PSYC 5270/BME 6086 - Statistical Analysis of Neural Data (Spring 2019)

Instructor: Ian Stevenson
Office: BOUS 112
Office hours by appointment
Meets Wed 9a-noon in Bousfield A101A

This course aims to give students a practical introduction to the analysis of neural data. Topics will include time series analysis, regression, clustering, and dimensionality reduction with an emphasis on how these techniques are used to interpret neural signals from membrane potentials and spikes to EEG and fMRI. We will review some of the recent developments/trends in neural data analysis, and students will gain hands-on experience with these methods through in-class labs, problem sets, and a course project. This course is suitable for both students with backgrounds in neuroscience but little programming experience and students with quantitative backgrounds who are new to neuroscience.

Lectures and homeworks will use student's choice of Matlab (available here or through Skybox), R (available here or in Rstudio), or Python (available here or in Anaconda).

Optional texts:
Matlab for Neuroscientists, 2nd Ed. Wallisch et al. 2013. Academic Press.
R for Data Science. Wickham and Grolemund. 2017. O'Reilly.
Python Data Science Handbook. VanderPlas. 2016. O'Reilly.
Information Theory, Inference, and Learning Algorithms. MacKay, 2003. Cambridge University Press.

TopicAssignments & Optional Readings
01.23Logistics and introduction to the course

01.30Matlab Basics Discussion Question 1
Matlab Tutorial, Matlab for Neuro Ch 2

02.06Math, Probability, and Statistics Review Discussion Question 2
Linear Algebra, Probability, Shalizi Notes, Matlab for Neuro Ch 3

02.13Regression and Hypothesis Testing Discussion Question 3
Heeger Notes, Matlab for Neuro Ch 10

02.20Frequency Analysis 1 - Fourier Transforms Problem Set 1
Olshausen Notes, Chronux Tutorial, Matlab for Neuro Ch 11-12

02.27Frequency Analysis 2 - Filtering & Wavelets Discussion Question 4
Heeger Notes, Matlab for Neuro Ch 13

03.06Neural Coding

03.13Neural Data Analysis "Journal Club"

03.20No Class - Spring Break

03.27Unsupervised Learning Problem Set 2

04.03Information Theory Discussion Question 5
PCA Tutorial, Matlab for Neuro Ch 19

04.10Bayesian Methods Discussion Question 6
ICA Tutorial, Matlab for Neuro Ch 20

04.17Voltage Models Discussion Question 7 (optional)
Olshausen Note

04.24Final Presentations Project Reports Due May 10 @ 5p

05.01No Class Problem Set 3

Think of the class project as an extended lab assignment. This is a chance to explore the ideas covered in class in more depth or as they relate to your own work. In addition to giving an oral presentation on your project at the end of the semester, you should turn in a short report (5-10 pages) that describes what you did, why you did it, what you found, and why it matters.

At the end of the course everyone will give a short oral presentation on their project. The format should be similar to a short conference talk. Allow 20min for the presentation with an additional 10min for questions.

Open Data

Further Reading

Enrollment information

3 credits. Instructor permission required. Open to graduate students.


Based on discussion questions and participation (25%), problem sets (25%) and final project (50%).


Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Center for Students with Disabilities as soon as possible to better ensure that such accommodations are implemented in a timely fashion.

Academic Student Code

Academic dishonesty of any type will not be tolerated. Students should refer to the Student Code for specific guidelines (see section on Academic Integrity).

Site built on Bootstrap v2.3.1