Cognitive Computational Neuroscience
NEU 502B
Princeton University, Spring 2025
Time: Tu/W 2:00–5:00 pm
Location: PNI A03
Instructor: Sam Nastase (snastase@princeton.edu)
AIs: Kirsten Ziman (kz0108@princeton.edu), Ariadne Letrou (ariadne@princeton.edu)
Syllabus: Syllabus
GitHub: GitHub
Scratch: Scratch
This lab course closely accompanies NEU 502A and surveys the methodological landscape of cognitive computational neuroscience research. Students will learn the fundamentals of experimental design, data collection, preprocessing, and statistical analysis for fMRI and EEG/MEG, with an emphasis on best practices in reproducible neuroscience. Lectures will set the conceptual foundation for interactive, hands-on lab work using Jupyter notebooks. Advanced topics include multivariate pattern analysis (MVPA), representational similarity analysis (RSA), and regularized encoding models. This year, we’re experimenting with a new format that will interleave two threads: empirical (E) classes focused on fMRI and MEG projects, and computational (C) classes focused on modeling and neural networks. E classes will provide the empirical backbone of the course, while C classes (typically on Wednesdays) will focus on interactive modeling exercises paralleling the topics discussed in 502A. These two threads will converge over the course of the term. Students will be expected to design, analyze, and write up both an fMRI experiment and an OPM-MEG experiment as graded projects.
Lecture schedule
| Date | Topic | Slides/code | Optional reading |
|---|---|---|---|
| Tu 1/28 (E) | Introduction and computing; MR physics and BOLD biology | Slides Code | Logothetis et al., 2001 Kriegeskorte & Douglas, 2018 Richards et al., 2019 |
| W 1/29 (E) | fMRI design, preprocessing, and subject-level modeling (GLM) | Slides Code | Friston et al., 1994 Power et al., 2012 Esteban et al., 2019 |
| Tu 2/4 (E) | fMRI group-level analysis and correction for multiple tests | Slides Code | Nichols & Holmes, 2002 Eklund et al., 2016 |
| W 2/5 (C) | Dynamics in perception | Hopfield et al., 1982 | |
| Tu 2/11 (E) | Multivariate pattern analysis (MVPA) fMRI project group formation and brainstorming | Haxby et al., 2001 Norman et al., 2006 | |
| W 2/12 (C) | Decision making | Bogacz et al., 2006 | |
| Tu 2/18 (E) | Representational similarity analysis (RSA) and searchlights fMRI project proposal presentations | Edelman, 1998 Kriegeskorte et al., 2006 Kriegeskorte et al., 2008 | |
| W 2/19 (C) | Reinforcement learning | Montague et al., 1996 Botvinick et al., 2020 | |
| Tu 2/25 (E) | Naturalistic neuroimaging and voxelwise encoding models fMRI project experimental design | Naselaris et al., 2011 Huth et al., 2016 Nastase et al., 2020 Hasson et al., 2020 | |
| W 3/4 (C) | Statistical learning and backpropagation | Rumelhart et al., 1986 McClelland & Rogers, 2003 | |
| Tu 3/4 (E) | fMRI project data collection | ||
| W 3/5 (E) | fMRI project data collection | ||
| Tu 3/11 | No class (spring recess) | ||
| W 3/12 | No class (spring recess) | ||
| Tu 3/18 (E) | fMRI project data analysis | ||
| W 3/19 (C) | Statistical learning and language processing | Manning et al., 2020 | |
| Tu 3/25 (E) | EEG/MEG signal, preprocessing, and modeling | Buzsáki et al., 2012 Fries, 2015 | |
| W 3/26 (C) | Selective attention, automaticity, and control | Cohen et al., 1990 | |
| Tu 4/1 (E) | MEG project proposals and experimental design | Baillet, 2017 Brooks et al., 2022 | |
| W 4/2 (C) | Conflict monitoring, effort, and control | Shenhav et al., 2017 | |
| Tu 4/8 (E) | MEG project data collection | ||
| W 4/9 (E) | MEG project data analysis | ||
| Tu 4/15 (E) | Deep learning models for vision | Kriegeskorte et al., 2015 Yamins & DiCarlo, 2016 Lindsay, 2021 | |
| W 4/16 (E) | Progress report presentations | ||
| Tu 4/22 (E) | Deep learning models for language | Schrimpf et al., 2021 Goldstein et al., 2022 Zada et al., 2024 | |
| W 4/23 (E) | fMRI and MEG project presentations | ||
| M 5/6 | No class (final written reports due) |
The content of this course is inspired by related courses designed by Jonathan Cohen, Leigh Nystrom, Jody Culham, and Jim Haxby, and built on top of lots of hard work by Younes Strittmatter, Zaid Zada, and many others.
Supplementary reading
Rokem, A., & Yarkoni, T. (2024). Data Science for Neuroimaging: An Introduction. Princeton University Press. link
Huettel, S. A., Song, A. W., & McCarthy, G. (2014). Functional Magnetic Resonance Imaging (3rd Ed.). Sinauer Associates. link
Poldrack, R. A., Mumford, J. A., & Nichols, T. E. (2011). Handbook of Functional MRI Data Analysis. Cambridge University Press. DOI
Bandettini, P. A. (2020). fMRI. MIT Press. link
Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique (2nd ed.). MIT Press. link
McClelland, J. L. (2015). Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises (2nd ed.). MIT Press. PDF
Duvernoy, H. M. (1999). The Human Brain: Surface, Three-Dimensional Sectional Anatomy with MRI, and Blood Supply (2nd ed.). Springer. DOI