Opened Access When Matrices Meet Brains
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Author: Zeng, Xiangxiang
Adorna, Henry N.
Martínez del Amor, Miguel Ángel
Pan, Linqiang
Department: Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial
Date: 2010
Published in: Proceedings of the Eighth Brainstorming Week on Membrane Computing, 311-320. Sevilla, E.T.S. de Ingeniería Informática, 1-5 de Febrero, 2010
ISBN/ISSN: 9788461423576
Document type: Presentation
Abstract: 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 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 vectors.
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