Introduction#

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: Sifan Liu and Ying Jin
Winter Quarter, 2022-23
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: MWF 11:30am-12:20pm

  • Location: MW in 380-380Y, F in Turing Auditorium (Polya Hall)

  • Grading: Credit or letter grade

  • Components: Lectures on Mon/Wed, in-class labs on Fri

  • Office Hours:

    • Sifan: 6:00-7:30pm Tues, Sequoia Hall, Room 207

    • Scott: 1:30-2:30pm Wed, Wu Tsai Neurosciences Institute, 2nd floor by the NeuroTheory center

    • Ying: 11:00-12:30pm Thurs, Sequoia Hall, Room 207

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

Schedule#

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. 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

Type

Topic

Mon, Jan 9

Lecture 1

Course overview [slides]

Wed, Jan 11

Lecture 2

Probabilistic modeling

Fri, Jan 13

Lecture 3
Lab 0

Basic neurobiology
Python and PyTorch primer (not graded)

Mon, Jan 16

MLK Day

No class

Wed, Jan 18

Lecture 4

Simple spike sorting [slides]

Fri, Jan 20

Lab 1

Spike sorting

Mon, Jan 23

Lecture 5

Spike sorting by deconvolution [slides]

Wed, Jan 25

Lecture 6

Demixing and deconvolving calcium imaging data [slides]

Fri, Jan 27

Lab 2

Calcium deconvolution

Mon, Jan 30

Lecture 7

Markerless pose tracking [slides]

Wed, Feb 1

Lecture 8

Markerless pose tracking [slides]

Fri, Feb 3

Lab 3

Markerless pose tracking

Unit II: Encoding and decoding models for neural data#

Date

Type

Topic

Mon, Feb 6

Lecture 9

Summary statistics and GLMs [slides]

Wed, Feb 8

Lecture 10

GLMs [slides]

Fri, Feb 10

Lab 4

Generalized linear models

Mon, Feb 13

Lecture 11

Poisson processes [slides]

Wed, Feb 15

Lecture 12

Bayesian decoding of neural spike trains [slides]

Fri, Feb 17

Lab 5

Bayesian decoding

Unit III: Unsupervised models of neural and behavioral data#

Date

Type

Topic

Mon, Feb 20

Pres. Day

No class

Wed, Feb 22

Lecture 13

Mixture Models, EM and Hidden Markov models (HMMs) [slides]

Fri, Feb 24

Lab 6

Autoregressive HMMs

Mon, Feb 27

Lecture 14

More HMMs [slides]
Final project proposal due

Wed, Mar 1

Lecture 15

Switching linear dynamical systems (SLDS) [slides]

Fri, Mar 3

Lab 7
(not graded)

Switching linear dynamical systems

Mon, Mar 6

Lecture 16

Variational Autoencoders (VAEs) [slides]

Wed, Mar 8

Lecture 17

Sequential VAEs [slides]

Fri, Mar 10

Lab 8
(not graded)

Sequential VAEs

Mon, Mar 13

Guest Lec.

Russ Poldrack: fMRI Data Analysis

Wed, Mar 15

Lecture 19

Current research

Fri, Mar 17

Presentations

Project presentations 11:20-1:20pm, with lunch provided

Labs#

  • You will work start the labs in class on Fridays, so attendance is required. (If you are sick or have a one-time conflict, please let me know as soon as possible.)

  • You will be automatically assigned to a team of 3 students.

  • The labs will probably take more than one class period to complete. You should find time to work with your teammates outside of class to finish them.

  • The labs are due the following Thursday night at 11:59pm.

  • Lab reports will be submitted via GradeScope.

Final project#

  • You will work on the final project in teams of 2-3 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 Monday, Feb 27 at 11:30am.

  • The final report will be due Friday, Mar 24 at 11:30am.

Grading#

  • First 6 labs: 10% each, total 60%

  • Final project presentation: 10%

  • Final project report: 20%

  • Participation: 5%

Note: We will work on Labs 7 and 8 in class, but they will not be graded. That way you can focus on final proejcts in March. Your attendance on those days will count toward the class participation grade.