CEBRA - Learnable Latent Embeddings for Joint Behavioural and Neural Analysis
CEBRA is a machine-learning method designed to compress time series data, particularly suited for simultaneous behavioural and neural recordings. It enables the decoding of neural activity, such as reconstructing viewed video from the visual cortex of a mouse brain.
CEBRA Product Information
What is CEBRA?
CEBRA is a novel encoding method that uses both behavioural and neural data to produce consistent and high-performance latent spaces. It can be used in a supervised or self-supervised manner, validating its accuracy across sensory and motor tasks, simple or complex behaviors, and across species. CEBRA allows for label-free usage and provides rapid, high-accuracy decoding of natural movies from the visual cortex.
How to Use CEBRA?
Record large neural and behavioural data, then apply CEBRA to compress time series data and reveal hidden structures in the variability of the data. Use the inferred latents for decoding and uncovering neural dynamics in a variety of tasks and behaviors.
Core Features of CEBRA
- Compression of time-series data
- Decoding of neural activity
- Consistent and high-performance latent spaces
- Rapid, high-accuracy decoding of natural movies from the visual cortex
Use Cases of CEBRA
- Behavioural and Neural Data Analysis
- Neural Representation Modeling
- Decoding of Neural Activity
FAQ about CEBRA
What is the Github link for the official implementation of the CEBRA algorithm?
You can access the official implementation of the CEBRA algorithm on GitHub at [GitHub Link].
Where can I find the pre-print of the CEBRA algorithm?
The pre-print of the CEBRA algorithm is available on arxiv at [Pre-Print Link].
How can I stay updated on future updates and releases of the CEBRA algorithm?
You can subscribe to the CEBRA newsletter for updates on the project or to be notified of future releases and updates.
How can I contact CEBRA for collaborations?
For collaborations, please contact CEBRA via [Contact Email].
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