Matrix representation of Spiking Neural P systems

TitleMatrix representation of Spiking Neural P systems
Publication TypeJournal Papers
Year of Publication2011
AuthorsZeng, X. X., Adorna H., Martínez-del-Amor M. A., Pan L., & Pérez-Jiménez M. J.
Journal TitleLecture Notes in Computer Science
ISBN Number978-84-9887-518-8
Place PublishedAmsterdam, The Netherlands

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.

ISSN Number0302-9743