Linqiang Pan

Huazhong University of Science and Technology

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Mario J. Pérez–Jiménez

Universidad de Sevilla

Bosheng Song

Hunan University

Xingyi Zhang

Anhui University

Tao Song

Universidad Politécnica de Madrid

Xiangxiang Zeng

Hunan University

Tingfang Wu

Soochow University

Gheorghe Pǎun

Romanian Academy

Cheng He

Huazhong University of Science and Technology

K. G. Subramanian

Liverpool Hope University

Lianghao Li

Huazhong University of Science and Technology

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Huazhong University of Science and Technology

Chinese Academy of Sciences

Peking University

Ministry of Education of the People's Republic of China

Zhejiang University

Universidad de Sevilla

Tongji University

Sun Yat-sen University

Anhui University

Beijing University of Posts and Telecommunications

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高論理解析による経路の協調規制の検出【Powered by NICT】

IEEE Conference Proceedings (2016)

Su Yansen Xingyi Zhang Linqiang Pan

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Normal Forms for Some Classes of Sequential Spiking Neural P Systems

IEEE Transactions on NanoBioscience (2013)

Tao Song Linqiang Pan Keqin Jiang Bosheng Song Wei Chen

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where each neuron can have several spiking rules and forgetting rules and neurons work in parallel in the sense that each neuron that can fire should fire at each computation step. In this work, we consider SN P systems with the restrictions: 1) systems are simple (resp. almost simple) in the sense that each neuron has only one rule (resp. except for one neuron); 2) at each step the neuron(s) with the maximum number of spikes among the neurons that can spike will fire. These restrictions correspond to that the systems are simple or almost simple and a global view of the whole network makes the systems sequential. The computation power of simple SN P systems and almost simple SN P systems working in the sequential mode induced by maximum spike number is investigated. Specifically, we prove that such systems are Turing universal as both number generating and accepting devices. The results improve the corresponding ones in Theor. Comput. Sci., 410 (2009), 2982-2991.

Turing

P system

Winner-take-all

10.1109/tnb.2013.2271278

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Small universal spiking neural P systems with exhaustive use of rules

Xingyi Zhang Yun Jiang Linqiang Pan

We consider the problem of looking for small universal spiking neural P systems with exhaustive use of rules, which was formulated as an open problem by Gheorghe Pąun in a survey paper. Here, spiking neural P systems are used in two versions: as devices computing functions and as devices generating sets of numbers, with two ways of encoding the result of a computation. As devices of computing functions, if we associate the result with the distance between the first two spikes emitted by the output neuron, we produce a universal computing spiking neural P system with exhaustive use of rules (without delay) having 125 neurons; if we introduce the usual way of defining the result of a computation in membrane systems to encode the result, namely, the number of spikes emitted during a computation, then a universal computing system (without delay) with 126 neurons is also obtained in the sense of the exhaustive use of rules. For spiking neural P systems used as generators of sets of numbers, we construct a universal system (without delay) by using 128 neurons under the first way of defining the computation result, and a system (without delay) by using 127 neurons under the second way of defining the computation result.

Membrane Computing

ENCODE

P system

Natural computing

Neural system

10.1109/bicta.2008.4656713

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Bio-Inspired Computing: Theories and Applications

Communications in computer and information science (2023)

Linqiang Pan Dongming Zhao Lianghao Li Jianqing Lin

10.1007/978-981-99-1549-1

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On Efficiency of P Systems with Symport/Antiport and Membrane Division

Luis Felipe Macías Ramos Bosheng Song Tao Song Linqiang Pan Mario J. Pérez–Jiménez

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The Computational Complexity of Tissue P Systems with Evolutional Symport/Antiport Rules

Complexity (2018)

Linqiang Pan Bosheng Song Luis Valencia‐Cabrera Mario J. Pérez–Jiménez

Tissue P systems with evolutional communication (symport/antiport) rules are computational models inspired by biochemical systems consisting of multiple individuals living and cooperating in a certain environment, where objects can be modified when moving from one region to another region. In this work, cell separation, inspired from membrane fission process, is introduced in the framework of tissue P systems with evolutional communication rules. The computational complexity of this kind of P systems is investigated. It is proved that only problems in class P can be efficiently solved by tissue P systems with cell separation with evolutional communication rules of length at most ( n , 1), for each natural number n ≥ 1. In the case where that length is upper bounded by (3,2), a polynomial time solution to the SAT problem is provided, hence, assuming that P ≠ N P a new boundary between tractability and NP ‐hardness on the basis of the length of evolutional communication rules is provided. Finally, a new simulator for tissue P systems with evolutional communication rules is designed and is used to check the correctness of the solution to the SAT problem.

Membrane Computing

10.1155/2018/3745210

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Spiking Neural P Systems with Neuron Division and Budding

Linqiang Pan Gheorge Paun Mario J. Pérez–Jiménez

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LOCALIZATION THEOREM ON HAMILTONIAN GRAPHS

Acta Mathematica Scientia (2000)

Linqiang Pan Kemin Zhang Guofei Zhou