Klaas Stephan: Katalogdaten im Frühjahrssemester 2013 |
| Name | Herr Prof. Dr. Klaas Stephan |
| Lehrgebiet | Translationale Neuromodellierung und Komputationale Psychiatrie |
| Adresse | Professur f. Transl. Neuromodeling ETH Zürich, WIL G 203 Wilfriedstrasse 6 8032 Zürich SWITZERLAND |
| Telefon | +41 44 634 91 25 |
| Fax | +41 44 634 91 31 |
| stephan@biomed.ee.ethz.ch | |
| Departement | Informationstechnologie und Elektrotechnik |
| Beziehung | Ordentlicher Professor |
| Nummer | Titel | ECTS | Umfang | Dozierende | |
|---|---|---|---|---|---|
| 227-0967-00L | Computational Neuroimaging Clinic  | 3 KP | 2V | K. Stephan | |
| Kurzbeschreibung | This 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. | ||||
| Lernziel | 1. 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. | ||||
| Inhalt | This 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. | ||||
| Voraussetzungen / Besonderes | 1. 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'. | ||||
| 227-0970-00L | Research Topics in Biomedical Engineering | 1 KP | 2K | K. P. Prüssmann, M. Rudin, M. Stampanoni, K. Stephan, J. Vörös | |
| Kurzbeschreibung | Current topics in Biomedical Engineering presented mostly by external speakers from academia and industry. | ||||
| Lernziel | see above | ||||
| 227-0971-00L | Translational Neuromodeling & Computational Neuroeconomics | 3 KP | 2S | K. Stephan | |
| Kurzbeschreibung | 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 with biological and clinical knowledge about the phenomenology and pathophysiology of psychiatric and neurological diseases. | ||||
| Lernziel | To understand current concepts about computational and physiological mechanisms of maladaptive behaviour and psychiatric diseases. | ||||
| Inhalt | In 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. | ||||
| 227-0973-00L | Bayesian Methods for Translational Neuromodeling and Computational Psychiatry Findet dieses Semester nicht statt. | 3 KP | 2V | K. Stephan | |
| Kurzbeschreibung | This 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. | ||||
| Lernziel | To 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. | ||||
| Inhalt | This 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. | ||||
| Literatur | See TNU website: http://www.biomed.ee.ethz.ch/research/tnu/teaching | ||||
| Voraussetzungen / Besonderes | Basic knowledge of Bayesian statistics, MATLAB programming skills | ||||
| 227-0974-00L | TNU Colloquium | 0 KP | 2K | K. Stephan | |
| Kurzbeschreibung | This 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. | ||||
| Lernziel | see above | ||||