CEBRA

About CEBRA

CEBRA revolutionizes the analysis of behavioral and neural data by providing a machine learning framework that creates consistent latent spaces. Targeted at neuroscience researchers, it allows for hypothesis-driven exploration of neural dynamics, improving data interpretation and unveiling hidden behavioral patterns from concurrent datasets.

CEBRA offers free access to its core features, with advanced functionalities available in premium plans. Users can unlock higher performance and support for multi-session datasets, enhancing their research capabilities. Its affordability and unique offerings make upgrading advantageous for comprehensive analysis in neuroscience studies.

CEBRA boasts an intuitive user interface designed for seamless navigation and efficient data interaction. Its clear layout and user-friendly features enable researchers to easily access tools for behavioral and neural data analysis, creating a streamlined experience that enhances productivity in scientific research.

How CEBRA works

Users interact with CEBRA by onboarding their behavioral and neural datasets into the platform. The user-friendly interface guides them through the process of data integration and analysis, allowing for flexible exploration of latent spaces. By employing both supervised and self-supervised learning, CEBRA enables researchers to decode complex neural activities from their data efficiently.

Key Features for CEBRA

Joint Behavioral and Neural Data Analysis

CEBRA’s core feature is its ability to analyze joint behavioral and neural data, offering insights into neural dynamics. This unique capability allows researchers to uncover hidden structures within the data, enabling more effective interpretation of complex neural responses during behavioral tasks, enhancing research outcomes significantly.

High-Performance Latent Space Generation

CEBRA excels in generating high-performance latent spaces that represent complex relationships in neural and behavioral data. This feature allows users to conduct hypothesis-driven research effectively, facilitating a deeper understanding of neural activity patterns and their correlation with behavior through advanced machine learning techniques.

Multi-Session Dataset Integration

CEBRA allows integration of both single and multi-session datasets, making it a versatile tool for longitudinal studies. This feature enables researchers to conduct robust hypothesis testing and uncover temporal changes in neural behavior over time, optimizing the analysis of dynamic neurological processes.