Languages in Membrane Computing: Some Details for Spiking Neural P Systems

Spiking neural P systems were introduced in the end of the year 2005, in the aim of incorporating in membrane computing the idea of working with unique objects ("spikes"), encoding the information in the time elapsed between consecutive spikes sent from a cell/neuron to another cell/neuron. More than one dozen of papers where written in the meantime, clarifying many of the basic properties of these devices, especially related to their computing power. The present paper quickly surveys the basic ideas and the basic results, presenting a complete to-date bibliography, and also giving a completing result related to the normal forms possible for spiking neural P systems: we prove that the indegree of such systems (the maximal number of incoming synapses of neurons) can be bounded by 2 without losing the computational completeness. A series of research topics and open problems are formulated.

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Fuzzy Membrane Computing: Theory and Applications

International Journal of Computers Communications & Control ◽

10.15837/ijccc.2015.6.2080 ◽

2015 ◽

Vol 10 (6) ◽

pp. 144 ◽

Cited By ~ 12

Author(s):

Tao Wang ◽

Gexiang Zhang ◽

Mario J. Pérez-Jiménez

Keyword(s):

Knowledge Representation ◽

Membrane Computing ◽

Fuzzy Theory ◽

Research Direction ◽

P Systems ◽

Future Research ◽

Knowledge Representation And Reasoning ◽

Spiking Neural P Systems ◽

Comprehensive Survey ◽

Fuzzy Knowledge

<p>Fuzzy membrane computing is a newly developed and promising research direction in the area of membrane computing that aims at exploring the complex in- teraction between membrane computing and fuzzy theory. This paper provides a comprehensive survey of theoretical developments and various applications of fuzzy membrane computing, and sketches future research lines. The theoretical develop- ments are reviewed from the aspects of uncertainty processing in P systems, fuzzifica- tion of P systems and fuzzy knowledge representation and reasoning. The applications of fuzzy membrane computing are mainly focused on fuzzy knowledge representation and fault diagnosis. An overview of different types of fuzzy P systems, differences between spiking neural P systems and fuzzy reasoning spiking neural P systems and newly obtained results on these P systems are presented.</p>

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On Applications of Spiking Neural P Systems

Applied Sciences ◽

10.3390/app10207011 ◽

2020 ◽

Vol 10 (20) ◽

pp. 7011 ◽

Cited By ~ 1

Author(s):

Songhai Fan ◽

Prithwineel Paul ◽

Tianbao Wu ◽

Haina Rong ◽

Gexiang Zhang

Keyword(s):

Power Systems ◽

Membrane Computing ◽

Fuzzy Reasoning ◽

P Systems ◽

Diverse Range ◽

Spiking Neural P Systems ◽

Advantages And Disadvantages ◽

New Ideas

Over the years, spiking neural P systems (SNPS) have grown into a popular model in membrane computing because of their diverse range of applications. In this paper, we give a comprehensive summary of applications of SNPS and its variants, especially highlighting power systems fault diagnoses with fuzzy reasoning SNPS. We also study the structure and workings of these models, their comparisons along with their advantages and disadvantages. We also study the implementation of these models in hardware. Finally, we discuss some new ideas which can further expand the scope of applications of SNPS models as well as their implementations.

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Spiking Neural P Systems

Advancing Artificial Intelligence through Biological Process Applications ◽

10.4018/978-1-59904-996-0.ch004 ◽

2011 ◽

pp. 60-73 ◽

Cited By ~ 4

Author(s):

Gheorghe Paun ◽

Mario J. Perez-Jimenez

Keyword(s):

Computational Complexity ◽

Membrane Computing ◽

Research Area ◽

P Systems ◽

Neural Computing ◽

Research Topics ◽

Computing Power ◽

Spiking Neural P Systems ◽

Basic Ideas ◽

Source Of Information

This chapter is a quick survey of spiking neural P systems, a branch of membrane computing which was recently introduced with motivation from neural computing based on spiking. Basic ideas, examples, some results (especially concerning the computing power and the computational complexity/efficiency), and several research topics are discussed. The presentation is succinct and informal, meant mainly to let the reader having a flavour of this research area. The additional references are an important source of information in this respect.

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Automatic Implementation of Fuzzy Reasoning Spiking Neural P Systems for Diagnosing Faults in Complex Power Systems

Complexity ◽

10.1155/2019/2635714 ◽

2019 ◽

Vol 2019 ◽

pp. 1-16 ◽

Cited By ~ 10

Author(s):

Haina Rong ◽

Kang Yi ◽

Gexiang Zhang ◽

Jianping Dong ◽

Prithwineel Paul ◽

...

Keyword(s):

Fault Diagnosis ◽

Power Systems ◽

Large Scale ◽

Membrane Computing ◽

Good Description ◽

Fuzzy Reasoning ◽

P Systems ◽

P System ◽

Spiking Neural P Systems ◽

Complex Power

As an important variant of membrane computing models, fuzzy reasoning spiking neural P systems (FRSN P systems) were introduced to build a link between P systems and fault diagnosis applications. An FRSN P system offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. However, the implementation of FRSN P systems is still at a manual process, which is a time-consuming and hard labor work, especially impossible to perform on large-scale complex power systems. This manual process seriously limits the use of FRSN P systems to diagnose faults in large-scale complex power systems and has always been a challenging and ongoing task for many years. In this work we develop an automatic implementation method for automatically fulfilling the hard task, named membrane computing fault diagnosis (MCFD) method. This is a very significant attempt in the development of FRSN P systems and even of the membrane computing applications. MCFD is realized by automating input and output, and diagnosis processes consists of network topology analysis, suspicious fault component analysis, construction of FRSN P systems for suspicious fault components, and fuzzy inference. Also, the feasibility of the FRSN P system is verified on the IEEE14, IEEE 39, and IEEE 118 node systems.

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A quick overview of membrane computing with some details about spiking neural P systems

Frontiers of Computer Science in China ◽

10.1007/s11704-007-0005-4 ◽

2007 ◽

Vol 1 (1) ◽

pp. 37-49 ◽

Cited By ~ 2

Author(s):

Gheorghe Păun

Keyword(s):

Membrane Computing ◽

P Systems ◽

Spiking Neural P Systems

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Delayed Spiking Neural P Systems with Scheduled Rules

Complexity ◽

10.1155/2021/6817636 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Qianqian Ren ◽

Xiyu Liu

Keyword(s):

Time Delay ◽

Membrane Computing ◽

P Systems ◽

Time Range ◽

P System ◽

System Delay ◽

Spiking Neural P Systems ◽

Time Control ◽

New Variant ◽

Delay Phenomenon

Due to the inevitable delay phenomenon in the process of signal conversion and transmission, time delay is bound to occur between neurons. Therefore, it is necessary to introduce the concept of time delay into the membrane computing models. Spiking neural P systems (SN P systems), as an attractive type of neural-like P systems in membrane computing, are widely followed. Inspired by the phenomenon of time delay, in our work, a new variant of spiking neural P systems called delayed spiking neural P systems (DSN P systems) is proposed. Compared with normal spiking neural P systems, the proposed systems achieve time control by setting the schedule on spiking rules and forgetting rules, and the schedule is also used to realize the system delay. A schedule indicates the time difference between receiving and outputting spikes, and it also makes the system work in a certain time, which means that a rule can only be used within a specified time range. We specify that each rule is performed only in the continuous schedule, during which the neuron is locked and cannot send or receive spikes. If the neuron is not available at a given time, it will not receive or send spikes due to the lack of a schedule for this period of time. Moreover, the universality of DSN P systems in both generating and accepting modes is proved. And a universal DSN P system having 81 neurons for computing functions is also proved.

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Simplified and Yet Turing Universal Spiking Neural P Systems with Communication on Request

International Journal of Neural Systems ◽

10.1142/s0129065718500132 ◽

2018 ◽

Vol 28 (08) ◽

pp. 1850013 ◽

Cited By ~ 46

Author(s):

Tingfang Wu ◽

Florin-Daniel Bîlbîe ◽

Andrei Păun ◽

Linqiang Pan ◽

Ferrante Neri

Keyword(s):

Neural Networks ◽

Membrane Computing ◽

P Systems ◽

P System ◽

Third Generation ◽

Turing Machines ◽

Spiking Neural P System ◽

Spiking Neural P Systems ◽

Turing Universality ◽

Theoretical Results

Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are a type of spiking neural P system where the spikes are requested from neighboring neurons. SNQ P systems have previously been proved to be universal (computationally equivalent to Turing machines) when two types of spikes are considered. This paper studies a simplified version of SNQ P systems, i.e. SNQ P systems with one type of spike. It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems. Theoretical results are shown in the cases of the SNQ P system used in both generating and accepting modes. Furthermore, the influence of the number of unbounded neurons (the number of spikes in a neuron is not bounded) on the computation power of SNQ P systems with one type of spike is investigated. It is found that SNQ P systems functioning as number generating devices with one type of spike and four unbounded neurons are Turing universal.

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