Computational Neurogenetic Modeling: Integration of Spiking Neural Networks, Gene Networks, and Signal Processing Techniques

  • Nikola Kasabov
  • Lubica Benuskova
  • Simei Gomes Wysoski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3697)


The paper presents a theory and a new generic computational model of a biologically plausible artificial neural network (ANN), the dynamics of which is influenced by the dynamics of internal gene regulatory network (GRN). We call this model a “computational neurogenetic model” (CNGM) and this new area of research Computational Neurogenetics. We aim at developing a novel computational modeling paradigm that can potentially bring original insights into how genes and their interactions influence the function of brain neural networks in normal and diseased states. In the proposed model, FFT and spectral characteristics of the ANN output are analyzed and compared with the brain EEG signal. The model includes a large set of biologically plausible parameters and interactions related to genes/proteins and spiking neuronal activities. These parameters are optimized, based on targeted EEG data, using genetic algorithm (GA). Open questions and future directions are outlined.


Gene Regulatory Network Local Field Potential Signal Processing Technique Spike Neural Network Artificial Neural Network Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kasabov, N., Benuskova, L.: Computational Neurogenetics. Journal of Computational and Theoretical Nanoscience 1, 47–61 (2004)CrossRefGoogle Scholar
  2. 2.
    Kasabov, N., Benuskova, L., Wysoski, S.G.: Computational neurogenetic modelling: gene networks within neural networks. In: Proc. IEEE Intl. Joint Conf. Neural Networks, vol. 2, pp. 1203–1208. IEEE Press, Budapest (2004)Google Scholar
  3. 3.
    Weaver, D.C., Workman, C.T., Stormo, G.D.: Modeling regulatory networks with weight matrices. In: Proc. Pacific Symp. Biocomputing, Hawai, vol. 4, pp. 112–123. World Scientific Publ. Co., Singapore (1999)Google Scholar
  4. 4.
    Wessels, L.F.A., van Someren, E.P., Reinders, M.J.T.: A comparison of genetic network models. In: Proc. Pacific Symp. Biocomputing, vol. 6, pp. 508–519 (2001)Google Scholar
  5. 5.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models. Cambridge Univ. Press, Cambridge (2002)zbMATHGoogle Scholar
  6. 6.
    Destexhe, A.: Spike-and-wave oscillations based on the properties of GABAB receptors. J. Neuroscience 18, 9099–9111 (1998)Google Scholar
  7. 7.
    Kleppe, I.C., Robinson, H.P.C.: Determining the activation time course of synaptic AMPA receptors from openings of colocalized NMDA receptors. Biophys. J. 77, 1418–1427 (1999)CrossRefGoogle Scholar
  8. 8.
    Destexhe, A., Contreras, D., Steriade, M.: Spatiotemporal analysis of local field potentials and unit discharges in cat cerebral cortex during natural wake and sleep states. J. Neuroscience 19, 4595–4608 (1999)Google Scholar
  9. 9.
    Lodish, H., Berk, A., Zipursky, S.L., Matsudaira, P., Baltimore, D., Darnell, J.: Molecular Cell Biology, 4th edn. W.H. Freeman & Co, New York (2000)Google Scholar
  10. 10.
    Quiroga, R.Q.: Dataset #3: Tonic-clonic (Grand Mal) seizures (1998),

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Nikola Kasabov
    • 1
  • Lubica Benuskova
    • 1
  • Simei Gomes Wysoski
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations