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The Self-Organising Kernel Memory (SOKM)

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Artificial Mind System - Kernel Memory Approach

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1))

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Abstract

In the previous chapter, various topological representations in terms of the kernel memory concept have been discussed together with some illustrative examples. In this chapter, a novel unsupervised algorithm to train the link weights between the KFs is given by extending the original Hebb’s neuropsychological concept, whereby the self-organising kernel memory (SOKM)1 is proposed.

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Authors and Affiliations

  1. RIKEN Brain Science Institute Laboratory for Advanced Brain Signal Processing, 2-1 Hirosawa, Wako-Shi Saitama, 351-0198, Japan

    Dr. Tetsuya Hoya

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  1. Dr. Tetsuya Hoya
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Hoya, T. The Self-Organising Kernel Memory (SOKM). In: Artificial Mind System - Kernel Memory Approach. Studies in Computational Intelligence, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10997444_4

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  • DOI: https://doi.org/10.1007/10997444_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26072-1

  • Online ISBN: 978-3-540-32403-4

  • eBook Packages: EngineeringEngineering (R0)

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