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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
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis 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.
Course description *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
Entry Requirements (not applicable to Visiting Students)
Pre-requisites 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. Neural Computation is recommended as preparation. Also, a reasonable level of familiarity with computational concepts and MATLAB is assumed.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2017/18, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
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 )
Assessment (Further Info) Written Exam 75 %, Coursework 25 %, Practical Exam 0 %
Additional Information (Assessment) There will be two assessed assignments, one on neural coding, one on probabilistic models for neural information processing.

You should expect to spend approximately 30 hours on the coursework for this course.

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.
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Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  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.
Reading List
Abbott and Dayan (2001) Theoretical Neuroscience. MIT press (recommended)
Additional Information
Course URL
Graduate Attributes and Skills Not entered
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Course organiserDr Matthias Hennig
Tel: (131 6)50 3080
Course secretaryMs Katey Lee
Tel: (0131 6)50 2701
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