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Neuroscience

Course name: Neuroscience
Course number: R-10
Area of instruction: Computer Science and Engineering

Administrative details

Faculty: Computer Science and Engineering
Year of instruction: 2dd year of MSc
Semester of instruction: 2nd semester
No. of Credits: 5 ECTS
Total workload on average: 180 hours overall
Frontal lecture hours: 2 hours per week.
Frontal tutorial hours: 0 hours per week.
Lab hours: 2 hours per week.
Individual lab hours: 2 hours per week.
Frequency: weekly throughout the semester.
Grading mode: letters: ???.

Course outline

This course is designed to serve an introduction to modern neuroscience and neurotechnologies. Because neuroscience is an interdisciplinary field including biological science, physics, mathematics, philosophy, and engineering, we will cover all the areas in lectures and seminars. The course goal is to make students familiar with basic principles of nervous system functioning and its applications for rehabilitation, robotics and control of human brain states. The students will learn about various methods of neuron modelling and biological bases of artificial intelligence. We will discussed the various neuroimaging methods and its applications for disease diagnostics and brain computer interfaces development. After the course the students will be able to conceptually understand the important terms and approaches of modern neuroscience and neural engineering. The theoretical introduction will be complemented by practical examples of task solving.

Expected learning outcomes

Be able to describe what modern neuroscience is and what a neurotechnologies might include or not include, and why, to someone not in the class
Learn about many of the features of the nervous system and body that may be useful to a specialist in artificial intelligence,
Be familiar with modern computational neuroscience methods and approaches,
Learn about some of the goals of neuronal engineering and neurotechnologies and who is involved (in research and uses)

Required background knowledge

Basic knowledge of physics and biology, mathematical background in differential equations and graph theory, digital signal processing

Prerequisite courses

Artificial Intelligence,
Behavioral and cognitive robotics,
High-Dimensional Data Analysis,
Mathematical Analysis.

Detailed topics covered in the course+

Neuroanatomy and Brain Systems
Neuroimaging Methods
Brain Diseases
Sensory Systems
Human Motion Control and Motor System
Computational Neuroscience
Brain Computer Interfaces

Textbook

Fundamental Neuroscience (3rd edition), Academic Press, Elsevier, 2008
Nam C. S., Nijholt A., Lotte F. (ed.) Brain–Computer Interfaces Handbook: Technological and Theoretical Advances. CRC Press, 2018.

Reference material

None

Required computer resources

Laptop

Evaluation

Assignments (20%)
Research Project (20%)
Quizzes (20%)
Mid-term exam (20%)
Final exam (20%)

MSc:Neuroscience old

Contents