Basic Characteristics and Learning Potential of a Digital Spiking Neuron

Hiroyuki TORIKAI

doi:10.1093/ietfec/e90-a.10.2093

Basic Characteristics and Learning Potential of a Digital Spiking Neuron

Hiroyuki TORIKAI

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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.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E90-A No.10 pp.2093-2100

Publication Date: 2007/10/01

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Online ISSN: 1745-1337

DOI: 10.1093/ietfec/e90-a.10.2093

Type of Manuscript: Special Section PAPER (Special Section on Nonlinear Theory and its Applications)

Category: Neuron and Neural Networks

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Hiroyuki TORIKAI, "Basic Characteristics and Learning Potential of a Digital Spiking Neuron" in IEICE TRANSACTIONS on Fundamentals, vol. E90-A, no. 10, pp. 2093-2100, October 2007, doi: 10.1093/ietfec/e90-a.10.2093.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e90-a.10.2093/_p

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@ARTICLE{e90-a_10_2093,
author={Hiroyuki TORIKAI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Basic Characteristics and Learning Potential of a Digital Spiking Neuron},
year={2007},
volume={E90-A},
number={10},
pages={2093-2100},
abstract={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.},
keywords={},
doi={10.1093/ietfec/e90-a.10.2093},
ISSN={1745-1337},
month={October},}

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TY - JOUR
TI - Basic Characteristics and Learning Potential of a Digital Spiking Neuron
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2093
EP - 2100
AU - Hiroyuki TORIKAI
PY - 2007
DO - 10.1093/ietfec/e90-a.10.2093
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E90-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2007
AB - 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.
ER -