Artículo
Sequential Spiking Neural P Systems with Structural Plasticity Based on Max/Min Spike Number
Autor/es | Cabarle, Francis George C.
Adorna, Henry N. Pérez Jiménez, Mario de Jesús |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2016 |
Fecha de depósito | 2021-07-21 |
Publicado en |
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Resumen | 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 ... 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) were 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, maxpseudo, 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. Speci cally, 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. |
Agencias financiadoras | Ministerio de Economía y Competitividad (MINECO). España |
Identificador del proyecto | TIN2012-37434 |
Cita | Cabarle, F.G.C., Adorna, H.N. y Pérez Jiménez, M.d.J. (2016). Sequential Spiking Neural P Systems with Structural Plasticity Based on Max/Min Spike Number. Neural Computing and Applications, 27 (5), 1337-1347. |
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