Learning Mechanisms in Networks of Spiking Neurons

Qingxiang Wu, TM McGinnity, LP Maguire, Brendan Glackin, Ammar Belatreche

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

18 Citations (Scopus)
128 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationStudies in Computational Intelligence
EditorsKe Chen, Lipo Wang
ISBN (Print)1860-949X
Publication statusPublished (in print/issue) - 1 Jan 2007

Bibliographical note

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  • spiking neural networks
  • learning
  • spiking neuron models
  • spike timing-dependent plasticity
  • neuron encoding
  • co-ordinate transformation.


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