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Single spiking neuron multi-objective optimization for pattern classification

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

Journal of Automation Mobile Robotics and Intelligent Systems

2020

Vol. 14, No. 1

73--80

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.