Dimensionality Reduction of Large-Scale Neural Recordings
Dimensionality Reduction of Large-Scale Neural Recordings
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
External Links
Prerequisites
- Linear algebra (matrices)
- Some basic statistics (probability and conditional probability)
Back to the course