Training spiking neural models using artificial bee colony.
Comput Intell Neurosci
; 2015: 947098, 2015.
Article
em En
| MEDLINE
| ID: mdl-25709644
Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, the lack of learning strategies for training these models do not allow to use them in several pattern recognition problems. On the other hand, several bioinspired algorithms have been proposed in the last years for solving a broad range of optimization problems, including those related to the field of artificial neural networks (ANNs). Artificial bee colony (ABC) is a novel algorithm based on the behavior of bees in the task of exploring their environment to find a food source. In this paper, we describe how the ABC algorithm can be used as a learning strategy to train a spiking neuron aiming to solve pattern recognition problems. Finally, the proposed approach is tested on several pattern recognition problems. It is important to remark that to realize the powerfulness of this type of model only one neuron will be used. In addition, we analyze how the performance of these models is improved using this kind of learning strategy.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Redes Neurais de Computação
/
Aprendizagem
/
Neurônios
Idioma:
En
Revista:
Comput Intell Neurosci
Assunto da revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
México
País de publicação:
Estados Unidos