Postgraduate Course: Neural Information Processing (INFR11035)
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://course.inf.ed.ac.uk/nip |
Taught in Gaelic? | No |
| Course description | This 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)
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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.
A good undergraduate-level grounding in mathematics is assumed, particularly probability and statistics, vectors and matrices. The Probabilistic Modelling & Reasoning (PMR) course is recommended as preparation. Also, a reasonable level of familiarity with computational concepts and MATLAB is assumed.
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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: 2013/14 Semester 2, Available to all students (SV1)
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Learn enabled: No |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
13/01/2014 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
76 )
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| Additional Notes |
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| Breakdown of Assessment Methods (Further Info) |
Written Exam
75 %,
Coursework
25 %,
Practical Exam
0 %
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| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
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| 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.
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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 |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: Katey.Lee@ed.ac.uk |
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© Copyright 2013 The University of Edinburgh - 13 January 2014 4:28 am
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