CEBRA

CEBRA

Uncover hidden structures in your data with CEBRA

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CEBRA is a self-supervised learning algorithm designed for obtaining interpretable embeddings of high-dimensional recordings using auxiliary variables. It excels in compressing time series data to reveal hidden structures, particularly in behavioral and neural data. The algorithm can decode neural activity, reconstruct viewed videos, decode trajectories, and determine position during navigation. CEBRA is a valuable tool for joint behavioral and neural analysis, providing consistent and high-performance latent spaces for hypothesis testing and label-free applications across various datasets and species.

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Features

Advantages

  • Reveals hidden structures in data
  • Provides consistent and high-performance latent spaces
  • Suitable for joint behavioral and neural analysis
  • Accurate decoding of natural movies from visual cortex
  • Flexible application across sensory and motor tasks

Disadvantages

  • Requires understanding of machine learning concepts
  • Complexity may be challenging for beginners
  • Limited documentation for certain advanced features

Frequently Asked Questions

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