Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

Automatic design of arithmetic operation spiking neural P systems

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

As one of the most widely studied membrane systems, a spiking neural P system consists of three fundamental elements: initial spikes, evolution rules and connection between neurons. The automatic design of an arithmetic operation spiking neural P system (SN P system) in the literature usually considered the selection of the set of redundant rules under the condition of known initial spikes and connection between neurons. In this work, an automatic design method of arithmetic operation SN P systems including the encoding method and evolutionary strategy of arithmetic operation SN P systems is proposed to evolve an SN P system to achieve arithmetic operation. Experimental results show that the automatic design method of arithmetic operation SN P systems is feasible and effective for arithmetic operations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Alhazov A (2010) Minimal parallelism and number of membrane polarizations. Comput Sci J Mold 18(18):149–170

    MathSciNet  MATH  Google Scholar 

  • Casauay L, Macababayao I, Cabarle F, de la Cruz R, Adorna H, Zeng X, Martínez-del-Amor M (2021) A framework for evolving spiking neural P systems. Int J Unconv Comput 16(2/3):83–119

    Google Scholar 

  • Ceterchi R, Orellana-Martín D, Zhang G (2021) Division rules for tissue P systems inspired by space filling curves. J Membr Comput 3(2):105–115

    Article  MathSciNet  MATH  Google Scholar 

  • Chen Y, Zhang GX, Wang T, Huang XL (2014) Automatic design of a P system for basic arithmetic operations. Chin J Electon 23(2):302–304

    Google Scholar 

  • Ciencialová L, Csuhaj-Varjú E, Cienciala L, Sosík P (2019) P colonies. J Membr Comput 1(3):178–197

    Article  MathSciNet  MATH  Google Scholar 

  • Dong J, Stachowicz M, Zhang G, Cavaliere M, Rong H, Paul P (2021) Automatic design of spiking neural P systems based on genetic algorithms. Int J Unconv Comput 16(2/3):201–216

    Google Scholar 

  • Dong J, Zhang G, Luo B, Yang Q, Guo D, Rong H, Zhu M, Zhou K (2022) A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems. Inform Sci 596(1):1–14

    Article  Google Scholar 

  • Escuela G, Naranjo MA (2010) An application of genetic algorithms to membrane computing. In: in Proc. 8th Brainstorming Week Membrane Comput

  • Freund R, Păun G, Pérez-Jiménez MJ (2005) Tissue P systems with channel states. Theoret Comput Sci 330(1):101–116

    Article  MathSciNet  MATH  Google Scholar 

  • Gheorghe M, Păun G, Pérez-Jiménez MJ, Rozenberg G (2013) Research frontiers of membrane computing: open problems and research topics. Int J Found Comput Sci 24(5):547–623

    Article  MathSciNet  MATH  Google Scholar 

  • Hu J, Wang Y, Kong D, Yan F, Xue J (2020) Hypergraph membrane system based F2 fully convolutional neural network for brain tumor segmentation. Appl Soft Comput 94:106454–110

    Article  Google Scholar 

  • Huang X, Zhang G, Rong H, Ipate F (2012) Evolutionary design of a simple membrane system. Membr Comput 7184:203–214

    Article  MathSciNet  MATH  Google Scholar 

  • Ionescu M, Păun G, Yokomori T (2006) Spiking neural P systems. Fundam Inform 71(2):279–308

    MathSciNet  MATH  Google Scholar 

  • Jiang Y, Su Y, Luo F (2019) An improved universal spiking neural P system with generalized use of rules. J Membr Comput 1(8):270–278

    Article  MathSciNet  MATH  Google Scholar 

  • Juico J, Silapan J, Cabarle F, Macababayao I, la Cruz RD (2020) Evolving spiking neural P systems with polarization. Philipp Comput J 14(2):11–20

    Google Scholar 

  • Leporati A, Manzoni L, Claudio Z, Porreca A, Zandron C (2020) A turing machine simulation by p systems without charges. J Membr Comput 2(2):71–79

    Article  MathSciNet  MATH  Google Scholar 

  • Li B, Peng H, Luo X, Wang J, Riscos-Núñez A (2020) Medical image fusion method based on coupled neural P systems in nonsubsampled shearlet transform domain. Int J Neural Syst 31(1):2050050–117

    Article  Google Scholar 

  • Lv Z, Yang Q, Peng H, Song X, Wang J (2021) Computational power of sequential spiking neural P systems with multiple channels. J Membr Comput 3(2):270–283

    Article  MathSciNet  MATH  Google Scholar 

  • Orellana-Martín D, Valencia-Cabrera L, Riscos-Núñez A, Pérez-Jiménez MJ (2019) Minimal cooperation as a way to achieve the efficiency in cell-like membrane systems. J Membr Comput 1(1):1–2

    MathSciNet  MATH  Google Scholar 

  • Ou Z, Zhang GX, Wang T, Huang XL (2013) Automatic design of cell like p systems through tuning membrane structures, initial objects and evolution rules. Int J Unconv Comput 9(5–6):425–443

    Google Scholar 

  • Pan L, Paun G, Zhang G, Neri F (2017) Spiking neural P systems with communication on request. Int J Neural Syst 27(8):1750042–1175004213

    Article  Google Scholar 

  • Pan LQ, Păun G, Zhang GX (2019) Foreword: starting JMC. J Membr Comput 1(1):1–2

    Article  Google Scholar 

  • Pan LQ, Orellana-Martín D, Song B, Pérez-Jiménez MJ (2020) Cell-like P systems with polarizations and minimal rules. Theoret Comput Sci 816:1–18

    Article  MathSciNet  MATH  Google Scholar 

  • Păun G (2000) Computing with membranes. J Comput Syst Sci 61(1):108–143

    Article  MathSciNet  MATH  Google Scholar 

  • Ren T, Cabarle FG, Adorna HN (2019) Generating context-free languages using spiking neural P systems with structural plasticity. J Membr Comput 1(8):161–177

    MathSciNet  MATH  Google Scholar 

  • Rong H, Yi K, Zhang G, Dong J, Paul P, Huang Z (2019) Automatic implementation of fuzzy reasoning spiking neural P systems for diagnosing faults in complex power systems. CompLex 2019:2635714–1263571416

    Article  Google Scholar 

  • Song BS, Zhang C, Pan LQ (2017) Tissue-like P systems with evolutional symport/antiport rules. Inform Sci 378:177–193

    Article  MathSciNet  MATH  Google Scholar 

  • Song B, Luo X, Peng H, Valencia-Cabrera L, Zeng X (2021) The computational power of cell-like P systems with one protein on membrane. J Membr Comput 2(4):332–340

    Article  MathSciNet  MATH  Google Scholar 

  • Tudose C, Lefticaru R, Ipate F (2011) Using genetic algorithms and model checking for P systems automatic design. Nat Ins Cooperative Strateg Optim 387:285–302

    MATH  Google Scholar 

  • Valencia-Cabrera L, Song B (2020) Tissue P systems with promoter simulation with mecosim and p-lingua framework. J Membr Comput 2(2):95–107

    Article  MathSciNet  MATH  Google Scholar 

  • Wang T, Zhang GX, Zhao JB, He ZY, Wang J, Pérez-Jiménez MJ, Cheng JX (2015) Fault diagnosis of electric power systems based on fuzzy reasoning spiking neural P systems. IEEE Trans Power Syst 30(3):1182–1194

    Article  Google Scholar 

  • Wang X, Zhang GX, Gou XT, Paul P, Zhang H (2020) A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning. Integr Comput Aided Eng 27(2):121–138

    Article  Google Scholar 

  • Wang X, Zhang GX, Gou XT, Paul P, Zhang H (2021) Multi-behaviors coordination controller design with enzymatic numerical P systems for robots. Integr Comput Aided Eng 28(2):119–140

    Article  Google Scholar 

  • Wu T, Jiang S (2021) Spiking neural P systems with a flat maximally parallel use of rules. J Membr Comput 3(3):221–231

    Article  MathSciNet  MATH  Google Scholar 

  • Xue J, Wang Y, Kong D, Wu F, Liu X (2021) Deep hybrid neural-like P systems for multiorgan segmentation in head and neck CT/MR images. Expert Syst Appl 168(27):114446–110

    Article  Google Scholar 

  • Zarate C, Cabarle F, Macababayao I, Cruz R (2020) Evolving spiking neural P systems by fixing neurons, and varying rules and synapses. Philipp Comput J 14(2):21–30

    Google Scholar 

  • Zhang GX (2021) Membrane computing. Int J Parallel Emerg Distrib Syst 36(1):1–2

    Article  Google Scholar 

  • Zhang XY, Zeng XX, Pan LQ, Luo B (2009) A spiking neural P system for performing multiplication of two arbitrary natural numbers. Chin J Comput 32(12):2362–2372

    MathSciNet  Google Scholar 

  • Zhang GX, Rong HN, Ou Z, Pérez-Jiménez MJ, Gheorghe M (2014) Automatic design of deterministic and non-halting membrane systems by tuning syntactical ingredients. IEEE Trans Nanobiosci 13(3):363–371

    Article  Google Scholar 

  • Zhang GX, Shang ZY, Verlan S, Martínez-Amor MA, Yuan C, Valencia-Cabrer L, Pérez-Jiménez MJ (2020) An overview of hardware implementation of membrane computing models. ACM Comput Surv 53(4):1–38

    Google Scholar 

  • Zhang G, Rong H, Paul P, He Y, Neri F, Pérez-Jiménez MJ (2021) A complete arithmetic calculator constructed from spiking neural P systems and it application to information fusion. Int J Neural Syst 31(1):2050055–1205005517

    Article  Google Scholar 

  • Zhang G, Zhang X, Rong H, Paul P, Zhu M, Neri F, Ong Y (2022) A layered spiking neural system for classification problems. Int J Neural Syst. https://doi.org/10.1142/S012906572250023X

    Article  Google Scholar 

  • Zhang G, Pérez-Jiménez MJ, Gheorghe M (2017) Real-life applications with membrane computing. In: Springer

  • Zhang G, Pérez-Jiménez MJ, Riscos-Núñez A, Verlan S, Konur S, Hinze T, Gheorghe M (2021) Membrane computing models: implementations. In: Springer

  • Zhu M, Zhang GX, Yang Q, Rong HN, Yuan WT, Pérez-Jiménez MJ (2018) P systems-based computing polynomials with integer coefficients: design and formal verification. IEEE Trans Nanobiosci 17(3):272–280

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (61972324, 61373047), the Sichuan Science and Technology Program (2021YFS0313, 2021YFG0133), Beijing Advanced Innovation Center for Intelligent Robots and Systems (2019IRS14).

Author information

Author notes

  1. Jianping Dong and Biao Luo have contributed equally to this work.

Authors and Affiliations

  1. School of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Chengdu, 610059, China

    Jianping Dong

  2. Research Center for Artificial Intelligence, Chengdu University of Technology, Chengdu, 610059, China

    Biao Luo & Gexiang Zhang

  3. School of Automation, Chengdu University of Information Technology, Chengdu, 610225, China

    Gexiang Zhang

Authors
  1. Jianping Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Biao Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gexiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gexiang Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, J., Luo, B. & Zhang, G. Automatic design of arithmetic operation spiking neural P systems. Nat Comput 22, 55–67 (2023). https://doi.org/10.1007/s11047-022-09902-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-022-09902-5

Keywords

Search

Navigation