A Learning Method for Synthesizing Spiking Neural Oscillators

Author

Kuroe, Yasuaki ; Lima, Harlley

Author_Institution

Kyoto Inst. of Technol., Kyoto

fYear

0

fDate

0-0 0

Firstpage

3882

Lastpage

3886

Abstract

In the biological systems there are numerous examples of autonomously generated periodic activities. Several different periodic patterns are generated simultaneously in a living body. It is known that in biological systems there are specific neurons which generate such periodic patterns. In spiking neural networks the information processing is carried out by spike trains in a manner similar to the generic biological neurons. This paper presents a method for synthesis of neural oscillators by spiking neural networks. We propose a learning method for synthesizing spiking neural networks which generate desired periodic spike trains with specified spike emission times. A method of stability analysis of the generated periodic spike trains is also discussed.

Keywords

neural nets; oscillators; stability; autonomously generated periodic activities; biological systems; periodic spike trains; spiking neural networks; stability analysis; synthesizing spiking neural oscillators; Artificial neural networks; Biological information theory; Biological neural networks; Biological systems; Filters; Learning systems; Network synthesis; Neural networks; Neurons; Oscillators;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2006. IJCNN '06. International Joint Conference on

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-9490-9

Type

conf

DOI

10.1109/IJCNN.2006.246885

Filename

1716633