Neural Computing and Applications

, Volume 27, Issue 5, pp 1337–1347

Sequential spiking neural P systems with structural plasticity based on max/min spike number

  • Francis George C. Cabarle
  • Henry N. Adorna
  • Mario J. Pérez-Jiménez
Original Article

Abstract

Spiking neural P systems (in short, SNP systems) are parallel, distributed, and nondeterministic computing devices inspired by biological spiking neurons. Recently, a class of SNP systems known as SNP systems with structural plasticity (in short, SNPSP systems) was introduced. SNPSP systems represent a class of SNP systems that have dynamism applied to the synapses, i.e. neurons can use plasticity rules to create or remove synapses. In this work, we impose the restriction of sequentiality on SNPSP systems, using four modes: max, min, max-pseudo-, and min-pseudo-sequentiality. We also impose a normal form for SNPSP systems as number acceptors and generators. Conditions for (non)universality are then provided. Specifically, acceptors are universal in all modes, while generators need a nondeterminism source in two modes, which in this work is provided by the plasticity rules.

Keywords

Membrane computing Spiking neural P systems Structural plasticity Sequential systems Turing universality 

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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  • Francis George C. Cabarle
    • 1
  • Henry N. Adorna
    • 1
  • Mario J. Pérez-Jiménez
    • 2
  1. 1.Algorithms and Complexity Lab, Department of Computer ScienceUniversity of the Philippines DilimanQuezon CityPhilippines
  2. 2.Department of Computer Science and AIUniversity of SevillaSevilleSpain

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