052319 VU Current topics in Neuroinformatics (2024S)
Continuous assessment of course work
Labels
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).
- Registration is open from Mo 12.02.2024 09:00 to Th 22.02.2024 09:00
- Deregistration possible until Th 14.03.2024 23:59
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
N
Tuesday
21.05.
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
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)
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