Syllabus

Syllabus#

Welcome to STATS320!

This course is cross-listed as STATS220, NBIO220 and CS339N. They are all the same. Enroll in the version that is best for your degree requirements.

Instructor: Scott Linderman
TAs: Noah Cowan and Amber Hu
Spring Quarter, 2024-25
Stanford University

Course Description#

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, 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 and calcium deconvolution techniques for extracting relevant signals from raw data; markerless tracking methods for estimating animal pose in behavioral videos; 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.

Prerequisites#

You should be comfortable with basic probability (STATS 116) as well as multivariate calculus and linear algebra. This course will emphasize implementing models and algorithms in Python, so coding proficiency is important. We will have a coding primer in the first week to help get you up to speed if you’re coming from R or Matlab.

Logistics#

  • Time: MW 1:30pm-2:20pm

  • Location: MW in STLC115

  • Grading: Credit or letter grade

  • Components: Lectures on Mon/Wed

  • Office Hours:

    • Scott: 10:30am-12:00pm Tuesday, Wu Tsai Neurosciences Institute, 2nd floor by the NeuroTheory center

    • Noah: 10am-11:30am Monday, CoDa room B06 (Remote 4/21, link on Ed)

    • Amber: 1:30-3pm Friday, CoDa room B40

  • This course will have a final project, not an exam

Book#

We will use an online textbook that I have been developing over the past few years called Machine Learning Methods for Neural Data Analysis. It’s a work in progress, and I will continue to update it throughout the quarter!

Schedule#

The lectures develop the theory behind the methods developed in the labs (i.e., homework assignments). I’ve organized the course into four units: signal extraction, encoding and decoding, unsupervised modeling, and current research. 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#

Date

Neuro Topic

ML Topic

Reading

Assignment

Mon, Mar 31

Course Overview [slides]

Basic neurobiology
Probabilistic modeling

Wed, Apr 2

Spike Sorting

Mixture Models

Spike Sorting by Clustering

Lab 1 Out

Mon, Apr 7

Spike Sorting[slides]

Matrix Factorization

Spike Sorting by Deconvolution

Wed, Apr 9

Calcium Deconvolution[slides]

Convex Optimization

Calcium Deconvolution

Lab 1 Due
Lab 2 Out

Mon, Apr 14

Markerless Pose Tracking [slides]

Logistic Regression

Markerless Pose Tracking

Wed, Apr 16

Markerless Pose Tracking [slides]

Convolutional Neural Networks (CNNs)

Markerless Pose Tracking

Lab 2 Due
Lab 3 Out

Unit II: Encoding and Decoding Models for Neural Data#

Date

Neuro Topic

ML Topic

Reading

Assignment

Mon, Apr 21

Neural Encoding [slides]

Generalized Linear Models (GLMs)

Summary Statistics and GLMs

Wed, Apr 23

Neural Encoding

Poisson Processes

Poisson Processes

Lab 3 Due
Lab 4 Out

Mon, Apr 28

Bayesian Decoding

Bayesian Inference

Wed, Apr 30

Bayesian Decoding

Markov Chain Monte Carlo (MCMC)

Lab 4 Due
Lab 5 Out

Unit III: Unsupervised models of neural and behavioral data#

Date

Neuro Topic

ML Topic

Reading

Assignment

Mon, May 5

Behavioral Segmentation

Hidden Markov Models (HMMs)

Wed, May 7

Behavioral Segmentation

Expectation Maximization (EM)

Lab 5 Due
Lab 6 Out

Mon, May 12

Neural Dynamics

Linear Dynamical Systems (LDS)

Wed, May 14

Neural Dynamics

Switching Linear Dynamical Systems (SLDS)

Lab 6 Due
Lab 7 Out

Unit IV: Current Research Topics#

Date

Topic

Reading

Assignment

Mon, May 19

(Sequential) Variational Autoencoders

Wed, May 21

Gaussian Process State Space Models

Lab 7 Due

Mon, May 26

Memorial Day, No Class

Wed, May 28

(Neuromodulated) Recurrent Neural Networks

Mon, Jun 2

Deep State Space Models

Wed, Jun 4

Foundation Models for Neuroscience

Mon, Jun 9

Final Project Due

Labs#

  • Each week, you will implement a minimal version of the method we discussed in lecture. These labs will be your assignments.

  • You must work in a team of two people.

  • There’s a catch! You may not work with the same person twice. (We will have a discussion forum on Ed to facilitate matching.)

  • Lab reports will be submitted via GradeScope.

  • All assignments are due at 11:59pm PT on the specified date.

Final project#

  • You will work on the final project in teams of 2 people (you choose your team!)

  • You must use real neural or behavioral data. We will provide links to suggested datasets, or if you are an experimentalist, you can use your own.

  • A project proposal will be due TBD.

  • The final report will be due Mon, June 9 at 11:59pm.

Late Policy#

We will allow 7 late days to be used as needed throughout the quarter. Since assignments are done in teams of two, both students must have sufficient late days to turn in a late assignment. Unfortunately, we cannot allow late days on the final project.

Grading#

  • 7 labs: 10% each, total 70%

  • Final project: 25%

  • Participation: 5%

Note: You must do a final project in order to pass.

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