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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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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://www.inf.ed.ac.uk/teaching/courses/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 For Informatics PG and final year MInf students only, or by special permission of the School. Experience in programming or simulation systems desirable. No background in Neuroscience or cognitive science is required.
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2012/13 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 11:10 - 12:00
CentralLecture1-11 11:10 - 12:00
First Class Week 1, Monday, 11:10 - 12:00, Zone: Central. Lecture Theatre 4, 7 Bristo Square
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 Theoretical Neuroscience, by Dayan and Abbott, MIT Press, 2000, will be used for the first part of the course. For the second part, readings will be based on recently published articles.
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 secretaryMiss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
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