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    Dimensionality Reduction of Large-Scale Neural Recordings

    Difficulty level
    Beginner
    Speaker
    Type
    Duration
    1:16:47

    This lesson introduces methods for dimensionality reduction of data, with 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
    • 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)