Computational power of dynamic threshold neural P systems for generating string languages

Abstract Inspired from spiking and dynamic mechanisms of neurons, dynamic threshold neural P systems (DTNP systems) have been developed and their computational completeness as numbergenerati ng/accepting devices and function computing devices has been investigated. However, a universality result of DTNP systems as language generators has not been established so far. This paper discusses computational power of DTNP systems as language generators. We first discuss the relationship between the languages generated by DTNP systems and finite languages, and then prove that regular languages can be generated by finite DTNP systems. Moreover, we prove that recursively enumerable languages can be characterized by projections of inverse-morphic images of the languages generated by DTNP systems.