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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2016

01.07.2016 | Original Article

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

verfasst von: Francis George C. Cabarle, Henry N. Adorna, Mario J. Pérez-Jiménez

Erschienen in: Neural Computing and Applications | Ausgabe 5/2016

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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.
Vorheriger Artikel Consensus problems for multi-agent systems with nonlinear algorithms
Nächster Artikel Projective synchronization of two different fractional-order chaotic systems via adaptive fuzzy control

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Fußnoten
1
See e.g. [13] and [14] and references therein.
 
2
An overview in [29] and the SNP systems chapter in [28].
 
3
Introduced in [1] and improved and extended in [2].
 
4
A good introduction is [26] and the P systems webpage at http://​ppage.​psystems.​eu/​, with a handbook in [28].
 
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Metadaten
Titel
Sequential spiking neural P systems with structural plasticity based on max/min spike number
verfasst von
Francis George C. Cabarle
Henry N. Adorna
Mario J. Pérez-Jiménez
Publikationsdatum
01.07.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1937-5

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