Memristors for More Than Just Memory: How to Use Learning to Expand Applications

  • Paul J. Werbos
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 4)


There has been a huge explosion of interest in the memristor since the first experimental confirmation by HP in 2008 (Strukov et al., Nature 453:80–83, 2008). Because the memristor and its variants provide a huge increase in memory density, compared with existing technologies like flash memory , many of us expect that they will move very quickly to a huge and important market in the memory area. But what about other large-scale markets and applications? What is the pathway which could open up those larger markets? The purpose of this chapter is to discuss what would be needed to capture those larger markets.


Mixed Integer Linear Programming Cellular Neural Network Grand Challenge Massive Parallelism Adaptive Dynamic Programming 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  1. 1.Energy, Power and Adaptive SystemsNational Science FoundationArlingtonUSA

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