Foundations of Computational Psychiatry

MSSM BSR 2708 01 Foundations of Computational Psychiatry

View the Project on GitHub Center-for-Computational-Psychiatry/course_fcp

Foundations of computational psychiatry - Fall 2022

Instructors: Vincenzo Fiore, Angela Radulescu

Teaching Assistant: Kaustubh Kulkarni

Time: Mondays 1-4pm

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

Format: This course will have a hybrid lecture and lab 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 encourage in person attendance, though 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.

Final project: You can find a final project overview here.

Recommended background:

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

Schedule:

Grading:

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