title: Spiking perceptrons.
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.
authors.forename: Phill
authors.initials: P
authors.lastname: Rowcliffe
authors.forename: Jianfeng
authors.initials: J
authors.lastname: Feng
authors.forename: Hilary
authors.initials: H
authors.lastname: Buxton
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*