<?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%">Henry Adorna</style></author><author><style face="normal" font="default" size="100%">Miguel A. Martínez-del-Amor</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><secondary-authors><author><style face="normal" font="default" size="100%">Marian Gheorghe</style></author><author><style face="normal" font="default" size="100%">Thomas Hinze</style></author><author><style face="normal" font="default" size="100%">Gheorghe Paun</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Matrix representation of Spiking Neural P Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Eleventh International Conference on Membrane Computing (CMC11)</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the Eleventh International Conference on Membrane Computing</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://cmc11.uni-jena.de/proceedings.html</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Verlag ProBusiness Berlin</style></publisher><pub-location><style face="normal" font="default" size="100%">Jena, Germany</style></pub-location><pages><style face="normal" font="default" size="100%">425-439</style></pages><isbn><style face="normal" font="default" size="100%">978-3-86805-721-8</style></isbn><abstract><style face="normal" font="default" size="100%">Spiking neural P systems (SN P systems, for short) are a
class of distributed parallel computing devices inspired from the way neurons
communicate by means of spikes. In this work, a discrete structure
representation of SN P systems with extended rules and without delay is
proposed. Specifically, matrices are used to represent SN P systems. In
order to represent the computations of SN P systems by matrices, configuration
vectors are defined to monitor the number of spikes in each
neuron at any given configuration; transition net gain vectors are also
introduced to quantify the total amount of spikes consumed and produced
after the chosen rules are applied. Nondeterminism of the systems
is assured by a set of spiking transition vectors that could be used at
any given time during the computation. With such matrix representation,
it is quite convenient to determine the next configuration from a
given configuration, since it involves only multiplication and addition of
matrices after deciding the spiking transition vector.</style></abstract></record></records></xml>