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How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm

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Cuckoo Search and Firefly Algorithm

Part of the book series: Studies in Computational Intelligence ((SCI,volume 516))

Abstract

Spiking neurons are neural models that try to simulate the behavior of biological neurons. This model generates a response (spikes or spike train) only when the model reaches a specific threshold. This response could be coded into a firing rate and perform a pattern classification task according to the firing rate generated with the input current. However, the input current must be carefully computed to obtain the desired behavior. In this paper, we describe how the Cuckoo Search algorithm can be used to train a spiking neuron and determine the best way to compute the input current for solving a pattern classification task. The accuracy of the methodology is tested using several pattern recognition problems.

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Acknowledgments

The authors would like to thank CONACYT-INEGI and Universidad La Salle for the economical support under grant number 187637 and I-061/12, respectively.

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Authors and Affiliations

  1. Intelligent Systems Group, Universidad La Salle, Benjamin Franklin 47 Col. Hipódromo Condesa, 06140, Mexico City, Mexico

    Roberto A. Vazquez, Guillermo Sandoval & Jose Ambrosio

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  1. Roberto A. Vazquez
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  2. Guillermo Sandoval
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  3. Jose Ambrosio
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Correspondence to Roberto A. Vazquez .

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Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, United Kingdom

    Xin-She Yang

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Vazquez, R.A., Sandoval, G., Ambrosio, J. (2014). How to Generate the Input Current for Exciting a Spiking Neural Model Using the Cuckoo Search Algorithm. In: Yang, XS. (eds) Cuckoo Search and Firefly Algorithm. Studies in Computational Intelligence, vol 516. Springer, Cham. https://doi.org/10.1007/978-3-319-02141-6_8

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  • DOI: https://doi.org/10.1007/978-3-319-02141-6_8

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