Rectifying Resistive Memory Devices as Dynamic Complementary Artificial Synapses

Dan Berco*

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

9 Scopus citations


Brain inspired computing is a pioneering computational method gaining momentum in recent years. Within this scheme, artificial neural networks are implemented using two main approaches: software algorithms and designated hardware architectures. However, while software implementations show remarkable results (at high-energy costs), hardware based ones, specifically resistive random access memory (RRAM) arrays that consume little power and hold a potential for enormous densities, are somewhat lagging. One of the reasons may be related to the limited excitatory operation mode of RRAMs in these arrays as adjustable passive elements. An interesting type of RRAM was demonstrated recently for having alternating (dynamic switching) current rectification properties that may be used for complementary operation much like CMOS transistors. Such artificial synaptic devices may be switched dynamically between excitatory and inhibitory modes to allow doubling of the array density and significantly reducing the peripheral circuit complexity.

Original languageEnglish
Article number755
JournalFrontiers in Neuroscience
StatePublished - 22 10 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright © 2018 Berco.


  • artificial neural networks
  • brain inspired computing
  • dynamic artificial synapses
  • memristors
  • rectifying synapses


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