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    Area of Science:

    • Neuroscience
    • Machine Learning
    • Computational Biology

    Background:

    • Deep learning models for neural activity analysis require larger datasets and model sizes.
    • Integrating diverse neural recordings from different animals into a unified model is a significant challenge.

    Conclusions:

    • The proposed framework and architecture offer a powerful new approach for analyzing neural data at scale.
    • This work establishes a clear path toward training deep learning models for large-scale neural data analysis.
    • The method facilitates the development of advanced deep learning tools for neuroscience research.