Woo-Young Ahn
Tore Erdmann
Stefan Frässle
Jakob Heinzle
Marcus Herdener
Quentin Huys
Sandra Iglesias
Roland von Känel
Andre Marquand
Christoph Mathys
Rosalyn Moran
Graham Murray
John Murray
Yael Niv
Gina Paolini
Mads Lund Pedersen
Inês Pereira
Frederike Petzschner
Lionel Rigoux
Jonathan Roiser
Marion Rouault
Saige Rutherford
Lianne Schmaal
Helen Schmidt
Jakob Siemerkus
Ryan Smith
Klaas Enno Stephan
Alexander Tschantz
Lilian Weber
Thomas Wolfers
Angela Yu
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.
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.
Duration
5 days + 1 day (Practical Tutorials)
(between 8am CEST and 6pm CEST)
Date
13th – 18th September 2021
Registration Start
TBA
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
ON SITE:
- CHF 150 for external participants, this includes 2 practical tutorials
- CHF 0 for UZH & ETH staff & students
ONLINE:
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 ToolboxA 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 Ahn | Seoul National University, South Korea |
| Tore Erdmann | Scuola Internazionale Superiore di Studi Avanzati, Italy |
| Stefan Frässle | University of Zurich & ETH Zurich, Switzerland |
| Jakob Heinzle | University of Zurich & ETH Zurich, Switzerland |
| Marcus Herdener | University of Zurich, Switzerland |
| Quentin Huys | Max Planck UCL Centre for Computational Psychiatry and Ageing Research, United Kingdom |
| Sandra Iglesias | University of Zurich & ETH Zurich, Switzerland |
| 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 |
| 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 Cologne, Germany |
| Jonathan Roiser | University College London, United Kingdom |
| Marion Rouault | École Normale Supérieure, France |
| Saige Rutherford | Radboud University Medical Center, Netherlands |
| Lianne 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 |
| Alexander Tschantz | University of Sussex, United Kingdom |
| Lilian Weber | University of Oxford, United Kingdom |
| Thomas Wolfers | Donders 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.
