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Author

Gheorghe Pun

Other affiliations: University of Seville
Bio: Gheorghe Pun is an academic researcher from Romanian Academy. The author has contributed to research in topics: Membrane computing & Spiking neural network. The author has an hindex of 9, co-authored 12 publications receiving 713 citations. Previous affiliations of Gheorghe Pun include University of Seville.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors introduce the notion of local synchronization into asynchronous SN P systems, where the use of spiking rules (even if they are enabled by the contents of neurons) is not obligatory.

177 citations

Journal ArticleDOI
TL;DR: It is proved that asynchronous systems, with extended rules, and where each neuron is either bounded or unbounded, are not computationally complete and the configuration reachability, membership, emptiness, infiniteness, and disjointness problems are shown to be decidable.

151 citations

Journal ArticleDOI
TL;DR: It is proved that i) if no limit is imposed on the number of spikes in any neuron during any computation, such systems can generate the sets of Turing computable natural numbers and thesets of vectors of positive integers computed by k-output register machine, which gives a positive answer to the problem formulated in Song et al. 2014.

118 citations

Journal ArticleDOI
TL;DR: If at least a rule from a set of rules associated with a membrane or a region can be used, then at least one rule from that membrane or region must be used , without any other restriction, and this minimal parallelism leads to universality.

109 citations

Journal ArticleDOI
TL;DR: In this article, the authors prove a series of normal forms for spiking neural P systems, concerning the regular expressions used in the firing rules, the delay between firing and spiking, the forgetting rules used, and the outdegree of the graph of synapses.

85 citations


Cited by
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Journal ArticleDOI
TL;DR: The emerging picture is that SNNs still lag behind ANNs in terms of accuracy, but the gap is decreasing, and can even vanish on some tasks, while SNN's typically require many fewer operations and are the better candidates to process spatio-temporal data.

756 citations

Journal ArticleDOI
TL;DR: This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants, and proposes a methodology based on the information given by the transition diagram associated with an SN P system which establishes the soundness and completeness of the system with respect to the problem it tries to resolve.
Abstract: This work deals with several aspects concerning the formal verification of SN P systems and the computing power of some variants. A methodology based on the information given by the transition diagram associated with an SN P system is presented. The analysis of the diagram cycles codifies invariants formulae which enable us to establish the soundness and completeness of the system with respect to the problem it tries to resolve. We also study the universality of asynchronous and sequential SN P systems and the capability these models have to generate certain classes of languages. Further, by making a slight modification to the standard SN P systems, we introduce a new variant of SN P systems with a special I/O mode, called SN P modules, and study their computing power. It is demonstrated that, as string language acceptors and transducers, SN P modules can simulate several types of computing devices such as finite automata, a-finite transducers, and systolic trellis automata.

408 citations

Journal ArticleDOI
TL;DR: An extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking Neural P system, are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems.
Abstract: Membrane systems (also called P systems) refer to the computing models abstracted from the structure and the functioning of the living cell as well as from the cooperation of cells in tissues, organs, and other populations of cells. Spiking neural P systems (SNPS) are a class of distributed and parallel computing models that incorporate the idea of spiking neurons into P systems. To attain the solution of optimization problems, P systems are used to properly organize evolutionary operators of heuristic approaches, which are named as membrane-inspired evolutionary algorithms (MIEAs). This paper proposes a novel way to design a P system for directly obtaining the approximate solutions of combinatorial optimization problems without the aid of evolutionary operators like in the case of MIEAs. To this aim, an extended spiking neural P system (ESNPS) has been proposed by introducing the probabilistic selection of evolution rules and multi-neurons output and a family of ESNPS, called optimization spiking neural P system (OSNPS), are further designed through introducing a guider to adaptively adjust rule probabilities to approximately solve combinatorial optimization problems. Extensive experiments on knapsack problems have been reported to experimentally prove the viability and effectiveness of the proposed neural system.

284 citations

Journal ArticleDOI
TL;DR: This simple extension of spiking neural P systems is shown to considerably simplify the universality proofs in this area, where all rules become of the form bc → b′ or bc → lambda , where b,b′ are spikes or anti-spikes.
Abstract: Besides usual spikes employed in spiking neural P systems, we consider “anti-spikes", which participate in spiking and forgetting rules, but also annihilate spikes when meeting in the same neuron. This simple extension of spiking neural P systems is shown to considerably simplify the universality proofs in this area: all rules become of the form bc → b′ or bc → lambda , where b,b′ are spikes or anti-spikes. Therefore, the regular expressions which control the spiking are the simplest possi- ble, identifying only a singleton. A possible variation is not to produce anti-spikes in neurons, but to consider some “inhibitory synapses", which transform the spikes which pass along them into anti-spikes. Also in this case, universality is rather easy to obtain, with rules of the above simple forms.

197 citations

Journal ArticleDOI
TL;DR: An intelligent cryptography approach, by which the cloud service operators cannot directly reach partial data is proposed, and experimental results depict that the approach can effectively defend main threats from clouds and requires with an acceptable computation time.

196 citations