Programmable current mode Hebbian learning neural network using Programmable Metallization Cell

B. Swaroop, W. C. West, G. Martinez, Michael Kozicki, L. A. Akers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The design and performance of a Hebbian learning based neural network is presented in this work. In situ analog learning was employed, thus computing the synaptic weight changes continuously during the normal operation of the artificial neural network (ANN). The complexity of a synapse is minimized by using a novel device called the Programmable Metallization Cell (PMC). Simulations with circuits with three PMCs per synapse showed that appropriate learning behaviour was obtained at the synaptic level.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Symposium on Circuits and Systems
Editors Anon
PublisherIEEE
Pages33-36
Number of pages4
Volume3
StatePublished - 1998
EventProceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS. Part 5 (of 6) - Monterey, CA, USA
Duration: May 31 1998Jun 3 1998

Other

OtherProceedings of the 1998 IEEE International Symposium on Circuits and Systems, ISCAS. Part 5 (of 6)
CityMonterey, CA, USA
Period5/31/986/3/98

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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