<?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%">Francis G. C. Cabarle</style></author><author><style face="normal" font="default" size="100%">Henry N. Adorna</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%">Sequential spiking neural P systems with structural plasticity based on max/min spike number</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Computing and Applications</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Sequential systems</style></keyword><keyword><style  face="normal" font="default" size="100%">spiking neural P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Structural plasticity</style></keyword><keyword><style  face="normal" font="default" size="100%">turing universality</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/article/10.1007/s00521-015-1937-5</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%">Amsterdam (The Netherlands)</style></pub-location><pages><style face="normal" font="default" size="100%">1-11</style></pages><abstract><style face="normal" font="default" size="100%">Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic computing devices inspired by biological spiking neurons. Recently, a class of SNP systems known as SNP systems with structural plasticity (in short, SNPSP systems) was introduced. SNPSP systems represent a class of SNP systems that have dynamism applied to the synapses, i.e. neurons can use plasticity rules to create or remove synapses. In this work, we impose the restriction of sequentiality on SNPSP systems, using four modes: max, min, max-pseudo-, and min-pseudo-sequentiality. We also impose a normal form for SNPSP systems as number acceptors and generators. Conditions for (non)universality are then provided. Specifically, acceptors are universal in all modes, while generators need a nondeterminism source in two modes, which in this work is provided by the plasticity rules</style></abstract><custom1><style face="normal" font="default" size="100%">1.569</style></custom1><custom2><style face="normal" font="default" size="100%">53/123 - Q2</style></custom2></record></records></xml>