COMPUTATIONAL PSYCHIATRY COURSE 2020
07th September 2020 - 12th September 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)
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 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, 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, Nathaniel Haines & Jaeyeong Jayce Yang
Practical Session D: Model Inversion using the Variational Bayes Toolbox with Lionel Rigoux & Eduardo A. Aponte
Practical Session G: Regression DCM - An Advanced Model of Connectivity for fMRI using Tapas rDCM with Stefan Frässle & Cao Tri Do
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, University of Newcastle, Australia
Jean Daunizeau, Brain and Spine Institute, ICM, France
Cao Tri Do, University of Zurich & ETH Zurich, Switzerland
Tore Erdmann, Scuola Internazionale Superiore di Studi Avanzati, Italy
Stefan Frässle, University of Zurich & ETH Zurich, Switzerland
Marta Garrido, University of Melbourne, Australia
Herman Galioulline, University of Zurich & ETH Zurich, Switzerland
Nathaniel Haines, Ohio State University, USA
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
Matthias Müller-Schrader, University of Zurich & ETH Zurich, Switzerland
Gina Paolini, Klinik für Psychiatrie und Psychotherapie, Clienia Schlössli AG, 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
Saige Rutherford, Donders Institute, Netherlands
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
Jaeyeong Jayce Yang, Seoul National University, South Korea
Yu Yao, University of Zurich & ETH Zurich, Switzerland
The Computational Psychiatry Course does not receive any sponsoring from the pharmaceutical industry.
Translational Neuromodeling Unit
University & ETH Zurich