Postgraduate Course: Computational Cognitive Neuroscience (INFR11036)
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 | In this course, we study how computations carried out by the nervous system leads to cognition, in particular perception, memory, learning, and decision-making. We learn to develop and simulate computational models that incorporate data from neurobiology and / or can be used to model aspects of cognition such as measured during behavioural experiments. Such models can be used to understand individual differences and mental disorders (e.g., autism, schizophrenia, addiction, and depression): a domain of application that is emphasised in the second half of the course is the emerging field of computational psychiatry. |
| Course description |
- Overview of computational neuroscience basics (models of neurons and networks)
- Reinforcement learning models for computational neuroscience
- Bayesian models for computational neuroscience (The Bayesian Brain)
- Computational modelling of behavioural data
- Models of decision-making
- Application to individual differences (e.g., autism) and mental disorders (e.g.,schizophrenia, addiction, and depression): introduction to Computational Psychiatry
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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 | |
Other requirements | 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 | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2022/23, Available to all students (SV1)
|
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 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
The course is assessed by one assignment and one essay, each worth 50%.
You should expect to spend approximately 40 hours on the coursework for this course.
If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year. |
| 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 scientific reports on topics related to computational cognitive neuroscience
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Additional Information
| Course URL |
http://course.inf.ed.ac.uk/ccn |
| Graduate Attributes and Skills |
Not entered |
| 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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