Spiking Neural Networks: Learning, Applications, and Analysis - Couverture souple

H. Amin, Hesham

 
9783845405155: Spiking Neural Networks: Learning, Applications, and Analysis
Couverture souple
ISBN 10 : 3845405155 ISBN 13 : 9783845405155
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

In this book, focus has been placed on SpikingNeural Networks (SNNs). Research on artificial SNNs has gained momentum in the last decadedue to its ability to emulate biological neural network signals and its enhanced computational capabilities. Input data arrives into a SNN as temporal data instead of values within a time window (rate code). The input data into such a neural network arrives in the shape of sequence of pulses or spikes in time, which called spike train patterns. Thus, there is a need for a pre-processing method, a learning algorithm, and deep analysis for a practical model is a highly postulated matter. Emphasis has been placed on finding a robust and practical SNNlearning algorithm and as well as an analysis of how various parameters affect the algorithm's behavior. A special pre-processingstage (the mapping stage) was used to convert spike train pattern inputs into spatio-temporal outputs. Another main point of this research was to achieve a learning organization that can be practically implemented. Hence, learningschemes have been developed in a way that avoids complex or costly designs.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

In this book, focus has been placed on SpikingNeural Networks (SNNs). Research on artificial SNNs has gained momentum in the last decadedue to its ability to emulate biological neural network signals and its enhanced computational capabilities. Input data arrives into a SNN as temporal data instead of values within a time window (rate code). The input data into such a neural network arrives in the shape of sequence of pulses or spikes in time, which called spike train patterns. Thus, there is a need for a pre-processing method, a learning algorithm, and deep analysis for a practical model is a highly postulated matter. Emphasis has been placed on finding a robust and practical SNNlearning algorithm and as well as an analysis of how various parameters affect the algorithm's behavior. A special pre-processingstage (the mapping stage) was used to convert spike train pattern inputs into spatio-temporal outputs. Another main point of this research was to achieve a learning organization that can be practically implemented. Hence, learningschemes have been developed in a way that avoids complex or costly designs.

Biographie de l'auteur

Hesham H. Amin is an Assist. professor at South Valley Univ., Egypt (Currently at Umm Alqura Univ., KSA). He received B.E. in Electrical Engineering (1997) from Assuit Univ., Egypt. His M.E. (2002) and PhD (2005) in Computer Science and Engineering from Aizu Univ., Japan. He is interested in neural networks, pattern recognition, computer vision.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3845405155
ISBN 13 
9783845405155
Reliure
Broché
Nombre de pages
132
Coordonnées du fabricant
non disponible
Personne responsable
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