Advertisement

Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling

  • Nikola Kasabov
  • Stefan Schliebs
  • Ammar Mohemmed
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7548)

Abstract

Computational neuro-genetic models (CNGM) combine two dynamic models – a gene regulatory network (GRN) model at a lower level, and a spiking neural network (SNN) model at a higher level to model the dynamic interaction between genes and spiking patterns of activity under certain conditions. The paper demonstrates that it is possible to model and trace over time the effect of a gene on the total spiking behavior of the SNN when the gene controls a parameter of a stochastic spiking neuron model used to build the SNN. Such CNGM can be potentially used to study neurodegenerative diseases or develop CNGM for cognitive robotics.

Keywords

Gene Regulatory Network Spike Neural Network Spike Neuron Model Gene Regulatory Network Model Probabilistic Spike 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abbott, L.F.: Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Research Bulletin 50(5-6) (1999)Google Scholar
  2. 2.
    Benuskova, L., Kasabov, N.: Computational Neurogenetic Modelling. Springer, NY (2007)CrossRefGoogle Scholar
  3. 3.
    Clopath, C., Jolivet, R., Rauch, A., Lüscher, H.R., Gerstner, W.: Predicting neuronal activity with simple models of the threshold type: Adaptive exponential integrate-and-fire model with two compartments. Neurocomput. 70(10-12), 1668–1673 (2007)CrossRefGoogle Scholar
  4. 4.
    Fogel, D.B.: Evolutionary computation - toward a new philosophy of machine intelligence, 3rd edn. Wiley-VCH (2006)Google Scholar
  5. 5.
    Gerstner, W., Kistler, W.M.: Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge (2002)zbMATHCrossRefGoogle Scholar
  6. 6.
    Grzyb, B.J., Chinellato, E., Wojcik, G.M., Kaminski, W.A.: Which model to use for the liquid state machine? In: IJCNN 2009: Proceedings of the 2009 International Joint Conference on Neural Networks, pp. 1692–1698. IEEE Press, Piscataway (2009)Google Scholar
  7. 7.
    Holter, J.L., Humphries, A., Crunelli, V., Carter, D.A.: Optimisation of methods for selecting candidate genes from cdna array screens: application to rat brain punches and pineal. Journal of Neuroscience Methods 112(2), 173–184 (2001)CrossRefGoogle Scholar
  8. 8.
    van Kampen, N.G.: Stochastic Processes in Physics and Chemistry. North-Holland (2007)Google Scholar
  9. 9.
    Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, London (2007)zbMATHGoogle Scholar
  10. 10.
    Kasabov, N.: Evolving intelligence in humans and machines: Integrative connectionist systems approach (feature article). IEEE Computational Intelligence Magazine 3(3), 23–37 (2008)CrossRefGoogle Scholar
  11. 11.
    Kasabov, N.: Integrative connectionist learning systems inspired by nature: current models, future trends and challenges. Natural Computing 8, 199–218 (2009), http://dx.doi.org/10.1007/s11047-008-9066-z, doi:10.1007/s11047-008-9066-zMathSciNetzbMATHCrossRefGoogle Scholar
  12. 12.
    Kasabov, N.: To spike or not to spike: A probabilistic spiking neuron model. Neural Networks 23(1), 16–19 (2010)CrossRefGoogle Scholar
  13. 13.
    Kasabov, N., Benuskova, L.: Computational neurogenetics. Journal of Computational and Theoretical Nanoscience 1, 47–61 (2004)CrossRefGoogle Scholar
  14. 14.
    Kasabov, N., Benuskova, L.: Theoretical and computational models for neuro, genetic, and neurogenetic information processing. In: Rieth, M., Schommers, W. (eds.) Handbook of Computational and Theoretical Nanotechnology, ch. 41. American Scientific Publishers, Los Angeles (2005)Google Scholar
  15. 15.
    Kasabov, N., Benuskova, L., Wysoski, S.G.: Biologically plausible computational neurogenetic models: Modeling the interaction between genes, neurons and neural networks. Journal of Computational and Theoretical Nanoscience 2, 569–573 (2005)CrossRefGoogle Scholar
  16. 16.
    Kasabov, N., Schliebs, R., Kojima, H.: Probabilistic computational neurogenetic framework: From modelling cognitive systems to alzheimer’s desease. IEEE Trans. on Autonomous Mental Development 3(4), 300–311 (2011)CrossRefGoogle Scholar
  17. 17.
    Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)zbMATHCrossRefGoogle Scholar
  18. 18.
    Maass, W., Zador, A.: Dynamic stochastic synapses as computational units. In: Advances in Neural Information Processing Systems, pp. 903–917. MIT Press (1999)Google Scholar
  19. 19.
    Marcus, G.F.: The Birth Of The Mind: How A Tiny Number of Genes Creates the Complexities of Human Thought. Basic Books (March 2005)Google Scholar
  20. 20.
    Meng, Y., Jin, Y., Yin, J., Conforth, M.: Human activity detection using spiking neural networks regulated by a gene regulatory network. In: The 2010 International Joint Conference on Neural Networks (IJCNN), pp. 1–6 (July 2010)Google Scholar
  21. 21.
    Morse, A., de Greeff, J., Belpeame, T., Cangelosi, A.: Epigenetic robotics architecture (era). IEEE Transactions on Autonomous Mental Development 2(4), 325–339 (2010)CrossRefGoogle Scholar
  22. 22.
    NCBI: The nervous system, in genes and disease. National Centre for Biotechnology Information (2003), http://www.ncbi.nlm.nih.gov/books/bv.fcgi?call=bv.View..ShowSection&rid=gnd.chapter.75
  23. 23.
    Villa, A.E.P., Asai, Y., Tetko, I.V., Pardo, B., Celio, M.R., Schwaller, B.: Cross-channel coupling of neuronal activity in parvalbumin-deficient mice susceptible to epileptic seizures. Epilepsia 46(suppl. 6), 359 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nikola Kasabov
    • 1
    • 2
  • Stefan Schliebs
    • 1
  • Ammar Mohemmed
    • 1
  1. 1.KEDRIAuckland University of TechnologyNew Zealand
  2. 2.Institute for NeuroinformaticsETH and University of ZurichSwitzerland

Personalised recommendations