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Computational Psychiatry
Last Updated: 2026-06-01 11:30:44
Abstract
This six-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples.
Objective
This course aims at bridging the gap between mathematical modelers and clinical neuroscientists by teaching computational techniques in the context of clinical applications. The hope is that the acquisition of a joint language and tool-kit will enable more effective communication and joint translational research between fields that are usually worlds apart.
Content
This six-day course teaches state-of-the-art methods in computational psychiatry. It covers various computational models of cognition (e.g., learning and decision-making) and brain physiology (e.g., effective connectivity) of relevance for psychiatric disorders. The course not only provides theoretical background, but also demonstrates open source software in application to concrete examples. Furthermore, practical exercises provide in-depth exposure to different software packages. Please see http://www.translationalneuromodeling.org/cpcourse/ for details.
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| seminar |
Computational Psychiatry
Block course 01.09.-06.09.2025
|
No time listed | 60 h semesterly |
Offered In
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Wahlfächer (Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden.)
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Wahlfächer (Von den angebotenen Wahlfächern müssen mindestens zwei Lerneinheiten erfolgreich abgeschlossen werden. Als Wahlfächer für Rechnergestützte Wissenschaften Master gelten automatisch (ohne Anrechnungsgesuch) auch alle Kernfächer/Vertiefungsfächer (aber nicht Wahlfächer!) aus folgenden Studiengängen: Informatik Master Mathematik Master Physik Master Elektrotechnik und Informationstechnologie Master Data Science Master Robotics, Systems and Control Master Statistik Master Neural Systems and Computation Master gemäss den angegebenen Abschnittsreferenzen.)
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Wahlfächer der Vertiefung (Diese Fächer sind für die Vertiefung in Bioimaging besonders empfohlen. Bei abweichender Fächerwahl konsultieren Sie bitte den Track Adviser.)
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Vertiefung: Biomedical Engineering (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Biomedical Engineering", see . The individual study plan is subject to the tutor's approval.)
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Vertiefungsfächer (These specialisation courses are particularly recommended for the area of "Biomedical Engineering" but you are free to choose courses from any other field in agreement with your tutor. Semester / Research Projects are not allowed in this category. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.)
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Elective Core Modules (Hier werden nur Fächer aufgelistet, die an der ETH Zürich angeboten werden. Eine vollständige Liste der Elective Core Modules und der dazugehörigen Fächer finden Sie hier: ETH-Kurse bitte an der ETH belegen, UZH-Kurse an der UZH.)
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