Cebra
Cebra is a latent embedding that can be learned and utilized for both behavioral and neural analysis, offering valuable insights for research purposes.
About Cebra
Best for: neuroscience researchers, computational neuroscientists, machine learning researchers
Key features
- Compresses time series data to reveal hidden structures in neural and behavioral recordings
- Supports both supervised and self-supervised learning for latent space creation
- Enables decoding of neural activity and behavioral trajectories
- Works with single and multi-session datasets across species
- Provides consistent latent spaces for various neural recording modalities
Use cases
- Analyzing neural activity to reconstruct visual stimuli or behavioral trajectories
- Mapping animal position and movement direction from hippocampal data
- Decoding neural data from motor and somatosensory cortices
Pros
- Open-source implementation available on GitHub
Limitations
- Patent pending may restrict non-academic use
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