Monash University Handbook 2010 Postgraduate - Unit

Synopsis

This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.

Objectives

At the completion of this unit students will:

• understand basic computational principles underlying the operations of biological neural systems;
• have knowledge of computational methods of simulating biological and artificial neural systems;
• have knowledge of supervised, unsupervised and self-organising neuronal learning systems;
• be able to use computer software to simulate behaviour of neurons and neural networks.

Assessment

Examination (3 hours): 60%; In-semester assessment: 40%

Chief examiner(s)

Dr Grace Rumantir

Contact hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Prohibitions

CSE5301

Additional information on this unit is available from the faculty at:

http://www.infotech.monash.edu.au/units/fit5167