Spiking Neural P Systems with Polarizations and Rules on Synapses
Suxia Jiang,
Jihui Fan,
Yijun Liu,
Yanfeng Wang,
Fei Xu
Affiliations
Suxia Jiang
Henan Key Lab of Information Based Electrical Appliances, School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Jihui Fan
Henan Key Lab of Information Based Electrical Appliances, School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Yijun Liu
Henan Key Lab of Information Based Electrical Appliances, School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Yanfeng Wang
Henan Key Lab of Information Based Electrical Appliances, School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, China
Fei Xu
Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China
Spiking neural P systems are a class of computation models inspired by the biological neural systems, where spikes and spiking rules are in neurons. In this work, we propose a variant of spiking neural P systems, called spiking neural P systems with polarizations and rules on synapses (PSNRS P systems), where spiking rules are placed on synapses and neurons are associated with polarizations used to control the application of such spiking rules. The computation power of PSNRS P systems is investigated. It is proven that PSNRS P systems are Turing universal, both as number generating and accepting devices. Furthermore, a universal PSNRS P system with 151 neurons for computing any Turing computable functions is given. Compared with the case of SN P systems with polarizations but without spiking rules in neurons, less number of neurons are used to construct a universal PSNRS P system.
