Arithmetic Logic Operation Based On Pulse Nerve Membrane System Research

Membrane computing is a branch of natural computing. It is a computational model which isabstracted from the structure and the functioning of living cells, and it has the advantage on distributed,parallel, and non-deterministic. So far, it has attracted strong attention from the biological scientists,information scientists and computer scientists. Spiking neural P systems is a new computational modelbased on Membrane Computing. It not only simulates the structural and functional of the biologicalnervous system, provides a new algorithm model for biological systems modeling and simulation. It alsointroduces a new computational ideas and methods for computer science.With the theory of spiking neural P systems, main research focus in two ways, a class of researchfocuses on the field of system’s own structure, operation principle and features. And the other is to explorethe application of spiking neural P systems. The former mainly focused on three aspects: the topology ofthe neurons, formal definitions of rule set and use of rule sets. Although this theoretical development timeis not so long, It is developing very fast. Scientists are constantly trying to use spiking neural P systems toresolve the open issues in the various areas. In 2008, foreign researchers have proposed spiking neural Psystems to the field of arithmetic and Boolean circuit field for the first time. Reference to the ideology ofdomestic and foreign researchers, in this paper, some areas work has been down as follows:First of all, through summarizing the domestic and foreign research, in this paper, the concept,development situation and research significance of spiking neural P system are expounded. Analysis thesystem’s research methods and research priorities detailed. These works provide a sufficient theoreticalbasis for the research of this paper.In this paper, through anglicizing the topology of the spiking neural P systems and the flexible usingof the rule set, addition, subtraction, and multiplication of binary natural number are designed respectivelyusing spiking neural P systems. And, the Computational efficiency of spiking neural p System was analysisand compared. The application of spiking neural P systems in arithmetic is proved.The third is using spiking neural P system to simulate arithmetic operations. In this paper division ofbinary natural number is designed using spiking neural p system by using the Auxiliary neural and the ruleset. This work provide further theoretical basis of CPU design based on Spiking Neural P system.The lasts work in this paper is Modeling and simulating logic operations by using spiking neural Psystem. In this paper, we studied the similarities and commonalities of Boolean circuit and spiking neural Psystems. Firstly, this article has designed a series of spiking neural P systems used to simulate the basiclogic operations. Such as OR gates、AND gates、XOR-gates、NOR-gates and NAND gates. Secondly, onthe basis of the above work, this article also applies spiking neural P systems to the simulation, of a trigger, sequential logic components. And then, two spiking neural P systems were designed for simulating thebasic RS flip-flop and clock control RS flip-flop, respectively. This work provide further theoretical basisof applications of spiking neural p system in the field of logic circuits.In this paper, the applications of spiking neural p system in Arithmetic and logic operation fieldanalyzed and studied. The system is designed for solving some basic problems in the two fields. Althoughthese problems are relatively simple in traditional computing science, in the emerging biologicalcomputing science, the research of these problems is very important. Through the research of these issues,this paper not only proved the possibility of realizing the biological computer, it also pointed out thedirection for the next research of biological computers. These researches play a very important role to thedevelopment of the physical realization and algorithm design of biological computing technology.