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A Novel Bifurcation-Based Synthesis of Asynchronous Cellular Automaton Based Neuron

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Artificial Neural Networks and Machine Learning – ICANN 2012 (ICANN 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7552))

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Abstract

A spiking neuron model described by an asynchronous cellular automaton is introduced. Our model can be implemented in an asynchronous sequential logic circuit and its control parameter is adjustable after implementation in an FPGA. It is shown that our model can reproduce the features of four groups into which biological and other model neurons are classified. In addition, underlying bifurcations of the four groups are analyzed, and the results yield basic guides to synthesis of our model.

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References

  1. Matsubara, T., Torikai, H., Hishiki, T.: A Generalized Rotate-and-Fire Digital Spiking Neuron Model and Its On-FPGA Learning. IEEE Trans. on Circuits and Systems II: Express Briefs 58(11), 677–681 (2011)

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Author information

Authors and Affiliations

  1. Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama-cho, Toyonaka, Osaka, 560-8531, Japan

    Takashi Matsubara & Hiroyuki Torikai

Authors
  1. Takashi Matsubara
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  2. Hiroyuki Torikai
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Editor information

Editors and Affiliations

  1. Neuro Heuristic Research Group, University of Lausanne, 1015, Lausanne, Switzerland

    Alessandro E. P. Villa

  2. Department of Informatics, Nicolaus Copernicus University, 87-100, Toruń, Poland

    Włodzisław Duch

  3. Center for Complex Systems Studies, Kalamazoo College, 49006, Kalamazoo, MI, USA

    Péter Érdi

  4. Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, 16146, Genoa, Italy

    Francesco Masulli

  5. Institut für Neuroinformatik, Universität Ulm, 89069, Ulm, Germany

    Günther Palm

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© 2012 Springer-Verlag Berlin Heidelberg

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Matsubara, T., Torikai, H. (2012). A Novel Bifurcation-Based Synthesis of Asynchronous Cellular Automaton Based Neuron. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33269-2_30

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  • DOI: https://doi.org/10.1007/978-3-642-33269-2_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33268-5

  • Online ISBN: 978-3-642-33269-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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