title: Digital spiking neuron and its learning for approximation of various spike-trains.
abstract: A digital spiking neuron is a wired system of shift registers and can generate various spike-trains by adjusting the wiring pattern. In this paper we analyze the basic relations between the wiring pattern and characteristics of the spike-train. Based on the relations, we present a learning algorithm which utilizes successive changes of the wiring pattern. It is shown that the neuron can reproduce spike-trains of another neuron which has an unknown wiring pattern. It is also shown that the neuron can approximate various spike-trains of a chaotic analog spiking neuron.
authors.affiliation: Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan. torikai@sys.es.osaka-u.ac.jp
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Torikai
authors.forename: Atsuo
authors.initials: A
authors.lastname: Funew
authors.forename: Toshimichi
authors.initials: T
authors.lastname: Saito
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000703:Analog-Digital Conversion; D000818:Animals; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*