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Handbook of Memristor Networks pp 369–389Cite as

Memristors and Memristive Devices for Neuromorphic Computing

Abstract

Memristors are an important emerging technology for memory and neuromorphic computing applications. In this chapter, we review the fundamentals of the memistor framework developed by Leon Chuan nearly 40 years ago, and examine resistive switching phenomena as the quintessential example of physical memristive systems. A special focus is given to the hardware emulation of biological synapses using memristors and groundbreaking results in the field are reviewed. Future research directions with spiking neural networks is outlined and the exciting prospect of emergent behavior in memristor networks is discussed.

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Notes

  1. 1.

    The equation listed is for a charge-controlled memristor; [1] includes a definition for flux-controlled memristors as well.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor MI, USA

    Patrick Sheridan & Wei Lu

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  1. Patrick Sheridan
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  2. Wei Lu
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Correspondence to Wei Lu .

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Editors and Affiliations

  1. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, USA

    Prof. Leon Chua

  2. Democritus University of Thrace, Xanthi, Greece

    Dr. Georgios Ch. Sirakoulis

  3. Department of Computer Science and Creative Technologies, University of the West of England, Bristol, UK

    Prof. Andrew Adamatzky

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Sheridan, P., Lu, W. (2019). Memristors and Memristive Devices for Neuromorphic Computing. In: Chua, L., Sirakoulis, G., Adamatzky, A. (eds) Handbook of Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-76375-0_13

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