 | Study programme 2025-2026 | Français | |
| Introduction to Computational Neuroscience | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| M-NEUR-065 |
|
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais | Anglais | 20 | 0 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
You will learn the foundations of Computational Neurosciences through these differets chapters
1 Why do we need data?
2 Bottom-up vs Top-down
3 Correlation versus causality
4 Filtering
5 Fitting
6 Prediction & perturbation
7 Model comparison principles
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Not applicable
Mode of delivery
- Face-to-face
Type of Teaching Activity/Activities
- Cours magistraux
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)
Location of learning activity
- Université de Mons - Mons
Location of assessment
- Université de Mons - Mons
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term