, unveiling previously obscured patterns in variability. It is adept at simultaneously processing behavioral and neural data, making it effective for dynamic neural analyses. The method can decode visual cortex activity in mice, allowing for the reconstruction of viewed videos. CEBRA offers a non-linear approach for mapping behaviors to neural dynamics, achieving high accuracy in decoding from complex datasets, such as those derived from both calcium imaging and electrophysiology. Its capability to generate consistent latent spaces across multiple data collection methods, including 2-photon and Neuropixels, positions it as a versatile instrument for hypothesis testing and behavioral analysis in neuroscience research.
Platform
Task
Features
• learnable latent embeddings
• compression of time series data
• joint analysis of behavioral and neural data
• decodes activity from visual cortex
• high-performance latent spaces
• used for both supervised and self-supervised learning
• validates accuracy across various dataset types
• handles single and multi-session datasets
• maps kinematic features
• rapid and high-accuracy decoding
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