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Diffusion Modelling

Topographic Error of SOM Under Control

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Abstract

The traditional self-organized map (SOM) is learned by Kohonen learning and the most common 2-dimensional grids defining the structure of the map are the hexagonal grid and the rectangular grid. A novel model of self-organization is based on hexagonal grid and diffusion modeling in continuous space which is a good approximation of endorphins propagation and nitric oxide generation in the real brain. Therefore the structure of the system is described by neuron coordinates instead of neighborhood relationships in traditional SOM. The discussed neuron activation using the diffusion process and novel diffusive learning algorithm is based on this activation mentioned above. The novel structure and algorithm are demonstrated on simple examples and real economic applications.

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Acknowledgements

The authors would like to acknowledge the support of the research grant SGS20/190/OHK4/3T/14. The second author also acknowledges the support of the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16_019/0000765 Research Center for Informatics.

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  1. FNSPE, CTU in Prague, Brehova 7, 115 19, Prague 1, Czech Republic

    Radek Hrebik & Jaromir Kukal

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  1. Radek Hrebik
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  2. Jaromir Kukal
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Correspondence to Radek Hrebik.

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Hrebik, R., Kukal, J. Diffusion Modelling. Neural Process Lett 54, 835–852 (2022). https://doi.org/10.1007/s11063-021-10660-1

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