Advertisement

Behavior Research Methods & Instrumentation

, Volume 15, Issue 6, pp 553–560 | Cite as

Visual receptive fields and clustering

  • Evangelia Micheli-Tzanakou
Methods & Designs
  • 187 Downloads

Abstract

A pattern recognition technique—clustering—has been used to analyze and evaluate meaningful characteristics of visual receptive fields in the frog tectum. The fields were mapped either by an automatic scanning technique or by a response-feedback method called ALOPEX. The data were then analyzed by the clustering technique, which separates the receptive field into isoresponse regions. The latter can be checked on line by stimulating the eye with each cluster and with combinations of clusters. In this way, existing nonlinearities can be checked objectively.

Keywords

Receptive Field Retinal Ganglion Cell Sensitive Area Response Matrix Rana Pipiens 
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.

Reference note

  1. 1.
    Tzanakou, E.Some nonlinear characteristics in the frog’s visual system. Manuscript submitted for publication, 1983.Google Scholar

References

  1. Arden, G. B. Types of response and organization of simple receptive fields in cells of the rabbit’s LGB.Journal of Physiology (London), 1963,166, 449–467.Google Scholar
  2. Babtz, M. R. Optimizing a video processor for OCR. InProceedings of the International Joint Conference on Artificial Intelligence. Bedford, Mass: Mitre Corporation, 1969.Google Scholar
  3. Doyle, W. Operations useful for similarity-invariant pattern recognition.Journal of Association for Computer Machinery, 1962,9, 259–267.Google Scholar
  4. Easter, S. S. Excitation in the goldfish retina: Evidence for a non-linear intensity code.Journal of Physiology (London), 1968,195, 253–271.Google Scholar
  5. Friedman, H. P., &Rubin, J. On some invariant criteria of grouping data.Journal of the American Statistical Association, 1967,62, 1159.CrossRefGoogle Scholar
  6. Harth, E., &Tzanakou, E. Alopex: A stochastic method for determining visual receptive fields.Vision Research, 1974,14, 1475–1482.CrossRefPubMedGoogle Scholar
  7. Hartline, H. K. The receptive fields of optic nerve fibers.American Journal of Physiology, 1940,130, 690–699.Google Scholar
  8. Hubel, D. H., &Wiesel, T. N. Receptive fields, binocular interactions and functional architecture in cat’s visual cortex.Journal of Physiology (London), 1962,150, 106–154.Google Scholar
  9. Hubel, D. H., &Wiesel, T. N. Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat.Journal of Neurophysiology, 1965,28, 229–289.PubMedGoogle Scholar
  10. Hubel, D. H., &Wiesel, T. N. Receptive fields and functional architecture of monkey striate cortex.Journal of Physiology(London), 1968,195, 212–243.Google Scholar
  11. Jacobson, M. The representation of the retina on the optic tectum of the frog. Correlation between retino-tectal magnification factor and retinal ganglion cell count.Quarterly Journal of Experimental Physiology, 1962,47, 170–178.Google Scholar
  12. Julesz, B.Foundations of cyclopean perception. Chicago: University of Chicago Press, 1971.Google Scholar
  13. Julesz, B. Textons, the elements of texture perception and their interactions.Nature, 1981,290, 91–97.CrossRefPubMedGoogle Scholar
  14. Julesz, B. A theory of preattentive texture discrimination based on first-order statistics of textons.Biological Cybernetics, 1981,41, 131–138.CrossRefPubMedGoogle Scholar
  15. Julesz, B. Textons, the fundamental elements in preattentive vision and perception of textures.Bell System Technical Journal, 1983,62(6), 1619–1645.Google Scholar
  16. Julesz, B., Gilbert, E. N., &Victor, J. D. Visual discrimination of textures with identical third-order statistics.Biological Cybernetics, 1978,31, 137–140.CrossRefPubMedGoogle Scholar
  17. Keating, M. J., &Gaze, R. M. Observations of the surround properties of the receptive field of frog retinal ganglion cells.Journal of Experimental Physiology, 1970,55, 129–142.Google Scholar
  18. Kuffler, S. W. Discharge patterns and functional organization of the mammalian retina.Journal of Neurophysiology, 1953,16, 37–68.PubMedGoogle Scholar
  19. Lettvin, J. Y., Maturana, C. R., McCuixoch, W. S., &Pitts, W. H. What the frog’s eye tells the frog’s brain.Proceedings of the Institute of Radio Engineers, 1959,47, 1940–1951Google Scholar
  20. Lino, R. F. On the theory and construction of k-clusters.Computer journal, 1972,15, 326–332.CrossRefGoogle Scholar
  21. Maturana, C. R., Lettvin, J. Y., McCuixoch, W. S., &Pitts, W. H. Anatomy and physiology of vision in the frog(Rana pipiens).Journal of General Physiology, 1960,43(Suppl. 2). 129–175.CrossRefPubMedGoogle Scholar
  22. McIllwain, J. T. Receptive fields of optic tract axons and lateral geniculate cells: Peripheral extent and barbiturate sensitivity.Journal of Neurophysiology, 1964,27, 1154–1173.Google Scholar
  23. Morrin, T. H. A black-white representation of a gray-scale picture.IEEE Transactions on Computers, 1974,23, 184–186.CrossRefGoogle Scholar
  24. Prewitt, J. M. S., &Mendelson, M. L. The analysis of cell images.Annals of the New York Academy of Science, 1966,128, 1035–1053.CrossRefGoogle Scholar
  25. Sasaki, H., Bear, D. M., &Ervin, F. R. Quantitative characterization of the unit response in the visual system.Experimental Brain Research, 1971,13, 239–255.Google Scholar
  26. Shepard, R. N., &Arabie, P. Additive clustering: Representation of similarities as combinations of discrete overlapping properties.Psychological Review, 1979,86, 87–123.CrossRefGoogle Scholar
  27. Sklansky, J. Image segmentation and feature extraction.IEEE Transactions on Systems, Man and Cybernetics, 1968,8, 237–247.CrossRefGoogle Scholar
  28. Spinelli, D. N. Visual receptive fields on the cat’s retina.Science, 1966,152, 1767–1769.CrossRefGoogle Scholar
  29. Stone, J., &Fabian, M. Summing properties of the cat’s retinal ganglion cells.Vision Research, 1968,8, 1023–1040.CrossRefPubMedGoogle Scholar
  30. Tzanakou, E., &Harth, E. Alopex: Self-portrait of a feature detector. In R. P. Dooley (Ed.),Advances in the psychophysical and visual aspects of image evaluation: A program summary. Washington, D.C: Society for Photographic Scientists and Engineers, 1977.Google Scholar
  31. Tzanakou, E., Michalak, R., &Harth, E. The Alopex process: Visual receptive fields by response feedback.Biological Cybernetics, 1979,35, 161–174.CrossRefPubMedGoogle Scholar
  32. Ullman, J. R. Binarization using associative addressing.Pattern Recognition, 1974,6, 127–135.CrossRefGoogle Scholar
  33. Wolfe, R. N. A dynamic thresholding scheme for quantization of scanned images. InProceedings of Automatic Pattern Recognition. Washington, D.C: National Security Industrial Association, 1969.Google Scholar

Copyright information

© Psychonomic Society, Inc. 1983

Authors and Affiliations

  • Evangelia Micheli-Tzanakou
    • 1
  1. 1.Department of Electrical Engineering (Bioengineering)Rutgers UniversityPiscataway

Personalised recommendations