13. September - 18. September 2021

This course is organized by the Translational Neuromodeling Unit (TNU), University of Zurich & ETH Zurich and is designed to provide students from different fields with the necessary toolkit to master challenges in computational psychiatry research.

The CPC Zurich is meant to be practically useful for students at all levels (MDs, Master, PhD, Postdoc, PI) and from diverse backgrounds (neuroscience, psychology, medicine, engineering, physics, etc.), who would like to apply modeling techniques to study cognition or brain physiology in mental health. The course will teach not only the theory of computational modeling, but also demonstrate open source software in application to example data sets.

Pre-requisites: The course is split into several parts. The first day features an introduction to Psychiatry and psychosomatic medicine. Days 2 - 4 will cover computational methods in detail. Day 5 presents concrete applications. The final day 6 (to be booked separately) consists of practical tutorials with open source software. Some background knowledge in statistics and computational methods is needed to master the more technical parts (Day 2-4). If you lack this background it is recommended that you prepare for this course. Here is a list of helpful (but not mandatory) introductory resources to get you started.


Welcome to the registration for the Computational Psychiatry Course 2021!

This is the first time we are offering a hybrid course: That means you can either join our Main Course online via Zoom or join the Main Course + hands-on Tutorials in person in Zurich.

Tutorials are only available to those who join the course in person.
In addition, you need to be registered to the Main Course (in person) event to be able to attend the tutorials!
You can attend max. two Tutorials, one in the morning and one afternoon.
For more details regarding the tutorials, please go to "structure" on this site.

Members of the host institutions (UZH and ETH Zurich) will receive a special access code, which will allow them to book the course free of charge. Please contact us in order to get your access code.

Please register HERE for the Computational Psychiatry Course Zurich 2021.

Registration closes on 3rd of September 2021. Note that spaces are limited, first come first served.


5 days + 1 day (Practical Tutorials)
(between 8am CEST and 6pm CEST)

13th – 18th September 2021

Registration Start

Registration Ends
03. September 2021

Designed For
Master Students, PhDs, PostDocs, Clinicians and anyone interested in Computational Psychiatry

Perfomance Assessment
Oral examination after the course, date TBA.

ECTS for University of Zurich & ETH Zurich students
You will be awarted 3 ECTS for completing the oral examination successfully. This course is part of the HS (fall term) 2021 and will be available on ETH MyStudies and the UZH course catalogue in due time. For ECTS you will need to sign up in ETH myStudies in addition to registering here. UZH students must register and sign up in ETH myStudies as “UZH Fachstudent/in”.

Course Fee
    - CHF 150 for external participants, this includes 2 practical tutorials
    - CHF 0 for UZH & ETH staff & students

    CHF 30, no practical tutorials available online


The CPC is divided into two parts: The main course (Day 1-5) and in-depth practical tutorials (Day 6), which can only be attended in person on site.


The first day will cover topics in Psychiatry providing a conceptual basis for the type of questions that Computational Psychiatry will need to address.

The second day will explain basic modelling principles (basic mathematical terminology, step-by-step guide on how to build a model, model fitting and model selection) and will finish with a first introduction to models of perception (Psychophysics, Bayesian Models od Perception).

The third day will continue with reinforcement learning, models of perception (Predicitve Coding), action selection (Markov Decision Processes, Active Inference, Drift Diffusion Models) and will end with an introduction to models of metacognition

The fourth day will include models of connectivity (Dynamic Causal Modeling for fMRI and EEG and biophysical network models) and Machine Learning (basics and advanced).

The fifth day will feature a series of talks on practical applications of computational models to problems from psychiatry.


The practical tutorials on the sixth day will provide 3-hour, small-group, in-depth and hands-on sessions on a specific modelling approach. Tutorials will take place on-site and will not be available to an online audience. To get the most out of the tutorial, students are advised to bring their own laptops along. The practical sessions cover only open-source software packages. The code can be found under the respective links below.

  • Practical Session A with Tore Erdmann, Sandra Iglesias, & Lilian Weber
    Bayesian Learning using the Hierarchical Gaussian Filter (HGF, TNU Tapas)

    In this tutorial, we will recap the theory behind the Hierarchical Gaussian Filter (HGF) and introduce the model in an accessible way. We will then discuss practical issues when fitting computational models to behavioral data in general and specific to the HGF. We will work through exercises to learn how to analyze data with the HGF using the HGF-toolbox (in Matlab).

  • Practical Session B with Alec Tschantz
    Active Inference (toolbox to be announced)

    This tutorial provides a practical guide on developing computational models. Students will be taught to build simple simulations using Matlab, and will use these to explore the behavioural and electrophysiological manifestations of active inference. We aim to develop an intuitive understanding of the theoretical principles, without requiring detailed technical knowledge.

  • Practical Session C with Woo-Young Ahn
    Reinforcement Learning using the hBayesDM Package

    A description of this tutorial will be published soon.

  • Practical Session D with Mads Lund Pedersen
    Drift Diffusion Models (toolbox to be announced)

    A description of this tutorial will be published soon.

  • Practical Session E with Lionel Rigoux
    Model Inversion using the Variational Bayes Toolbox

    A description of this tutorial will be published soon..

  • Practical Session F with Thomas Wolfers & Saige Rutherford
    Machine Learning using NISPAT

    A description of this tutorial will be published soon.

  • Practical Session G with Rosalyn Moran
    Dynamic Causal Modelling for EEG (toolbox to be announced)

    A description of this tutorial will be published soon.

  • Practical Session H with Jakob Heinzle
    Dynamic Causal Modelling for fMRI using the SPM-DCM Package

    In this tutorial you will learn how to use the SPM software to perform a dynamic causal modeling (DCM) analysis in MATLAB. We will first guide you through all steps of a basic DCM analysis of a single subject: Data extraction, Model setup, Model inversion and, finally, inspection of Results. We will then proceed to look at a group of subjects. Here, we will focus on model comparison and inspection of model parameters.
    We will provide a point-by-point recipe on how to perform the analysis. However, it is of advantage if you have worked with neuroimaging (fMRI) data and MATLAB before.

  • Practical Session I with Marion Rouault
    Metacognition (toolbox to be announced)

    In this tutorial, we will recap the theory underlying the hMeta-d model for quantifying metacognitive efficiency, our ability to monitor and evaluate our own decisions. We will introduce the model in an accessible way, then discuss practical issues when fitting computational models to behavioral data and go through code examples using the hMeta-d toolbox.

  • Practical Session J with Stefan Frässle
    Advanced Models of Connectivity: rDCM using Tapas rDCM

    A description of this tutorial will be published soon.

Woo-Young AhnSeoul National University, South Korea
Tore ErdmannScuola Internazionale Superiore di Studi Avanzati, Italy
Stefan FrässleUniversity of Zurich & ETH Zurich, Switzerland
Jakob HeinzleUniversity of Zurich & ETH Zurich, Switzerland
Marcus HerdenerUniversity of Zurich, Switzerland
Quentin HuysMax Planck UCL Centre for Computational Psychiatry and Ageing Research, United Kingdom
Sandra IglesiasUniversity of Zurich & ETH Zurich, Switzerland
Roland von KänelUniversity Hospital Zurich, Switzerland
Andre MarquandDonders Institute, Netherlands
Christoph MathysAarhus University, Denmark
Rosalyn MoranKing's College London, United Kingdom
Graham MurrayUniversity of Cambridge, United Kingdom
John MurrayYale School of Medicine, USA
Yael NivPrinceton University, USA
Gina PaoliniKlinik für Psychiatrie und Psychotherapie, Clienia Schlössli AG Switzerland
Mads Lund PedersenUniversity of Oslo, Norway
Inês PereiraUniversity of Zurich & ETH Zurich, Switzerland
Frederike PetzschnerBrown University, USA
Lionel RigouxMax Planck Institute Cologne, Germany
Jonathan RoiserUniversity College London, United Kingdom
Marion RouaultÉcole Normale Supérieure, France
Saige RutherfordRadboud University Medical Center, Netherlands
Lianne SchmaalUniversity of Melbourne, Australia
Helen SchmidtUniversity of Zurich & ETH Zurich, Switzerland
Jakob SiemerkusUniversity of Zurich & ETH Zurich, Switzerland
Ryan Smith Laureate Institute for Brain Research, USA
Klaas Enno StephanUniversity of Zurich & ETH Zurich, Switzerland
Alexander Tschantz University of Sussex, United Kingdom
Lilian WeberUniversity of Oxford, United Kingdom
Thomas WolfersDonders Institute, Netherlands
Angela Yu University of California, USA

The Translational Neuromodeling Unit (TNU) has been organizing the Computational Psychiatry Course in Zurich since 2015. All materials from previous courses can be found here.

Dr. Frederike Petzschner


Prof. Klaas Enno Stephan


Katharina V. Wellstein


Nicole Jessica Zahnd

Contact Person

Heidi Brunner


Inês Pereira

Contact Person