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://www.inf.ed.ac.uk/teaching/courses/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
| 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-requisites |
None |
| Displayed in Visiting Students Prospectus? |
Yes |
Course Delivery Information
|
| Delivery period: 2010/11 Semester 2, Available to all students (SV1)
|
WebCT enabled: No |
Quota: None |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
| Central | Lecture | | 1-11 | 09:00 - 09:50 | | | | | | Central | Lecture | | 1-11 | | | | 09:00 - 09:50 | |
| First Class |
Week 1, Monday, 09:00 - 09:50, Zone: Central. Seminar Room 5, Chrystal Macmillan Building |
| Exam Information |
| Exam Diet |
Paper Name |
Hours:Minutes |
Stationery Requirements |
Comments |
| Main Exam Diet S2 (April/May) | | 2:00 | 12 sides | |
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 |
| 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 |
|
copyright 2011 The University of Edinburgh -
31 January 2011 7:53 am
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