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:**

* Neural Latent Embeddings: Use for hypothesis testing and discovery-driven analysis.
* Validated Accuracy: Efficacy proven on calcium and electrophysiology datasets, sensory and motor tasks, and simple or complex behaviours across species.
* Multi-session and Label-free: Can be used with single or multi-session datasets and without labels.
* High-accuracy Decoding: Provides rapid decoding of natural movies from visual cortex.
* Code Availability: Access the tool's code on GitHub and read the pre-print on arxiv.org.
**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.

Learnable latent embeddings for joint behavioral and neural analysis

Cebra

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