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Machine Learning Methods for Neural Data Analysis

Introduction

Foundations

Probabilistic Modeling
Basic Neurobiology

Unit I: Signal Extraction

Spike Sorting by Clustering
Spike Sorting by Matrix Factorization
Spike Sorting by Deconvolution
Demixing Calcium Imaging Data
Markerless Pose Tracking

Unit II: Encoding & Decoding

Summary Statistics
Generalized Linear Models
Poisson Processes
Decoding Models

Unit III: Unsupervised Learning

Mixture Models and the EM Algorithm
Hidden Markov Models
fMRI Analysis

Labs

PyTorch Primer
Spike Sorting by Clustering
Spike Sorting by Deconvolution
Calcium Deconvolution
Markerless Pose Tracking
Generalized Linear Models
Bayesian Decoding
Autoregressive HMMs
Switching LDS
Sequential Variational Autoencoders

References

References

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By Scott Linderman

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