Spiking neural P systems with a flat maximally parallel use of rules - Wu, Tingfang; Jiang, Suxia (Journal of membrane computing, 2021)
Spiking neural P systems (SN P systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information by means of spikes, where rules in each neuron are applied in a sequential mode in the sense that at every step at most one rule is executed in each neuron. In this work, a flat maximally parallel mode of using rules is introduced into SN P systems, where at every step, a maximal set of applicable rules in each neuron is chosen and each rule in the chosen set is applied exactly once. The computation power of SN P systems working in the flat maximally parallel mode is investigated. Specifically, it is demonstrated that such systems are Turing universal as both number generating devices and function computing devices. Moreover, it is shown that 68 neurons are sufficient for constructing a universal SN P working in the flat maximally parallel mode and using standard rules as a function computing device. These results indicate that the computation power of SN P systems is robust regarding their mode of flat maximal parallelism.