Matrix representation of spiking neural P systems

Authors:
Xiangxiang Zeng;Henry Adorna;Miguel Ángel Martínez-del-Amor;Linqiang Pan;Mario J. Pérez-Jiménez
Affiliations:
Image Processing and Intelligent Control Key Laboratory of Education Ministry, Department of Control Science and Engineering, Huazhong University of Science and Technology, Hubei, China;Department of Computer Science, University of the Philippines, Quezon City, Philippines;Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Spain;Image Processing and Intelligent Control Key Laboratory of Education Ministry, Department of Control Science and Engineering, Huazhong University of Science and Technology, Hubei, China;Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Spain
Venue:
CMC'10 Proceedings of the 11th international conference on Membrane computing
Year:
2010

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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.