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International Conference on Artificial Neural Networks

ICANN 1996: Artificial Neural Networks — ICANN 96 pp 575–580Cite as

Analysis of multi-fluorescence signals using a modified Self-Organizing Feature Map

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1112))

Abstract

This paper introduces an algorithm for the multi-sensory integration of signals from the fluorescence microscopy. For the cluster analysis a Self-Organizing Feature Map (SOFM) is used. One basic property of these artificial neural nets is the smoothing of the input vectors and thus a certain insensitivity to clusters of low feature density. While classifying clusters of highly different feature density this property is undesirable. A modification of the learning algorithm of the SOFM, which makes a reproduction of low feature density clusters on a SOFM possible, is described.

This work was supported by the DFG/BMBF grant (Innovationskolleg 15/A1).

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Author information

Authors and Affiliations

  1. Institute for Measurement and Electronics, Otto-von-Guericke University Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Stefan Schünemann & Bernd Michaelis

  2. Institute for Medical Neurobiology, Otto-von-Guericke University Magdeburg, P.O. Box 4120, D-39016, Magdeburg, Germany

    Walter Schubert

Authors
  1. Stefan Schünemann
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  2. Bernd Michaelis
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  3. Walter Schubert
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Editor information

Christoph von der Malsburg Werner von Seelen Jan C. Vorbrüggen Bernhard Sendhoff

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© 1996 Springer-Verlag Berlin Heidelberg

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Schünemann, S., Michaelis, B., Schubert, W. (1996). Analysis of multi-fluorescence signals using a modified Self-Organizing Feature Map. In: von der Malsburg, C., von Seelen, W., Vorbrüggen, J.C., Sendhoff, B. (eds) Artificial Neural Networks — ICANN 96. ICANN 1996. Lecture Notes in Computer Science, vol 1112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61510-5_98

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  • DOI: https://doi.org/10.1007/3-540-61510-5_98

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61510-1

  • Online ISBN: 978-3-540-68684-2

  • eBook Packages: Springer Book Archive

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