2018
About

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.

Details

Schedule

TNU

Location

Social

CourseDetails

Duration
4 days + 1 day (Practical Sessions)

Date
10th – 14th September 2018

Registration Start
February 2018

Registration Ends
August 2018

Designed for
Master Students, PhDs, PostDocs, Clinicians and anyone deeply interested in Computational Psychiatry

ECTS Points
3 (only for members of the University of Zurich and ETH)

Course Fee
500 CHF for the main course
(free for students of the ETH and University of Zurich)
50 CHF per Practical Session

Requirements
Basic programming skills
(e.g. Matlab or Python)
Basic knowledge of statistics

CourseStructure
Psychiatry
Baysian Inference & Machine Learning & Drift-Diffusion Model
Models of Planning & Biophysical Models & Model Inversion
Computational Psychiatry Applied
Practical Tutorials


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!!!)

Speakers 2018
 
David
Read
Rick
Hanneke
Klaas Enno
Valerie
Marcus
Andre
Dominik
Martin
Helene
Frederike
Stephen
Lilian
Christoph
Rafael
Lionel
Woo-Young
Philipp
Jakob
Dario
Andreea
Adam
Jean
Tore
Stefan
Redish
Montague
Adams
den Ouden
Stephan
Voon
Herdener
Marquand
Bach
Paulus
Haker
Petzschner
Fleming
Weber
Mathys
Polania
Rigoux
Ahn
Schwartenbeck
Heinzle
Schöbi
Diaconescu
Checkroud
Daunizeau
Erdmann
Frässle
University of Minnesota, USA
Virginia Tech, USA
UCL, London
Radboud University, Netherlands
University of Zurich & ETH, Zurich
Cambridge, UK
UZH, Zurich
Donders, Nijmegen
University of Zurich, Zurich
Laureate Institute, Tulsa, USA
University of Zurich & ETH, Zurich
University of Zurich & ETH, Zurich
University of Zurich & ETH, Zurich
UCL, London
SISSA, Trieste
ETH, Zurich
MPI, Cologne
Seoul National University
UCL & Oxford University
University of Zurich & ETH, Zurich
University of Zurich & ETH, Zurich
University of Basel, Basel
Spring Health, New York
Brain and Spine Institute, ICM, Paris
SISSA, Trieste
University of Zurich & ETH, Zurich
Registration

Thank you for your interest!

CourseMaterial

Software

Video

Slides

Reading

Team