02.09.2019 - 06.09.2019


This course is designed to provide students across fields (neuroscience, psychiatry, physics, biology, psychology....) with the necessary toolkit to master challenges in computational psychiatry research.

The CPC is meant to be practically useful for students at all levels (MDs, Master, PhD, Postdoc, PI) coming from diverse backgrounds (neuroscience, psychology, medicine, engineering, physics, etc.), who would like to apply modeling techniques to study learning, decision-making or brain physiology in patients with psychiatric disorders. The course will teach not only the theory of computational modeling, but also demonstrate software in application to example data sets.
You can find detailed information on our website or follow us on twitter and facebook.


The CPC is divided into two parts: The main course (day 1-4) and in-depth practical tutorials (day 5).

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

The second day explains basic modelling principles and covers models of perception and learning  (Bayesian Models of Perception, Bayesian Hierarchical Learning, Predictive Coding, Reinforcement Learning)

The third day includes models of planning and decision making (Active Inference, Drift Diffusion Models), Machine Learning, Biophysical Models (DCMs), and advanced models of connectivity (Whole Brain Models).

The fourth day features a series of talks by leading scientists on the applications of Computational Psychiatry, including a panel discussion on the challenges and future of Computational Psychiatry.

Practical Tutorial Sessions

The practical tutorials will provide 3h, small-group, in-depth and hands-on sessions on a specific modelling approach. 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.

Bayesian Learning using the Hierarchical Gaussian Filter from Tapas
- Active inference using the Active Inference Toolbox

Reinforcement Learning using the hBayesDM Package
- Model Inversion using the Variational Bayes Toolbox
- Machine Learning (Toolbox to be announced)
- Dynamic Causal Modelling
using the SPM-DCM Package
- Whole Brain Models
using the Tapas MPDCM Toolbox
- Metacognition
using the HMeta-d Toolbox



Slides (available September 2019)


Reading (available July 2019)

Videos (available ca. 2 months after course date)



Woo-Young Ahn, Seoul National University, South Korea

Dominik Bach, University of Zurich, Switzerland

Michael Browning, Universtity of Oxford, UK

Philip Corlett, Yale School of Medicine, USA

Tore Erdmann, Scuola Internazionale Superiore di Studi Avanzati, Italy

Olivia Faull, University of Zurich & ETH Zurich, Switzerland

Stephen Fleming, UCL London, UK

Stefan Frässle, University of Zurich & ETH Zurich, Switzerland

Claire Gillan, Trinity College Dublin, Ireland

Samuel Harrison, University of Zurich & ETH Zurich, Switzerland

Jakob Heinzle, University of Zurich & ETH Zurich, Switzerland

Roland von Känel, University Hospital Zurich, Switzerland

Andre Marquand, Donders Institute, Netherlands

Christoph Mathys, SISSA, Italy

Michael Moutoussis, Max Planck UCL Centre London, UK

Martin Paulus, Laureate Institute Tulsa, USA

Frederike Petzschner, University of Zurich & ETH Zurich, Switzerland

Lionel Rigoux, Max Planck Institute for Metabolism Research Cologne, Germany

Philipp Schwartenbeck, UCL London, UK

Klaas Enno Stephan, University of Zurich & ETH Zurich, Switzerland

Wesley Thompson, University of San Diego, USA

Lilian Weber, University of Zurich & ETH Zurich, Switzerland

Thomas Wolfers, Donders Institute, Netherlands

Yu Yao, University of Zurich & ETH Zurich, Switzerland

Ariel Zylberberg, Columbia University New York, USA


Translational Neuromodeling Unit
University & ETH Zurich



Klaas Enno Stephan
Frederike Petzschner
Administration: Heidi Brunner
Contact: Nicole Zahnd & Katharina Wellstein