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    Dimensionality Reduction of Large-Scale Neural Recordings

    Difficulty level
    Beginner
    Speaker
    Type
    Duration
    1:16:47

    In this lesson, you will learn about methods for dimensionality reduction of data, with a focus on factor analysis.

    Topics covered in this lesson
    • Large noisy multi-channel datasets from new methods of large scale neural recording.
    • Covariation among channels.
    • Can we summarize the data using a smaller number of variables? Binning and resolution.
    • Dimensionality reduction.
    • Denoising.
    • Dimensionality reduction models: principal component analysis (PCA), factor analysis (FA), Gaussian-process factor analysis (GPFA), latent dynamical systems, non-linear systems.
    • Explicit noise models.
    • Closer explanation of factor analysis, with computations.  
    Prerequisites
    • Linear algebra (matrices)
    • Some basic statistics (probability and conditional probability)