Postgraduate Course: Computational Neuroscience of Vision (INFR11037)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Year 4 Undergraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This 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
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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.
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
| 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:
- Describe the roles of computational models in biology and informatics
- Summarise the basic architecture, development, and known computational functions of early visual areas in humans and monkeys
- Describe and evaluate different types of computational models and how they relate to relevant experimental data
- mplement simple models of feature map development and function
- Analyse the results of models to make predictions for future experiments
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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.
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Contacts
| Course organiser | Dr James Bednar
Tel:
Email: James.Bednar@ed.ac.uk |
Course secretary | Ms Sarah Larios
Tel: (0131 6)51 5170
Email: sarah.larios@ed.ac.uk |
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