Matrix Representation of Spiking Neural P Systems
Adorna, Henry N.
Martínez del Amor, Miguel Ángel
Pérez Jiménez, Mario de Jesús
|Department||Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial|
|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 ...
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.
|Funding agencies||Ministerio de Ciencia e Innovación (MICIN). España
Junta de Andalucía
|Citation||Zeng, X., Adorna, H.N., Martínez del Amor, M.Á., Pan, L. y Pérez Jiménez, M.d.J. (2011). Matrix Representation of Spiking Neural P Systems. En CMC 2010: 11th International Conference on Membrane Computing (377-392), Jena, Germany: Springer.|