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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Computational Cognitive Neuroscience (INFR11036)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryIn 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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Computational Neuroscience (INFR11209)
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-requisitesAs above.
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) Assignment 50%
Essay 50%
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. describe current computational theories of the brain and mental illness
  2. read, understand, and have a critical opinion on scientific articles related to computational cognitive neuroscience and computational psychiatry
  3. write and analyse simple computational models related to brain function in Python or MATLAB
  4. write scientific reports on topics related to computational cognitive neuroscience
Reading List
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.
Keywordslinear differential equations,Bayesian inference models,model fitting,model comparison
Course organiserDr Peggy Series
Tel: (0131 6)50 3088
Course secretaryMs Lindsay Seal
Tel: (0131 6)50 2701
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