Unit name | Information Processing and the Brain |
---|---|
Unit code | COMSM0034 |
Credit points | 10 |
Level of study | M/7 |
Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
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 |
This unit explores information processing, statistical and deep learning in neuroscience. It starts out with an overview of information, statistical theory and the probabilistic brain before focusing on computational models of neural circuits and learning, including unsupervised, supervised and reinforcement learning, visual and auditory system, convolution and recurrent neural networks and the backpropagation algorithm in the brain. Finally the unit explains how to relate these models to neural data. Overall, the unit will enable students to understand how concepts from data science, machine learning and computational modelling are being used to solve one of the most challenging problems in science: how do our brains work.
On successful completion of this unit, students will be able to:
Lectures and computer laboratories.
24 hours of contact time consisting of:
• 18 lecture hours
• 6 hours of computer labs – either 3x2hour or 2x3 hour
2 course works; problem sheet (10%) and coursework (20%)
2 hour exam (70%) (all learning outcomes)
A list of textbooks and relevant papers will be provided.