<?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%">Miguel A. Gutiérrez-Naranjo</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%">Hebbian learning from spiking neural P systems view</style></title><secondary-title><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/d548306632p77n64/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin / Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Heidelberg, Alemania</style></pub-location><volume><style face="normal" font="default" size="100%">5391</style></volume><pages><style face="normal" font="default" size="100%">217-230</style></pages><isbn><style face="normal" font="default" size="100%">978-3-540-95884-0</style></isbn><abstract><style face="normal" font="default" size="100%">Spiking neural P systems and artificial neural networks are computational devices which share a biological inspiration based on the flow of information among neurons. In this paper we present a first model for Hebbian learning in the framework of spiking neural P systems by using concepts borrowed from neuroscience and artificial neural network theory. </style></abstract></record></records></xml>