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Learning Mechanisms in Networks of Spiking Neurons

Wu, Qingxiang, McGinnity, TM, Maguire, LP, Glackin, Brendan and Belatreche, Ammar (2007) Learning Mechanisms in Networks of Spiking Neurons. In: Studies in Computational Intelligence. (Eds: Chen, Ke and Wang, Lipo), Springer-Verlag, pp. 171-197. ISBN 1860-949X [Book section]

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URL: http://www.springerlink.com/content/n605v2m520478859/

DOI: 10.1007/978-3-540-36122-0_7

Abstract

In spiking neural networks, signals are transferred by action potentials. The information is encoded in the patterns of neuron activities or spikes. These features create significant differences between spiking neural networks and classical neural networks. Since spiking neural networks are based on spiking neuron models that are very close to the biological neuron model, many of the principles found in biological neuroscience can be used in the networks. In this chapter, a number of learning mechanisms for spiking neural networks are introduced. The learning mechanisms can be applied to explain the behaviours of networks in the brain, and also can be applied to artificial intelligent systems to process complex information represented by biological stimuli.

Item Type:Book section
Keywords:spiking neural networks, learning; spiking neuron models, spike timing-dependent plasticity, neuron encoding, co-ordinate transformation.
Faculties and Schools:Ulster Business School > Department of Management and Leadership
Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Ulster Business School
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Business and Management Research Institute
Computer Science Research Institute
ID Code:20648
Deposited By: Dr Qingxiang Wu
Deposited On:17 Jan 2012 15:33
Last Modified:09 Dec 2015 11:00

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