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Title: Multiple self-organised spiking neural networks
Author: Amin, Muhamad Kamal M.
ISNI:       0000 0004 2672 4998
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2009
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This thesis presents a Multiple Self-Organised Spiking Neural Networks (MSOSNN). The aim of this architecture is to achieve a more biologically plausible artificial neural network. Spiking neurons with delays are proposed to encode the information and perform computations. The proposed method is further implemented to enable unsupervised competitive and self-organising learning. The method is evaluated by application to real world datasets. Computer simulation results show that the proposed method is able to function similarly to conventional neural networks i.e. the Kohonen Self-Organising Maps. The SOSNN are further combined to form multiple networks of the Self-Organised Spiking Neural Networks. This network architecture is structured into n component modules with each module providing a solution to the sub-task and then combined with other modules to solve the main task. The training is made in such a way that a module becomes a winner at each step of the learning phase. The evaluation using different data sets as well as comparing the network to a single unity network showed that the proposed architecture is very useful for high dimensional input vectors. The Multiple SOSNN architecture thus provides a guideline for a complex large-scale network solution.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Neural networks (Computer science) ; Engineering