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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Computational Neuroscience of Vision (INFR11037)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course focuses on understanding the computational mechanisms underlying animal visual systems that are similar to those of humans. The main emphasis is on how the properties of neurons across the two-dimensional surface of the visual cortex are organised topographically to represent and transform the relevant features of visual stimuli. Because the visual cortex is the primary model system for understanding the cortex in general, the course also acts as an introduction to computational processing in all topographically organised cortical regions.
Course description *Biological Background
Role of computational models in biology, relation of biological models to computer vision, early visual processing, primary visual cortex, face and object processing, visual system development

*Computational Modeling Levels and Approaches
Unit models, topographic map models

*Models of the Development of Feature Maps in V1
E.g. separate and combined maps for topography, orientation, ocular dominance, motion direction, colour, and spatial frequency

*Modeling Adult Processing in V1
E.g. orientation and motion direction estimation, visual aftereffects, plasticity, contour segmentation and grouping

*Higher-level Processing
E.g. modeling face detection and recognition, object detection and recognition, invariant responses (viewpoint, size, translation)

Relevant QAA Computing Curriculum Sections: Simulation and Modeling, Artificial Intelligence, Computer Vision and Image Processing
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.

It is expected that students will have a general background in computer science, including some programming experience, and will be comfortable with basic mathematics. Compared to Probabilistic Modelling and Reasoning, and Neural Computation, this course is not as heavily mathematical, focusing more on biological concepts and computational implementation using existing primitives. Biological and/or neuroscience background would be very helpful but is not required. Computational Cognitive Neuroscience, Neural Information Processing, and Neural Computation are worthwhile companion or prior courses.
Information for Visiting Students
Course Delivery Information
Academic year 2014/15, 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 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) There will be two assessed assignments consisting of literature reviews, modeling project design, and simulations of biological visual systems using the Topographica simulator.

You should expect to spend approximately 40 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.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
1 - Describe the roles of computational models in biology and informatics
2 - Summarise the basic architecture, development, and known computational functions of early visual areas in humans and monkeys
3 - Search the neuroscientific literature for relevant experimental data
4 - Describe and evaluate different types of computational models
5 - Implement simple models of feature map development and function
6 - Analyse the results of models to make predictions for future experiments
Reading List
* Miikkulainen, Bednar, Choe, and Sirosh, Computational Maps in Visual Cortex (Springer, 2005), ISBN 0-387-22024-0.
* Other notes as distributed in class.
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Jim Bednar
Tel: (0131 6)51 3092
Course secretaryMiss Claire Edminson
Tel: (0131 6)51 4164
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