Postgraduate Course: Computational Cognitive Neuroscience (INFR11036)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Available to all students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Informatics |
Other subject area | None |
| Course website |
http://www.inf.ed.ac.uk/teaching/courses/ccn |
Taught in Gaelic? | No |
| Course description | In 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.
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| Additional Costs | None |
Information for Visiting Students
| Pre-requisites | None |
| Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
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| Delivery period: 2011/12 Semester 2, Available to all students (SV1)
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WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 11:10 - 12:00 | | | | | | Central | Lecture | | 1-11 | | | | 11:10 - 12:00 | |
| First Class |
Week 1, Monday, 11:10 - 12:00, Zone: Central. George Sq 07 S.1 |
| 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++
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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 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk |
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© Copyright 2011 The University of Edinburgh - 16 January 2012 6:17 am
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