Cross-listed as NBIO220 and CS339N
Instructor: Prof. Scott Linderman
Winter Quarter, 2021
Stanford University

With modern high-density electrodes and optical imaging techniques, neuroscientists routinely measure the activity of hundreds, if not thousands, of cells simultaneously. Coupled with high-resolution behavioral measurements, genetic sequencing, and connectomics, these datasets offer unprecedented opportunities to learn how neural circuits function. This course will study statistical machine learning methods for analysing such datasets, including: spike sorting, calcium deconvolution, and voltage smoothing techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; network models for connectomics and fMRI data; state space models for analysis of high-dimensional neural and behavioral time-series; point process models of neural spike trains; and deep learning methods for neural encoding and decoding. We will develop the theory behind these models and algorithms and then apply them to real datasets in the in-class coding labs and final project.

The course is organized around eight coding labs. Each lab develops a minimal implementation of a state-of-the-art method from scratch in a self-contained Google Colab notebook. These are widely used techniques for analyzing neural and behavioral data, and through the labs you'll get a deep understanding of how these methods work under the hood:

1. A simple spike sorting algorithm
2. Kilosort: Spike sorting by Deconvolution
3. CNMF: Calcium deconvolution via constrained NMF
4. DeepLabCut: Markerless pose tracking with CNNs
5. DeepRetina: Deep encoding models of retinal spike trains
6. Kalman Smoothers: Decoding movement from neural data
7. MoSeq: Autoregressive HMMs for animal movements
8. SLDS: Switching LDS model of neural data

The lectures develop the theory behind the methods developed in lab. I've organized the course into three units: signal extraction, encoding and decoding, and unsupervised modeling of neural and behavioral data. At the end, you'll work on a final project in which you will use, explore, or extend the techniques studied in class.

Unit I: Extracting biological signals from raw data

Unit II: Encoding and decoding models for neural data

Unit III: Unsupervised models of neural and behavioral data

Final Projects