Torikai Hiroyuki, Funew Atsuo, Saito Toshimichi
Department of Systems Innovation, Graduate School of Engineering Science, Osaka University, Osaka 560-8531, Japan.
Neural Netw. 2008 Mar-Apr;21(2-3):140-9. doi: 10.1016/j.neunet.2007.12.045. Epub 2008 Jan 5.
A digital spiking neuron is a wired system of shift registers and can generate various spike-trains by adjusting the wiring pattern. In this paper we analyze the basic relations between the wiring pattern and characteristics of the spike-train. Based on the relations, we present a learning algorithm which utilizes successive changes of the wiring pattern. It is shown that the neuron can reproduce spike-trains of another neuron which has an unknown wiring pattern. It is also shown that the neuron can approximate various spike-trains of a chaotic analog spiking neuron.
数字脉冲神经元是一种由移位寄存器组成的有线系统，通过调整布线模式可以生成各种脉冲序列。在本文中，我们分析了布线模式与脉冲序列特征之间的基本关系。基于这些关系，我们提出了一种利用布线模式连续变化的学习算法。结果表明，该神经元可以重现具有未知布线模式的另一个神经元的脉冲序列。还表明，该神经元可以逼近混沌模拟脉冲神经元的各种脉冲序列。
