Numerical P systems are a class of P systems inspired both from the structure of living cells and from economics. In this work, we further investigate the generative capacity of numerical P systems as language generators. The families of languages generated by non-enzymatic, by enzymatic, and by purely enzymatic (all programs are enzymatic) numerical P systems working in the sequential mode are compared with the language families in the Chomsky hierarchy. Especially, a characterization of recursively enumerable languages is obtained by using purely enzymatic numerical P systems working in the sequential mode.
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Spiking neural P systems are a class of distributed parallel computing models inspired from the way the neurons communicate with each other by means of electrical impulses (called "spikes"). In this paper, we consider a restricted variant of spiking neural P systems, called homogeneous spiking neural P systems, where each neuron has the same set of rules. The universality of homogeneous spiking neural P systems is investigated. One of universality results is that it is sufficient for homogeneous spiking neural P system to have only one neuron that behaves nondeterministically in order to achieve Turing completeness.
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The operations of symport and antiport, directly inspired from biology, are already known to be rather powerful when used in the framework of P systems. In this paper we confirm this observation with a quite surprising result: P systems with symport/antiport rules using only three objects can simulate any counter machine, while systems with only two objects can simulate any blind counter machine. In the first case, the universality (of generating sets of numbers) is obtained also for a small number of membranes, four.
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