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Molecular Approach to Hopfield Neural Network

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Book cover Artificial Intelligence and Soft Computing (ICAISC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9119))

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Abstract

The present article puts forward a completely new technology development , a spin glass-like molecular implementation of the Hopfield neural structure. This novel approach uses magnetic molecules homogenously distributed in mesoporous silica matrix, which forms a base for a converting unit, an equivalent of a neuron in the Hopfield network. Converting units interact with each other via a fully controlled magnetic fields, which corresponds to weighted interconnections in the Hopfield network. This novel technology enables building fast, high-density content addressable associative memories. In particular, it is envisaged that in the future this approach can be scaled up to mimic memory with human-like characteristics. This would be a breakthrough in artificial brain implementations and usher in a new type of highly intelligent beings. Another application relates to systems designed for multi-objective optimization (multiple criteria decision making).

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Czestochowa University of Technology, Al. A.K. 36, 42-200, Czestochowa, Poland

    Łukasz Laskowski & Jerzy Jelonkiewicz

  2. Institute of Physics, Czestochowa University of Technology, Al. Armii Krajowej 19, PL-42-200, Czestochowa, Poland

    Łukasz Laskowski & Magdalena Laskowska

  3. Chimie Moléculaire et Organisation du Solide, Institut Charles Gerhardt, UMR 5253 CC 1701, Université Montpellier II, 2 Place E. Bataillon, F-34095, Montpellier Cedex 5, France

    Arnaud Boullanger

Authors
  1. Łukasz Laskowski
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  2. Magdalena Laskowska
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  3. Jerzy Jelonkiewicz
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  4. Arnaud Boullanger
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Corresponding author

Correspondence to Łukasz Laskowski .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafal Scherer

  4. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Laskowski, Ł., Laskowska, M., Jelonkiewicz, J., Boullanger, A. (2015). Molecular Approach to Hopfield Neural Network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-19324-3_7

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19323-6

  • Online ISBN: 978-3-319-19324-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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