title: New approximation method for smooth error backpropagation in a quantron network.
abstract: In this work, we propose a new approximation method to perform error backpropagation in a quantron network while avoiding the silent neuron problem that usually affects networks of realistic neurons. In our experiments, we train quantron networks to solve the XOR problem and other nonlinear classification problems. We achieve this while using less parameters than the number necessary to solve the same problems with networks of perceptrons or spiking neurons.
authors.affiliation: Department of Mathematics and Industrial Engineering, Polytechnique Montreal, 2900 boul. Édouard-Montpetit, Campus de l'Université de Montréal, 2500 Chemin de Polytechnique, Montreal, Quebec, H3T 1J4, Canada. Electronic address: simon.de-montigny@polymtl.ca.
authors.forename: Simon
authors.initials: S
authors.lastname: de Montigny
mesh_terms: D000465:Algorithms; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*
keywords: Backpropagation; Classification; Quantrons; Smooth approximation; Spiking neurons