Lv Zeqiong, Bao Tingting, Zhou Nan, Peng Hong, Huang Xiangnian, Riscos-Núñez Agustín, Pérez-Jiménez Mario J
School of Computer and Software Enginering, Xihua University, Chengdu, 610039, P. R. China.
Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, 41012, Spain.
Int J Neural Syst. 2021 Jan;31(1):2050049. doi: 10.1142/S0129065720500495. Epub 2020 Aug 18.
This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP-ECR systems). SNP-ECR systems are a class of distributed parallel computing models. In SNP-ECR systems, a new type of spiking rule is introduced, called ECR. With an ECR, a neuron can send the different numbers of spikes to its subsequent neurons. Therefore, SNP-ECR systems can provide a stronger firing control mechanism compared with SNP systems and the variant with multiple channels. We discuss the Turing universality of SNP-ECR systems. It is proven that SNP-ECR systems as number generating/accepting devices are Turing universal. Moreover, we provide a small universal SNP-ECR system as function computing devices.
本文讨论了脉冲神经P系统(简称为SNP系统)的一种新变体,即具有扩展通道规则的脉冲神经P系统(简称为SNP-ECR系统)。SNP-ECR系统是一类分布式并行计算模型。在SNP-ECR系统中,引入了一种新型的脉冲规则,称为ECR。通过ECR,一个神经元可以向其后续神经元发送不同数量的脉冲。因此,与SNP系统和具有多个通道的变体相比,SNP-ECR系统可以提供更强的激发控制机制。我们讨论了SNP-ECR系统的图灵通用性。结果证明,作为数字生成/接受装置的SNP-ECR系统是图灵通用的。此外,我们提供了一个小型通用SNP-ECR系统作为函数计算装置。