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Machine Learning Methods for Neural Data Analysis

Introduction

Labs

Lab 0: PyTorch Primer
Lab 1: Spike Sorting
Lab 2: Calcium Deconvolution
Lab 3: Markerless Pose Tracking
Lab 4: Generalized Linear Models
Lab 5: Bayesian Decoding
Lab 6: Autoregressive HMMs
Lab 7: Switching LDS
Lab 8: Sequential VAEs

Foundations

Probabilistic Modeling
Basic Neurobiology

Unit I: Signal Extraction

Simple Spike Sorting
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

References

References

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By Scott Linderman

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