Analog VLSI Excitatory Feedback Circuits for Attentional Shifts and Tracking

  • T. G. Morris
  • S. P. DeWeerth
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 447)


In biological vision systems, the term attention describes the way that information is prioritized and selected [23]. Selective attention is necessary in visual processing in order to handle the overwhelming amount of sensory information that is available. Visual systems, such as those found in primates, have a hybrid architecture in which low-level processing is performed in parallel across the entire visual field, and high-level processing is only performed on a selected subregion of the visual field [2]. Low-level tasks that are computed entirely in parallel are described as preattentive. Attentive processing uses this preattentive information to select a smaller region of interest for subsequent high-level processing. The duality of parallel computation and serial selections of regions of interest exemplifies the trade-off between speed and processing sophistication that results from the utilization of a limited amount of processing circuitry. If the attentional selection were not performed, an overwhelming amount of neural circuitry would be required in order to perform the high-level processing in parallel over the entire visual field [1].


Selective Attention Local Excitation Space Constant Excitatory Current Analog VLSI 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Y. Aloimonos. Active Perception. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1993.Google Scholar
  2. [2]
    J. R. Bergen and B. Julesz. Parallel versus serial processing in rapid pattern discrimination. Nature, 303, June 1983.Google Scholar
  3. [3]
    K. A. Boahen and A. G. Andreou. A contrast sensitive silicon retina with reciprocal synapses. Advances in Neural Information Processing Systems, 4:764–772, 1992.Google Scholar
  4. [4]
    C. L. Colby. The neuroanatomy and neurophysiology of attention. Journal of Child Neurology, 6:90–118, 1991.CrossRefGoogle Scholar
  5. [5]
    T. Delbrück. Silicon retina with correlation-based velocity-tuned pixels. IEEE Transactions on Neural Networks, 4(3):529–541, May 1993.CrossRefGoogle Scholar
  6. [6]
    S. P. DeWeerth. Converting spatially encoded sensory information to motor signals using analog VLSI circuits. Autonomous Robots, 2:93–104, 1995.CrossRefGoogle Scholar
  7. [7]
    S. P. DeWeerth and T. G. Morris. Analog VLSI circuits for primitive sensory attention. In Proceedings of the IEEE International Symposium on Circuits and Systems, volume 6, pages 507–510, 1994.Google Scholar
  8. [8]
    S. P. DeWeerth and T. G. Morris. CMOS current mode winner-take-all circuit with distributed hysteresis. Electronics Letters, 31(13):1051–1053, 1995.CrossRefGoogle Scholar
  9. [9]
    B. Gilbert. A monolithic 16-channel analog array normalizer. IEEE Journal of Solid State Circuits, SC-19(6):956–963, December 1984.CrossRefGoogle Scholar
  10. [10]
    J. G. Harris. Analog Models for Early Vision. PhD thesis, California Institute of Technology, 1991.Google Scholar
  11. [11]
    T. K. Horiuchi, B. Bishofberger, and C. Koch. An analog VLSI saccadic eye movement system. In Cowan, Tesauro, and Alspector, editors, Advances in Neural Information Processing Systems 6, pages 582–589, San Francisco, 1994. Morgan Kaufman.Google Scholar
  12. [12]
    H. Kobayashi, J. L. White, and A. A. Abidi. An active resistor network for gaussian filtering of images. IEEE Journal of Solid-State Circuits, 26(5):738–748, May 1991.CrossRefGoogle Scholar
  13. [13]
    C. Koch and S. Ullman. Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4:219–227, 1985.Google Scholar
  14. [14]
    N. Kumar, P. O. Pouliquen, and A. G. Andreou. Device mismatch limitations on the performance of an associative memory system. In Proceedings of the 36 th Midwest Symposium on Circuits and Systems, volume 1, pages 570–573, 1993.CrossRefGoogle Scholar
  15. [15]
    J. Lazzaro, S. Ryckebusch, M. A. Mahowald, and C. A. Mead. Winner-take-all networks of o(n) complexity. Technical Report CS-TR-88-21, Computer Science Department, California Institute of Technology, Pasadena, CA, 1989.Google Scholar
  16. [16]
    M. Mahowald. VLSI Analogs of Neuronal Visual Processing: a Synthesis of Form and Function. Computation and neural systems, California Institute of Technology, 1992.Google Scholar
  17. [17]
    C. A. Mead. Analog VLSI and Neural Systems. Addison-Wesley, Reading, MA, 1989.MATHGoogle Scholar
  18. [18]
    T. G. Morris and S. P. DeWeerth. Analog VLSI circuits for covert attentional shifts. In Proceedings of the Fifth International Conference on Microelectronics for Neural Networks and Fuzzy Systems, pages 30–37, Lausanne, Switzerland, February 1996. IEEE Computer Society Press.Google Scholar
  19. [19]
    T. G. Morris, D. M. Wilson, and S. P. DeWeerth. Analog VLSI circuits for manufacturing inspection. In Proceedings of the 16th Conference on Advanced Research in VLSI, pages 241–255, Los Alamitos, CA, 1995. IEEE Computer Society Press.Google Scholar
  20. [20]
    E. Niebur and C. Koch. A model for the neuronal implementation of selectiave visual attention based on temporal correlation among neurons. Journal of Computational Neuroscience, 1:141–158, 1994.CrossRefGoogle Scholar
  21. [21]
    E. Niebur and C. Koch. Modeling the ‘where’ visual pathway. In Proceedings of 2nd Joint Symposium on Neural Computation, volume 5. Caltech-UCSD Institute for Neural Computation, 1995.Google Scholar
  22. [22]
    B. Olshausen, C. Anderson, and D. van Essen. A neural model of visual attention and invariant pattern recognition. CNS Memo 18, August 6 1992.Google Scholar
  23. [23]
    M. I. Posner and S. E. Petersen. The attention system of the human brain. Annual Review of Neuroscience, 13:25–42, 1990.CrossRefGoogle Scholar
  24. [24]
    R. W. Remington and L. Pierce. Moving attention: Evidence for time-invariant shifts of visual selection attention. Perception & Psychophysics, 35:393–399, 1984.Google Scholar
  25. [25]
    G. Sperling and E. Weichselgartner. Episodic theory of the dynamics of spatial attention. Psychological Review, 3:503–532, 1995.CrossRefGoogle Scholar
  26. [26]
    E. Vittoz and X. Arreguit. Linear networks based on transistors. Electronics Letters, 29(3):297–299, February 1993.CrossRefGoogle Scholar
  27. [27]
    S. Yantis. Control of visual attention. In H. Pashler, editor, Control of Visual Attention. University College Press, London, in press.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • T. G. Morris
    • 1
  • S. P. DeWeerth
    • 1
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA

Personalised recommendations