Skip to main content

Koniocortex-Like Network Unsupervised Learning Surpasses Supervised Results on WBCD Breast Cancer Database

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10338)


Koniocortex-Like Network is a novel category of Bio-Inspired Neural Networks whose architecture and properties are inspired in the biological koniocortex, the first layer of the cortex that receives information from the thalamus. In the Koniocortex-Like Network competition and pattern classification emerges naturally due to the interplay of inhibitory interneurons, metaplasticity and intrinsic plasticity. Recently proposed, it has shown a big potential for complex tasks with unsupervised learning. Now for the first time, its competitive results are proved in a relevant standard real application that is the objective of state-of-the-art research: the diagnosis of breast cancer data from the Wisconsin Breast Cancer Database.


  • Metaplasticity
  • Koniocortex
  • Plasticity
  • KLN
  • WBCD
  • Feature extraction
  • Competition

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions


  1. Abraham, W.C., Bear, M.F.: Metaplasticity: the plasticity of synaptic plasticity. Trends Neurosci. 19, 126–130 (1996)

    CrossRef  Google Scholar 

  2. Abraham, W.C., Tate, W.P.: Metaplasticity: a new vista across the field of synaptic plasticity. Prog. Neurobiol. 52, 303–323 (1997)

    CrossRef  Google Scholar 

  3. Andina, D., Alvarez-Vellisco, A., Jevtic, A., Fombellida, J.: Artificial metaplasticity can improve artificial neural network learning. Intell. Autom. Soft Comput. Spec. Issue Sig. Process. Soft Comput. 15(4), 681–694 (2009)

    Google Scholar 

  4. Andina, D., Ropero-Pelaez, J.: On the biological plausibility of artificial metaplasticity learning algorithm. Neurocomputing (2012).

  5. Artola, A., Brocher, S., Singer, W.: Different voltage-dependent threshold for inducing long-term depression and long-term potentiation in slices of rat visual córtex. Nature 347, 69–72 (1990)

    CrossRef  Google Scholar 

  6. Desai, N.S.: Homeostatic plasticity in the CNS: synaptic and intrinsic forms. J. Physiol. 97(4–6), 391–402 (2003)

    Google Scholar 

  7. Desai, N.S., Rutherford, L.C., Turrigiano, G.G.: Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nat. Neurosci. 2, 515–520 (1999)

    CrossRef  Google Scholar 

  8. Ferster, D., Chung, S., Wheat, H.: Orientation selectivity of thalamic input to simple cells of cat visual cortex. Nature 380(6571), 249–252 (1996)

    CrossRef  Google Scholar 

  9. Fukai, T., Tanaka, S.: A simple neural network exhibiting selective activation of neuronal ensembles: from winner-take-all to winners-share-all. Neural Comput. 9(1), 77–97 (1997)

    CrossRef  MATH  Google Scholar 

  10. Kaski, S., Kohonen, T.: Winner-take-all networks for physiological models of competitive learning. Neural Netw. 7(6/7), 973–984 (1994)

    CrossRef  MATH  Google Scholar 

  11. Mao, Z.H., Massaquoi, S.G.: Dynamics of Winner-Take-All competition in recurrent neural networks with lateral inhibition. IEEE Trans. Neural Netw. 18, 55–69 (2007)

    CrossRef  Google Scholar 

  12. Marcano-Cedeño, A., Quintanilla-Dominguez, J., Andina, D.: Breast cancer classification applying artificial metaplasticity algorithm. Neurocomputing 74(8), 1243–1250 (2011)

    CrossRef  Google Scholar 

  13. Miller, K.D.: Synaptic economics: competition and cooperation in synaptic plasticity. Neuron 17, 371–374 (1996)

    CrossRef  Google Scholar 

  14. Quintanilla-Dominguez, J., Cortina-Januchs, M.G., Ojeda-Magaa, B., Jevtic, A., Vega-Corona, A., Andina, D.: Microcalcification detection applying artificial neural networks and mathematical morphology in digital mammograms. In: World Automation Congress (WAC) (2010)

    Google Scholar 

  15. Ropero-Peláez, F.J., Andina, D.: Do biological synapses perform probabilistic computations? Neurocomputing (2012).

  16. Ropero-Peláez, F.J., Andina, D.: The Koniocortex-like network: a new biologically plausible unsupervised neural network. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9107, pp. 163–174. Springer, Cham (2015). doi:10.1007/978-3-319-18914-7_17

    CrossRef  Google Scholar 

  17. Ropero-Peláez, F.J., Aguiar-Furucho, M.A., Andina, D.: Intrinsic plasticity for natural competition in Koniocortex-like neural networks. Int. J. Neural Syst. 26(5), 1650040 (2016).

  18. Yang, J.F., Chen, C.M.: Winner-Take-All neural network using the highest threshold. IEEE Trans. Neural Netw. 11, 194–199 (2000)

    CrossRef  Google Scholar 


Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to J. Fombellida .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Fombellida, J., Ropero-Peláez, F.J., Andina, D. (2017). Koniocortex-Like Network Unsupervised Learning Surpasses Supervised Results on WBCD Breast Cancer Database. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Biomedical Applications Based on Natural and Artificial Computing. IWINAC 2017. Lecture Notes in Computer Science(), vol 10338. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59772-0

  • Online ISBN: 978-3-319-59773-7

  • eBook Packages: Computer ScienceComputer Science (R0)