Neuronal Morphology Modeling Based on Microscopy Reconstruction Data in the Public Repositories

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)


Neuronal morphology modeling is one of the key steps for reverse engineering the brain at the micro level. It creates a realistic digital version of the neuron obtained by microscopy reconstruction in a visualized way so that the structure of the whole neuron (including soma, dendrite, axon, spin, etc.) is visible in different angles in a three dimensional space. Whether the modeled neuronal morphology matches the original neuron in vivo is closely related to the details captured by the manually sampled morphological points. Many data in public neuronal morphology data repositories (such as the NeuroMorpho project) focus more on the morphology of dendrites and axons, while there are only a few points to represent the neuron soma. The lack of enough details for neuron soma makes the modeling on the soma morphology a challenging task. In this paper, we provide a general method to neuronal morphology modeling (including the soma and its connections to surrounding dendrites, and axons, with a focus on how different components are connected) and handle the challenging task when there are not many detailed sample points for soma.


Neuron Morphology Reconstruction Neuronal Morphology Modeling Soma Reconstruction 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Perry, W., Broers, A., El-Baz, F., Harris, W., Healy, B., Hillis, W.D., et al.: Grand challenges for engineering. National Academy of Engineering, Washington, DC (2008)Google Scholar
  2. 2.
    Grand Challenges: Reverse-engineering the Brain. National Academy of Engineering,
  3. 3.
    The Human Brain Project: A Report to the European Commission (2012)Google Scholar
  4. 4.
    Zhong, N., Bradshaw, J.M., Liu, J., Taylor, J.G.: Brain Informatics. IEEE Intelligent Systems 26(5), 16–21 (2011)CrossRefGoogle Scholar
  5. 5.
    Cauwenberghs, G.: Reverse engineering the cognitive brain. Proceedings of the National Academy of Sciences 110(39), 15512–15513 (2013)CrossRefGoogle Scholar
  6. 6.
    Cannon, R.C., Turner, D.A., Pyapali, G.K., Wheal, H.V.: An on-line archive of reconstructed hippocampal neurons. Journal of Neuroscience Methods 84(1-2), 49–54 (1998)CrossRefGoogle Scholar
  7. 7.
    Halavi, M., Polavaram, S., Donohue, D.E., Hamilton, G., Hoyt, J., Smith, K.P., et al.: NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF. Neuroinformatics 6(3), 241–252 (2008)CrossRefGoogle Scholar
  8. 8.
    Brito, J.P., Mata, S., Bayona, S., Pastor, L., Defelipe, J., Benavides-Piccione, R.: Neuronize: a tool for building realistic neuronal cell morphologies. Frontiers in Neuroanatomy 7(15) (2013)Google Scholar
  9. 9.
    Zerouni, C.: Houdini on the Spot: Power User Tips and Techniques. Focal Press (2007)Google Scholar
  10. 10.
    Catmull, E., Clark, J.: Recursively generated B-spline surfaces on arbitrary topological meshes. Seminal graphics, pp. 183–188. ACM (1998)Google Scholar
  11. 11.
    Shreiner, D., Sellers, G., Kessenich, J.M., Licea-Kane, B.M.: OpenGL Programming Guide: The Official Guide to Learning OpenGL, Version 4.3, 8th edn. Addison-Wesley Professional (2013)Google Scholar
  12. 12.
    Terzopoulos, D., Platt, J., Barr, A., Fleischer, K.: Elastically deformable models. SIGGRAPH Comput. Graph. 21(4), 205–214 (1987)CrossRefGoogle Scholar
  13. 13.
    Nealen, A., Muller, M., Keiser, R., Boxerman, E., Carlson, M.: Physically based deform-able models in computer graphics. Comput. Graph. Forum 25(4), 809–836 (2006)CrossRefGoogle Scholar
  14. 14.
    Lasserre, S., Hernando, J., Hill, S., Schumann, F., Anasagasti, P.M., Jaoude, G.A., et al.: A neuron membrane mesh representation for visualization of electrophysiological simulations. IEEE Transactions on Visualization and Computer Graphics 18(2), 214–227 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations