| Unit name | Neural Information Processing |
|---|---|
| Unit code | EMATM0041 |
| Credit points | 10 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Rosalyn Moran |
| Open unit status | Not open |
| Pre-requisites |
EMAT20920 or an equivalent unit in Matlab |
| Co-requisites |
None |
| School/department | Department of Computer Science |
| Faculty | Faculty of Engineering |
This unit aims to train students in fundamental concepts related to information processing in the brain and how these theories and methods can be applied in the derivation of principled approaches to learning problems and assimilation, quantification and interpretation of time dependent, probabilistic data (looking outwards). It also aims to equip students with the theoretical and mathematical background and foundation to undertake research in computational and systems neuroscience using modelling and empirical data analysis to advance our knowledge of how neural systems operate (looking inwards).
1. To be able to model how neurons & the nervous system encode and exchange information
2. To be able to apply small and large scale brain networks (forward models)
3. To be able to model learning, including value learning and deep learning and inference.
Lectures and computer laboratories.
24 hours of contact time consisting of:
• 18 lecture hours
• 6 hours of computer labs requiring Matlab – either 3x2hour or 2x3 hour
Coursework comprising a Matlab Project on evaluation of neural codes which demonstrate either simulation of behavioural outputs or neural firing. (25%) (all learning outcomes)
2 hour exam (75%) (all learning outcomes)
A number of key papers will be made available for each individual topic area.
