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Pattern representation and recognition with accelerated analog neuromorphic systems

Abstract · Mar 17, 2017 14:59 ·

q-bio-nc cs-ne stat-ml

Arxiv Abstract

  • Mihai A. Petrovici
  • Sebastian Schmitt
  • Johann Klähn
  • David Stöckel
  • Anna Schroeder
  • Guillaume Bellec
  • Johannes Bill
  • Oliver Breitwieser
  • Ilja Bytschok
  • Andreas Grübl
  • Maurice Güttler
  • Andreas Hartel
  • Stephan Hartmann
  • Dan Husmann
  • Kai Husmann
  • Sebastian Jeltsch
  • Vitali Karasenko
  • Mitja Kleider
  • Christoph Koke
  • Alexander Kononov
  • Christian Mauch
  • Paul Müller
  • Johannes Partzsch
  • Thomas Pfeil
  • Stefan Schiefer
  • Stefan Scholze
  • Anand Subramoney
  • Vasilis Thanasoulis
  • Bernhard Vogginger
  • Robert Legenstein
  • Wolfgang Maass
  • René Schüffny
  • Christian Mayr
  • Johannes Schemmel
  • Karlheinz Meier

Despite being originally inspired by the central nervous system, artificial neural networks have diverged from their biological archetypes as they have been remodeled to fit particular tasks. In this paper, we review several possibilites to reverse map these architectures to biologically more realistic spiking networks with the aim of emulating them on fast, low-power neuromorphic hardware. Since many of these devices employ analog components, which cannot be perfectly controlled, finding ways to compensate for the resulting effects represents a key challenge. Here, we discuss three different strategies to address this problem: the addition of auxiliary network components for stabilizing activity, the utilization of inherently robust architectures and a training method for hardware-emulated networks that functions without perfect knowledge of the system’s dynamics and parameters. For all three scenarios, we corroborate our theoretical considerations with experimental results on accelerated analog neuromorphic platforms.

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