The Retinomorphic Approach: Pixel-Parallel Adaptive Amplification, Filtering, and Quantization

  • Kwabena A. Boahen
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 447)


The migration of sophisticated signal processing down to the pixel level is driven by shrinking feature sizes in CMOS technology, allowing higher levels of integration to be achieved [24, 18]. New pixel-parallel architectures are required to take advantage of the increasing numbers of transistors available [1]. Inspired by the pioneering work of Mahowald and Mead [6], I describe in this paper a retinomorphic vision system that addresses this need by mimicking biological sensory systems.


Spike Train Automatic Gain Control Interspike Interval Outer Plexiform Layer Analog Integrate Circ 
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  1. [1]
    A. Andreou and K. Boahen. Translinear circuits in subthreshold MOS. J. Analog Integrated Circ. Sig. Proc., 9:141–166, 1996.CrossRefGoogle Scholar
  2. [2]
    J. Atick and N. Redlich. What does the retina know about natural scenes. Neural Computation, 4(2):196–210, 1992.CrossRefGoogle Scholar
  3. [3]
    K. Boahen. The adaptive neuron and the diode-capacitor integrator. In preparation.Google Scholar
  4. [4]
    K. Boahen. Communication neuronal ensembles between neuromorphic chips. In preparation.Google Scholar
  5. [5]
    K. Boahen. Toward a second generation silicon retina. Technical Report CNS-TR-90-06, California Institute of Technology, Pasadena CA, 1990.Google Scholar
  6. [6]
    K. Boahen. Spatiotemporal sensitivity of the retina: A physical model. Technical Report CNS-TR-91-06, California Institute of Technology, Pasadena CA, 1991.Google Scholar
  7. [7]
    K. Boahen. Retinomorphic vision systems. In Int. Conf. on Microelectronics for Neural Networks, volume 16-5, pages 30–39, Los Alamitos, CA, 1996. EPFL/CSEM/IEEE.Google Scholar
  8. [8]
    K. Boahen. Retinomorphic vision systems II: Communication channel design. In Proceedings of the IEEE International Symposium on Circuits and Systems, volume supplement, pages 9–14, Atlanta, GA, May 1996.Google Scholar
  9. [9]
    K. Boahen and A. Andreou. A contrast-sensitive retina with reciprocal synapses. In J E Moody, editor, Advances in Neural Information Processing, volume 4, San Mateo CA, 1991. Morgan Kaufman.Google Scholar
  10. [10]
    K. A. Boahen. A retinomorphic vision system. IEEE Micro, 16(5):30–39, October 1996.CrossRefGoogle Scholar
  11. [11]
    J. Buhman, M. Lades, and Eeckman F. Illumination-invariant face recognition with a contrast sensitive silicon retina. In J D Cowan, G Tesauro, and J Alspector, editors, Advances in Neural Information Processing, volume 6, San Mateo CA, 1994. Morgan Kaufman.Google Scholar
  12. [12]
    K. Bult and G. J. Geelen. An inherently linear and compact MOST-only current division technique. IEEE J. Solid-State Circ., 27(12):1730–1735, 1992.CrossRefGoogle Scholar
  13. [13]
    P. C. Chen and A. W. Freeman. A model for spatiotemporal frequency responses in the x cell pathway of cat’s retina. Vision Res., 29:271–291, 1989.CrossRefGoogle Scholar
  14. [14]
    R. R. de Ruyter van Steveninck and S. B. Laughlin. The rate of information transfer at graded-potential synapses. Nature, 379:642–645, February 1996.CrossRefGoogle Scholar
  15. [15]
    T. Delbrück and C. Mead. Photoreceptor circuit with wide dynamic range. In Proceedings of the International Circuits and Systems Meeting, IEEE Circuits and Systems Society, London, England, 1994.Google Scholar
  16. [16]
    A. Dickinson, B. Ackland, E. El-Sayed, D. Inglis, and E. R. Fossum. Standard CMOS active pixel image sensors for multimedia applications. In William Dally, editor, Proceedings of the 16th Conference on Advanced Research in VLSI, pages 214–224, Chapel Hill, North Carolina, 1995. IEEE Press, Los Alamitos CA.Google Scholar
  17. [17]
    D. Dong and J. Atick. Statistics of natural time-varying scenes. Network: Computation in Neural Systems, 6(3):345–358, 1995.MATHCrossRefGoogle Scholar
  18. [18]
    D. Dong and J. Atick. Temporal decorrelation: A theory of lagged and non-lagged responses in the lateral geniculate nucleus. Network: Computation in Neural Systems, 6(2):159–178, 1995.MATHCrossRefGoogle Scholar
  19. [19]
    M. Eckert and G. Buchsbaum. Efficient coding of natural time-varying images in the early visual system. Phil. Trans. Royal Soc. Lond. Biol, 339(1290):385–395, 1993.CrossRefGoogle Scholar
  20. [20]
    C. Enroth-Cugell, J. G. Robson, D. E. Schweitzer-Tong, and A B. Watson. Spatiotemporal interactions in cat retinal ganglion cells showing linear spatial summation. J. Physiol., 341:279–307, 1983.Google Scholar
  21. [21]
    D. J. Field. Relations between statistics of natural images and the response properties of cortical cells. J. Opt. Soc. Am., 4:2379–2394, 1987.Google Scholar
  22. [22]
    B. Fowler, A. E. Gamal, and D. Yang. A CMOS area image sensor with pixel-level A/D conversion. In John H. Wuorinen, editor, Digest of Technical Papers, volume 37 of IEEE International Solid-State Circuits Conference, pages 226–227, San Francisco, California, 1994.Google Scholar
  23. [23]
    K. Fujikawa, I. Hirota, H. Mori, T. Matsuda, M. Sato, Y. Takamura, S. Kitayama, and J. Suzuki. A 1/3 inch 630k-pixel IT-CCD image sensor with multi-function capability. In John H. Wuorinen, editor, Digest of Technical Papers, volume 38 of IEEE International Solid-State Circuits Conference, pages 218–219, San Francisco, CA, 1995.Google Scholar
  24. [24]
    B. Hoeneisen and C. Mead. Fundamental limitations in microelectronics-I: MOS technology. IEEE J. Solid-State Circ., 15:819–829, 1972.Google Scholar
  25. [25]
    C. Jansson, I. Per, C. Svensson, and R. Forchheimer. An addressable 256 × 256 photodiode image sensor array with 8-bit digital output. Analog Integr. Circ. & Sig. Proc., 4:37–49, 1993.CrossRefGoogle Scholar
  26. [26]
    D. H. Kelly. Motion and vision II: Stabilized spatiotemporal threshold surface. J. Opt. Soc. Am., 69(10):1340–1349, 1979.Google Scholar
  27. [27]
    John Lazzaro. Temporal adaptation in a silicon auditory nerve. In John E. Moody, Steve J. Hanson, and Richard P. Lippmann, editors, Advances in Neural Information Processing Systems, volume 4, pages 813–820. Morgan Kaufmann Publishers, Inc., 1992.Google Scholar
  28. [28]
    M. Mahowald and R. Douglas. A silicon neuron. Nature, 354:515–518, 1991.CrossRefGoogle Scholar
  29. [29]
    M. Mahowald and C. Mead. The silicon retina. Scientific American, 264(5):76–82, 1991.CrossRefGoogle Scholar
  30. [30]
    C. Mead. A sensitive electronic photoreceptor. In H. Fuchs, editor, 1985 Chapel Hill Conference on VLSI, pages 463–471, Rockville MD, 1985. Computer Science Press, Inc.Google Scholar
  31. [31]
    C. Mead. Scaling of MOS technology to submicrometer feature sizes. J. of VLSI Signal Processing, 8:9–25, 1994.CrossRefGoogle Scholar
  32. [32]
    C. A. Mead and M. Ismail, editors. Analog VLSI Implementation of Neural Systems. Kluwer, Norwell, MA, 1989.Google Scholar
  33. [33]
    S. Ohshima, T. Yagi, and Y. Funashi. Computational studies on the interaction between red cone and H1 horizontal cell. Vision Res., 35(1):149–160, 1994.CrossRefGoogle Scholar
  34. [34]
    W. A. Richards. A lightness scale for image intensity. Appl. Opt., 21:2569–2582, 1982.Google Scholar
  35. [35]
    B. Sakman and O. D. Creutzfeldt. Scotopic and mesopic light adaptation in the cat’s retina. Pflúgers Archiv fúr die gesamte physiologie, 313:168–185, 1969.CrossRefGoogle Scholar
  36. [36]
    C. E. Shannon and W. Weaver. The Mathematical Theory of Communication. Univ. Illinois Press, Urbana IL, 1949.MATHGoogle Scholar
  37. [37]
    R. G. Smith. Simulation of an anatomically defined local circuit: The cone-horizontal cell network in cat retina. Visual Neurosci., 12(3):545–561, May–Jun 1995.CrossRefGoogle Scholar
  38. [38]
    W. R. Softky. Fine analog coding minimizes information transmission. Neural Networks, 9(1):15–24, 1996.CrossRefGoogle Scholar
  39. [39]
    J. H. van Hateren. A theory of maximizing sensory information. Biol. Cybern., 68:23–29, 1992.MATHCrossRefGoogle Scholar
  40. [40]
    E. Vittoz and X. Arreguit. Linear networks based on transistors. Electronics Letters, 29(3):297–299, February 1993.CrossRefGoogle Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Kwabena A. Boahen
    • 1
  1. 1.Physics of Computation Laboratory, MS 136-93California Institute of TechnologyPasadena

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