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.
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Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
It is RECOMMENDED that students have passed
Computational Neuroscience (INFR11209)
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Co-requisites | |
Prohibited Combinations | Students MUST NOT also be taking
Computational Cognitive Neuroscience (INFR11036)
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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
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Academic year 2023/24, Available to all students (SV1)
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Quota: None |
Course Start |
Semester 2 |
Timetable |
Timetable |
Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 15,
Supervised Practical/Workshop/Studio Hours 15,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
68 )
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Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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Additional Information (Assessment) |
Assignment 50%
Essay 50% |
Feedback |
Not entered |
No Exam Information |
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
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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: |
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