Ponencia
Nondeterminism in Spiking Neural P Systems: Algorithms and Simulations
Autor/es | Carandang, Jym Paul
Cabarle, Francis George C. Adorna, Henry N. Hernández, Nestine Hope S. Martínez del Amor, Miguel Ángel |
Departamento | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial |
Fecha de publicación | 2017 |
Fecha de depósito | 2021-11-29 |
Publicado en |
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Resumen | Spiking Neural P system (or SN P system) is a computing
model based on the neurons in a living being. It is composed of neurons
containing spikes interconnected by synapses. Each neuron contain a set
of rules which will ... Spiking Neural P system (or SN P system) is a computing model based on the neurons in a living being. It is composed of neurons containing spikes interconnected by synapses. Each neuron contain a set of rules which will determine how the spikes are passed in the system. It is a non-deterministic and parallel system which makes GPU a good candidate for simulating this computing model. A matrix representation for system without delay was previously developed and an algorithm for simulating deterministic systems with delay was also presented. In this work, an algorithm for simulating non-deterministic Spiking Neural P System was presented. To accelerate simulations of Spiking Neural P Systems, this algorithm was then implemented and used to simulate nonuniform and uniform solution to the subset sum problem as a case study. Time and space resources in the GPU of such simulations are then compared and analyzed |
Cita | Carandang, J.P., Cabarle, F.G.C., Adorna, H.N., Hernández, N.H.S. y Martínez del Amor, M.Á. (2017). Nondeterminism in Spiking Neural P Systems: Algorithms and Simulations. En ACMC 2017: The 6th Asian Conference on Membrane Computing Chengdu, China: Xihua University. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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(ACMC-2017) 6th.pdf | 11.83Mb | [PDF] | Ver/ | |