Computational Neuroscience

Course content

This course will introduce the field computational neuroscience. 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. 

Education

MSc in Neuroscience - Elective course

Not open for credit transfer students or other external students

Learning outcome

After completing the course the student is expected to 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

 

Competences

  • Apply basic modeling in neuroscience
  • Implement general tools of measurements in neuroscience
  • Independently process experimental 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.

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).

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
Presentation by student (10 minutes) followed by a discussion (10 minutes)
Without preparation.
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
  • 28
  • Class Instruction
  • 12
  • Preparation
  • 157
  • Exercises
  • 10
  • English
  • 207