- spiking neural networks
- spiking neuron models
- spike timing-dependent plasticity
- neuron encoding
- co-ordinate transformation.
Learning Mechanisms in Networks of Spiking Neurons. / Wu, Qingxiang; McGinnity, TM; Maguire, LP; Glackin, Brendan; Belatreche, Ammar.Studies in Computational Intelligence. ed. / Ke Chen; Lipo Wang. Vol. 35 Springer, 2007. p. 171-197.
Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
TY - CHAP
T1 - Learning Mechanisms in Networks of Spiking Neurons
AU - Wu, Qingxiang
AU - McGinnity, TM
AU - Maguire, LP
AU - Glackin, Brendan
AU - Belatreche, Ammar
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PY - 2007/1/1
Y1 - 2007/1/1
N2 - 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.
AB - 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.
KW - spiking neural networks
KW - learning
KW - spiking neuron models
KW - spike timing-dependent plasticity
KW - neuron encoding
KW - co-ordinate transformation.
UR - http://www.springerlink.com/content/n605v2m520478859/
UR - http://www.springerlink.com/content/n605v2m520478859/
U2 - 10.1007/978-3-540-36122-0_7
DO - 10.1007/978-3-540-36122-0_7
M3 - Chapter
SN - 1860-949X
VL - 35
SP - 171
EP - 197
BT - Studies in Computational Intelligence
A2 - Chen, Ke
A2 - Wang, Lipo
PB - Springer