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Adaptive hierarchical structures

  • Neuroscience
  • Conference paper
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From Natural to Artificial Neural Computation (IWANN 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

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Abstract

The construction of a hierarchical system to distinguish classes of patterns can be improved by the combination of a dynamical elastic matching with a time dependent image resolution. Using the elastic matching as a preprocessing for synergetic computers one achieves an invariant perception by means of arbitrary spatial transformations. On the other hand a time dependent resolution, realized with a Gaussian distribution, can be interpreted as a dynamical change of the available image information. The coupling of these two dynamics results in a hierarchy of locality of pattern transformations. Therefore this system utilizes a dynamical feature extraction and implies the definition several classes of patterns.

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Authors and Affiliations

  1. Institute for Theoretical Physics and Synergetics, University of Stuttgart, Pfaffenwaldring 57/4, D-70550, Stuttgart, Germany

    A. Daffertshofer & H. Haken

Authors
  1. A. Daffertshofer
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  2. H. Haken
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José Mira Francisco Sandoval

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© 1995 Springer-Verlag Berlin Heidelberg

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Daffertshofer, A., Haken, H. (1995). Adaptive hierarchical structures. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_159

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  • DOI: https://doi.org/10.1007/3-540-59497-3_159

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

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