Postgraduate - UnitFIT5167 - Natural computation for intelligent systems

This unit entry is for students who completed this unit in 2013 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

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6 points, SCA Band 2, 0.125 EFTSL

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Level	Postgraduate
Faculty	Faculty of Information Technology
Offered	Caulfield First semester 2013 (Day)

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.

Outcomes

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.