Computational Neuroscience of Vision (P01491)

? Credit Points : 10 ? SCQF Level : 11 ? Acronym : INF-P-CNV

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.

Entry Requirements

? Co-requisites : For Informatics or Neuroscience PG students only, or by special permission of the School. 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 PMR, LFD, and NC, 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. CCN, NIP, and NC are worthwhile companion or prior courses.

Subject Areas

Home subject area

Cognitive Neuroscience, (School of Informatics, Schedule O)

Other subject areas

Neuroinformatics, (School of Informatics, Schedule O)

Vision, Perception and Action, (School of Informatics, Schedule O)

Delivery Information

? Normal year taken : Postgraduate

? Delivery Period : Semester 2 (Blocks 3-4)

? Contact Teaching Time : 2 hour(s) per week for 10 weeks

First Class Information

Date	Start	End	Room	Area	Additional Information
08/01/2007	14:00	14:50	Room G.02, William Robertson Building	Central

All of the following classes

Type	Day	Start	End	Area
Lecture	Monday	14:00	14:50	Central
Lecture	Thursday	14:00	14:50	Central

Summary of Intended Learning Outcomes

Students who complete the course should 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.
* Search the neuroscientific literature for relevant experimental data.
* Describe and evaluate different types of computational models.
* Implement simple models of feature map development and function.
* Analyse the results of models to make predictions for future experiments .