Single spiking neuron multi-objective optimization for pattern classification

Carlos Juarez-Santini, Manuel Ornelas-Rodriguez, Jorge Alberto Soria-Alcaraz, Alfonso Rojas-Domínguez, Hector J. Puga-Soberanes, Andrés Espinal, Horacio Rostro-Gonzalez

Producció científica: Article en revista indexadaArticleAvaluat per experts

1 Citació (Scopus)


As neuron models become more plausible, fewer computing units may be required to solve some problems; such as static pattern classification. Herein, this problem is solved by using a single spiking neuron with rate coding scheme. The spiking neuron is trained by a variant of Multi-objective Particle Swarm Optimization algorithm known as OMOPSO. There were carried out two kind of experiments: the first one deals with neuron trained by maximizing the inter distance of mean firing rates among classes and minimizing standard deviation of the intra firing rate of each class; the second one deals with dimension reduction of input vector besides of neuron training. The results of two kind of experiments are statistically analyzed and compared again a Mono-objective optimization version which uses a fitness function as a weighted sum of objectives.

Idioma originalAnglès
Pàgines (de-a)73-80
Nombre de pàgines8
RevistaJournal of Automation, Mobile Robotics and Intelligent Systems
Estat de la publicacióPublicada - 2020
Publicat externament


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