Universität Wien

052319 VU Current topics in Neuroinformatics (2024S)

Continuous assessment of course work
ON-SITE

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 05.03. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Monday 18.03. 16:45 - 20:00 PC-Seminarraum 3, Kolingasse 14-16, OG02
Tuesday 19.03. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 09.04. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 16.04. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 23.04. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 30.04. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 07.05. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 14.05. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 28.05. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 04.06. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 11.06. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 18.06. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG
Tuesday 25.06. 13:15 - 14:45 Seminarraum 3, Währinger Straße 29 1.UG

Information

Aims, contents and method of the course

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.

Assessment and permitted materials

The overall grade is composed as follows:
40% programming assignment
40% paper presentation
20% weekly quizzes

Minimum requirements and assessment criteria

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)

Examination topics

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.

Reading list

Will be provided on moodle

Association in the course directory

Last modified: Mo 04.03.2024 20:05