Neuromorphology: A Case Study Based on Data Mining and Statistical Techniques in an Educational Setting

  • F. Maiorana
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


The importance of neuromorphology cannot be underestimated since knowledge of the brain supports interesting and disparate applications ranging from obtaining an insight into mental diseases to try a comprehension of the human cognitive processes while performing complex tasks. Indeed, it is widely recognized that the knowledge of the morphological structure of the neurons is a fundamental step towards studying neurons and understanding their functions and interactions. This paper presents a case study of an engineering course provided with a data mining module whose final projects were dedicated to manage computational tools for neuron morphology. The results suggest how such tools may be reused in neuroscience and bioengineering courses as a basis for the important and difficult task of neuron modeling and understanding.


Neuromorhpology Data mining Medical education Matlab 


  1. 1.
    Kaiser M, Hilgetag CC, van Ooyen A (2009) A simple rule for axon outgrowth and synaptic competition generates realistic connection lengths and filling fractions. Cereb Cortex 19(12):3001–3010CrossRefGoogle Scholar
  2. 2.
    Faro A, Giordano D, Maiorana F, Spampinato C (2009) Discovering genes-diseases associations from specialized literature using the grid. IEEE Trans Inf Technol Biomed 13(4):554–560CrossRefGoogle Scholar
  3. 3.
    Faro A, Giordano D, Maiorana F (2011) Mining massive datasets by an unsupervised parallel clustering on a GRID: novel algorithms and case study. Future Gener Comput Syst 27(6):711–724CrossRefGoogle Scholar
  4. 4.
    Giordano D, Faro A, Maiorana F, Pino C, Spampinato C (2009) Feeding back learning resources repurposing patterns into the “information loop”: opportunities and challenges. In: 9th International conference on information technology and applications in biomedicine ITAB 2009, pp 1–6. IEEEGoogle Scholar
  5. 5.
    Faro A, Giordano D, Spampinato C (2012) Combining literature text mining with microarray data: advances for system biology modeling. Briefings Bioinform 13(1):61–82CrossRefGoogle Scholar
  6. 6.
    Maiorana F (2012) A teaching experience on a data mining module. In: 2012 Federated conference on computer science and information systems (FedCSIS), pp 871–874. IEEEGoogle Scholar
  7. 7.
    Faro A, Giordano D (1998) Concept formation from design cases: why reusing experience and why not. Knowl Based Syst J 11(7):437–448Google Scholar
  8. 8.
    Faro A, Giordano D (2003) Design memories as evolutionary systems: socio-technical architecture and genetics. In: IEEE Proceedings international conference on systems, man and cybernetics, vol 5. Washington, D.C. USA, pp 4288–4293, IEEEGoogle Scholar
  9. 9.
    Parekh R, Ascoli GA (2013) Neuronal morphology goes digital: a research hub for cellular and system neuroscience. Neuron 77(6):1017–1038CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Ascoli GA, Donohue DE, Halavi M (2007) Neuromorpho.Org: a central resource for neuronal morphologies. J Neurosci 27(35):9247–9251CrossRefGoogle Scholar
  12. 12.
    Gillette TA, Ascoli GA (2012) Measuring and modeling morphology: how dendrites take shape. In: Computational systems neurobiology. Springer,Dordrecht, pp 387–427Google Scholar
  13. 14.
    Donohue DE, Ascoli GA (2005) Local diameter fully constrains dendritic size in basal but not apical trees of CA1 pyramidal neurons. J Comput Neurosci 19(2):223–238CrossRefMATHGoogle Scholar
  14. 13.
    Donohue DE, Ascoli GA (2008) A comparative computer simulation of dendritic morphology. PLoS Comput Biol 4(6):e1000089CrossRefMathSciNetGoogle Scholar
  15. 15.
    Giordano D (2002) Evolution of interactive graphical representations into a design language: a distributed cognition account. Int J Human Comput Stud 57(4):317–345CrossRefMathSciNetGoogle Scholar
  16. 16.
    Ahmed S (2005) Encouraging reuse of design knowledge: a method to index knowledge. Des Stud 26(6):565–592CrossRefGoogle Scholar
  17. 17.
    Faro A, Giordano D (1998) Story net : an evolving network of cases to learn information systems design. IEEE Proc Softw 145(4):119–127CrossRefGoogle Scholar
  18. 18.
    Giordano D, Maiorana F, Leonardi R (2012) Effects of monitor size on accuracy and time needed to detect cephalometric radiographs landmarks. Displays 33(4–5):206–213CrossRefGoogle Scholar
  19. 19.
    Maiorana F, Leonardi R, Giordano D (2012) Eye-tracker data analysis in cephalometric land marking. In: 2012 International conference on computer and information science (ICCIS), vol 2, pp 1025–1029Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Electrical, Electronic and Informatics EngineeringUniversity of CataniaCataniaItaly

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