Monash University Handbook 2011 Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

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