@article {699,
title = {Matrix representation of Spiking Neural P Systems},
journal = {Eleventh International Conference on Membrane Computing (CMC11)},
year = {2010},
month = {08/2010},
pages = {425-439},
publisher = {Verlag ProBusiness Berlin},
address = {Jena, Germany},
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 with extended rules and without delay 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 multiplication and addition of
matrices after deciding the spiking transition vector.},
isbn = {978-3-86805-721-8},
url = {http://cmc11.uni-jena.de/proceedings.html},
author = {XiangXiang Zeng and Henry Adorna and Miguel A. Mart{\'\i}nez-del-Amor and Linqiang Pan and Mario J. P{\'e}rez-Jim{\'e}nez},
editor = {Marian Gheorghe and Thomas Hinze and Gheorghe Paun}
}