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 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:

https://github.com/BergLab

 

 

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
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   (5-7b7e776e6b497c7e776d37747e376d74)
  • Elias Najarro   (6-677066373a3543646f7870716c316e7831676e)
Teacher

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

Saved on the 01-05-2024

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