SNEU20007U Computational Neuroscience

Volume 2023/2024

MSc in Neuroscience - Elective course

Not open for credit transfer students or other external students


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.

Learning Outcome

After completing the course the student is expected to be able to:


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



  • Discuss basic models in neuroscience
  • Basic coding using python
  • Process experimentally acquired neuroscience data



  • Apply basic modeling in neuroscience
  • Implement python models of neurons
  • Process data in neuroscience


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




A completed Bachelor degree within the Biomedical and Natural Sciences (e.g. biology, biochemistry, molecular biomedicine, medicine, or similar).
Basic knowledge in neuroscience, mathematics and programming
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.
  • Category
  • Hours
  • Lectures
  • 28
  • Class Instruction
  • 12
  • Preparation
  • 157
  • Exercises
  • 10
  • Total
  • 207
Continuous feedback during the course of the semester
Peer feedback (Students give each other feedback)
7,5 ECTS
Type of assessment
Continuous assessment, 20 minutes
Type of assessment details
4-6 written individual assignments. At least 4 out of 6 assignments need to be passed to successfully complete the course.
Exam registration requirements


All aids allowed
Marking scale
passed/not passed
Censorship form
No external censorship
Internal examiners

Resubmission of unsuccessfully completed assignments.

Criteria for exam assesment

To achieve the grade Passed, the student must adequately be able to:


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



  • Discuss basic models in neuroscience
  • Perform basic programming on computers
  • Perform computer models of neurons
  • Process experimentally acquired neuroscience data