A NEURAL NETWORK MODEL OF THE OLFACTORY SYSTEM FOR GLOMERULAR ACTIVITY PREDICTION

Zu Soh, Ryuji Inazawa, Toshio Tsuji, Noboru Takiguchi, Hisao Ohtake

Abstract

Recently, the importance of odors has begun to be emphasized as well as methods for their evaluation, especially in the fragrance and food industries. Although odors can be characterized by their odorant components, their chemical information cannot be directly related to the flavors we perceive. Recent research has revealed that neuronal activity related to glomeruli (which form part of the olfactory system) is closely connected to odor qualities. In this paper, we propose a neural network model of the olfactory system in mice to predict glomerular activity from odorant molecules. To adjust the parameters included in the model, a learning algorithm is also proposed. The results of simulation proved that the relationship between glomerular activity and odorant molecules could be approximated using the proposed model. In addition, the model could predict glomerular activity to a certain extent. These results suggest that the proposed model could be utilized to predict odor qualities for future application.

References

  1. Amoore, J. (1963). Stereochemical theory of olfaction. Nature, 198:271-272.
  2. Aungst, J. L., Heyward, P. M., Puche, A. C., Karnup, S. V., Hayar, A., Szabo, G., and Shipley, M. T. (2003). Centre-surround inhibition among olfactory bulb glomeruli. Nature, 426:623-329.
  3. Aznar, M., Lopez, R., Cacho, J., and Ferreira, V. (2001). Identification and quantification of impact odorants of aged red wines from rioja. gc-olfactometry, quantitative gc-ms, and odor evaluation of hplc fractions. Journal of Agricultural and Food Chemistry, 49(6):2924-2929.
  4. Buck, L. and Axel, R. (1991). A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell, 65:175-187.
  5. Chen, S., Cowan, C., and Grant, P. (1991). Orthogonal least squares learning algorithm for radial basis function networks. IEEE Transactions on Neural Networks, 2(2):302-309.
  6. Gutierrez-Osuna, R. (2002). Pattern analysis for machine olfaction: A review. IEEE Sensors Journal, 2(3):189- 202.
  7. Herz, R. and Engen, T. (1996). Odor memory review and analysis. Psychonomic Bulletin and Review, 3(3):300 - 313.
  8. Johnson, B. A., Farahbod, H., and Leon, M. (2005). Interactions between odorant functional group and hydrocarbon structure influence activity in glomerular response modules in the rat olfactory bulb. The Journal of Comparative Neurology, 483(2):205-216.
  9. Johnson, B. A. and Leon, M. (2007). Chemotopic odorant coding in a mammalian olfactory. The Journal of Comparative Neurology, 503(1):1-34.
  10. Kashima, H., Tsuda, K., and Inokuschi, A. (2004). Kernels for graphs. MIT Press.
  11. Leon, M. and Johnson, B. A. (2009). Glomerular response archive: http://leonserver.bio.uci.edu/.
  12. Limpawattana, M., Yang, S., Yang, D., Kays, S., and Shewfelt, R. (2001). Relating sensory descriptors to volatile components in flavor of specialty rice types. Journal of Food Science, 73(9):S456-S460.
  13. Mori, K. and Yoshihara, Y. (1995). Molecular recognition and olfactory processing in the mammalian olfactory system. Progress in Neurobiology, 45:585-619.
  14. Nelder, J. and Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4):308- 313.
  15. Semmelroch, P. and Grosch, W. (1995). Analysis of roasted coffee powders and brews by gas chromatographyolfactometry of headspace samples. Food Science and Technology, 28(3):310 - 313.
  16. van Ruth, S. (2001). Methods for gas chromatographyolfactometry: a review. Biomolecular Engineering, 17(4-5):121 - 128.
  17. Youngentob, Johnson, B., Leon, M., Sheehe, P., and Kent, P. (2006). Predicting odorant quality perceptions from multidimensional scaling of olfactory bulb glomerular activity patterns. Behavioral Neuroscience, 120(6):1337-1345.
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Paper Citation


in Harvard Style

Soh Z., Inazawa R., Tsuji T., Takiguchi N. and Ohtake H. (2009). A NEURAL NETWORK MODEL OF THE OLFACTORY SYSTEM FOR GLOMERULAR ACTIVITY PREDICTION . In Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009) ISBN 978-989-674-014-6, pages 379-385. DOI: 10.5220/0002320203790385


in Bibtex Style

@conference{icnc09,
author={Zu Soh and Ryuji Inazawa and Toshio Tsuji and Noboru Takiguchi and Hisao Ohtake},
title={A NEURAL NETWORK MODEL OF THE OLFACTORY SYSTEM FOR GLOMERULAR ACTIVITY PREDICTION},
booktitle={Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)},
year={2009},
pages={379-385},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002320203790385},
isbn={978-989-674-014-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Joint Conference on Computational Intelligence - Volume 1: ICNC, (IJCCI 2009)
TI - A NEURAL NETWORK MODEL OF THE OLFACTORY SYSTEM FOR GLOMERULAR ACTIVITY PREDICTION
SN - 978-989-674-014-6
AU - Soh Z.
AU - Inazawa R.
AU - Tsuji T.
AU - Takiguchi N.
AU - Ohtake H.
PY - 2009
SP - 379
EP - 385
DO - 10.5220/0002320203790385