Rowcliffe, P., Feng, Jianfeng and Buxton, H. (2006) Spiking perceptrons. IEEE Transactions on Neural Networks, Vol.17 (No.3). pp. 803-807. ISSN 1045-9227Full text not available from this repository.
Official URL: http://dx.doi.org/10.1109/TNN.2006.873274
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|
|Official Date:||May 2006|
|Number of Pages:||5|
|Page Range:||pp. 803-807|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Restricted or Subscription Access|
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