Postgraduate Course: Neural Information Processing (INFR11035)
Course Outline
School | School of Informatics |
College | College of Science and Engineering |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
SCQF Credits | 10 |
ECTS Credits | 5 |
Summary | 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. |
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
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Entry Requirements (not applicable to Visiting Students)
Pre-requisites |
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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. Neural Computation is recommended as preparation. Also, a reasonable level of familiarity with computational concepts and MATLAB is assumed.
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Information for Visiting Students
Pre-requisites | None |
High Demand Course? |
Yes |
Course Delivery Information
Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand response analysis techniques and their limitations when applied to neural responses.
- Analyse neural responses in Information theoretic terms
- Describe perceptual processing from a probabilistic point of view.
- Implement some of the methods discussed in class in Matlab.
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Reading List
Abbott and Dayan (2001) Theoretical Neuroscience. MIT press (recommended) |
Contacts
Course organiser | Dr Matthias Hennig
Tel: (131 6)50 3080
Email: m.hennig@ed.ac.uk |
Course secretary | Mrs Sam Stewart
Tel: (0131 6)51 3266
Email: Sam.Stewart@ed.ac.uk |
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