| Unit name | Unit 4 - Computational Neuroscience |
|---|---|
| Unit code | COMS35103 |
| Credit points | 15 |
| Level of study | H/6 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Bogacz |
| Open unit status | Not open |
| Pre-requisites |
Unit 1 Foundations of Neuroscience and Unit 2 Concepts and Techniques for In Vivo Research (MRes Systems Neuroscience) |
| Co-requisites |
None |
| School/department | Department of Computer Science |
| Faculty | Faculty of Engineering |
This unit introduces the quantitative theory and models of computations performed by the brain. The lectures will progress through three different levels of abstraction: from detailed models of single neuron based on neurophysiology to high-level models of interactions between networks of neurons explaining human behaviour. Computational neuroscience is an interdisciplinary subject hence this unit is addressed to students of various backgrounds: computer science, neuroscience, psychology and engineering mathematics.
The following topics are discussed:
1. Models of a single neurone: integrate and fire model based on neurophysiology.
2. Models of neural networks: models of different types of memory in hippocampus and cortex, models of feature extraction in thalamus and visual cortex.
3. Models of neural systems: models of decision making, reinforcement learning and cognitive control.
Lecture notes. Background reading to include:
* Dayan P & Abbott LF (2001) Theoretical Neuroscience: Computational and Mathematical Modelling of Neural Systems, MIT Press. Recommended. * O'Reilly RC & Munataka Y (2000) Computational Explorations in Cognitive Neuroscience, MIT Press. * Feng J (Ed.) (2003) Computational Neuroscience: A Comprehensive Approach, Chapman & Hall. * Eliasmith C & Anderson C (2002) Neural Engineering: Computation, Representation and Dynamics in Neurobiological Systems, Bradford Book. * Rolls E & Deco G (2002) Computational Neuroscience of Vision, Oxford University Press.
