Evolving Dendritic Morphology and Parameters in Biologically Realistic Model Neurons for Pattern Recognition

  • Giseli de Sousa
  • Reinoud Maex
  • Rod Adams
  • Neil Davey
  • Volker Steuber
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7552)

Abstract

This paper addresses the problem of how dendritic topology and other properties of a neuron can determine its pattern recognition performance. In this study, dendritic trees were evolved using an evolutionary algorithm, which varied both morphologies and other parameters. Based on these trees, we constructed multi-compartmental conductance-based models of neurons. We found that dendritic morphology did have a considerable effect on pattern recognition performance. The results also revealed that the evolutionary algorithm could find effective morphologies, with a performance that was five times better than that of hand-tuned models.

Keywords

Neuronal model dendritic morphology evolutionary algorithm pattern recognition 

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References

  1. 1.
    Cuntz, H., Borst, A., Segev, I.: Optimization principles of dendritic structure. Theoretical Biology and Medical Modelling 4(1), 21 (2007)CrossRefGoogle Scholar
  2. 2.
    Graham, B.P.: Pattern recognition in a compartmental model of a ca1 pyramidal neuron. Network: Computation in Neural Systems 12(4), 473–492 (2001)Google Scholar
  3. 3.
    Gulledge, A.T., Kampa, B.M., Stuart, G.J.: Synaptic integration in dendritic trees. Journal of Neurobiology 64(1), 75–90 (2005)CrossRefGoogle Scholar
  4. 4.
    Hines, M.L., Carnevale, N.T.: The neuron simulation environment. Neural Computation 9(6), 1179–1209 (1997)CrossRefGoogle Scholar
  5. 5.
    Mainen, Z.F., Sejnowski, T.J.: Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363–366 (1996)CrossRefGoogle Scholar
  6. 6.
    van Ooyen, A., Duijnhouwer, J., Remme, M., van Pelt, J.: The effect of dendritic topology on firing patterns in model neurons. Network: Computation in Neural Systems 13, 311–325 (2002)CrossRefGoogle Scholar
  7. 7.
    van Pelt, J., Verwer, R.: Growth models (including terminal and segmental branching) for topological binary trees. Bulletin of Mathematical Biology 47, 323–336 (1985), doi:10.1007/BF02459919MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    de Sousa, G., Maex, R., Adams, R., Davey, N., Steuber, V.: The effect of dendritic morphology on pattern recognition in the presence of active conductances. BMC Neuroscience 12(suppl. 1), P315 (2011)Google Scholar
  9. 9.
    Wen, Q., Chklovskii, D.B.: A cost-benefit analysis of neuronal morphology. J. Neurophysiol. 99(5), 2320–2328 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Giseli de Sousa
    • 1
  • Reinoud Maex
    • 1
  • Rod Adams
    • 1
  • Neil Davey
    • 1
  • Volker Steuber
    • 1
  1. 1.Science and Technology Research InstituteUniversity of HertfordshireHatfieldUK

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