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Journal of Computer and System Sciences
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Journal of Computer and System Sciences
Article . 2000
License: Elsevier Non-Commercial
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Computing with Membranes

Computing with membranes
Authors: Gheorghe Paun;

Computing with Membranes

Abstract

New models of massive parallel computation devices are suggested. The basic idea of these devices is the modelling of the evolution of a family of cells. It is a modification of DNA-computers or neural networks or abstract chemistry machines. Three versions of the device are defined, the first one computes subsets of natural numbers, the second (or third) type of the device computes subsets of strings -- languages -- using rewriting (or splicing) rules. It is proved that all suggested models compute exactly recursive enumerable sets, even strong restricted conditions on them are given. Some open problems concerning effectiveness of computations are formulated.

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Keywords

Computer Networks and Communications, Applied Mathematics, recursively enumerable set, natural computing, a recursively enumerable set, Formal languages and automata, Models of computation (Turing machines, etc.), Theoretical Computer Science, splicing, Computational Theory and Mathematics, a membrane structure, membrane structure, a natural computing, P system, matrix grammar

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2K
Top 0.1%
Top 0.01%
Top 1%
hybrid