052319 VU Current topics in Neuroinformatics (2024S)
Prüfungsimmanente Lehrveranstaltung
Labels
VOR-ORT
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Mo 12.02.2024 09:00 bis Do 22.02.2024 09:00
- Abmeldung bis Do 14.03.2024 23:59
Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Dienstag
05.03.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Montag
18.03.
16:45 - 20:00
PC-Seminarraum 3, Kolingasse 14-16, OG02
Dienstag
19.03.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
09.04.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
16.04.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
23.04.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
30.04.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
N
Dienstag
07.05.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
14.05.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
21.05.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
28.05.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
04.06.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
11.06.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
18.06.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Dienstag
25.06.
13:15 - 14:45
Seminarraum 3, Währinger Straße 29 1.UG
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
What brain regions are involved in the processing of certain information? How can we decode a person’s intentions from neuronal data and use it to build a Brain-Computer Interface? And how does high-level cognition arise from the joint activity of individual neurons? These are only some of the questions that the methods developed in the field of neuroinformatics can help answer. In this course, you will get to know a variety of recording modalities of neuronal data (e.g., fMRI, EEG, recordings of single neurons) and methods to analyze the obtained signals.The course will be in the style of a seminar, where students will independently read and present scientific papers, which will then be discussed in class. Prior knowledge of signal processing and/or machine learning methods will be helpful, but not required to follow the course.
Art der Leistungskontrolle und erlaubte Hilfsmittel
The overall grade is composed as follows:
40% programming assignment
40% paper presentation
20% weekly quizzes
40% programming assignment
40% paper presentation
20% weekly quizzes
Mindestanforderungen und Beurteilungsmaßstab
To receive a passing grade, you may be absent without excuse a maximum of three times, and at least 20% of the available points must be earned on each task.Grades will be assigned according to the following scheme:
At least 90%: very good (1)
At least 80%: good (2)
At least 70%: satisfactory (3)
At least 60%: sufficient (4)
Less than 60%: not sufficient (5)
At least 90%: very good (1)
At least 80%: good (2)
At least 70%: satisfactory (3)
At least 60%: sufficient (4)
Less than 60%: not sufficient (5)
Prüfungsstoff
The participants must achieve at least 20% of the available points in all three tasks. The topics of the quizzes cover everything presented during the lectures.
Literatur
Will be provided on moodle
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 04.03.2024 20:05