Unit information: Computational Neuroscience in 2019/20

Unit name	Computational Neuroscience
Unit code	COMSM2127
Credit points	10
Level of study	M/7
Teaching block(s)	Teaching Block 2 (weeks 13 - 24)
Unit director	Dr. O'Donnell
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 introduces the quantitative theory and models of computations performed by the brain. The lectures will progress through different levels of abstraction: from detailed models of single neurones based on neurophysiology to high-level models of interactions between networks of neurones explaining human behaviour. 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.

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

Assessment Information

Examination (70%). Three pieces of coursework, 10% each (30%)

Reading and References

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.