Spiking neurons in 3D growing self-organising maps

In Kohonen’s Self-Organising Maps (SOM) learning, preserving the map topology to simulate the actual input features appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the S...

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Main Author:	Yusob, Bariah
Format:	Thesis
Language:	English
Published:	2018
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/98241/1/BariahYusobPSC2018.pdf
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id	my-utm-ep.98241
record_format	uketd_dc
spelling	my-utm-ep.982412022-11-23T08:10:07Z Spiking neurons in 3D growing self-organising maps 2018 Yusob, Bariah QA75 Electronic computers. Computer science In Kohonen’s Self-Organising Maps (SOM) learning, preserving the map topology to simulate the actual input features appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. Nonetheless, it is a challenging task as most of the real problems are composed of complex and insufficient data. Spiking Neural Network (SNN) is the third generation of Artificial Neural Network (ANN), in which information can be transferred from one neuron to another using spike, processed, and trigger response as output. This study, hence, embedded spiking neurons for SOM learning in order to enhance the learning process. The proposed method was divided into five main phases. Phase 1 investigated issues related to SOM learning algorithm, while in Phase 2; datasets were collected for analyses carried out in Phase 3, wherein neural coding scheme for data representation process was implemented in the classification task. Next, in Phase 4, the spiking SOM model was designed, developed, and evaluated using classification accuracy rate and quantisation error. The outcomes showed that the proposed model had successfully attained exceptional classification accuracy rate with low quantisation error to preserve the quality of the generated map based on original input data. Lastly, in the final phase, a Spiking 3D Growing SOM is proposed to address the surface reconstruction issue by enhancing the spiking SOM using 3D map structure in SOM algorithm with a growing grid mechanism. The application of spiking neurons to enhance the performance of SOM is relevant in this study due to its ability to spike and to send a reaction when special features are identified based on its learning of the presented datasets. The study outcomes contribute to the enhancement of SOM in learning the patterns of the datasets, as well as in proposing a better tool for data analysis. 2018 Thesis http://eprints.utm.my/id/eprint/98241/ http://eprints.utm.my/id/eprint/98241/1/BariahYusobPSC2018.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:141942 phd doctoral Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing Faculty of Engineering - School of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Yusob, Bariah Spiking neurons in 3D growing self-organising maps
description	In Kohonen’s Self-Organising Maps (SOM) learning, preserving the map topology to simulate the actual input features appears to be a significant process. Misinterpretation of the training samples can lead to failure in identifying the important features that may affect the outcomes generated by the SOM model. Nonetheless, it is a challenging task as most of the real problems are composed of complex and insufficient data. Spiking Neural Network (SNN) is the third generation of Artificial Neural Network (ANN), in which information can be transferred from one neuron to another using spike, processed, and trigger response as output. This study, hence, embedded spiking neurons for SOM learning in order to enhance the learning process. The proposed method was divided into five main phases. Phase 1 investigated issues related to SOM learning algorithm, while in Phase 2; datasets were collected for analyses carried out in Phase 3, wherein neural coding scheme for data representation process was implemented in the classification task. Next, in Phase 4, the spiking SOM model was designed, developed, and evaluated using classification accuracy rate and quantisation error. The outcomes showed that the proposed model had successfully attained exceptional classification accuracy rate with low quantisation error to preserve the quality of the generated map based on original input data. Lastly, in the final phase, a Spiking 3D Growing SOM is proposed to address the surface reconstruction issue by enhancing the spiking SOM using 3D map structure in SOM algorithm with a growing grid mechanism. The application of spiking neurons to enhance the performance of SOM is relevant in this study due to its ability to spike and to send a reaction when special features are identified based on its learning of the presented datasets. The study outcomes contribute to the enhancement of SOM in learning the patterns of the datasets, as well as in proposing a better tool for data analysis.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Yusob, Bariah
author_facet	Yusob, Bariah
author_sort	Yusob, Bariah
title	Spiking neurons in 3D growing self-organising maps
title_short	Spiking neurons in 3D growing self-organising maps
title_full	Spiking neurons in 3D growing self-organising maps
title_fullStr	Spiking neurons in 3D growing self-organising maps
title_full_unstemmed	Spiking neurons in 3D growing self-organising maps
title_sort	spiking neurons in 3d growing self-organising maps
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Computing
granting_department	Faculty of Engineering - School of Computing
publishDate	2018
url	http://eprints.utm.my/id/eprint/98241/1/BariahYusobPSC2018.pdf
_version_	1776100564058243072

Spiking neurons in 3D growing self-organising maps

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