Abstract
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.
Highlights
Artificial neural networks (ANNs) are applied in a broad range of problems
We described how the artificial bee colony algorithm can be used as a learning strategy to train a spiking neural model
We observed that the spiking neuron model provides acceptable results during the pattern recognition task, regardless of the swarm intelligence algorithms used as a learning strategy, the spiking neuron model provides acceptable results during the pattern recognition task
Summary
Artificial neural networks (ANNs) are applied in a broad range of problems. One interesting alternative to designing the topology and training and exploiting the capabilities of an ANN is to adopt a learning strategy based on evolutionary and swarm intelligence algorithms. It is well-known that designing and training tasks can be stated as optimization problems; for that reason, it is possible to apply different types of evolutionary and swarm intelligence algorithms. Particle swarm optimization [1] and differential evolution [2] have been used to design and train ANNs automatically
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