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Deep Learning with Dense Random Neural Networks

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Book cover Man-Machine Interactions 5 (ICMMI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 659))

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Abstract

We exploit the dense structure of nuclei to postulate that in such clusters, the neuronal cells will communicate via soma-to-soma interactions, aswell as through synapses. Using the mathematical structure of the spiking Random Neural Network, we construct a multi-layer architecture for Deep Learning. An efficient training procedure is proposed for this architecture. It is then specialized to multi-channel datasets, and applied to images and sensor-based data.

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Author information

Authors and Affiliations

  1. Intelligent Systems and Networks Group, Department of Electrical and Electronic Engineering, Imperial College, London, SW7 2BT, UK

    Erol Gelenbe & Yonghua Yin

Authors
  1. Erol Gelenbe
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  2. Yonghua Yin
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Corresponding author

Correspondence to Erol Gelenbe .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Katarzyna Harezlak

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Piotrowska

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Gelenbe, E., Yin, Y. (2018). Deep Learning with Dense Random Neural Networks. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-67792-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67791-0

  • Online ISBN: 978-3-319-67792-7

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