@article {727,
	title = {Matrix representation of Spiking Neural P systems},
	journal = {Lecture Notes in Computer Science},
	volume = {6501},
	year = {2011},
	pages = {377-392},
	publisher = {Springer},
	address = {Amsterdam, The Netherlands},
	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 = {978-84-9887-518-8},
	issn = {0302-9743},
	doi = {10.1007/978-3-642-18123-8_29},
	url = {http://link.springer.com/chapter/10.1007/978-3-642-18123-8_29},
	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}
}
