On String Languages Generated by Sequential Spiking Neural P Systems Based on Maximum Spike Number

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, we consider SN P systems with the restriction: at each step the neuron with the maximum number of spikes among the neurons that can spike will fire (if there is a tie for the maximum number of spikes stored in the active neurons, only one of the neurons containing the maximum is chosen non-deterministically). We investigate the computational power of such sequential SN P systems that are used as language generators. We prove that recursively enumerable languages can be characterized as projections of inverse-morphic images of languages generated by that sequential SN P systems. The relationships of the languages generated by these sequential SN P systems with finite and regular languages are also investigated.