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Computing with Biophysical and Hardware-Efficient Neural Models

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Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10305))

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Abstract

In this paper we evaluate how seminal biophysical Hodgkin Huxley model and hardware-efficient TrueNorth model of spiking neurons can be used to perform computations on spike rates in frequency domain. This side-by-side evaluation allows us to draw connections how fundamental arithmetic operations can be realized by means of spiking neurons and what assumptions should be made on input to guarantee the correctness of the computed result. We validated our approach in simulation and consider this work as a first step towards FPGA hardware implementation of neuromorphic accelerators based on spiking models.

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Acknowledgment

This research is supported by the project HARMONIA (845631), funded by a national Austrian grant from FFG (Österreichische Forschungsförderungsgesellschaft) under the program IKT der Zukunft and the EU ICT COST Action IC1402 on Runtime Verification beyond Monitoring (ARVI).

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Authors and Affiliations

  1. Vienna University of Technology, Vienna, Austria

    Konstantin Selyunin, Ramin M. Hasani, Denise Ratasich, Ezio Bartocci & Radu Grosu

Authors
  1. Konstantin Selyunin
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  2. Ramin M. Hasani
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  3. Denise Ratasich
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  4. Ezio Bartocci
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  5. Radu Grosu
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Corresponding author

Correspondence to Konstantin Selyunin .

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Editors and Affiliations

  1. Universidad de Granada , Granada, Spain

    Ignacio Rojas

  2. University of Malaga , Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Selyunin, K., Hasani, R.M., Ratasich, D., Bartocci, E., Grosu, R. (2017). Computing with Biophysical and Hardware-Efficient Neural Models. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10305. Springer, Cham. https://doi.org/10.1007/978-3-319-59153-7_46

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  • DOI: https://doi.org/10.1007/978-3-319-59153-7_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59152-0

  • Online ISBN: 978-3-319-59153-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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