Spiking neural P systems (SN P systems) are a class of parallel and distributed spiking neural network models, which are inspired from the way biological neurons spiking and communicating by means of spikes. White hole rules, abstracted from the biological observation of neural information rejection, were recently introduced into SN P systems, by which a neuron consumes its complete contents when it fires. In this work, SN P systems with white hole neurons are proposed, in which each neuron has only white hole rules. The computational power of general and bounded SN P systems with white hole neurons are obtained. Specifically, it is achieved in a constructive way that i) general SN P systems (having both bounded and unbounded) white hole neurons are Turing universal as number generators; ii) bounded SN P systems with white hole neurons can only characterize semi-linear sets of numbers. These results show that "information storage capacity" of certain key neurons provides some "programming capacity" useful for SN P systems achieving a desired computation power.

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.

Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational hard problems in feasible (linear or polynomial) time with a “time-space trade-off” strategy. In this work, a new mechanism called neuron dissolution is introduced, by which redundant neurons produced during the computation can be removed. As applications, uniform solutions to two NP-hard problems: SAT problem and Subset Sum problem are constructed in linear time, working in a deterministic way. The neuron dissolution strategy is used to eliminate invalid solutions, and all answers to these two problems are encoded as indices of output neurons. Our results improve the one obtained in Science China Information Sciences, 2011, 1596-1607 by Pan et al.

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Consensus clustering algorithm, which integrates several clustering results obtained by common algorithms, can find a better result that is independent on parameter settings. However, this kind of algorithm is often designed based on simple, such as K -means, algorithms, which is limited by the time complexity. In this work, a P system, a novel branch of bio-inspired computing with inherent parallel and distributed computation, is combined with the consensus clustering algorithm. As a result, an improved consensus clustering algorithm is constructed using the hierarchical membrane structure and parallel evolution mechanism in a cell-like P system with multi-catalysts, where the catalysts are utilized to regulate the parallelism of objects evolution. The integration strategy of the algorithm is based on a revised PAM where only the q-nearest neighbors of the original medoids are considered as candidates for the new medoids. The experimental results indicate that the clustering quality of the proposed algorithm is more robust than well-known consensus clustering algorithms on data sets with noises and outliers. This work gives evidence that the effectiveness and efficiency of consensus clustering algorithms can be improved using P systems.

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