Postgraduate Course: Computational Cognitive Neuroscience (UG) (INFR11233)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course follows the delivery and assessment of Computational Cognitive Neuroscience (INFR11036) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11036 instead. |
| Course description |
This course follows the delivery and assessment of Computational Cognitive Neuroscience (INFR11036) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11036 instead.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
It is RECOMMENDED that students have passed
Computational Neuroscience (INFR11209)
|
Co-requisites | |
| Prohibited Combinations | Students MUST NOT also be taking
Computational Cognitive Neuroscience (INFR11036)
|
Other requirements | This course follows the delivery and assessment of Computational Cognitive Neuroscience (INFR11036) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11036 instead.
This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.
No prior biology / neuroscience knowledge is required. The course was developed assuming a background in computer science or related quantitative field. We use a small subset of not very advanced math and machine learning in the lectures.
Basics of Python or MATLAB is required. |
Information for Visiting Students
| Pre-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- describe current computational theories of the brain and mental illness
- read, understand, and have a critical opinion on scientific articles related to computational cognitive neuroscience and computational psychiatry
- write and analyse simple computational models related to brain function in Python or MATLAB
- write and analyse simple computational models related to brain function in Python or MATLAB
|
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Additional Class Delivery Information |
Students should expect to spend approximately 40 hours on the coursework for this course. |
| Keywords | linear differential equations,Bayesian inference models,model fitting,model comparison |
Contacts
| Course organiser | Dr Peggy Series
Tel: (0131 6)50 3088
Email: |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 2701
Email: |
|
|
University Homepage