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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2013/2014
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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
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://course.inf.ed.ac.uk/ccn Taught in Gaelic?No
Course descriptionIn this course we study computational approaches to understanding cognitive processes, using massively parallel networks. We study biologically-inspired learning rules for connectionist networks, and their application in connectionist models of perception, memory and language.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites 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.

Some background in statistics, and calculus. Background in linear algebra and programming in Matlab is desirable.
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2013/14 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Web Timetable Web Timetable
Course Start Date 13/01/2014
Breakdown of 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 )
Additional Notes
Breakdown of Assessment Methods (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
No Exam Information
Summary of Intended Learning Outcomes
1 - Describe a cognitive architecture of the brain.
2 - Contrast the applicability of several connectionist learning rules.
3 - Understand the limitation of current connectionist models.
4 - Design a simple computational model of a cognitive process and relate it to the literature and understand the underlyng assumptions.
5 - Write a simple memory model in PDP++
Assessment Information
Written Examination 0
Assessed Assignments 100
Oral Presentations 0

Assessment
The course is assessed by four assignments and a report.

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.
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus *Encoding Information in populations of neurons.
*Decoding Information from populations of neurons.
*Models of Neurons and Networks of Neurons.
*Information transmission and Attention.
*Models of Learning and Plasticity.
*Models of Memory.
*Models of Decision Making.
*Models of Mental disorders.
*The Bayesian Brain.

Relevant QAA Computing Curriculum Sections: Artificial Intelligence
Transferable skills Not entered
Reading list Computational Modelling in Cognition: Principles and Practice by Stephen Lewandowsky and Simon Farrell, Sage 2011
Study Abroad Not entered
Study Pattern Lectures 15
Tutorials 0
Timetabled Laboratories 15
Non-timetabled assessed assignments 40
Private Study/Other 30
Total 100
KeywordsNot entered
Contacts
Course organiserDr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk
Course secretaryMs Katey Lee
Tel: (0131 6)50 2701
Email: Katey.Lee@ed.ac.uk
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