Course details

8.3128

Modeling in Cognitive Neuroscience

 L + P
WS 2023 Prof. Dr. Sebastian Musslick OFFLINE
4h/wk
8 ECTS 		B.Sc modules:
CS-BWP-CNP - Cognitive (Neuro-)Psychology
CS-BWP-MCS - Methods of Cognitive Science
CS-BWP-NI - Neuroinformatics
CS-BWP-NS - Neuroscience
KOGW-WPM-KNP - Cognitive (Neuro-)Psychology
KOGW-WPM-NW - Neuroscience
M.Sc modules:
CC-MWP-NI - Neuroinformatics
CC-MWP-NS - Neuroscience
CS-MWP-CNP - Cognitive (Neuro-)Psychology
CS-MWP-NI - Neuroinformatics
CS-MWP-NS - Neuroscience

CS-BW - Bachelor elective course
CS-MW - Master elective course 		Tue: 12-14

Course Overview: This course provides students with a comprehensive introduction to the fundamental principles of computational modeling in the field of cognitive neuroscience. Through a combination of theoretical lectures and practical programming exercises, participants will acquire a solid understanding of the theoretical underpinnings, applications, and techniques involved in computational modeling of human cognition and brain function. This course serves as a foundation for further exploration and research in the emerging field of computational neuroscience. Course Structure: The course will consist of a series of lectures and programming exercises, allowing students to acquire both theoretical knowledge and practical skills. The lectures will provide a comprehensive overview of the various modeling techniques, their theoretical foundations, and their applications in cognitive neuroscience research. The programming exercises, conducted using the Python programming language, will enable students to implement and simulate computational models, apply parameter estimation methods, and compare models using appropriate statistical techniques. By the end of this course, students will have gained the necessary knowledge and practical skills to develop, analyze, and interpret computational models in cognitive neuroscience. Prerequisites: Students are expected to have a basic understanding of cognitive neuroscience concepts and programming fundamentals. Familiarity with Python programming language is preferred but not mandatory. Assessment: Assessment methods may include programming assignments, model implementation projects, in-class exercises, and written examinations. These assessments will gauge students' comprehension of the theoretical concepts and their ability to apply computational modeling techniques in cognitive neuroscience research.