Matrix Representation of Spiking Neural P Systems
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.
KeywordsMatrix Representation Regular Expression Output Neuron Total Order Algebraic Representation
Unable to display preview. Download preview PDF.
- 4.Ionescu, M., Păun, G., Yokomori, T.: Spiking Neural P Systems with Exhaustive Use of Rules. International Journal of Unconventional Computing 3, 135–154 (2007)Google Scholar
- 5.Nelson, J.K., McCormac, J.C.: Structural Analysis: Using Classical and Matrix Methods, 3rd edn. Wiley, Chichester (2003)Google Scholar
- 10.The P System Web Page, http://ppage.psystems.eu