Translational Neuromodeling


This course provides a systematic introduction to Translational Neuromodeling (the development of mathematical models for diagnostics of brain diseases) and their application to concrete clinical questions (Computational Psychiatry/Psychosomatics). It focuses on a generative modeling strategy and discusses (hierarchical) Bayesian models of neuroimaging data and behaviour in detail.

Lecture slides and exercise sheets will be published below on the day of the respective lecture. Solutions to the exercises are due at 2pm on the day of the exercise. Please send-in your solutions via email to tnu-teaching(at) or hand them in at the beginning of the exercise session. To download the slides, please use the password distributed during the first lecture. Slides and exercises will be distributed via moodle.

Please note that admission to the final project is subject to having successfully completed at least 50% of the exercises during the semester. For more information, please refer to the course catalogue.

Tentative Schedule

Lectures take place on Tuesdays from 9:15am to 12:00pm in HG G26.1 (ETH main building). Exercises take place on Fridays from 2:15pm to 4:00pm in ETZ E6 (Gloriastrasse).

Date Lecture Date Exercise
02/18 Introduction to Translational Neuromodeling & Computational Psychiatry 02/21 No exercise
02/25 Psychiatric nosology and pathophysiology 02/28 Basics of statistical modeling
03/03 Generative models and principles of Bayesian inference 03/06 No exercise
03/10 A Bayesian framework for understanding psychiatric and psychosomatic diseases 03/13 Exercise 1: Bayesian inference
03/17 Generative models of behavioural data: HGF and Predictive Coding 03/20 Crash course: fMRI data analysis
03/24 Generative models of fMRI data: DCM for fMRI 03/27 Exercise 2: HGF
03/31 Generative models of EEG data: DCM for EEG 04/03 Exercise 3: DCM for fMRI
04/07 Computational concepts of schizophrenia, autism, and depression 04/10 No exercise (Easter week)
04/14 No lectures (Easter week) 04/17 No exercise (Easter week)
04/21 Clinical applications in Computational Psychiatry 04/24 Exercise 4: DCM for EEG
04/28 Variational Bayes 05/01 No exercise (public holiday)
05/05 Bayesian model selection 05/08 Exercise 5: Variational Bayes
05/12 Markov Chain Monte Carlo   05/15 Exercise 6: Bayesian model selection
05/19 Project work (No presence required) 05/22 Project work/ Exercise MCMC
05/26 Project work (No presence required) 05/29 Project presentations (2pm - 6pm)


General Information

For the project you may work in groups of up to 3 students. Please sign-up your group until 19 May 2020 by sending an email to the TAs containing the names of all group members and a tentative project title (if available). If you do not sign-up until May 19th, we will assume you are working on your project alone. Each group is expected to prepare a presentation of its project and hand in a report one week after the presentation. Please note that admission to the final project is subject to having successfully completed at least 50% of the exercises during the semester.

Additional Information

If you have questions concerning the lecture or exercise, you can contact the TAs via email: tnu-teaching(at)

For further information, please consult the course catalogue.