Calibration of a Visual System with Receptor Drop-out

  • Albert J. AhumadaJr.
  • Kathleen Turano
Part of the Springer Series in Perception Engineering book series (SSPERCEPTION)

Abstract

Maloney and Ahumada (1989) have proposed a network learning algorithm that allows the visual system to compensate for irregularities in the positions of its photoreceptors. Weights in the network are adjusted by a process tending to make the internal image representation translation-invariant. We report on the behavior of this translation-invariance algorithm calibrating a visual system that has lost receptors. To attain robust performance in the presence of aliasing noise, the learning adjustment was limited to the receptive field of output units whose receptors were lost. With this modification the translation-invariance learning algorithm provides a physiologically plausible model for solving the recalibration problem posed by retinal degeneration.

Keywords

Steam Retina Sine Remote Sensor Blindness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahumada, Jr., A. J. (1992). Learning receptor positions. In M. S. Landy & J. A. Movshon (Eds.), Computational Models of Visual Processing (pp. 23–34). Cambridge, Massachusetts: MIT Press.Google Scholar
  2. Ahumada, Jr., A. J. & Mulligan, J. B. (1990). Learning receptor positions from imperfectly known motions. In Rogowitz, B. & Allebach, J. (Eds.), Human Vision, Visual Processing, and Digital Display, Proceedings of the SPIE, Volume 1249 (pp. 124–134).Google Scholar
  3. Ahumada, Jr., A. J. & Mulligan, J. B. (1991). Network compensation for missing sensors. In Rogowitz, B., Brill, M. H. & Allebach, J. (Eds.), Human Vision, Visual Processing, and Digital Display, Proceedings of the SPIE, Volume 1453 (pp. 134–146).Google Scholar
  4. Ahumada, Jr., A. J. & Tabernero, A. (1994). Anti-hebbian learning and cortical receptive field calibration. Investigative Ophthalmology and Visual Science, 35(4, ARVO Suppl.), 1257 (Abstract).Google Scholar
  5. Ahumada, Jr., A. J. & Turano, K. (1990). Calibration of a visual system with progressive receptor drop-out. Perception, 19, 337 (Abstract).Google Scholar
  6. Barlow, H. B. (1979). Reconstructing the visual image in space and time. Nature, 279, 189–190.CrossRefGoogle Scholar
  7. Craik, K. J. W. (1966). The nature of psychology. Cambridge, UK: Cambridge University Press.Google Scholar
  8. Flannery, J. G., Farber, D. B., Bird, A. C. & Bok, D. (1989). Degenerative changes in a retina affected with autosomal dominant retinitis pigmentosa. Investigative Ophthalmology & Visual Science, 30, 191–211.Google Scholar
  9. Maloney, L. T. & Ahumada, Jr., A. J. (1989). Learning by Assertion: Two methods for calibrating a linear visual system. Neural Computation, 1, 392–401.CrossRefGoogle Scholar
  10. Massof, R. W. & Finkelstein, D. (1987). A two-stage hypothesis for the natural course of retinitis pigmentosa. In E. Zrenner, H. Krastel & H. Goebel (Eds.), Advances in the Biosciences: Research in Retinitis Pigmentosa (pp. 29–58). New York: Pergamon Press.Google Scholar
  11. Stone, G. O. (1986). An analysis of the delta rule and the learning of statistical associations. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel Distributed Processing, Vol. I (pp. 444–459). Cambridge, Massachusetts: MIT Press.Google Scholar
  12. Szamier, R. B., Berson, E. L., Klein, R. & Meyers, S. (1979). Sex-linked retinitis pigmentosa: Ultrastructure of photoreceptors and pigment epithelium. Investigative Ophthalmology and Visual Science, 30, 191–211.Google Scholar
  13. Turano, K. (1991). Bisection judgments in patients with retinitis pigmentosa. Clinical Vision Science, 6, 119–130.Google Scholar
  14. Widrow, A. B. & Hoff, M. E. (1960). Adaptive switching circuits. In WESCON Convention Record, Part 4 (pp. 96–104).Google Scholar
  15. Widrow, A. B. & Stearns, S. D. (1985). Adaptive signal processing. Englewood Cliffs, New Jersey: Prentice-Hall.MATHGoogle Scholar

Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • Albert J. AhumadaJr.
    • 1
  • Kathleen Turano
    • 2
  1. 1.NASA Ames Research CenterUSA
  2. 2.Wilmer InstituteJohns Hopkins UniversityUSA

Personalised recommendations