Spiking Neural P Systems with a Generalized Use of Rules

Zhang, Xingyi; Wang, Bangju; Pan, Linqiang

doi:10.1162/neco_a_00665

Cited by 38 publications

(16 citation statements)

References 18 publications

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“…The cells in SN P systems are neurons that have only one type of objects called spikes. Zhang et al [24,25], Song et al [26], and Zeng et al [27] provided good examples. Also, some other data mining algorithms can be improved by using parallel evolution mechanisms and graph membrane structures, such as spectral clustering, support vector machines, and genetic algorithms [1].…”

Section: Discussionmentioning

confidence: 99%

An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

Liu

Zhao

Sun

2017

Discrete Dynamics in Nature and Society

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Apriori algorithm, as a typical frequent itemsets mining method, can help researchers and practitioners discover implicit associations from large amounts of data. In this work, a fast Apriori algorithm, called ECTPPI-Apriori, for processing large datasets, is proposed, which is based on an evolution-communication tissue-like P system with promoters and inhibitors. The structure of the ECTPPI-Apriori algorithm is tissue-like and the evolution rules of the algorithm are object rewriting rules. The time complexity of ECTPPI-Apriori is substantially improved from that of the conventional Apriori algorithms. The results give some hints to improve conventional algorithms by using membrane computing models.

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Section: Discussionmentioning

confidence: 99%

An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

Liu

Zhao

Sun

2017

Discrete Dynamics in Nature and Society

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“…It is of interest to use spiking neural networks computing models to do the optimization [19–22]. In the framework of membrane computing, cell-like [23] and tissue-like [24] computing models have been proved to be powerful as bioinspired computing models.…”

Section: Discussionmentioning

confidence: 99%

A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network

Sun

Xin

et al. 2016

BioMed Research International

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Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.

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“…Recently, the sequential use of rules combines with the exhaustive use of rules to form generalized use of rules [46]. This new way of applying rules is similar to the minimal parallelism mode in P systems [45].…”

Section: Introductionmentioning

confidence: 99%

An improved universal spiking neural P system with generalized use of rules

Jiang

Luo

2019

J Membr Comput

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Taken inspiration from biological phenomenon that neurons communicate via spikes, spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices. So far firing rules in most of the SN P systems usually work in a sequential way or in an exhaustive way. Recently, a combination of the two ways mentioned above is considered in SN P systems. This new strategy of using rules, which is called a generalized way of using rules, is applicable for both firing rules and forgetting rules. In SN P systems with generalized use of rules (SNGR P systems, for short), if a rule is used at some step, it can be applied any possible number of times, nondeterministically chosen. In this work, the computational completeness of SNGR P systems is investigated. Specifically, a universal SNGR P system is constructed, where each neuron contains at most 5 rules, and for each time each firing rule consumes at most 6 spikes and each forgetting rule removes at most 4 spikes. This result makes an improvement regarding to these related parameters, thus provides an answer to the open problem mentioned in original work. Moreover, with this improvement we can use less resources (neurons and spikes involved in the evolution of system) to construct universal SNGR P systems.

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Spiking Neural P Systems with a Generalized Use of Rules

Cited by 38 publications

References 18 publications

An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

An Improved Apriori Algorithm Based on an Evolution-Communication Tissue-Like P System with Promoters and Inhibitors

A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network

An improved universal spiking neural P system with generalized use of rules

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