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for a past academic year. Please see the current academic year for up to date information.
| Unit name |
Computational Neuroscience |
| Unit code |
COMS30127 |
| Credit points |
10 |
| Level of study |
H/6
|
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24)
|
| Unit director |
Dr. Houghton |
| Open unit status |
Not open |
| Pre-requisites |
None
|
| Co-requisites |
None
|
| School/department |
Department of Computer Science |
| Faculty |
Faculty of Engineering |
Description including Unit Aims
This unit has two versions, COMS30127 for undergraduate students, and COMSM2127 for postgraduate students.
Aims are to provide the student with an understanding of computational principles of biological computations performed in the brain by: single neurons, network of neurons, and interacting brain areas.
Intended Learning Outcomes
After successful completion of this unit, the student will
- Be inspired by the computational principles of the brain in their future engineering work.
- Be prepared to do research on the brain with understanding of brain s purpose (i.e., information processing).
- For each levels of abstraction (single neuron, network of neurons, interacting brain areas): understand the assumptions made by the models, validity of the assumptions, and computational principles.
- Be able to simulate simple models of neurons, networks, and cortical areas in Matlab or Python.
Teaching Information
20 hours of lectures, 10 hours of laboratory sessions
Assessment Information
Coursework (20%), exam (80%)
All students need to do two courseworks in Matlab or Python which address the fourth learning outcome (Be able to simulate simple models of neurons, networks, and cortical areas in Matlab or Python).
Reading and References
Lecture notes. Background reading to include: Peter Dayan, Larry F Abbott Theoretical Neuroscience MIT-Press, 2001 ISBN: 0-262-04199-5 Price: £31.57 Recommended
