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Pattern Recognition Using a Recurrent Neural Network Inspired on the Olfactory Bulb

  • Lucas Baggio Figueira
  • Antonio Carlos Roque
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6687)

Abstract

The olfactory system is a remarkable system capable of discriminating very similar odorant mixtures. This is in part achieved via spatio-temporal activity patterns generated in mitral cells, the principal cells of the olfactory bulb, during odor presentation. In this work, we present a spiking neural network model of the olfactory bulb and evaluate its performance as a pattern recognition system with datasets taken from both artificial and real pattern databases. Our results show that the dynamic activity patterns produced in the mitral cells of the olfactory bulb model by pattern attributes presented to it have a pattern separation capability. This capability can be explored in the construction of high-performance pattern recognition systems.

Keywords

Granule Cell Olfactory Bulb Recurrent Neural Network Olfactory System Mitral Cell 
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.

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References

  1. [Abbott, 2008]
    Abbott, L.F.: Theoretical neuroscience rising. Neuron 60, 489–495 (2008)CrossRefGoogle Scholar
  2. [Adrian, 1950]
    Adrian, E.D.: The electrical activity of the olfactory bulb. Electroencephalography and Clinical Neurophysiology 2, 377–388 (1950)CrossRefGoogle Scholar
  3. [Auer et al., 2002]
    Auer, P., Burgsteiner, H., Maass, W.: Reducing communication for distributed learning in neural networks. In: Dorronsoro, J.R. (ed.) ICANN 2002. LNCS, vol. 2415, p. 123. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  4. [Auer et al., 2008]
    Auer, P., Burgsteiner, H., Maass, W.: A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks 21(5), 786–795 (2008)CrossRefzbMATHGoogle Scholar
  5. [Blake and Merz, 1998]
    Blake, C.L., Merz, C.J.: Uci repository of machine learning databases (1998)Google Scholar
  6. [Cleland and Linster 2005]
    Cleland, T.A., Linster, C.: Computation in the olfactory system. Chemical Senses 30, 801–813 (2005)CrossRefGoogle Scholar
  7. [Davison et al., 2003]
    Davison, A.P., Feng, J., Brown, D.: Dendrodendritic inhibition and simulated odor responses in a detailed olfactory bulb network model. Journal of Neurophysiology 90, 1921–1935 (2003)CrossRefGoogle Scholar
  8. [Freeman and Skarda, 1985]
    Freeman, W.J., Skarda, C.A.: Spatial EEG patterns, non-linear dynamics and perception: the neo-Sherringtonian view. Brain Research 357, 147–175 (1985)CrossRefGoogle Scholar
  9. [Heyward et al., 2001]
    Heyward, P., Ennis, M., Keller, A., Shipley, M.T.: Membrane bistability in olfactory bulb mitral cells. The Journal of Neuroscience 21(14), 5311–5320 (2001)Google Scholar
  10. [Harris et al., 2011]
    Harris, K.D., Bartho, P., Chadderton, P., Curto, C., de la Rocha, J., Hollender, L., Itskov, V., Luczak, A., Marguet, S.L., Renart, A., Sakata, S.: How do neurons work together? Lessons from auditory cortex. Hearing Research 271, 37–53 (2011)CrossRefGoogle Scholar
  11. [Izhikevich, 2007]
    Izhikevich, E.M.: Dynamical Systems in Neuroscience: Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. The MIT Press, Cambridge (2007)Google Scholar
  12. [Jaeger, 2002]
    Jaeger, H.: Short term memory in echo state networks. GMD-Report 152, GMD - German National Research Institute for Computer Science (2002)Google Scholar
  13. [Jin et al., 2011]
    Jin, J., Wang, Y., Swadlow, H.A., Alonso, J.M.: Population receptive fields of ON and OFF thalamic inputs to an orientation column in visual cortex. Nature Neuroscience 14, 232–240 (2011)CrossRefGoogle Scholar
  14. [Kay and Stopfer, 2006]
    Kay, L.M., Stopfer, M.: Information processing in the olfactory systems of insects and vertebrates. Seminars in Cell & Developmental Biology 17, 433–442 (2006)CrossRefGoogle Scholar
  15. [Kay et al., 2008]
    Kay, L.M., Beshel, J., Brea, J., Martin, C., Rojas-Líbano, D., Kopell, N.: Olfactory oscillations: the what, how and what for. Trends in Neuroscience 32, 207–214 (2008)CrossRefGoogle Scholar
  16. [Laurent, 1997]
    Laurent, G.: Olfactory processing: maps, time and codes. Current Opinion in Neurobiology 7, 547–553 (1997)CrossRefGoogle Scholar
  17. [Laurent, 2002]
    Laurent, G.: Olfactory network dynamics and the coding of multidimensional signals. Nature Reviews Neuroscience 3, 884–895 (2002)CrossRefGoogle Scholar
  18. [Laurent et al., 2001]
    Laurent, G., Stopfer, M., Friedrich, R.W., Rabinovich, M.I., Volkovskii, A., Abarbanel, H.D.I.: Odor encoding as an active, dynamical process: Experiments, computation, and theory. Annual Review of Neuroscience 24, 263–297 (2001)CrossRefGoogle Scholar
  19. [Lledo et al., 2005]
    Lledo, P.M., Gheusi, G., Vincent, J.D.: Information processing in the mammalian olfactory system. Physiological Reviews 85, 281–317 (2005)CrossRefGoogle Scholar
  20. [Markram et al., 1998]
    Markram, H., Wang, Y., Tsodyks, M.: Differential signaling via the same axon of neocortical pyramidal neurons. Proceedings of the National Academy of Sciences (USA) 95, 5323–5328 (1998)CrossRefGoogle Scholar
  21. [Maass et al., 2002]
    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)CrossRefzbMATHGoogle Scholar
  22. [Mori et al., 1999]
    Mori, K., Nagao, H., Yoshihara, Y.: The olfactory bulb: coding and processing of odor molecule information. Science 286(5440), 711–715 (1999)CrossRefGoogle Scholar
  23. [Natschläger et al., 2002]
    Natschläger, T., Maass, W., Markram, H.: The “liquid computer”: A novel strategy for real-time computing on time series. Special Issue on Foundations of Information Processing of TELEMATIK, 8 (2002)Google Scholar
  24. [Shepherd, 2004]
    Shepherd, G.M.: The synaptic organization of the brain, 5th edn. Oxford University Press, Oxford (2004)CrossRefGoogle Scholar
  25. [Shepherd et al., 2007]
    Shepherd, G.M., Chen, W.R., Willhite, D., Migliore, M., Greer, C.A.: The olfactory granule cell: from classical enigma to central role in olfactory processing. Brain Res. Rev. 55(2), 373–382 (2007)CrossRefGoogle Scholar
  26. [Simões-de-Souza and Roque, 2004]
    Simões-de-Souza, F.M., Roque, A.C.: A biophysical model of vertebrate olfactory epithelium and bulb exhibiting gap junction dependent odor-evoked spatiotemporal patterns of activity. BioSystems 73, 25–43 (2004)CrossRefGoogle Scholar
  27. [Stopfer et al. 2003]
    Stopfer, M., Jayaraman, V., Laurent, G.: Intensity versus identity coding in an olfactory system. Neuron 39, 991–1004 (2003)CrossRefGoogle Scholar
  28. [Tsodyks et al., 1998]
    Tsodyks, M., Pawelzik, K., Markram, H.: Neural networks with dynamic synapses. Neural Computation 10(4), 821–835 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Lucas Baggio Figueira
    • 1
  • Antonio Carlos Roque
    • 1
  1. 1.Laboratory of Neural Systems, Department of Physics, FFCLRPUniversity of São PauloRibeirão PretoBrazil

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