Small universal spiking neural P systems

Păun, Andrei; Păun, Gheorghe

doi:10.1016/j.biosystems.2006.06.006

Cited by 206 publications

(112 citation statements)

References 6 publications

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“…2.1 P Systems P systems could be divided into three groups: cell-like P systems, tissue-like P systems and neural-like P systems [30]. The structure of cell-like P systems is the basic structure of other P systems.…”

Section: P Systems and Membrane Algorithmmentioning

confidence: 99%

An Approximate Algorithm Combining P Systems and Active Evolutionary Algorithms for Traveling Salesman Problems

Song¹,

Wang²

2014

INT J COMPUT COMMUN

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An approximate algorithm combining P systems and active evolutionary algorithms (AEAPS) to solve traveling salesman problems (TSPs) is proposed in this paper. The novel algorithm uses the same membrane structure, subalgorithms and transporting mechanisms as Nishida's algorithm, but adopts two classes of active evolution operators and a good initial solution generating method. Computer experiments show that the AEAPS produces better solutions than Nishida's shrink membrane algorithm and similar solutions with an approximate optimization algorithm integrating P systems and ant colony optimization techniques (ACOPS) in solving TSPs. But the necessary number of iterations using AEAPS is less than both of them.

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Section: P Systems and Membrane Algorithmmentioning

confidence: 99%

An Approximate Algorithm Combining P Systems and Active Evolutionary Algorithms for Traveling Salesman Problems

Song¹,

Wang²

2014

INT J COMPUT COMMUN

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“…SN P systems were proved to be computationally complete, i.e., Turing universal, as number generators [5,11,12,13,14,15], language generators [3,21], and function computing devices [16]. SN P systems with cell division or budding can generate new neurons during the computation, thus provide a way to generate exponential working space in polynomial or linear time.…”

Section: Introductionmentioning

confidence: 99%

“…In the previous obtained SN P systems in [3,5,6,7,8,12,16,20], a global clock is generally assumed marking the time for the system. The systems work in a synchronized manner in the global level (all neurons apply their rules in a parallel manner), and for each neuron, only one of the enabled rules can be used on the tick of the clock.…”

Section: Introductionmentioning

confidence: 99%

Solving Subset Sum Problems by Time-free Spiking Neural P Systems

Song¹,

Luo²,

He³

et al. 2014

Appl. Math. Inf. Sci.

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Abstract:In membrane computing, spiking neural P systems (shortly called SN P systems) are a group of neural-like computing models inspired from the way spiking neurons communication in form of spikes. In previous works, SN P systems working in a nondeterministic manner have been used to solve numerical NP-complete problems, such as SAT, vertex cover, in feasible time. In these works, the application of any rule should complete in exactly one time unit, and the precise execution time of rules plays a crucial role on solving the problems in polynomial (or even in linear) time. However, the restriction does not coincide with the biological fact, since in biological systems, bio-chemical reactions may cost different execution time due to the external uncontrollable conditions. In this paper, we consider timed and time-free SN P systems, where the precise execution time of the rules is removed. To investigate the computational efficiency of time-free SN P systems, we solve Subset Sum problem by a family of uniform time-free SN P systems.

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“…Since then, many computational properties of SN P systems have been studied; for example, it has been proved that they are Turing-complete when considered as number computing devices [6], when used as language generators [3,1] and also when computing functions [12].…”

Section: Introductionmentioning

confidence: 99%

Spiking neural P systems with neuron division and budding

Pan

Păun

Pérez–Jiménez

2011

Sci. China Inf. Sci.

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162

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Summary. In order to enhance the efficiency of spiking neural P systems, we introduce the features of neuron division and neuron budding, which are processes inspired by neural stem cell division. As expected (as it is the case for P systems with active membranes), in this way we get the possibility to solve computationally hard problems in polynomial time. We illustrate this possibility with SAT problem.

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Small universal spiking neural P systems

Cited by 206 publications

References 6 publications

An Approximate Algorithm Combining P Systems and Active Evolutionary Algorithms for Traveling Salesman Problems

An Approximate Algorithm Combining P Systems and Active Evolutionary Algorithms for Traveling Salesman Problems

Solving Subset Sum Problems by Time-free Spiking Neural P Systems

Spiking neural P systems with neuron division and budding

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