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Organic Memristive Devices and Neuromorphic Circuits

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Memristor Networks

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Abstract

Bio-inspired computational systems must be based on elements, involved, similarly to the brain, in both memorizing and processing of the information. This paper is dedicated to organic memristive devices—elements that were designed and constructed for mimicking the most important properties of synapses, responsible for Hebbian type of learning. We will consider the architecture of the device and its properties, as well as circuits and networks with adaptive features.

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Acknowledgements

We acknowledge the financial support of the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under the Collaborative project PhyChip, grant agreement number 316366.

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Authors and Affiliations

  1. CNR-IMEM (National Council of the Researches—Institute of Materials for Electronic and Magnetism), Parco Area delle Scienze 37A, 43124, Parma, Italy

    Victor Erokhin

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  1. Victor Erokhin
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Correspondence to Victor Erokhin .

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Editors and Affiliations

  1. Computer Science Department, University of the West of England, Bristol, United Kingdom

    Andrew Adamatzky

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

    Leon Chua

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Erokhin, V. (2014). Organic Memristive Devices and Neuromorphic Circuits. In: Adamatzky, A., Chua, L. (eds) Memristor Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-02630-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-02630-5_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02629-9

  • Online ISBN: 978-3-319-02630-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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