<?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%">A first model for hebbian learning with spiking neural P systems</style></title><secondary-title><style face="normal" font="default" size="100%">6th Brainstorming Week on Membrane Computing</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Proceedings of the Sixth Brainstorming Week on Membrane Computing</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.gcn.us.es/6BWMC/volume/learning_br.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Fenix Editora</style></publisher><pub-location><style face="normal" font="default" size="100%">Sevilla, Spain</style></pub-location><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">211-233</style></pages><isbn><style face="normal" font="default" size="100%">978-84-612-44 </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 transmission 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>