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Learnable latent embeddings for joint behavioral and neural analysis
AI Categories: research
Cebra is a machine learning tool that uses non-linear techniques to create consistent and high-performance latent spaces from joint behavioural and neural data recorded simultaneously.
Key Features:
Use Cases:
• Analyze and decode behavioural and neural data to reveal underlying neural representations.
• Map and uncover complex kinematic features in neuroscience research.
• Produce consistent latent spaces across various data types and experiments.
Cebra is a valuable tool for neuroscientists who wish to analyze and decode behavioural and neural data, allowing them to better understand the underlying neural representations involved in adaptive behaviours.
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