Neural Information Processing (P01489)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-NIP

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 : For Informatics PG 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 Probalistic Modelling & Reasoning (PMR) course is recommended as preparation. Also, a reasonable level of familiarity with computational concepts and MATLAB is assumed.

Subject Areas

Home subject area

Neuroinformatics, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
08/01/2007	15:00	15:50

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	15:00	15:50	Central
Lecture	Thursday	15:00	15:50	Central

Summary of Intended Learning Outcomes

It is anticipated that students who complete this course will be able to:
- Demonstrate knowledge of recent models of neural information processing.
- Critically evaluate mathematical models of information processing.
- To implement models in Matlab.