Abstract
A collective computational architecture and real-time, analog VLSI implementation for localizing and tracking a stimulus in a sensory image are developed. This architecture is presented as a layered two-dimensional computationalframework which generates signals to autonomously control a mechanical system that tracks the stimulus. The framework is a schematic representation of the described computation. The input to the framework is a spatially encoded sensory image and the outputs are a set of pulse trains that are used to control a robotic motor system. The analog VLSI implementation is based upon circuits that provide a real-time, small-size, low-power implementation technology for this and other sensorimotor applications. The circuits perform the desired computation entirely in parallel on a single VLSI chip. The layer-to-layer communications occur via arrays of currents which are modified at each level in the framework and then communicated to the subsequent layer. The outputs generated by the circuit are a set of pulse-encoded signals sufficient to antagonistically control DC actuators. A system implementation and resulting data are also presented. The system combines a visual imaging array, computational circuitry, and a mechanical plant, which, through negative feedback, moves the imager to hold a stimulus (a bright spot on a darker background) stationary in the sensory field.
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Ballard, D.A. 1986. Cortical connections and parallel processing: structure and function.Behavioral and Brain Sciences, 9:67–120.
Blake, A. and Yuille, A. (Eds.), 1992.Active Vision, Cambridge, MA: The MIT Press.
Boahen, K.A. and Andreou, A.G. 1992. A Contrast Sensitive Silicon Retina with Reciprocal Synapses. In J.E. Moody (Ed.),Neural Information Processing Systems, Vol. 4, San Mateo, CA: Morgan Kaufmann.
Brawer, J.R., Morest, D.K., and Kane, E.C. 1974. The Neuronal Architecture of the Cochlear Nucleus of the Cat.J. Comp. Neurol., 155:251–300.
Brooks, R.A. 1986. A robust layered control system for a mobile robot.IEEE Journal of Robotics and Automation, RA-2(1):14–23.
Brown, C., Coombs, D., and Soong, J. 1992. Real-Time Smooth Pursuit Tracking. InActive Vision, A. Blake and A. Yuille (Eds.), MIT Press, Cambridge, MA, pp. 123–136.
Clark, J.J. and Ferrier, N.J. 1992. Attentive Visual Servoing. InActive Vision, A. Blake and A. Yuille (Eds.), MIT Press, Cambridge, MA, pp. 137–154.
Coombs, D. and Brown, C. 1993. Real-Time Binocular Smooth Pursuit.International Journal of Computer Vision, 11:147–164.
Crowley, J.L., Bobet, P., and Mesrabi, M. 1992. Gaze Control for a Binocular Camera Head. InComputer Vision-ECCV'92, G. Sandini (Ed.), Springer, Berlin, pp. 588–596.
DeWeerth, S.P. 1991.Analog VLSI Circuits for Sensorimotor Feedback. Ph.D. Thesis, Computation and Neural Systems, California Institute of Technology, Pasadena, CA.
DeWeerth, S.P. 1992. Analog VLSI Circuits for Stimulus Localization and Centroid Computation.International Journal of Computer Vision, 8(3):191–202.
DeWeerth, S.P. and Morris, T. 1994. Analog VLSI Circuits for Primitive Sensory Attention.Proceedings of the 1994 IEEE International Symposium on Circuits and Systems, Short Run Press, Exeter, U.K., pp. 507–510.
DeWeerth, S.P., Nielsen, L., Mead, C.A., and Åström, K.J. 1991. A Simple Neuron Servo.IEEE Transactions on Neural Networks, 2(2):248–251.
Gilbert, B. 1984. A 16-channel array normalizer.IEEE Journal of Solid-State Circuits, 19:956–963.
Horiuchi, T., Bishofberger, B., and Koch, C. 1994. An analog VLSI saccadic eye movement system. In J. Cowan et al. (Eds.),Advances in Neural Information Processing Systems, Vol. 6, San Francisco: Morgan Kaufman, pp. 582–589.
Hubel, D.H. and Wiesel, T.N. 1959. Receptive Fields of Single Neurones in the Cat's Striate Cortex.J. Physiol. (Lond.), 148:574–591.
Knudsen, E.I. and Konishi, M. 1978. A Neural Map of Auditory Space in the Owl.Science, 200:795–797.
Koch, C. and Ullman, S. 1985. Shifts in Selective Visual Attention: Towards the Underlying Neural Circuitry.Human Neurobiology, 4:219–227.
Krauzlis, R.J. and Lisberger, S.G. 1989. A control systems model of smooth pursuit eye movements with realistic emergent properties.Neural Computation, 1:116–122.
Landy, M.S. and Movshon, J.A. 1991.Computational Models of Visual Processing, Cambridge, MA: The MIT Press.
Lazzaro, J. and Mead, C.A. 1989. A silicon model of auditory localization.Neural Computation, 1(1):47–57.
Lazzaro, J., Ryckebusch, S., Mahowald, M.A., and Mead, C.A. 1989. Winner-take-all networks ofO(n) complexity. InAdvances in Neural Information Processing Systems, Vol. 1, D.S. Touretzky (Ed.), Morgan Kaufmann, San Mateo, CA, pp. 703–711.
Ling, Y.L.C., Olson, K.W., Orin, D.E., and Sadayappan, P. 1987. A layered restructurable VLSI architecture. InProceedings of the 1987 IEEE International Conference on Computer Design: VLSI in Computers and Processors, New York: IEEE Press, pp. 267–272.
Mead, C.A. 1985. A Sensitive Electronic Photoreceptor. InProceedings of the 1985 Chapel Hill Conference on Very Large-Scale Integration, H. Fuchs (Ed.), Computer Society Press, Rockville, MD, pp. 463–471.
Mead, C.A. 1989.Analog VLSI and Neural Systems. Addison-Wesley, Reading, MA.
Mead, C.A. and Mahowald, M.A. 1988. A silicon model of early visual processing.Neural Networks, 1:91–97.
Pola, J. and Wyatt, H.J. 1991. Smooth pursuit: response, characteristics, stimuli and mechanisms. InVision and Visual Dysfunction, Vol. 8 (R.H.S. Carpenter), CRC Press Inc., Boston, MA, pp. 244–276.
Posner, Michael I. and Petersen, Steven E. 1990. The Attention System of the Human Brain.Annual Review of Neuroscience, 13:25–42.
Robinson, D.A. 1981. Control of eye movements. InHandbook of Physiology. The Nervous System. Motor Control, Vol. 2, J.M. Brookhart et al. (Eds.), American Physiological Society, Bethesda, MD, pp. 1275–1320.
Robinson, D.A. 1990. A Computational View of the Oculomotor Systems. InComputational Neuroscience, E.L. Schwartz (Ed.), The MIT Press, Cambridge, MA, pp. 319–330.
Rowe, M.H. 1991. Functional Organization of the Retina.Vision and Visual Dysfunction, Vol. 3, Bogdan Dreher and Stephen R. Robinson (Eds.), CRC Press, Inc., Boston, MA, pp. 1–68.
Scott, P.D. 1990. Applied Machine Vision. InScience of Vision, K.N. Leibovic (Ed.), Springer-Verlag, New York, pp. 439–465.
Sparks, D. 1986. Translation of sensory signals into commands for control of saccadic eye movements: role of primate superior colliculus.Physiological Reviews, 66(1):118–171.
Tanner, J.E. 1986.Integrated Optical Motion Detection. Ph.D. Thesis, Department of Computer Science, California Institute of Technology, Pasadena, CA.
Wurtz, R.H. and Albano, J.E. 1980. Visual-motor function of the primate superior colliculus.Ann. Rev. Neurosci., 3:189–226.
Wyatt, J., Keast, C., Seidel, M., Standley, D., Horn, B., Knight, T., Sodini, C., Lee, H., and Poggio, T. 1992. Analog VLSI systems for image acquisition and fast early vision processing.International Journal of Computer Vision, 8(3):217–230.
Zuber, B.L. (Ed.) 1981.Models of Oculomotor Behavior and Control, CRC Press, Boca Raton, FL.
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Deweerth, S.P. Converting spatially encoded sensory information to motor signals using analog VLSI circuits. Auton Robot 2, 93–104 (1995). https://doi.org/10.1007/BF00735429
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DOI: https://doi.org/10.1007/BF00735429