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Short- and long-term dynamics in a stochastic pulse stream neuron implemented in FPGA

  • M. Rossmann
  • A. Bühlmeier
  • G. Manteuffel
  • K. Goser
Part VIII: Implementations
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1327)

Abstract

This paper presents the implementation of the Hebbian learning rule in a hardware-friendly architecture based on a stochastic pulse representation of the signals. We compare implementation costs and speed of this approach with those of a parallel and a bit-serial implementation. The neural model includes both short- and long-term dynamics. Hence, networks composed of these neurons solve delayed reinforcement and adaptive timing tasks which has been shown in several real-world applications.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • M. Rossmann
    • 1
  • A. Bühlmeier
    • 1
    • 2
  • G. Manteuffel
    • 3
  • K. Goser
    • 1
  1. 1.Dept. of MicroelectronicsUniversity of DortmundDortmundGermany
  2. 2.FB 3 Computer ScienceUniversity of BremenBremenGermany
  3. 3.FBNDummerstorfGermany

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