Cebra is an advanced machine learning tool that uses cutting-edge non-linear techniques to create consistent and high-performance latent spaces from joint behavioural and neural data recorded simultaneously. Here are the key features and benefits of this tool:
Neural Latent Embeddings: Use for hypothesis testing and discovery-driven analysis, allowing neuroscientists to reveal underlying neural representations involved in adaptive behaviours.
Validated accuracy: The efficacy of Cebra has been proven on calcium and electrophysiology datasets, sensory and motor tasks, and simple or complex behaviours across species, ensuring that the results are highly accurate.
Multi-session and Label-free: Cebra can be used with both single and multi-session datasets and without labels, increasing its versatility when analyzing different types of data.
High-accuracy Decoding: Cebra provides rapid decoding of natural movies from the visual cortex, accelerating the research process.
Code Availability: The tool's code is available on GitHub, and users can read the pre-print on arxiv.org, providing transparency and facilitating collaboration.
Use cases for Cebra involve analyzing and decoding behavioural and neural data, mapping and uncovering complex kinematic features in neuroscience research, and producing consistent latent spaces across various data types and experiments.
Overall, Cebra is a highly valuable and necessary tool for neuroscientists who wish to enhance their research capabilities and better understand the neural underpinnings of complex behaviours.
