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

TitleSequential spiking neural P systems with structural plasticity based on max/min spike number
Publication TypeJournal Papers
Year of Publication2015
AuthorsCabarle, F. G. C., Adorna H. N., & Pérez-Jiménez M. J.
Journal TitleNeural Computing and Applications
PublisherSpringer
Place PublishedAmsterdam (The Netherlands)
Pages1-11
Date Published06/2015
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

KeywordsMembrane computing, Sequential systems, spiking neural P systems, Structural plasticity, turing universality
URLhttp://link.springer.com/article/10.1007/s00521-015-1937-5
Impact Factor

1.569

Ranking

53/123 - Q2

ISSN Number0941-0643
DOI10.1007/s00521-015-1937-5