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Seit SoSe 2024

English

Data Science for Cognitive Neuroscience

6

Deniz, Fatma

benotet

Mündliche Prüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34353100 FG Sprache und Kommunikation in biologischen und künstlichen Systemen

No information

Kontakt


MAR 6-4

Groiß, Camilla

camilla.groiss@tu-berlin.de

Learning Outcomes

- Understanding of experiments and data used in cognitive neuroscience research. - First hand experience with real brain recorded data, eg. EEG and fMRI. - Identifying and retrieving relevant information from brain data and interpreting them using data analysis techniques. - Learn how to develop and test a hypothesis.

Content

The human brain is an intricate and complex information processing system, giving rise to an exciting interdisciplinary field of research. Understanding how it works is a challenging scientific quest. In recent decades, new brain imaging techniques have allowed us to "see" the brain in action, leading to the growth of cognitive neuroscience. Cognitive neuroscience explores the neural mechanisms that underlie a wide range of cognitive functions, linking brain activity to the tasks it performs. This field fosters exciting collaborations with psychology, biology, physics, and computer science, offering a comprehensive approach to understanding the brain. If you are intrigued by the inner workings of the brain and eager to employ cutting-edge brain imaging and data analysis tools for it’s exploration, this course is tailored for you! During this course, you will acquire proficiency in utilizing Python programming for comprehending, manipulating, and exploring human brain recordings (such as ECoG, EEG, MEG, and fMRI). You will also learn how to create and test hypotheses about how the brain processes information using real data. Additionally, you will learn invaluable analysis techniques to make sense of brain recording data. This course provides hands-on experience in the data analysis of brain data, enabling you to gain a deep insight into the experiments and data employed in the cognitive neuroscience field. Furthermore, the data analysis techniques and investigative approaches you learn will be readily transferable to research in other disciplines.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Data Science for Cognitive NeuroscienceIVWiSe/SoSeEnglish4

Workload and Credit Points

Data Science for Cognitive Neuroscience (IV):

Workload descriptionMultiplierHoursTotal
Attendance15.04.0h60.0h
Pre/post processing15.08.0h120.0h
180.0h(~6 LP)
The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

Main lecture, tutorial, and homework assignments. During the lecture, the foundational concepts are introduced. Students receive programming and homework assignments. A pre-requisite to take part in the final oral exam is successful participation in the programming and homework assignments.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Python programming

Mandatory requirements for the module test application:

1. Requirement
[CoCo] Data Science for Cognitive Neuroscience: Successful completion of assignments

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

20 min

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Winter- und Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 20.

Registration Procedures

All interested students should register via E-Mail/ISIS and have to attend the first lecture to activate the registration.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
Computational and Inferential Thinking: The Foundations of Data Science (https://inferentialthinking.com/chapters/intro.html)

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Informatik (B. Sc.)12SoSe 2024WiSe 2024/25
Medieninformatik (B. Sc.)12SoSe 2024WiSe 2024/25
Technische Informatik (B. Sc.)12SoSe 2024WiSe 2024/25
Wirtschaftsinformatik (B. Sc.)23SoSe 2024WiSe 2024/25

Students of other degrees can participate in this module without capacity testing.

Miscellaneous

No information