Klaas Stephan: Katalogdaten im Frühjahrssemester 2017 |
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 neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. | ||||
Lernziel | 1. Consolidation of theoretical knowledge (obtained in one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry') in a practical setting. 2. Acquisition of practical problem solving strategies for computational modeling of neuroimaging data. | ||||
Inhalt | This seminar teaches problem solving skills for computational neuroimaging, based on joint analyses of neuroimaging and behavioural data. It deals with a wide variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g. mass-univariate and multivariate analyses of fMRI/EEG data, or generative models of fMRI, EEG, or behavioural data. . | ||||
Voraussetzungen / Besonderes | The participants are expected to have successfully completed at least one of the following courses: 'Methods & models for fMRI data analysis', 'Translational Neuromodeling', 'Computational Psychiatry' | ||||
227-0970-00L | Research Topics in Biomedical Engineering | 0 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 | ||||
227-0973-00L | Translational Neuromodeling Findet dieses Semester nicht statt. | 6 KP | 4G | K. Stephan | |
Kurzbeschreibung | This lecture deals with computational modeling of neuronal and cognitive processes for diagnostic applications in psychiatry ("Translational Neuromodeling"). A particular focus is on Bayesian methods and generative models, e.g. dynamic system models for inferring neuronal mechanisms from neuroimaging data, and hierarchical learning models for inference on cognitive mechanisms from behaviour. | ||||
Lernziel | To obtain an understanding of the goals and methods of translational neuromodeling, particularly with regard to Bayesian models of neuroimaging (fMRI, EEG) and behavioural data. | ||||
Inhalt | This lecture deals with computational modeling of neuronal and cognitive processes for diagnostic applications in psychiatry ("translational neuromodeling"). A particular focus is on Bayesian methods and generative models, e.g. dynamic causal models (DCMs) for inferring neuronal mechanisms from neuroimaging data, and hierarchical learning models for inference on cognitive mechanisms from behavioural data. The course illustrates the application of these models to various psychiatric diseases and outlines a general research strategy. | ||||
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 |