Neural Information Processing Efforts to Restore Vision in the Blind

  • Rolf Eckmiller
  • Oliver Baruth
  • Dirk Neumann
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


Retina Implants belong to the most advanced and truly ‘visionary’ man-machine interfaces. Such neural prostheses for retinally blind humans with previous visual experience require technical information processing modules (in addition to implanted microcontact arrays for communication with the remaining intact central visual system) to simulate the complex mapping operation of the 5-layered retina and to generate a parallel, asynchronous data stream of neural impulses corresponding to a given optical input pattern. In this paper we propose a model of the human visual system from the information science perspective. We describe the unique information processing approaches implemented in a learning Retina Encoder (RE), which functionally mimics parts the central human retina and which allows an individual optimization of the RE mapping operation by means of iterative tuning using learning algorithms in a dialog between implant wearing subject and RE.


Ganglion Cell Blind Subject Mapping Operation Optical Pattern Visual Percept 
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.


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  1. 1.
    Chalupa, L.M., Werner, J.S.: The Visual Neurosciences, Volume 1+2. MIT Press Cambridge, Cambridge (2004)Google Scholar
  2. 2.
    Cuenca, N., Pinilla, I., Sauve, Y., Lu, B., Wang, S., Lund, R.D.: Regressive and Reactive Changes in the Connectivity Patterns of Rod and Cone Pathways of P23H Transgenic Rat Retina. Neuroscience 127, 301–317 (2004)CrossRefGoogle Scholar
  3. 3.
    Dacey, D.M., Peterson, M.R.: Dendritic Field Size and Morphology of Midget and Parasol Ganglion Cells of the Human Retina. Proc. Natl. Acad. Sci. 89, 9666–9670 (1992)CrossRefGoogle Scholar
  4. 4.
    Eckmiller, R.: Learning Retina Implants with Epiretinal Contacts. Ophthalmic Res. 29, 281–289 (1997)CrossRefGoogle Scholar
  5. 5.
    Eckmiller, R., Hünermann, R., Becker, R.: Exploration of a Dialog-Based Tunable Retina Encoder for Retina Implants. Neurocomputing 26, 1005–1011 (1999)Google Scholar
  6. 6.
    Eckmiller, R., Hornig, R., Gerding, H., Dapper, M., Böhm, H.: Test Technology for Retina Implants in Primats. ARVO, Invest. Ophthal. Vis. Sci. 41, 942 (2001)Google Scholar
  7. 7.
    Eckmiller, R.: Adaptive Sensory-Motor Encoder for Visual or Acoustic Prosthesis. US Patent 6, 400–989 (2002)Google Scholar
  8. 8.
    Eckmiller, R., Hornig, R., Ortmann, V., Gerding, H.: Test Technology for Acute Clinical Trials of Retina Implants. ARVO, Invest. Ophthal. Vis. Sci. 43, 2848 (2002)Google Scholar
  9. 9.
    Eckmiller, R., Neumann, D., Baruth, O.: Specification of Single Cell Stimulation Codes for Retina Implants. ARVO Conf. Assoc. Res. Vis. Ophthal, 3401 (2004)Google Scholar
  10. 10.
    Eckmiller, R., Neumann, D., Baruth, O.: Tunable Retina Encoders for Retina Implants: Why and How. J. Neural Eng. submitted (2004)Google Scholar
  11. 11.
    Humayun, M.S., Weiland, J.D., Fujii, G.Y., et al.: Visual Perception in a Blind Subject with a Chronic Microelectronic Retinal Prosthesis. Vision Res. 43, 2573–2581 (2003)CrossRefGoogle Scholar
  12. 12.
    Humphreys, G.W., Bruce, V.: Visual Cognition: Computational, Experimental, and Neuropsychological Perspectives. Lawrence Erlbaum Publ, London (1989)zbMATHGoogle Scholar
  13. 13.
    Lee, B.B., Pokorny, J., Smith, V.C., Kremers, J.: Responses to Pulses and Sinusoids in Macaque Ganglion Cells. Vision Res. 34, 3081–3096 (1994)CrossRefGoogle Scholar
  14. 14.
    Martinez-Conde, S., Macknik, S.L., Hubel, D.H.: The Role of Fixational Eye Movements in Visual Perception. Nature Rev. Neurosci. 5, 229–240 (2004)CrossRefGoogle Scholar
  15. 15.
    Noe, A.: Action in Perception. MIT Press, Cambridge (2004)Google Scholar
  16. 16.
    Noe, A., Regan, O., J.K.: Perception, Attention and the Grand Illusion. Psyche 6, 6–15 (2000)Google Scholar
  17. 17.
    Regan, O., J.K.: Solving the.Real. Mysteries of Visual Perception: The World as an Outside Memory. Can. J. Psychol. 46, 461–488 (1992)Google Scholar
  18. 18.
    Rucci, M., Desbordes, G.: Contributions of Fixational Eye Movements to the Discrimination of Briefly Presented Stimuli. J. Vision 3, 852–864 (2003)CrossRefGoogle Scholar
  19. 19.
    Santos, A., Humayun, M.S., de Juan, E.J., Greenberg, R.J., Marsh, M.J., Klock, I.B., et al.: Preservation of the Inner Retin in Retinitis Pigmentosa. A Morphometric Analysis. Arch. Ophthalmol 115, 511–515 (1997)Google Scholar
  20. 20.
    Schwefel, H.-P., Wegener, I., Weinert, K.: Advances in Computational Intelligence, Theory and Practice. Springer Publisher, Berlin (2003)zbMATHGoogle Scholar
  21. 21.
    Watanabe, M., Rodieck, R.W.: Parasol and Midget Ganglion Cells of the Primate Retina. J. Comp. Neurol. 289, 434–454 (1989)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Rolf Eckmiller
    • 1
  • Oliver Baruth
    • 1
  • Dirk Neumann
    • 1
  1. 1.Department of Computer ScienceUniversity of BonnBonnGermany

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