COMPUTATIONAL PSYCHIATRY COURSE 2020
07.09.2020 - 12.09.2020
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 1-5) and in-depth practical tutorials (day 6)
ETH Zurich and UZH students can get 3 ECTS for participating in this course (performance assessment: short oral exam (date to be announced) at the TNU. This course is part of the HS (fall term) 2020 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 (www.myStudies.ethz.ch) as "UZH Fachstudent/in".
Registration will open on 28.02.2020.
Course fees for the main course are CHF 500 (external participants) or CHF 100 (ETH Zurich or UZH staff & students). Course fees for the practical tutorial sessions are CHF 50 each.
We are offering CP Course Stipends for students who can not afford to pay the course fee! If you are interested in applying klick here for more information. Applications are due on March 17th 2020.
The main course consists of lectures (day 1 until day 5) on psychiatry, modelling and model applications in computational psychiatry.
The first day will cover topics in psychiatry providing a conceptual basis for the type of questions that compuatational 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, model inversion and model selection) and will finish with a first introduction to a possible learning model (Reinforcement Learning).
The third day will include models of perception (Psychophysics, Bayesian Models of Perception, Predictive Coding) and action selection (Markov Decision Processes, Active Inference, Drift Diffusion Models).
The fourth day (only half a day) will include Machine Learning (basics and advanced) and models of connectivity (Dynamic Causal Modelling and Advanced Models of Connectivity).
The fifth day will feature a series of talks on practical applications of computational models to problems from psychiatry.
Practical Tutorial Sessions
The sixth day of the course will provide in-depth practical sessions of a subset of the presented models for a smaller fraction of students (separate registration).
Practical Session A: Bayesian Learning using the Hierarchical Gaussian Filter (HGF, TNU Tapas) with Tore Erdmann & Sandra Iglesias
Practical Session B: Active Inference using the Active Inference Toolbox with Thomas Parr & Philipp Schwartenbeck
Practical Session C: Reinforcement Learning & Decision-Making using the hBayesDM Package with Woo-Young Ahn
Practical Session D: Model Inversion using the Variational Bayes Toolbox with Lionel Rigoux
Practical Session E: Machine Learning using Skit-Learn & NISPAT with Thomas Wolfers
Practical Session F: Dynamic Causal Modelling using SPM with Jakob Heinzle
Practical Session G: Regression DCM - An Advanced Model of Connectivity for fMRI using Tapas rDCM with Stefan Frässle
Practical Session H: Hierarchical Unsupervised Generative Embedding - An Advanced Model of Connectivity for fMRI using Tapas HUGE with Yu Yao
Detailed information and more material can be found on the course website.
Woo-Young Ahn, Seoul National University, South Korea
Eduardo A. Aponte, Pharma Research & Early Development Informatics, Roche Innovation Center Basel, Switzerland
Sonia Bishop, UC Berkeley, USA
Michael Breakspear, QIMR Berghofer, Australia
Jean Daunizeau, Brain and Spine Institute, ICM, France
Tore Erdmann, Scuola Internazionale Superiore di Studi Avanzati, Italy
Stefan Frässle, University of Zurich & ETH Zurich, Switzerland
Marta Garrido, University of Melbourne, Australia
Jakob Heinzle, University of Zurich & ETH Zurich, Switzerland
Marcus Herdener, University of Zurich, Switzerland
Philipp Homan, University Hospital of Psychiatry Zurich, Switzerland
Sandra Iglesias, University of Zurich & ETH Zurich, Switzerland
Sahib Khalsa, Laureate Institute for Brain Research, USA
Roland von Känel, University Hospital Zurich, Switzerland
Andre Marquand, Donders Institute, Netherlands
Christoph Mathys, SISSA, Italy
Gina Paolini, University of Zurich & ETH Zurich, Switzerland
Thomas Parr, UCL London, UK
Mads Lund Pedersen, University of Oslo, Norway
Frederike Petzschner, Brown University, USA
Lionel Rigoux, Max Planck Institute for Metabolism Research Cologne, Germany
Helen Schmidt, University of Zurich & ETH Zurich, Switzerland
Philipp Schwartenbeck, UCL London, UK
Jakob Siemerkus, University of Zurich & ETH Zurich, Switzerland
Klaas Enno Stephan, University of Zurich & ETH Zurich, Switzerland
Lilian Weber, University of Zurich & ETH Zurich, Switzerlan
Katja Wiech, University of Oxford, UK
Thomas Wolfers, Donders Institute, Netherlands
Yu Yao, University of Zurich & ETH Zurich, Switzerland
Translational Neuromodeling Unit
University & ETH Zurich
Klaas Enno Stephan
Katharina V. Wellstein
Administration: Heidi Brunner
Contact: Nicole Jessica Zahnd & Inês Perreira