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dc.creatorZeng, Xiangxianges
dc.creatorAdorna, Henry
dc.creatorMartínez del Amor, Miguel Ángeles
dc.creatorPan, Linqianges
dc.description.abstractSpiking 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 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 multiplying vectors to a matrix and adding
dc.publisherFénix Editoraes
dc.relation.ispartofProceedings of the Eighth Brainstorming Week on Membrane Computing, 311-320. Sevilla, E.T.S. de Ingeniería Informática, 1-5 de Febrero, 2010es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.titleWhen Matrices Meet Brainses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
dc.contributor.groupUniversidad de Sevilla. TIC193: Computación Natural

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional