Postgraduate Course: Neural Information Processing (INFR11035)
|School||School of Informatics
||College||College of Science and Engineering
||Availability||Available to all students
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
|Home subject area||Informatics
||Other subject area||None
||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)
|| It is RECOMMENDED that students have passed
Probabilistic Modelling and Reasoning (INFR11050)
||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.
|Additional Costs|| None
Information for Visiting Students
|Displayed in Visiting Students Prospectus?||Yes
Course Delivery Information
|Delivery period: 2013/14 Semester 2, Available to all students (SV1)
||Learn enabled: No
|Course Start Date
|Breakdown of Learning and Teaching activities (Further Info)
Lecture Hours 20,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Breakdown of Assessment Methods (Further Info)
||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.
|Written Examination 75|
Assessed Assignments 25
Oral Presentations 0
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.
||*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
||Abbott and Dayan (2001) Theoretical Neuroscience. MIT press (recommended)
Timetabled Laboratories 0
Non-timetabled assessed assignments 30
Private Study/Other 50
|Course organiser||Dr Iain Murray
Tel: (0131 6)51 9078
|Course secretary||Ms Katey Lee
Tel: (0131 6)50 2701
© Copyright 2013 The University of Edinburgh - 13 January 2014 4:28 am