Science in China Series C: Life Sciences

, Volume 44, Issue 5, pp 469–478 | Cite as

A neural network model on self-organizing emergence of simple-cell receptive field with orientation selectivity in visual cortex



In order to probe into the self-organizing emergence of simple cell orientation selectivity, we tried to construct a neural network model that consists of LGN neurons and simple cells in visual cortex and obeys the Hebbian learning rule. We investigated the neural coding and representation of simple cells to a natural image by means of this model. The results show that the structures of their receptive fields are determined by the preferred orientation selectivity of simple cells. However, they are also decided by the emergence of self-organization in the unsupervision learning process. This kind of orientation selectivity results from dynamic self-organization based on the interactions between LGN and cortex.


receptive field orientation selectivity dynamic self-organization neural sparse coding unsupervision learning 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hubel, D. H., Wiesel, T. N., Receptive fields of single neuron in the cat striate cortex, Journal of Physiology, 1959, 148: 574–591.PubMedGoogle Scholar
  2. 2.
    Hubel, D. H., Wiesel, T. N., Functional architecture macaque monkey visual cortexm, Proc. Roy. Soc. B, 1977, 198: 1–59.CrossRefGoogle Scholar
  3. 3.
    Shou, T. D., Brain Mechanisms of Visual Information Processing (in Chinese), Shanghai: Shanghai Science-Technology and Education Press, 1997, 188–197.Google Scholar
  4. 4.
    Ferster, D., Chung, S., Wheat, H., Orientation selectivity of thalamic input to simple cells of cat visual cortex, Nature, 1996, 380: 249–252.PubMedCrossRefGoogle Scholar
  5. 5.
    Vidyasagar, T. R., Pei, X., Volgushev, M., Multiple mechanisms underlying the orientation selectivity of visual cortical neurons, TINS, 1996, 19: 272–277.PubMedGoogle Scholar
  6. 6.
    Artun, O. B., Shouval, H. Z., Cooper, L. N., The effect of dynamic synapses on spatiotemporal receptive fields in visual cortex, Proc. Natl. Acad. Sci. USA, 1998, 95: 11999–12003.PubMedCrossRefGoogle Scholar
  7. 7.
    Rolls, E. T., Tovee, M. J., Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex, J. Neurophysiology, 1995, 73: 713–726.Google Scholar
  8. 8.
    Olshausen, B. A., Field, D. J., Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 1997, 37: 3311–3325.PubMedCrossRefGoogle Scholar
  9. 9.
    Bell, A. J., Sejnoswski, T. J., The “Independent components” of natural scenes are edge filters, Vision Research, 1997, 37: 3327–3338.PubMedCrossRefGoogle Scholar
  10. 10.
    Dan, Y., Atick, J. J., Reid, R. C., Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory, Journal of Neuroscience, 1996, 16: 3351–3362.PubMedGoogle Scholar
  11. 11.
    Field, D. J., Relations between the statistics of natural images and the response properties of cortical cells, Journal of the Optical Society of America A, 1987, 4: 2379–2394.Google Scholar
  12. 12.
    DeAngelis, G. C., Ohzawa, I., Freeman, R. D., Receptive filed dynamics in the central visual pathway, TINS, 1995, 18: 451–458.PubMedGoogle Scholar
  13. 13.
    Wang, Y. J., Qi, X. L., Chen, Y. Z., Simulations of receptive fields dynamics, TINS, 1996, 19: 385–386.PubMedGoogle Scholar
  14. 14.
    Blais, B. S., Intrator, N., Shouval, H. et al., Receptive field formation in natural science environments: Comparison of single-cell learning rules, Neural Computation, 1998, 10: 1797–1813.PubMedCrossRefGoogle Scholar
  15. 15.
    Reid, R. C., Alonso, J. M., Specificity of monosynaptic connections from thalamic to visual cortex, Nature, 1995, 378: 281–284.PubMedCrossRefGoogle Scholar
  16. 16.
    Champman, B., Zahs, K. R., Stryker, M. P., Relation of cortical cell orientation selectivity to alignment of receptive fields of the geniculocortical afferents that arborize within a single orientation column in ferret visual cortex, J. Neurosci., 1991, 11: 1347–1358.Google Scholar
  17. 17.
    Chung, S., Ferster, D., Strength and orientation tuning of the thalamic input to simple cells revealed by electrically evoked cortical suppression, Neuron, 1998, 20: 1177–1189.PubMedCrossRefGoogle Scholar

Copyright information

© Science in China Press 2001

Authors and Affiliations

  1. 1.Laboratory of Visual Information Processing, Institute of BiophysicsChinese Academy of SciencesBeijingChina

Personalised recommendations