Cluster Analysis of Cortical Pyramidal Neurons Using SOM

  • Andreas Schierwagen
  • Thomas Villmann
  • Alan Alpár
  • Ulrich Gärtner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5998)


A cluster analysis using SOM has been performed on morphological data derived from pyramidal neurons of the somatosensory cortex of normal and transgenic mice.


Cluster analysis Kohonen’s SOM transgenic mouse somatosensory cortex pyramidal neurons dendritic morphology 


  1. 1.
    Schierwagen, A.: Mathematical and Computational Modeling of Neurons and Neuronal Ensembles. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2009. LNCS, vol. 5717, pp. 159–166. Springer, Heidelberg (2009)Google Scholar
  2. 2.
    Alpár, A., Palm, K., Schierwagen, A., Arendt, T., Gärtner, U.: Expression of constitutively active p21H-rasVal12 in postmitotic pyramidal neurons results in increased dendritic size and complexity. J. Comp. Neurol. 467, 119–133 (2003)CrossRefGoogle Scholar
  3. 3.
    Cannon, R.C.: Structure editing and conversion with cvapp (2000),
  4. 4.
    Van Pelt, J., Schierwagen, A.: Morphological analysis and modeling of neuronal dendrites. Math. Biosciences 188, 147–155 (2004)zbMATHCrossRefGoogle Scholar
  5. 5.
    Schierwagen, A.: Neuronal morphology: Shape characteristics and models. Neurophysiology 40, 366–372 (2008)CrossRefGoogle Scholar
  6. 6.
    Schierwagen, A., Costa, L.F., Alpár, A., Gärtner, A.U., Arendt, T.: Neuromorphological Phenotyping in Transgenic Mice: A Multiscale Fractal Analysis. In: Deutsch, A., et al. (eds.) Mathematical Modeling of Biological Systems, vol. II, pp. 191–199. Birkhuser, Boston (2007)Google Scholar
  7. 7.
    Scorcioni, R., Polavaram, S., et al.: L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protocols 3, 866–876 (2008)CrossRefGoogle Scholar
  8. 8.
    Kohonen, T.: Self-Organizing Maps. Springer, Heidelberg (1997)zbMATHGoogle Scholar
  9. 9.
    Heskes, T.: Energy functions for Self-Organizing Maps. In: Oja, E., Kaski, S. (eds.) Kohonen Maps, pp. 303–316. Elsevier, Amsterdam (1999)CrossRefGoogle Scholar
  10. 10.
    Bauer, H.-U., Herrmann, M., Villmann, T.: Neural Maps and Topographic Vector Quantization. Neural Networks 12, 659–676 (1999)CrossRefGoogle Scholar
  11. 11.
    Villmann, T., Der, R., Herrmann, M., Martinetz, T.: Topology Preservation in Self–Organizing Feature Maps: Exact Definition and Measurement. IEEE Transactions on Neural Networks 8, 256–266 (1997)CrossRefGoogle Scholar
  12. 12.
    Bauer, H.-U., Villmann, T.: Growing a Hypercubical Output Space in a Self–Organizing Feature Map. IEEE Transactions on Neural Networks 8, 218–226 (1997)CrossRefGoogle Scholar
  13. 13.
    Bauer, H.-U., Pawelzik, K.: Quantifying the neighborhood preservation of Self-Organizing Feature Maps. IEEE Transactions on Neural Networks 3, 570–579 (1992)CrossRefGoogle Scholar
  14. 14.
    Ultsch, A., Siemon, H.P.: Kohonen’s self–organizing feature maps for exploratory data analysis. In: Proceedings of ICNN 1990, International Neural Network Conference, pp. 305–308. Kluwer, Dordrecht (1990)Google Scholar
  15. 15.
    Vesanto, J., Himberg, J., Alhoniemi, E., Parhankangas, J.: SOM Toolbox for Matlab 5. Report A57, April 2000. Helsinki University of Technology, Finland (2000)Google Scholar
  16. 16.
    Hammer, B., Villmann, T.: Generalized Relevance Learning Vector Quantization. Neural Networks 15, 1059–1068 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Andreas Schierwagen
    • 1
  • Thomas Villmann
    • 2
  • Alan Alpár
    • 3
  • Ulrich Gärtner
    • 3
  1. 1.Institute for Computer ScienceUniversity of LeipzigLeipzigGermany
  2. 2.Department of Mathematics/Physics/Computer SciencesUniversity of Applied Sciences MittweidaMittweidaGermany
  3. 3.Department of Neuroanatomy, Paul Flechsig Institut for Brain ResearchUniversity of LeipzigLeipzigGermany

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