Spiking neural P systems with inhibitory rules

被引:104
作者
Peng, Hong [1 ]
Li, Bo [1 ]
Wang, Jun [2 ]
Song, Xiaoxiao [2 ]
Wang, Tao [2 ]
Valencia-Cabrera, Luis [3 ]
Perez-Hurtado, Ignacio [3 ]
Riscos-Nunez, Agustin [3 ]
Perez-Jimenez, Mario J. [3 ]
机构
[1] Xihua Univ, Sch Comp & Software Engn, Chengdu 610039, Peoples R China
[2] Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu 610039, Peoples R China
[3] Univ Seville, Dept Comp Sci & Artificial Intelligence, Res Grp Nat Comp, E-41012 Seville, Spain
基金
中国国家自然科学基金;
关键词
Membrane computing; Spiking neural P systems; Spiking neural P systems with inhibitory rules; Inhibitory synapse; MEMBRANE CONTROLLERS; FAULT-DIAGNOSIS; POWER; ALGORITHM; IMAGES;
D O I
10.1016/j.knosys.2019.105064
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from the usual firing rules in SNP systems, the firing condition of an inhibitory rule not only depends on the state of the neuron associated with the rule but also is related to the states of other neurons. Moreover, from the perspective of topological structure, the new variant is shown as a directed graph with inhibitory arcs, and therefore seems to have more powerful control. The computational completeness of SNP-IR systems is discussed. In particular, it is proved that SNP-IR systems are Turing universal number accepting/generating devices. Moreover, we obtain a small universal function-computing device for SNP-IR systems consisting of 100 neurons. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页数:10
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