A Synthesis Method of Spiking Neural Oscillators with Considering Asymptotic Stability

In artificial Spiking Neural Networks (SNNs) the information processing and transmission are carried out by spike trains in a manner similar to the generic biological neurons. Recently it has been reported that they are computationally more powerful than the conventional neural networks. In 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. 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. We also propose a method for making the periodic trajectory generated by the synthesized spiking neural oscillator asymptotically stable.