Nonlinear neural P systems for generating string languages

Abstract

Abstract Nonlinear spiking neural P (NSNP) system is a distributed parallel computing model inspired from the mechanisms of spiking neurons. Computational completeness of NSNP systems as number generating/accepting devices and function computing devices has been already discussed. However, universality result of NSNP systems as language generating devices has not been established so far. This paper investigates computational power of NSNP systems as language generating devices. The relationships of languages generated by NSNP systems with regular languages are investigated. Moreover, we prove that recursively enumerable languages can be characterized by the projection of inverse-morphic images of languages generated by NSNP systems.