Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (1): 270-275.doi: 10.11896/jsjkx.211100091

• Artificial Intelligence • Previous Articles     Next Articles

SNPT Systems Working in Global Asynchronous and Local Synchronous Mode

ZHANG Luping, XU Fei   

  1. Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China,School of Artificial Intelligence and Automation,Huazhong University of Science and Technology,Wuhan 430074,China
  • Received:2021-11-08 Revised:2022-06-14 Online:2023-01-15 Published:2023-01-09
  • About author:ZHANG Luping,born in 1982,master,associated professor.Her main research interests include natural computing and membrane computing.
    XU Fei,born in 1984,Ph.D,associate researcher.His main research interests include natural computing,bio-inspired computing,DNA computing,and their applications in biomedicine.
  • Supported by:
    National Natural Science Foundation of China(62072201),National Key R & D Program of China for InternationalS & T Cooperation Projects(2021YFE0102100),Provincial Key R & D Program of Hubei Province(2021BAA168) and Fundamental Research Funds for the Central Universities(2019kfyXMBZ056).

PDF (PC)

302

Abstract: Spiking neural P systems with thresholds(SNPT systems) are a class of bio-inspired computing models,inspired by the association between the potential changes in neurons and the neural activities.It is proved that SNPT systems working in the maximally parallel mode are computationally universal since they can achieve the equivalent computation power with Turing machines as number generators and acceptors.The computing power of SNPT systems working in other modes is a topic of concern.In this work,we investigate the number generating power of SNPT systems working in the global asynchronous and local synchronous way(ASNPlocsynT systems).It is proved that ASNPlocsynT systems with integer weights are universal,and ASNPlocsynT systems with positive-integer weights can only generate the semilinear sets of numbers.The results show that the range of synaptic weights affects the computation power of ASNPlocsynT systems.

Key words: Bio-inspired computing, Spiking neural P system, Global asynchronization, Local synchronization, Computation power

CLC Number: 

  • TP301
[1]FORBES N.Imitation of Life:How Biology is Inspiring Computing [M].Mit Press,2004.
[2]AKERKAR R,SAJJA P S.Bio-inspired Computing:Constitu-ents and Challenges [J].International Journal of Bio-Inspired Computation,2009,1(3):135-150.
[3]PĂUN G H.A Quick Introduction to Membrane Computing[J].The Journal of Logic and Algebraic Programming,2010,79(6):291-294.
[4]ZHANG G,GHEORGHE M,PAN L,etal.Evolutionary Mem-brane Computing:A Comprehensive Survey and New Results [J].Information Sciences,2014,279:528-551.
[5]PĂUN G H.A Dozen of Research Topics in Membrane Computing [J].Theoretical Computer Science,2018,736:76-78.
[6]ZHANG G,SHANG Z,VERLAN S,et al.An Overview ofHardware Implementation of Membrane Computing Models [J].ACM Computing Surveys(CSUR),2020,53(4):1-38.
[7]BUIU C,FLOREA A G.Membrane Computing Models and Robot Controller Design,Current Results and Challenges [J].Journal of Membrane Computing,2019,1(4):262-269.
[8]BARFEH D P Y.Basic Mathematical Operations in Membrane Computing [C]//2019 International Conference on Computational Intelligence and Knowledge Economy(ICCIKE).IEEE,2019:278-282.
[9]SONG B,LI K,ORELLANA-MARTÍN D,et al.A Survey of Nature-Inspired Computing:Membrane Computing [J].ACM Computing Surveys(CSUR),2021,54(1):1-31.
[10]IONESCU M,PĂUN G H,YOKOMORI T,Spiking Neural P Systems[J].Fundamenta Informaticae,2006,71(23):279-308.
[11]PAN L,PĂUN G H.Spiking Neural P Systems with Anti-Spikes[J].International Journal of Computers Communications &Control,2009,4(3):273-282.
[12]PAN L,PĂUN G H,ZHANG G.Spiking Neural P Systems with Communication on Request[J].International Journal of Neural Systems,2017,27(8):1750042.
[13]ZHANG X,ZENG X,PAN L Weighted Spiking Neural P Systems with Rules on Synapses[J].Fundamenta Informaticae,2014,134(12):201-218.
[14]CABARLE F G C,DE LA CRUZ R T A,CAILIPAN D P P,et al.On Solutions and Representations of Spiking Neural P Systems with Rules on Synapses [J].Information Sciences,2019,501:30-49.
[15]PAN L,WU T,SU Y,et al.Cell-like Spiking Neural P Systems with Request Rules [J].IEEE Transactions on Nanobioscience,2017,16(6):513-522.
[16]JIANG Y, SU Y S, LUO F.An Improved Universal Spiking Neural P System with Generalized Use of Rules[J].Journal of Membrane Computing,2019,1(4):270-278.
[17]ZHANG L,XU F,RAMANUJANB A.Control Languages Accepted by Labeled Spiking Neural P Systems with Rules on Synapses [J].Theoretical Computer Science,2021,893:60-71.
[18]RONG H,WU T,PAN L,et al.Spiking Neural P Systems:Theo-retical Rresults and Applications[M]//Enjoying Natural Computing.Springer,2018:256-268.
[19]SONG T,PAN L.Spiking Neural P Systems with Request Rules [J].Neurocomputing,2016,193:193-200.
[20]PENG H,WANG J,PÉREZ-JIMÉNZ M J,et al.DynamicThreshold Neural P Systems [J].Knowledge-Based Systems,2019,163:875-884.
[21]PAN L,PĂUN G H,PÉREZ-JIMÉNZ M J.Spiking Neural P Systems with Neuron Division and Budding[J].Science China Information Sciences,2011,54(8):1596-1607.
[22]VERLAN S,FREUND R,ALHAZOV A,et al.A FormalFramework for Spiking Neural P Systems[J].Journal of Membrane Computing,2020,2(4):355-368.
[23]DE LA CRUZ R T A,CABARLE F G C,MACABABAYAO I C H,et al.Homogeneous Spiking Neural P Systems with Structu-ral Plasticity[J].Journal of Membrane Computing,2021,3(1):10-21.
[24]SOSÍK P P Systems Attacking Hard Problems Beyond NP:A Survey [J].Journal of Membrane Computing,2019,1(3):198-208.
[25]CETERCHI R,ZHANG L,SUBRAMANIAN K G,et al.Hilbert Words as Arrays Generated with P Systems [J].Journal of Membrane Computing,2021,3(3):163-169.
[26]LIU Y,CHEN Y,PAUL P,et al.A Review of Power System Fault Diagnosis with Spiking Neural P Systems [J].Applied Sciences,2021,11(10):4376.
[27]DÍAZ-PERNIL D,GUTIÉRREZ-NARANJO M A,PENG H.Membrane Computing and Image Processing:A Short Survey [J].Journal of Membrane Computing,2019,1(1):58-73.
[28]DONG J,ZHANG G,LUO B,et al.A Distributed Adaptive Optimization Spiking Neural P System for Approximately Solving Combinatorial Optimization Problems [J].Information Sciences,2022,596:1-14.
[29]ZENG X,ZHANG X,SONG T,et al.Spiking Neural P Systems with Thresholds [J].Neural Computation,2014,26(7):1340-1361.
[30]SONG T,PAN L,PĂUN G H.Asynchronous Spiking Neural P Systems with Local Synchronization [J].Information Sciences,2013,219:197-207.
[31]AMAN B,CIOBANU G.Synchronization of Rules in MembraneComputing [J].Journal of Membrane Computing,2019,1(4):233-240.
[32]SONG B,PAN L.Rule Synchronization for Tissue P Systems [J].Information and Computation,2021,281:104685.
[33]DASSOW J,PĂUN G H.Regulated Rewriting in Formal Language Theory [M].Berlin:Springer,2012.
[34]ROBERT N M,MICHAEL AA,KFOURY A J.An Introduction to Formal Language Theory [M].New York:Springer,1988.
[35]MINSKY M L.Finite and Infinite Machines [M].Englewood Cliffs Prentice-Hall,1967.
[36]IBARRA O H,WOODWORTH S.Characterizations of Some Re-stricted Spiking Neural P Systems [C]//International Workshop on Membrane Computing.Berlin:Springer,2006:424-442.
[37]JIANG S X,FAN J H,GENG S T,et al.The Computational Power of Spiking Neural P Systems with Polarizations and Extended Rules on Synapses[J].Journal of Chongqing University of Technology(Natural Science),2020, 34(12):177-185.
[1] ZHANG Lu-ping, XU Fei. Survey on Spiking Neural P Systems with Rules on Synapses [J]. Computer Science, 2022, 49(8): 217-224.
[2] YIN Xiu, LIU Xi-lin, LIU Xi-yu. Study on Computing Capacity of Novel Numerical Spiking Neural P Systems with MultipleSynaptic Channels [J]. Computer Science, 2022, 49(6A): 223-231.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.