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Memristive Models for the Emulation of Biological Learning

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Memristor Computing Systems

Abstract

Memristive devices offer a number of new possibilities for neuromorphic electronics, and there are currently strong efforts being made to find suitable device structures for this application. In this chapter, the important biological mechanisms for learning and memory emulation and their transfer to memristive devices are presented. Therefore, we use the framework of Hebbian learning models and show how these models can be applied to memristive devices. We explain important cellular paradigms of information processing that allow a transition from the cellular to the network level and address the existing challenges that need to be solved for the development of cognitive electronics.

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Acknowledgements

The authors acknowledge financial support via the Deutsche Forschungsgemeinschaft (DFG) by the Research Unit 2093: memristive devices for neuronal systems, Carl Zeiss Foundation via MemWerk: memristive materials for neuromorphic electronics, and Nick Diederich for carefully reading the manuscript. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 434434223 – SFB 1461.

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Authors and Affiliations

  1. Department of Micro- and Nanoelectronic Systems, Ilmenau University of Technology, 98684, Ilmenau, Germany

    Martin Ziegler

  2. Nanoelektronik, Technische Fakultät, Christian-Albrechts-Universität zu Kiel, 24143, Kiel, Germany

    Hermann Kohlstedt

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  1. Martin Ziegler
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  2. Hermann Kohlstedt
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Correspondence to Martin Ziegler .

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Editors and Affiliations

  1. Department of Electrical Engineering & Computer Science, University of California, Berkeley, Berkley, CA, USA

    Leon O. Chua

  2. Fundamentals of Electrical Engineering, TU Dresden, Dresden, Germany

    Ronald Tetzlaff

  3. Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Angela Slavova

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Ziegler, M., Kohlstedt, H. (2022). Memristive Models for the Emulation of Biological Learning. In: Chua, L.O., Tetzlaff, R., Slavova, A. (eds) Memristor Computing Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-90582-8_11

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  • DOI: https://doi.org/10.1007/978-3-030-90582-8_11

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