Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2015:2015:947098.
doi: 10.1155/2015/947098. Epub 2015 Feb 1.

Training spiking neural models using artificial bee colony

Roberto A Vazquez  1 , Beatriz A Garro  2
Affiliations

Training spiking neural models using artificial bee colony

Roberto A Vazquez et al. Comput Intell Neurosci. 2015.

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.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Simulation of the Izhikevich neuron model 100v˙=0.7v+60v+40-u+I, u˙=0.03-2v+60-u, if v ≥ 35 then v ← −50 and u ← u + 100. (a) Injection of the step of DC current I = 70 pA. (b) Injection of the step of DC current I = 100 pA.
Figure 2
Figure 2
(a)–(d) Some of the images used to train the proposed method. (f)–(i) Some of the images were used to test the proposed method.
Figure 3
Figure 3
Training error achieved by the spiking model during the learning phase using ABC algorithm. (a) 30 experiments using the wine dataset are shown. (b) 30 experiments using the iris dataset are shown. (c) 30 experiments using the glass dataset are shown. (d) 30 experiments using the diabetes dataset are shown. (e) 30 experiments using the liver dataset are shown. (f) 30 experiments using the object recognition dataset are shown.
Figure 4
Figure 4
Experimental results obtained with proposed methodology for different datasets. Notice that different firing rates which correspond to different classes can be observed. Each dot represents the time that the neuron generate an spike. (a) Wine dataset. (b) Iris plant dataset. (c) Object recognition dataset. (d) Glass dataset. (e) Diabetes dataset. (f) Liver dataset.
Pseudocode 1
Pseudocode 1
See this image and copyright information in PMC

References

    1. Garro B. A., Sossa H., Vazquez R. A. Design of artificial neural networks using a modified particle swarm optimization algorithm. Proceedings of the International Joint Conference on Neural Networks (IJCNN '09); June 2009; pp. 938–945. - DOI
    1. Garro B. A., Sossa H., Vázquez R. A. Proceedings of the 17th International Conference on Neural Information Processing: Models and Applications—Part II (ICONIP '10) Vol. 6444. Berlin, Germany: Springer; 2010. Design of artificial neural networks using differential evolution algorithm; pp. 201–208. (Lecture Notes in Computer Science).
    1. Dorigo M., Blum C. Ant colony optimization theory: a survey. Theoretical Computer Science. 2005;344(2-3):243–278. doi: 10.1016/j.tcs.2005.05.020. - DOI
    1. Karaboga D., Basturk B. A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of Global Optimization. 2007;39(3):459–471. doi: 10.1007/s10898-007-9149-x. - DOI
    1. Karaboga D., Gorkemli B., Ozturk C., Karaboga N. A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artificial Intelligence Review. 2014;42(1):21–57. doi: 10.1007/s10462-012-9328-0. - DOI

Publication types