PSYC 5107 - Statistical Analysis of Neural Data (Spring 2023)

Instructor: Ian Stevenson
Office: BOUS 112
Office hours by appointment
Meets Thurs 9:00-noon Bous 254

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 Anyware), 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
Jan 19Logistics and introduction to the course

Jan 26Programming Basics Matlab Tutorial, Matlab for Neuro Ch 2
The Good Research Code Handbook - Mineault

Feb 2Math, Probability, and Statistics Foundations Linear Algebra, Probability, Shalizi Notes, Matlab for Neuro Ch 3
Math for Neuro - Ella Batty (Wk 1, 2, 6)

Feb 9Null Hypothesis Significance Testing

Feb 16Regression Heeger Notes, Matlab for Neuro Ch 10

Feb 23Frequency Analysis 1 - Fourier Transforms Problem Set 1
Olshausen Notes, Chronux Tutorial, Matlab for Neuro Ch 11-12

Mar 2Frequency Analysis 2 - Filtering & Wavelets Heeger Notes, Matlab for Neuro Ch 13

Mar 9Journal Club/Data Showcase - Project Preview Presentations

Mar 16No Class - Spring Break

Mar 23Neural Coding Math for Neuro (Wk 8)

Mar 30Unsupervised Learning Problem Set 2
PCA Tutorial, Matlab for Neuro Ch 19, Math for Neuro (Wk 11)

Apr 6Information Theory ICA Tutorial, Matlab for Neuro Ch 20

Apr 13Bayesian Methods Olshausen Notes

Apr 20Voltage Models Problem Set 3

Apr 27Final Presentations

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 15min for the presentation with an additional 5min for questions.

Open Data

Further Reading

Enrollment information

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


Based on participation (30%), problem sets (30%) and final project (40%).


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).

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