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Efficient Pattern Recognition Using the Frequency Response of a Spiking Neuron

  • Sergio Valadez-Godínez
  • Javier González
  • Humberto SossaEmail author
Conference paper
  • 943 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10267)

Abstract

In previous works, a successful scheme using a single Spiking Neuron (SN) to solve complex problems in pattern recognition has been proposed. This consists in using the firing frequency response to classify a given input pattern, which is multiplied by a weight vector to produce a constant stimulation current. The weight vector is adjusted by an evolutionary strategy where the objective is to obtain an optimal frequency separation. The problem is that the SN has to be numerically simulated several times when the weight vector is being adjusted. In this work, we propose fitting the SN frequency response curve to a piecewise linear function to be used instead of the costly SN simulation. A high fitting degree was found, but, more importantly, the computational cost of the training and testing phases was drastically reduced.

Keywords

Spiking Neuron Izhikevich Pattern recognition Curve fitting Frequency Response Curve Piecewise linear function Firing Rate Evolutionary strategy Differential evolution Computational cost 

Notes

Acknowledgments

S. Valadez-Godínez would like to thank CONACYT and SIP-IPN for the scholarship granted in pursuit of his doctoral studies. J. González would like to thank CONACYT and SIP-IPN for undertaking his Master studies. H. Sossa would like to thank SIP-IPN and CONACYT under grants 20170693 and 65 (Frontiers of Science) to carry out this research. We are also very grateful to reviewers for their helpful comments.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Sergio Valadez-Godínez
    • 1
  • Javier González
    • 1
  • Humberto Sossa
    • 1
    Email author
  1. 1.Centro de Investigación en ComputaciónInstituto Politécnico NacionalMexico CityMexico

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