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)
|
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
|
| Academic year 2024/25, Available to all students (SV1)
|
Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
98 )
|
| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Exam 70%
Coursework 30% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Computational Cognitive Neuroscience PG (INFR11036 and UG (INFR11233) | :120 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Describe fundamental neuroscience and psychiatry concepts as well as current computational theories of the brain and mental illness.
- Abstract neuroscience and behavioural experimental data into an appropriate computational model and critically evaluate such a model from a biological and/or nd data fitting methods in mathemcomputational and/or clinical perspective.
- Write computational models learned in lectures and data fitting methods in mathematical form and implement and analyse them in Python or MATLAB.
- Critically evaluate model simulations and report the results in the form of a scientific paper.
- Compare the strengths and weaknesses of alternative modelling approaches and develop a critical understanding of the power and limitations of current research.
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Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/ccn |
| 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: pseries@informatics.ed.ac.uk |
Course secretary | Ms Lindsay Seal
Tel: (0131 6)50 5194
Email: lindsay.seal@ed.ac.uk |
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