<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">XiangXiang Zeng</style></author><author><style face="normal" font="default" size="100%">Linqiang Pan</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Small universal simple Spiking Neural P Systems with weights</style></title><secondary-title><style face="normal" font="default" size="100%">Science China. Information Sciences</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bio-inspired computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">P system</style></keyword><keyword><style  face="normal" font="default" size="100%">Spiking neural P system</style></keyword><keyword><style  face="normal" font="default" size="100%">universal computing device</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/article/10.1007%2Fs11432-013-4848-z</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">1-11</style></pages><abstract><style face="normal" font="default" size="100%">Spiking neural P systems with weights (WSN P systems, for short) are a new variant of spiking neural P systems, where the rules of a neuron are enabled when the potential of that neuron equals a given value. It is known that WSN P systems are universal by simulating register machines. However, in these universal systems, no bound is considered on the number of neurons and rules. In this work, a restricted variant of WSN P systems is considered, called simple WSN P systems, where each neuron has only one rule. The complexity parameter, the number of neurons, to construct a universal simple WSN P system is investigated. It is proved that there is a universal simple WSN P system with 48 neurons for computing functions; as generator of sets of numbers, there is an almost simple (that is, each neuron has only one rule except that one neuron has two rules) and universal WSN P system with 45 neurons.</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><custom1><style face="normal" font="default" size="100%">0.702</style></custom1><custom2><style face="normal" font="default" size="100%">95/135 - Q3</style></custom2></record></records></xml>