13th September 2021 - 18th September 2021


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.

The goal of the Computational Psychiatry Course (CPC) is to create a scientific and educational space for students, scientists, and other professionals to share and advance the state of knowledge in CP. Everyone is welcome at the CPC.
To this end, we encourage all participants to treat each other respectfully. This code of conduct defines a set of guidelines to facilitate this.

You can find detailed information on our website or follow us on twitter and facebook.

Detailed information and more material can be found on the course website.




The CPC is divided into two parts: The main course (Day 1-5) and in-depth practical tutorials (Day 6).



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. The practical sessions cover only open-source software packages. The code can be found under the respective links below. If you sign up, you will receive an installation guide and further information before the course takes place.

  • Practical Session A with Tore Erdmann, Alexander Hess & 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 Conor Heins & Daphne Demekas
    Active Inference using the PYMDP Toolbox

    This tutorial provides a practical guide on developing computational models using `pymdp`, a Python package for solving partially-observed Markov Decision Processes (POMDPs) with Active Inference. Students will build simple simulations in interactive, cloud-hosted Python notebooks (Google Colab). We aim to help students build generative models for POMDPs and to develop a conceptual understanding of the theoretical principles behind active inference, without requiring detailed technical knowledge.

  • Practical Session C with Woo-Young Ahn, Mina Kwon & Hoyoung Doh
    Reinforcement Learning using the hBayesDM Package

    In this tutorial, participants will learn how to use a Bayesian package called hBayesDM for modeling various reinforcement learning and decision making (RLDM) tasks. A short overview of (hierarchical) Bayesian modeling will be also provided. Participants will also learn important steps and issues to check when reporting modeling results in publications.

  • Practical Session D with Mads Lund Pedersen
    Drift Diffusion Models using the HDDM Toolbox

    The tutorial will provide a practical introduction to analyzing decision making data with the drift diffusion model (DDM) using the open source python toolbox HDDM. We will go through practical steps of applying the models to data, from importing data to running and validating models. We will also briefly go over extensions to the DDM, including the reinforcement learning drift diffusion model (RLDDM).

  • Practical Session E with Lionel Rigoux & Matthias Müller-Schrader
    Model Inversion using the Variational Bayes Toolbox

    This hands-on tutorial is a crash course on practical computational modelling. You will build a simple model (delay discounting) and learn how to apply it on empirical data to perform parameter estimation and model selection. We will use the VBA-toolbox which contains all the tools to simulate, estimate, and diagnose your models, as well as a collection of ready-to-use models (eg. Q-learning, DCM). No previous experience with modelling is required, but basic knowledge of Matlab is recommended.

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

    Would you like to learn more about modeling individual differences and heterogeneity in psychiatry? In this tutorial, we will abandon the classical patient vs. healthy control framework. You will be guided through how to run an analysis using normative modeling implemented in the PCNtoolkit.

  • Practical Session G with Rosalyn Moran
    Dynamic Causal Modelling for EEG using the SPM-DCM Package

    This tutorial will examine specific features of EEG data that can be used to optimize a cell and receptor specific model of brain connectivity. EEG data acquired from event related (ERP) and temporally extended / resting state studies (Spectral responses) will be examined. The neural mass models - their assumptions and parametrizations will be compared. Students will learn to use the SPM graphical user interface and to write batch code in MATLAB to perform Dynamic Causal Modeling of EEG.

  • Practical Session H with Jakob Heinzle & Birte Toussaint
    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 & Ashraya Indrakanti
    Metacognition using the hMeta-d Toolbox

    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

    In this tutorial, you will learn how to use the regression dynamic causal modeling (rDCM) toolbox to perform effective (directed) connectivity analyses in whole-brain networks from functional magnetic resonance imaging (fMRI) data. We will provide you with the necessary theoretical background of the rDCM approach and detail practical aspects that are relevant for whole-brain connectivity analyses. After having laid the foundation, a hands-on part will allow you to obtain a better feeling for the behavior of the model as well as provide you with in-depth experience on how to apply the model to empirical fMRI data. The goal of this tutorial is to familiarize you with the theoretical and practical aspects of rDCM, which will allow you to seamlessly integrate the approach into your own research. We will provide clear instructions on how to perform the analyses. However, experience with the analysis of fMRI data (already some experience with classical DCM for fMRI would be ideal) as well as experience with MATLAB are beneficial.





Woo-Young Ahn, Seoul National University, South Korea

Daphne Demekas, Imperial College London, United Kingdom

Hoyoung Doh, Seoul National university, South Korea

Tore Erdmann, Scuola Internazionale Superiore di Studi Avanzati, Italy

Michele Ferrante, National Institute of Mental Health (NIMH), USA

Stefan Frässle, University of Zurich & ETH Zurich, Switzerland

Conor Heins, Max Planck Institute of Animal Behavior and University of Konstanz, Germany

Jakob Heinzle, University of Zurich & ETH Zurich, Switzerland

Marcus Herdener, University of Zurich, Switzerland

Alex Hess, University of Zurich & ETH Zurich, Switzerland

Quentin Huys, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, United Kingdom

Ashraya Indrakanti, University of Zurich & ETH Zurich, Switzerland

Mina Kwon, Seoul National University, South Korea

Roland von Känel, University Hospital Zurich, Switzerland

Andre Marquand, Donders Institute, Netherlands

Christoph Mathys, Aarhus University, Denmark

Rosalyn Moran, King's College London, United Kingdom

Graham Murray, University of Cambridge, United Kingdom

John Murray, Yale School of Medicine, USA

Matthias Müller-Schrader, University of Zurich & ETH Zurich, Switzerland

Matthew Nassar, Brown University, USA

Yael Niv, Princeton University, USA

Gina Paolini,  Klinik für Psychiatrie und Psychotherapie, Clienia Schlössli AG, Switzerland

Mads Lund Pedersen, University of Oslo, Norway

Inês Pereira, University of Zurich & ETH Zurich, Switzerland

Frederike Petzschner, Brown University, USA

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

Jonathan Roiser, University College London, United Kingdom

Marion Rouault, École Normale Supérieure, France

Saige Rutherford, Donders Institute, Netherlands

Liane Schmaal, University of Melbourne, Australia

Helen Schmidt, University of Zurich & ETH Zurich, Switzerland

Jakob Siemerkus, University of Zurich & ETH Zurich, Switzerland

Ryan Smith, Laureate Institute for Brain Research, USA

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

Birte Toussaint, University of Zurich & ETH Zurich, Switzerland

Lilian Weber, University of Zurich & ETH Zurich, Switzerlan

Thomas Wolfers, Donders Institute, Netherlands

Angela Yu, University of California, USA



Translational Neuromodeling Unit
University & ETH Zurich

Mail: cpcourse(at)



Klaas Enno Stephan
Frederike Petzschner
Katharina V. Wellstein

Administration: Heidi Brunner
Contact: Nicole Jessica ZahndInês Pereira