A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 34; H. 1; S. 394 - 408
Hauptverfasser:	Sakemi, Yusuke, Morino, Kai, Morie, Takashi, Aihara, Kazuyuki
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Coding Computational neuroscience Edge computing Encoding Energy efficiency Firing pattern Information processing Integrated circuits Large scale integration Learning Machine learning Manufacturing Manufacturing industry Mathematical models Microprocessors Multilayers Neural coding Neural networks Neurons Nonhomogeneous media Performance enhancement Recognition Robustness (mathematics) Spiking spiking neural network (SNN) Supervised learning Task analysis temporal coding Temporal variations Timing Very large scale integration very-large-scale integration (VLSI)
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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