Department of Computer Science, Xiamen Unviersity, Xiamen 361005, Fujian, China.
IEEE Trans Nanobioscience. 2012 Dec;11(4):366-74. doi: 10.1109/TNB.2012.2211034. Epub 2012 Aug 6.
Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bio-inspired computing devices-spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information is encoded as the time interval between spikes). In this work, four SN P systems are constructed as adder, subtracter, multiplier, and divider, respectively. In these systems, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes.
最近,Gutiérrez-Naranjo 和 Leporati 考虑在一类新的仿生计算设备——尖峰神经网络脉冲系统(简称 SNP 系统)上进行基本的算术运算。然而,他们研究中使用的二进制编码机制看起来更像是电子电路的编码方法,而不是尖峰神经元的编码方式(在通常的 SNP 系统中,信息被编码为两个尖峰之间的时间间隔)。在这项工作中,分别构建了四个 SNP 系统作为加法器、减法器、乘法器和除法器。在这些系统中,输入到系统的一个数字是两个输入神经元接收到的尖峰之间经过的时间间隔,计算结果是输出神经元尖峰时刻之间的时间间隔。