Dimensionality reduction of large-scale neural recordings

# Dimensionality reduction of large-scale neural recordings

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.
- 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.

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Prerequisites

Linear algebra (matrices). Some basic statistics (probability and conditional probability).

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