Klaas Stephan: Catalogue data in Spring Semester 2013

Name Prof. Dr. Klaas Stephan
FieldTranslational Neuromodelling and Computational Psychiatry
Address
Professur f. Transl. Neuromodeling
ETH Zürich, WIL G 203
Wilfriedstrasse 6
8032 Zürich
SWITZERLAND
Telephone+41 44 634 91 25
Fax+41 44 634 91 31
E-mailstephan@biomed.ee.ethz.ch
DepartmentInformation Technology and Electrical Engineering
RelationshipFull Professor

NumberTitleECTSHoursLecturers
227-0967-00LComputational Neuroimaging Clinic Information 3 credits2VK. Stephan
AbstractThis seminar teaches problem solving skills for computational modeling of neuroimaging data (fMRI, EEG). It deals with a wide variety of real-life problems (from the neuroimaging community at Zurich.) Examples may include mass-univariate/multivariate analyses of fMRI data, dynamic causal modeling, or computational analyses of neuroimaging data based on Bayesian models of cognition.
Learning objective1. Consolidation of theoretical knowledge (obtained in the course Methods & models for fMRI data analysis) in a practical, setting with real-world problems from ongoing research.
2. Acquisition of practical problem solving strategies for computational modeling of neuroimaging data.
ContentThis seminar teaches problem solving skills for computational modeling of neuroimaging data (fMRI, EEG). It deals with a wide variety of real-life problems (from the neuroimaging community at Zurich.) Examples may include mass-univariate/multivariate analyses of fMRI data, dynamic causal modeling, or computational analyses of neuroimaging data based on Bayesian models of cognition.
Prerequisites / Notice1. Basic knowledge of neuroimaging procedures (e.g., fMRI, EEG),
knowledge of statistics and neuroimaging data analysis procedures.
2. Successful attendance and completion of the course
'Methods and models for fMRI data analysis'.
227-0970-00LResearch Topics in Biomedical Engineering1 credit2KK. P. Prüssmann, M. Rudin, M. Stampanoni, K. Stephan, J. Vörös
AbstractCurrent topics in Biomedical Engineering presented mostly by external speakers from academia and industry.
Learning objectivesee above
227-0971-00LTranslational Neuromodeling & Computational Neuroeconomics Information 3 credits2SK. Stephan
AbstractCurrent methods and concepts for deciphering mechanisms of maladaptive behaviour, such as aberrant learning and decision-making in healthy individuals and psychiatric patients.The key goal is to connect methodological training with biological and clinical knowledge about the phenomenology and pathophysiology of psychiatric and neurological diseases.
Learning objectiveTo understand current concepts about computational and physiological mechanisms of maladaptive behaviour and psychiatric diseases.
ContentIn this seminar, we discuss current methods and concepts for deciphering mechanisms of maladaptive behaviour, such as aberrant learning and decision-making in healthy individuals and psychiatric patients. The key goal is to connect methodological training (in computational and statistical techniques for analyzing behavioural, fMRI and EEG data) with biological and clinical knowledge about the phenomenology and pathophysiology of psychiatric and neurological diseases. This seminar aims at bridging the gap between mathematical modelers and clinical neuroscientists, enabling more effective communication and joint translational research.
227-0973-00LBayesian Methods for Translational Neuromodeling and Computational Psychiatry Information
Does not take place this semester.
3 credits2VK. Stephan
AbstractThis course provides an overview of current Bayesian methods in translational neuromodeling and computational psychiatry. The course focuses on (i) dynamic causal models (DCM) for inferring neuronal system mechanisms from fMRI and EEG data, and (ii) hierarchical Bayesian and Bayesian reinforcement learning models for inference on computational mechanisms from behavioural data.
Learning objectiveTo obtain a comprehensive overview of state-of-the-art Bayesian methods for translational neuromodeling and computational psychiatry, with applications to neuroimaging (fMRI, EEG) and behavioural data.
ContentThis course provides an overview of current Bayesian methods in translational neuromodeling and computational psychiatry. The course focuses on (i) dynamic causal models (DCM) for inferring neuronal system mechanisms from fMRI and EEG data, and (ii) hierarchical Bayesian and Bayesian reinforcement learning models for inference on computational mechanisms from behavioural data. A particular emphasis is on how these models can be applied for characterizing neuronal and computational mechanisms of aberrant learning and pathological belief formation in psychiatric diseases.
LiteratureSee TNU website:
http://www.biomed.ee.ethz.ch/research/tnu/teaching
Prerequisites / NoticeBasic knowledge of Bayesian statistics, MATLAB programming skills
227-0974-00LTNU Colloquium0 credits2KK. Stephan
AbstractThis colloquium for MSc and PhD students at D-ITET discusses current research topics in Translational Neuromodeling, a new discipline concerned with the development of mathematical models for diagnostics of brain diseases. The range of topics is broad, incl. statistics and computational modeling, experimental paradigms (fMRI, EEG, behaviour), and clinical questions.
Learning objectivesee above