Introductory description

This module provides an introduction to the theory and implementation of neural networks and an understanding of the important computational neural network architecture and methodology. It aims to give students sufficient knowledge to enable employment or postgraduate study involving neural networks.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

-Introduction: history of neural computing; relationship to Artificial Intelligence.
-Neurons: structure and behaviour of biological neurons; simple models of neurons; nonlinear and
dynamical models.
-Networks of Neurons: how neuronal networks are arranged in the brain; common architectures
for artificial networks.
Coding and Representation: how information is represented in neural networks; place coding; distributed representations.
-Learning and Memory: plasticity in biological neurons; theories of memory; learning in artificial
networks.
-Vision: structure of the human visual system; function of the retina, LGN and cortical processing;
artificial network models for vision.

Learning outcomes

By the end of the module, students should be able to:

• Students completing the module should be able to demonstrate: an understanding of the principles of Neural Networks and a knowledge of their main areas of application;
• -the ability to design, implement and analyse the behaviour of simple neural networks.

Indicative reading list

Haykin S, Neural Networks: a Comprehensive Foundation, Macmillan, 2009.
Schalkoff R J, Artificial Neural Networks, New York, McGraw-Hill, 1997.

Subject specific skills

tbc

Transferable skills

tbc