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.
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About the Author
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.
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Spiking Neural Networks Learning, Applications, and Analysis
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 132 pp. Englisch. Seller Inventory # 9783845405155
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: H. Amin HeshamHesham 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 Com. Seller Inventory # 5480667
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Spiking Neural Networks
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch. Seller Inventory # 9783845405155