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Computing with Biophysical and Hardware-Efficient Neural Models

  • Konstantin Selyunin
  • Ramin M. Hasani
  • Denise Ratasich
  • Ezio Bartocci
  • Radu Grosu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10305)

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.

Keywords

TrueNorth model Hodgkin-Huxley model Rate encoding Arithmetic operations Simulations 

Notes

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Konstantin Selyunin
    • 1
  • Ramin M. Hasani
    • 1
  • Denise Ratasich
    • 1
  • Ezio Bartocci
    • 1
  • Radu Grosu
    • 1
  1. 1.Vienna University of TechnologyViennaAustria

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