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Small Spiking Neural P Systems with Structural Plasticity

  • Francis George C. Cabarle
  • Ren Tristan A. de la Cruz
  • Henry N. Adorna
  • Ma. Daiela Dimaano
  • Faith Therese Peña
  • Xiangxiang Zeng
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11270)

Abstract

Spiking neural P systems or SN P systems are computing models inspired by spiking neurons. The SN P systems variant we focus on are SN P systems with structural plasticity or SNPSP systems. Unlike SN P systems, SNPSP systems have a dynamic topology for creating or removing synapses among neurons. In this work we construct small universal SNPSP systems: 62 and 61 neurons for computing functions and generating numbers, respectively. We then provide some new directions, e.g. parameters to consider, in the search for such small systems.

Notes

Acknowledgements

The first three authors are grateful for the ERDT project (DOST-SEI), Project 171722 PhDIA and Semirara Mining Corp. Professorial Chair (UP Diliman OVCRD). X. Zeng is supported by Juan de la Cierva position (code: IJCI-2015-26991) and the National Natural Science Foundation of China (Grant Nos. 61472333, 61772441, 61472335). The authors are grateful for useful comments from two anonymous referees.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Francis George C. Cabarle
    • 1
    • 2
  • Ren Tristan A. de la Cruz
    • 1
  • Henry N. Adorna
    • 1
  • Ma. Daiela Dimaano
    • 1
  • Faith Therese Peña
    • 1
  • Xiangxiang Zeng
    • 2
    • 3
  1. 1.Department of Computer ScienceUniversity of the Philippines DilimanQuezon CityPhilippines
  2. 2.School of Information Science and TechnologyXiamen UniversityXiamenChina
  3. 3.Departamento de Inteligencia ArtificialUniversidad Politécnica de Madrid (UPM)MadridSpain

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