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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. ISSN 1045-9227
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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 |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Science > Centre for Scientific Computing |
| Journal or Publication Title: | IEEE Transactions on Neural Networks |
| Publisher: | IEEE |
| ISSN: | 1045-9227 |
| Date: | May 2006 |
| Volume: | Vol.17 |
| Number: | No.3 |
| Number of Pages: | 5 |
| Page Range: | pp. 803-807 |
| Identification Number: | 10.1109/TNN.2006.873274 |
| Status: | Not Peer Reviewed |
| Publication Status: | Published |
| Access rights to Published version: | Restricted or Subscription Access |
| URI: | http://wrap.warwick.ac.uk/id/eprint/33558 |
Data sourced from Thomson Reuters' Web of Knowledge
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