<?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%">Linqiang Pan</style></author><author><style face="normal" font="default" size="100%">Gheorghe Paun</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%">Spiking neural P systems with neuron division and budding</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%">Membrane computing</style></keyword><keyword><style  face="normal" font="default" size="100%">Natural computing</style></keyword><keyword><style  face="normal" font="default" size="100%">neuron division</style></keyword><keyword><style  face="normal" font="default" size="100%">Spiking neural P system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/h583132743t72273/</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">54</style></edition><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%">8</style></volume><pages><style face="normal" font="default" size="100%">1596-1607</style></pages><abstract><style face="normal" font="default" size="100%">Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons. In this work, the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems, which are processes inspired by neural stem cell division. With neuron division and neuron budding, a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes. In this way, spiking neural P systems can efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with an efficient solution to SAT problem. </style></abstract></record></records></xml>