227-0967-00L Computational Neuroimaging Clinic
| Semester | Frühjahrssemester 2023 |
| Dozierende | K. Stephan |
| Periodizität | jedes Semester wiederkehrende Veranstaltung |
| Lehrsprache | Englisch |
| Kurzbeschreibung | This seminar teaches problem solving skills for computational neuroimaging (incl. associated computational analyses of behavioural data). It deals with a variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g., concerning mass-univariate and multivariate analyses of fMRI/EEG/OPM data, or generative models of fMRI/EEG/OPM, 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 (incl. associated computational analyses of behavioural data). It deals with a variety of real-life problems that are brought to this meeting from the neuroimaging community at Zurich, e.g., concerning mass-univariate and multivariate analyses of fMRI/EEG/OPM data, or generative models of fMRI/EEG/OPM or behavioural data. | ||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | The participants are expected to be familiar with general principles of statistics and have successfully completed at least one of the following courses: Methods & models for fMRI data analysis, Translational Neuromodeling, Computational Psychiatry | ||||||||||||||||||||||||||||||||||||||||||||||||
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