Science in China Series C: Life Sciences

, Volume 46, Issue 4, pp 358–369 | Cite as

Function of attention in learning process in the olfactory bulb

  • Baosheng Ma
  • Shunpeng Wang
  • Yan Li
  • Chunhua Feng
  • Aike Guo


It has been suggested that in the olfactory bulb, odor information is processed through parallel channels and learning depends on the cognitive environment. The synapse’s spike effective time is defined as the effective time for a spike from pre-synapse to post-synapse, which varies with the type of synapse. A learning model of the olfactory bulb was constructed for synapses with varying spike effective times. The simulation results showed that such a model can realize the multi-channel processing of information in the bulb. Furthermore, the effect of the cognitive environment on the learning process was also studied. Different feedback frequencies were used to express different attention states. Considering the information’s multi-channel processing requirement for learning, a learning rule considering both spike timing and average spike frequency is proposed. Simulation results showed that habituation and anti-habituation of an odor in the olfactory bulb might be the result of learning guided by a common local learning rule but at different attention states.


olfactory bulb spike effective time learning rule feedback attention 


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  1. 1.
    Laurent, G., Dynamical representation of odors by oscillating and evolving neural assemblies, Trends in Neurosciences, 1996, 19: 489–496.CrossRefPubMedGoogle Scholar
  2. 2.
    Laurent, G., Wehr, M., Davidowitz, H., Temporal representations of odors in an olfactory network, J. Neuroscience, 1996, 16: 3837–3847.Google Scholar
  3. 3.
    Laurent, G., Macleod, K., Stopfer, M. et al., Spatiotemporal structure of olfactory inputs to the mushroom bodies, Learning & Memory, 1998, 5: 124–132.Google Scholar
  4. 4.
    Laurent, G., A systems perspective on early olfactory coding, Science, 1999, 286: 723–728.CrossRefPubMedGoogle Scholar
  5. 5.
    MacLeod, K., Laurent, G., Distinct mechanisms for synchronization and temporal patterning of odor-encoding neural assemblies, Science, 1996, 274: 976–979.CrossRefPubMedGoogle Scholar
  6. 6.
    Mori, K., Relation of chemical structure to specificity of response in olfactory glomeruli, Current Opinion in Neurobiology, 1995, 5: 467–474.CrossRefPubMedGoogle Scholar
  7. 7.
    Mori, K., Nago, H., Yohihara, Y., The olfactory bulb: Coding and processing of odor molecule information, Science, 1999, 286: 711–715.CrossRefPubMedGoogle Scholar
  8. 8.
    Stopfer, M., Bhagavan, S., Smith, B. H. et al., Impaired odour discrimination on desynchronization of odour-ecoding neural assemblies, Nature, 1997, 390: 70–74.CrossRefPubMedGoogle Scholar
  9. 9.
    Buonviso, N., Chaput, M., Olfactory experience decreases responsiveness of the olfactory bulb in the adult rat, Neuroscience, 2000, 95(2): 325–332.CrossRefPubMedGoogle Scholar
  10. 10.
    Faber, T., Joerges, J., Menzel, R., Associative learning modifies neural representations of odors in the insect brain, Nature Neuroscience, 1999, 2: 74–78.CrossRefPubMedGoogle Scholar
  11. 11.
    Wilson, D. A., Sullivan, R. M., Leon, M., Odor familiarity alters mitral cell response in the olfactory bulb of neonatal rats, Brain Research, 1985, 22: 314–317.CrossRefGoogle Scholar
  12. 12.
    Gray, C. M., Skinner, J. E., Field potential response change in the rabbit olfactory bulb accompany behavioral habituation during the repeated presentation of unreinforced odors, Experimental Brain Research, 1998, 73: 189–197.CrossRefGoogle Scholar
  13. 13.
    Hiroshi, Fujii, Hiroyuki, Ito, Kazuyuki, Aihara et al., Dynamical cell assembly hypothesis theoretical possibility of spa- tio-temporal coding in the cortex, Neural Networks, 1996, 9(8): 1303–1350.CrossRefGoogle Scholar
  14. 14.
    Hendin, O., Horn, D., Tsodyks, M. V., Associative memory and segmentation in an oscillatory neural model of the olfactory bulb, Journal of Computational Neuroscience, 1998, 5(2): 157–169.CrossRefPubMedGoogle Scholar
  15. 15.
    Nicoll, R. A., Jahr, C. E., Self-excitation of olfactory bulb neurons, Nature, 1982, 296(5856): 441–444.CrossRefPubMedGoogle Scholar
  16. 16.
    Davison, A. P., Feng, J., Brown, D., Structure of lateral inhibition in an olfactory bulb model, Lecture Notes in Computer Science, 1999, 1606: 189–196.CrossRefGoogle Scholar
  17. 17.
    Bi, G. Q., Poo, M. M., Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type, J. Neuroscience, 1998, 18(24): 10464–10472.Google Scholar
  18. 18.
    Sejnowski, T. J., The book of Hebb, Neuron, 1999, 24(4): 773–776.CrossRefPubMedGoogle Scholar
  19. 19.
    Tsien, J. Z., Linking Hebb’s coincidence-detection to memory formation, Current Opinion in Neurobiology, 2000, 10(2): 266–273.CrossRefPubMedGoogle Scholar
  20. 20.
    Turrigiano, G. G., Nelson, S. B., Hebb and homeostasis in neuronal plasticity, Current Opinion in Neurobiology, 2000, 10(3): 358–364.CrossRefPubMedGoogle Scholar
  21. 21.
    Viana, D., Prisco, G., Hebb synaptic plasticity, Progress in Neurobiology, 1984, 22(2): 89–102.CrossRefGoogle Scholar
  22. 22.
    Hendin, O., Horn, D., Tsodyks, M. V., The role of inhibition in an associative memory model of the olfactory bulb, Journal of Computational Neuroscience, 1997, 4(2): 173–182.CrossRefPubMedGoogle Scholar
  23. 23.
    Linster, C., Hasselmo, M., Modulation of inhibition in a model of olfactory bulb reduces overlap in the neural representation of olfactory stimuli, Behavioural Brain Research, 1997, 84(1–2): 117–127.CrossRefPubMedGoogle Scholar

Copyright information

© Science in China Press 2003

Authors and Affiliations

  • Baosheng Ma
    • 1
  • Shunpeng Wang
    • 1
  • Yan Li
    • 1
  • Chunhua Feng
    • 1
  • Aike Guo
    • 1
    • 2
  1. 1.Laboratory of Visual Information Processing, Institute of BiophysicsChinese Academy of SciencesBeijingChina
  2. 2.Institute of NeuroscienceChinese Academy of SciencesShanghaiChina

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