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.

Education

MSc in Neuroscience - Elective course

Learning outcome

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 total of 6 weekly assignments, where each covers the material and topic of the week and which has to be handed in the following week. The assignments are not mandatory, but encouraged to do as they form the questions for the oral exam.

 

Most of the content from the previous year can be found on the following GitHub page

Basic knowledge in neuroscience, mathematics, especially linear algebra and differential calculus and programming (Python).

A completed Bachelor degree within the Biomedical and Natural Sciences (e.g. biology, biochemistry, molecular biomedicine, medicine, or similar).

Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
ECTS
7,5 ECTS
Type of assessment
Oral examination, 20 minutes under invigilation
Type of assessment details
There will be 6 topics for the oral exam. Each topic is based on the assignments for every covering the material that week.
Examination prerequisites

None

Aid
No aids allowed
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 (Python)
  • 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
  • 154
  • Exercises
  • 10
  • Project work
  • 6
  • 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   (5-787b746b6846797b746a34717b346a71)
  • Elias Najarro   (6-677066373a3543646f7870716c316e7831676e)
Teacher

Elias Najarro, Mogens Høgh Jensen, Tobias Andersen, Rune W. Berg

Saved on the 25-03-2025

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