It is our pleasure to announce the 4th international Computational Psychiatry Course organized by the Translational Neuromodeling Unit (TNU) at the University of Zurich and ETH Zurich. The CPC2018 will take place from 10-14 September 2018 in Zurich.
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.
We strongly believe in open source and open science, therefore, the content of the course will be made freely accessible on the web.
The course will consist of five parts:
The first day covers topics in Clinical Psychiatry providing a conceptual basis for the type of questions that Computational Psychiatry will need to address, including an open panel discussions led by scientists with a clinical background in Psychiatry.
The second day explains basic modelling principles, Bayesian Inference (Bayesian Models of Perception, Bayesian Hierarchical Learning, Predictive Coding), Machine Learning & the Drift-Diffusion Model
The third day includes models of planning and decision making (MDPs, POMDPs, Reinforcement Learning, Active Inference), Biophysical Models (DCMs) and more advanced model inversion techniques (model selection, model averaging, MCMC, Variational Bayes)
The fourth day features a series of talks by leading scientists on the applications of Computational Psychiatry, including a panel discussion on the future of Computational Psychiatry.
The fifth day is optional and includes hands-on practical programming sessions for a subset of the presented models (IMPORTANT NOTICE: Seats for the practical sessions need to be booked separately and are limited!!!)
Practical Session A: Bayesian Learning using the Hierarchical Gaussian Filter
Practical Session B: Active inference using the Active Inference Toolbox
Practical Session C: Model Inversion using the Variational Bayes Toolbox
Practical Session D: Reinforcement Learning using the hBayesDM Package
David Redish, University of Minnesota, USA
Read Montague, Virginia Tech, USA
Rick Adams, UCL, London
Klaas Enno Stephan, University of Zurich & ETH, Zurich
Valerie Voon, Cambridge,
Marcus Herdener, UZH, Zurich
Andre Marquand, Donders, Nijmegen
Dominik Bach, University of Zurich, Zurich
Martin Paulus, Laureate Institute, Tulsa, USA
Helene Haker, University of Zurich & ETH, Zurich
Frederike Petzschner, University of Zurich & ETH, Zurich
Lilian Weber, University of Zurich & ETH, Zurich
Christoph Mathys, SISSA, Trieste
Rafael Polania, ETH, Zurich
Lionel Rigoux, MPI, Cologne
Woo-Young Ahn, Seoul National University
Philipp Schwartenbeck, UCL & Oxford University
Jakob Heinzle, University of Zurich & ETH, Zurich
Dario Schöbi, University of Zurich & ETH, Zurich
Andreea Diaconescu, University of Basel, Basel
Adam Checkroud, Spring Health, New York
Jean Daunizeau, Brain and Spine Institute, ICM, Paris
Tore Erdmann, SISSA, Trieste
Stefan Frässle, University of Zurich & ETH, Zurich
… and more
Translational Neuromodeling Unit
University & ETH Zurich