Basic Characteristics and Learning Potential of a Digital Spiking Neuron(Neuron and Neural Networks,<Special Section>Nonlinear Theory and its Applications)

スポンサーリンク

概要

• 論文の詳細を見る

• The digital spiking neuron (DSN) consists of digital state cells and behaves like a simplified neuron model. By adjusting wirings among the cells, the DSN can generate spike-trains with various characteristics. In this paper we present a theorem that clarifies basic relations between change of wirings and change of characteristics of the spike-train. Also, in order to explore learning potential of the DSN, we propose a learning algorithm for generating spike-trains that are suited to an application example. We then show significances and basic roles of the presented theorem in the learning dynamics.

• 2007-10-01

著者

• Torikai Hiroyuki

  Graduate School Of Engineering Science Osaka University

• TORIKAI Hiroyuki

  Graduate School of Engineering Science, Osaka University

関連論文

• Basic Characteristics and Learning Potential of a Digital Spiking Neuron(Neuron and Neural Networks,Nonlinear Theory and its Applications)
• Response of a Chaotic Spiking Neuron to Various Periodic Inputs and Its Potential Applications
• A Discrete-State Spiking Neuron Model and its Learning Potential (Expansion of Integrable Systems)
• Bifurcation-based synthesis of asynchronous cellular automaton based neuron

スポンサーリンク