Title :
A Learning Method for Synthesizing Spiking Neural Oscillators
Author :
Kuroe, Yasuaki ; Lima, Harlley
Author_Institution :
Kyoto Inst. of Technol., Kyoto
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;
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
DOI :
10.1109/IJCNN.2006.246885
