Matrix representation of Spiking Neural P Systems

TitleMatrix representation of Spiking Neural P Systems
Publication TypeConference Contributions
Year of Publication2010
AuthorsZeng, X. X., Adorna H., Martínez-del-Amor M. A., Pan L., & Pérez-Jiménez M. J.
EditorsGheorghe, M., Hinze T., & Paun G.
Conference NameEleventh International Conference on Membrane Computing (CMC11)
Volume TitleProceedings of the Eleventh International Conference on Membrane Computing
ISBN Number978-3-86805-721-8
PublisherVerlag ProBusiness Berlin
Place PublishedJena, Germany
Date Published08/2010

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.