Foundations of computational psychiatry - Spring 2025

Instructors: Vincenzo Fiore, Angela Radulescu

Teaching Assistant: Ülgen Kilic

Time: Mondays 1-4pm

Location: Sinai Center for Computational Psychiatry, 55 W 125th St, Floor 13

Format: This course will have a hybrid lecture, lab and flipped journal club structure. Lectures will be held in person and will be followed by a live lab portion during which we will implement some of the key concepts covered in lecture. We will then have a student-led discussion of papers in the literature. We will make available recordings of each lecture.

Overview: At the intersection of psychology, neuroscience and AI, computational models are aimed at understanding the mechanisms underlying cognitive processes that drive behavior, and how these processes are altered in neuropsychiatric disorders. In this course, we will discuss some of the goals, foundational ideas, and technical concepts behind computational modeling. We will survey several modeling approaches, including Bayesian inference, reinforcement learning and neural modeling. And we will get hands on experience with building and fitting models to data from different modalities.

Pre-requisites: The course assumes beginner-to-intermediate proficiency in programming tools for data analysis. For each class, coding materials will be provided in MATLAB or Python. In general, materials will take the form of self-contained codebases which students can modify to suit the problem at hand. If you are unsure of the expected coding level, you are encouraged to consult with the instructors.

Recommended background:

• MATLAB: Getting Started with MATLAB: Basic commands
• Matlab Programming fundamentals
• Python 101 Google Colab notebook
• The Python Tutorial
• Mathesaurus

Readings: Reading for the course (~4 hours / week) will consist of selections from three textbooks, as well as recent literature in computational psychiatry. Textbooks:

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.

• Ma, W., & Kording, K. P., & Goldreich D. (2022). Bayesian models of perception and action.

• Dayan, P., & Abbott, L. (2001). Theoretical neuroscience.

Schedule:

• Feb. 10th: Intro to computational psychiatry

  • Intro paper
  • S&B Ch. 1
  • S&B Ch. 2
  • Slides
  • Recording
  • Code
  • Solutions

• Feb. 24th: Anxiety, avoidance and uncertainty

  • Intro paper
  • S&B Ch. 3
  • S&B Ch. 4
  • JC: Zorowitz et al.
  • Slides
  • Recording
  • Code
  • Solutions

• Mar. 3rd: Mood instability and learning

  • Intro paper
  • S&B Ch. 6
  • JC: Rutledge et al.
  • Slides
  • Recording
  • Code
  • Solutions

• Mar. 10th: Depression, effort and planning

  • Intro paper
  • S&B Ch. 8
  • JC: Huys et al.
  • Slides
  • Recording
  • Code
  • Solutions

• Mar. 17th: Naturalistic computational psychiatry

  • Intro paper
  • S&B Ch. 9
  • JC: Berwian et al.
  • Slides
  • Recording
  • Code
  • Solutions

• Mar. 24th: Belief updating with discrete evidence

  • Intro paper
  • M&K&G Ch. 1
  • M&K&G Ch. 2
  • Slides
  • Recording
  • Code

• Mar. 31st: Hierarchical thinking

  • Intro paper
  • M&K&G Ch. 5
  • M&K&G Ch. 12
  • JC: Cassidy et al.
  • Slides
  • Recording
  • Code
  • Solutions

• Apr. 7th: Non-linear dynamics in the brain

  • Intro paper
  • D & A Ch. 7
  • JC: Suryanarayana et al.
  • Slides
  • Recording
  • Code
  • Solutions

• Apr. 14th: Neurocomputational phenotypes

  • Intro paper
  • M&K&G Section C
  • JC: Wilson & Collins
  • Slides
  • Recording
  • Code

• Apr. 28th (tentative): Social cognition & multi-agent interactions

  • Intro paper
  • Slides
  • Recording

• May. 5th: Student presentations

Grading:

This is a P/F course. The course is considered passed based on attendance and participation, paper presentations, completion of coding exercises and the final paper.