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 | Students MUST NOT also be taking
Computational Cognitive Neuroscience (UG) (INFR11233)
|
Other requirements | MSc students must register for this course, while Undergraduate students must register for INFR11233 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% and 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 computational 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.
- 5. Compare the strengths and weaknesses of alternative modelling approaches and develop a critical understanding of the power and limitations of current research.
|
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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