%0 Generic
%D 2011
%T Matrix representation of Spiking Neural P systems
%A XiangXiang Zeng
%A Henry Adorna
%A Miguel A. Martínez-del-Amor
%A Linqiang Pan
%A Mario J. Pérez-Jiménez
%C Amsterdam, The Netherlands
%I Springer
%P 377-392
%R 10.1007/978-3-642-18123-8_29
%U http://link.springer.com/chapter/10.1007/978-3-642-18123-8_29
%V 6501
%X 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.
%@ 978-84-9887-518-8