Time after Time: Notes on Delays in Spiking Neural P Systems

  • Francis George C. Cabarle
  • Kelvin C. Buño
  • Henry N. Adorna
Part of the Proceedings in Information and Communications Technology book series (PICT, volume 7)


Spiking Neural P systems, SNP systems for short, are biologically inspired computing devices based on how neurons perform computations. SNP systems use only one type of symbol, the spike, in the computations. Information is encoded in the time differences of spikes or the multiplicity of spikes produced at certain times. SNP systems with delays (associated with rules) and those without delays are two of several Turing complete SNP system variants in literature. In this work we investigate how restricted forms of SNP systems with delays can be simulated by SNP systems without delays. We show the simulations for the following spike routing constructs: sequential, iteration, join, and split.


Membrane Computing Spiking Neural P systems delays routing simulations 


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  1. 1.
    Alhazov, A., Freund, R., Oswald, M., Slavkovik, M.: Extended Spiking Neural P Systems. In: Hoogeboom, H.J., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2006. LNCS, vol. 4361, pp. 123–134. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Cabarle, F.G.C., Adorna, H., Martínez, M.A.: A Spiking Neural P system simulator based on CUDA. In: Gheorghe, M., Păun, G., Rozenberg, G., Salomaa, A., Verlan, S. (eds.) CMC 2011. LNCS, vol. 7184, pp. 87–103. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Cabarle, F.G.C., Adorna, H.N., Martínez-del-Amor, M.A., Pérez-Jiménez, M.J.: Improving GPU Simulations of Spiking Neural P Systems. Romanian Journal of Information Science and Technology 15(1) (2012)Google Scholar
  4. 4.
    Chen, H., Ionescu, M., Ishdorj, T.-O., Pǎun, A., Pǎun, G., Pérez-Jiménez, M.J.: Spiking neural P systems with extended rules: universality and languages. Natural Computing 7(2), 147–166 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Gutiérrez-Naranjo, Leporati, A.: First Steps Towards a CPU Made of Spiking Neural P Systems. Int. J. of Computers, Communications and Control IV(3), 244–252 (2009)Google Scholar
  6. 6.
    Ibarra, O., Pǎun, A., Pǎun, G., Rodríguez-patón, A., Sosik, P., Woodworth, S.: Normal forms for spiking neural P systems. Theor. Comput. Sci. 372(2-3), 196–217 (2007)zbMATHCrossRefGoogle Scholar
  7. 7.
    Ibarra, O., Pérez-Jiménez, M.J., Yokomori, T.: On spiking neural P systems. Natural Computing 9, 475–491 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  8. 8.
    Ionescu, M., Pǎun, G., Yokomori, T.: Spiking Neural P Systems. Fundamenta Informaticae 71(2, 3), 279–308 (2006)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Pan, L., Pǎun, G., Pérez-Jiménez, M.J.: Spiking neural P systems with neuron division and budding. In: Proc. of the 7th Brainstorming Week on Membrane Computing, RGNC, Sevilla, Spain, pp. 151–168 (2009)Google Scholar
  10. 10.
    Pǎun, G.: Membrane Computing: An Introduction. Springer (2002)Google Scholar
  11. 11.
    Pǎun, G., Pérez-Jiménez, M.J.: Spiking Neural P Systems. Recent Results, Research Topics. In: Condon, A., et al. (eds.) Algorithmic Bioprocesses. Springer (2009)Google Scholar
  12. 12.
    Pǎun, G., Rozenberg, G., Salomaa, A.: The Oxford Handbook of Membrane Computing. Oxford University Press (2010)Google Scholar
  13. 13.
    Zeng, X., Adorna, H., Martínez-del-Amor, M.Á., Pan, L., Pérez-Jiménez, M.J.: Matrix Representation of Spiking Neural P Systems. In: Gheorghe, M., Hinze, T., Păun, G., Rozenberg, G., Salomaa, A. (eds.) CMC 2010. LNCS, vol. 6501, pp. 377–391. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer Tokyo 2013

Authors and Affiliations

  • Francis George C. Cabarle
    • 1
  • Kelvin C. Buño
    • 1
  • Henry N. Adorna
    • 1
  1. 1.Algorithms & Complexity Lab, Department of Computer ScienceUniversity of the Philippines DilimanQuezon CityPhilippines

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