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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Neural Information Processing (INFR11035)

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/nip Taught in Gaelic?No
Course descriptionThis course builds one recent insights that in many cases the computation done by the nervous system can be described in machine learning terms and using information theory. This course focusses on the more mathematical models of the brain and sensory processing. The solutions found by the nervous system might transfer to engineering applications such as compression, parallel processing, and dealing with complex data.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites It is RECOMMENDED that students have passed Probabilistic Modelling and Reasoning (INFR11050)
Co-requisites
Prohibited Combinations Other requirements For Informatics PG and final year MInf students only, or by special permission of the School. The background needed to successfully take this course is a good grounding in mathematics, particularly with regard to probability and statistics, vectors and matrices. The mathematical level required is similar to that which would be obtained by students who did not have significant difficulties with the courses Mathematics for Informatics 1-4 taken in the first two years of the Informatics undergraduate syllabus. The Probabilistic Modelling & Reasoning (PMR) course is recommended as preparation. Also, a reasonable level of familiarity with computational concepts and MATLAB is assumed.
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 09:00 - 09:50
CentralLecture1-11 09:00 - 09:50
First Class Week 1, Monday, 09:00 - 09:50, Zone: Central. 2.01, 10 Buccleuch Place
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Summary of Intended Learning Outcomes
1 - Understand response analysis techniques and their limitations when applied
to neural responses.
2 - Analyse neural responses in Information theoretic terms
3 - Describe perceptual processing from a probabilistic point of view.
4 - Implement some of the methods discussed in class in Matlab.
Assessment Information
Written Examination 75
Assessed Assignments 25
Oral Presentations 0

Assessment
There will be two assessed assignments, one on neural coding, one on probabilistic models for neural information processing.

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 *Neural coding: reverse correlation, higher order kernels, stimulus reconstruction. Application to the fly visual system.
*Information theory as applied to neural coding: mutual information measures, whitening. Application to retinal and LGN coding.
*Networks based on information-theoretic cost functions: Helmholtz machine, Linsker's info-max principle. Application to V1 coding.
*Independent Component Analysis. Basics, variants of ICA, ICA as model for visual cortex.
*Predictive Coding: Kalman filters. Application to cortical coding
*Bayesian approaches: Stimulus estimation, probabilistic interpretation of populations codes.

Relevant QAA Computing Curriculum Sections: Simulation and Modelling, Artificial intelligence
Transferable skills Not entered
Reading list Abbott and Dayan (2001) Theoretical Neuroscience. MIT press (recommended)
Study Abroad Not entered
Study Pattern Lectures 20
Tutorials 0
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
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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