TNU

COMPUTATIONAL PSYCHIATRY COURSE 2020

7th September 2020 - 12th September 2020

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.
You can find detailed information on our website or follow us on twitter and facebook. More material can be found on the course website.

DETAILS

The CPC is divided into two parts: The main course (day 1-5) and in-depth practical tutorials (day 6).

Main Course

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 E:Machine Learning using NISPAT with Thomas Wolfers & Saige Rutherford

Practical Session F: Dynamic Causal Modelling using SPM with Jakob Heinzle & Herman Galioulline

Practical Session G: Regression DCM - An Advanced Model of Connectivity for fMRI using Tapas rDCM with Stefan Frässle & Cao Tri Do

Practical Session H: Hierarchical Unsupervised Generative Embedding - An Advanced Model of Connectivity for fMRI using Tapas HUGE with Yu Yao & Matthias Müller-Schrader

MATERIAL

SPEAKERS

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