Spiking neural P systems with multiple channels

Hong Peng; Jinyu Yang; Jun Wang; Tao Wang; Zhang Sun; Xiaoxiao Song; Xiaohui Luo; Xiangnian Huang

doi:10.1016/j.neunet.2017.08.003

Spiking neural P systems with multiple channels

Neural Netw. 2017 Nov:95:66-71. doi: 10.1016/j.neunet.2017.08.003. Epub 2017 Aug 24.

Authors

Hong Peng¹, Jinyu Yang², Jun Wang³, Tao Wang⁴, Zhang Sun⁴, Xiaoxiao Song⁴, Xiaohui Luo², Xiangnian Huang²

Affiliations

¹ School of Computer and Software Engineering, Xihua University, Chengdu 610039, China. Electronic address: ph@mail.xhu.edu.cn.
² School of Computer and Software Engineering, Xihua University, Chengdu 610039, China.
³ School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China. Electronic address: wangjun@mail.xhu.edu.cn.
⁴ School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, China.

PMID: 28892672
DOI: 10.1016/j.neunet.2017.08.003

Abstract

Spiking neural P systems (SNP systems, in short) are a class of distributed parallel computing systems inspired from the neurophysiological behavior of biological spiking neurons. In this paper, we investigate a new variant of SNP systems in which each neuron has one or more synaptic channels, called spiking neural P systems with multiple channels (SNP-MC systems, in short). The spiking rules with channel label are introduced to handle the firing mechanism of neurons, where the channel labels indicate synaptic channels of transmitting the generated spikes. The computation power of SNP-MC systems is investigated. Specifically, we prove that SNP-MC systems are Turing universal as both number generating and number accepting devices.

Keywords: Membrane computing; Multiple channels; Spiking neural P systems; Universality.

MeSH terms

Computer Communication Networks
Neural Networks, Computer*