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Rowcliffe, P., Feng, Jianfeng and Buxton, H. (2006) Spiking perceptrons. IEEE Transactions on Neural Networks, Vol.17 (No.3). pp. 803-807. doi:10.1109/TNN.2006.873274
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Official URL: http://dx.doi.org/10.1109/TNN.2006.873274
Abstract
A more plausible biological version of the traditional perceptron is presented here with a learning rule which enables training of the neuron on nonlinear tasks. Three different models are introduced with varying inhibitory and excitatory, synaptic connections. Using the derived learning rule. a single neuron is trained to successfully classify the XOR problem.
Item Type: | Journal Item | ||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Centre for Scientific Computing | ||||
Journal or Publication Title: | IEEE Transactions on Neural Networks | ||||
Publisher: | IEEE | ||||
ISSN: | 1045-9227 | ||||
Official Date: | May 2006 | ||||
Dates: |
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Volume: | Vol.17 | ||||
Number: | No.3 | ||||
Number of Pages: | 5 | ||||
Page Range: | pp. 803-807 | ||||
DOI: | 10.1109/TNN.2006.873274 | ||||
Status: | Not Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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