Computational Neuroscience
Course content
This course will introduce the field of computational neuroscience. The course will put an emphasis on "learning-by-doing" by having a weekly coding workshop on the lecture content. Computational neuroscience is also known as theoretical neuroscience or mathematical neuroscience and is a branch within neuroscience that employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern the development, structure, physiology, and cognitive abilities of the nervous system. In this course, the student will get an overview of computational neuroscience and touch on some of the most important parts of the field. This includes simulating and visualizing biological neuronal networks, basic data analysis, and machine learning.
MSc in Neuroscience - Elective course
After completing the course the student is expected to be able to:
Knowledge
Explain the knowledge acquired in the following fields/subjects:
- Dynamical systems of two or more variables
- Neuronal networks and network theory
- Single neuron modeling
- Data processing
- Basic coding with Python
- Artificial intelligence and machine learning for neuroscience problems
Skills
- Discuss basic models in neuroscience
- Basic coding using python
- Process experimentally acquired neuroscience data
Competences
- Apply basic modeling in neuroscience
- Implement python models of neurons
- Process data in neuroscience
The student will participate in lectures, small practical groups and employ hands-on using computer programming (Matlab or python). During the course students will do a small project and write a report to present in plenum.
Most of the content from the previous year can be found on the following GitHub page:
Basic knowledge in neuroscience, mathematics and programming
A completed Bachelor degree within the Biomedical and Natural Sciences (e.g. biology, biochemistry, molecular biomedicine, medicine, or similar).
Open for credit transfer students. Apply for acceptance as a credit transfer student at SUND
- ECTS
- 7,5 ECTS
- Type of assessment
-
Oral examination, 20 minutes under invigilation
- Type of assessment details
- Presentation by student (10 minutes) followed by a discussion (10 minutes). Without preparation.
- Exam registration requirements
-
None
- Aid
- Without aids
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Internal examiners
Criteria for exam assessment
To achieve the grade Passed, the student must adequately be able to:
Knowledge
Explain the knowledge acquired in the following fields/subjects:
- Basic physics theory of biology and self-organization
- Neuronal networks and models of networks
- Neuronal modelling
- Mathematics and statistics in neuroscience
- Acquisition of data in neuroscience
- Basic programming on computers
- Neuro-prosthetics and the use of neural implants in medicine, e.g. cochlear implants and brain-machine interfaces
- Artificial intelligence and machine learning
Skills
- Discuss basic models in neuroscience
- Perform basic programming on computers
- Perform computer models of neurons
- Process experimentally acquired neuroscience data
- Category
- Hours
- Lectures
- 36
- Preparation
- 160
- Exercises
- 10
- English
- 206
Kursusinformation
- Language
- English
- Course number
- SNEU20007U
- ECTS
- 7,5 ECTS
- Programme level
- Full Degree Master
- Duration
-
1 semester
- Placement
- Spring
- Schedulegroup
-
Syllabus
- Capacity
- 20
- Studyboard
- Study Board for Human Biology, Immunology and Neuroscience
Contracting department
- Department of Neuroscience
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinators
- Rune W. Berg (5-75787168654376787167316e7831676e)
- Elias Najarro (6-6a73693a3d384667727b73746f34717b346a71)
Teacher
Elias Najarro, Mogens Høgh Jensen, Tobias Andersen, Rune W. Berg
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Kursusinformation for indskrevne studerende