Neural Systems for Motion Analysis: Single Neuron and Network Approaches

  • Terry L. Huntsberger
  • John R. Rose
  • Dudley Girard
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 42)


This chapter employs large-scale computer simulations to investigate properties of the visual cortex, in particular the motion pathway. We have modeled the direction and orientation sensitive portions of the pathway using a biological model of the dendritic structure of a neuron and with a Jordan-Elman recurrent network. While biologically motivated, our study is not meant to imply a functional underlying architecture based on our simulations, but to provide a computational system for possible use in machine vision applications. The construction and simulation of large-scale neuronal networks using simplified models is governed by the tradeoff between biological fidelity for a full model and the decrease in computational complexity for the simplified one.


Apparent Motion Dendritic Structure Lateral Geniculate Nucleus Sensor Fusion Input Field 
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  1. 1.
    Ajjimarangsee, P. and T. L. Huntsberger (1988). Neural network model for the fusion of visible and thermal infrared sensor output. In Proc. SPIE Sympos. Sesnor Fusion I: Spatial Reasoning and Scene Interpretation, Volume 1003, Cambridge, MA, pp. 153–160.Google Scholar
  2. 2.
    Beck, J. (1972). Surface Color Perception. Ithaca, NY: Cornell University Press.Google Scholar
  3. 3.
    Bower, J. M. and D. Beeman (1995). The Book of GENESIS. Santa Clara, CA: Springer-Verlag/TELOS.CrossRefMATHGoogle Scholar
  4. 4.
    Broida, T. and R. Chellappa (1991). Estimating the kinematics and structure of a rigid object from a sequence of monocular images. IEEE Transactions on Pattern Analysis and Machine Intelligence13, 497–513.CrossRefGoogle Scholar
  5. 5.
    Connor, J. A. and C. F. Stevens (1971). Prediction of repetitive firing behavior from voltage clamp data on an isolated neuron soma. J. Physiology (London) 213, 31–54.Google Scholar
  6. 6.
    Creutzfeldt, O. D. and H. C. Nothdurft (1978). Representation of complex visual stimuli in the brain. Naturwissenschaften65, 307–318.CrossRefGoogle Scholar
  7. 7.
    DeAngelis, G. C, I. Ohzawa, and R. D. Freeman (1993a). Spatiotem-poral organization of simple-cell receptive fields in the cat’s striate cortex. I: General characteristics and postnatal development. Journal of Neurophysiology69, 1091–1117.Google Scholar
  8. 8.
    DeAngelis, G. C, I. Ohzawa, and R. D. Freeman (1993b). Spatiotem-poral organization of simple-cell receptive fields in the cat’s striate cortex. II: Linearity of spatial and temporal summation. Journal of Neurophysiology69, 1118–1135.Google Scholar
  9. 9.
    Eccles, J. C. (1957). The Physiology of Nerve Cells. New York: Academic Press.Google Scholar
  10. 10.
    Ellman, J. (1990). Finding structure in time. Cognitive Science14, 179–211.CrossRefGoogle Scholar
  11. 11.
    Faugeras, O. D. and K. E. Price (1981). Semantic description of aerial images using stochastic labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence3, 633–642.CrossRefGoogle Scholar
  12. 12.
    Fukushima, K. (1987). Neural network model for selective attention in visual pattern recognition and associate recall. Applied Optics26, 4985–4992.CrossRefGoogle Scholar
  13. 13.
    Gibson, J. J. (1979). The Ecological Approach to Visual Perception. Boston, MA: Houghton Mifflin Co.Google Scholar
  14. 14.
    Goris, R. C. and S. Terashima (1973). Central response to infrared stimulation of the pit receptors in a Crotaline snake, trimeresurus flavoviridis. Journal of Experimental Biology58, 59–76.Google Scholar
  15. 15.
    Grossberg, S. (1987). Cortical dynamics of three-dimensional form, color, and brightness perception, I: Monocular theory. Perception and Psychophysics41, 87–116.CrossRefGoogle Scholar
  16. 16.
    Grossberg, S. and D. Levine (1987). Neural dynamics of attention-ally modulated Pavlovian conditioning: Blocking, interstimulus interval, and secondary reinforcement. Applied Optics26, 5015–5030.CrossRefGoogle Scholar
  17. 17.
    Grossberg, S. and M. E. Rudd (1989). A neural architecture for visual motion perception: Group and element apparent motion. Neural Networks2(6), 421–450.CrossRefGoogle Scholar
  18. 18.
    Hill, A. V. (1936). Excitation and accomodation in nerve. Proc. Royal Soc. London, Ser. B 119, 305–355.CrossRefGoogle Scholar
  19. 19.
    Hodgkin, A. L. and A. F. Huxley (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiology (London) 117, 500–544.Google Scholar
  20. 20.
    Hoffman, W. C. (1966). The lie algebra of visual perception. Journal of Mathematical Psychology3, 65–98.CrossRefMATHGoogle Scholar
  21. 21.
    Holmes, W. R. and W. Rail (1992). Electrotonic models of neuronal dendrites and single neuron computation. In T. McKenna, J. Davis, and S. F. Zornetzer (Eds.), Single Neuron Computation, pp. 7–26. Boston, MA: Academic Press, Inc.Google Scholar
  22. 22.
    Huntsberger, T. and M. Hilton (1995). Distributed multisensor integration in a cooperative multirobot system. In P. S. Schenker and G. T. McKee (Eds.), Proc. SPIE Symposium on Sensor Fusion and Networked Robotics, Volume 2589, Philadelphia, PA, pp. 134–141.CrossRefGoogle Scholar
  23. 23.
    Huntsberger, T. and J. Rose (1997). Computer simulation of bimodal neurons and networks: Integrating infrared and visual stimuli. In S. S. Iyengar (Ed.), A Computational Paradigm for Synthesizing Models and Simulations of Complex Biological Systems. Boca Raton, FL: CRC Press.Google Scholar
  24. 24.
    Huntsberger, T. L. (1990). Comparison of techniques for disparate sensor fusion. In Proc. SPIE Sympos. Sensor Fusion III: 3-D Perception and Recognition, Volume 1383, Boston, MA, pp. 589–595.Google Scholar
  25. 25.
    Huntsberger, T. L. (1992a). Data fusion: A neural networks implementation. In M. A. Abidi and R. C. Gonzalez (Eds.), Data Fusion in Robotics and Machine Intelligence. Orlando, FL: Academic Press.Google Scholar
  26. 26.
    Huntsberger, T. L. (1992b). Sensor fusion in a dynamic environment. In P. S. Schenker (Ed.), Proc. SPIE Symposium on Sensor Fusion V, Volume 1828, Boston, MA, pp. 175–182.CrossRefGoogle Scholar
  27. 27.
    Huntsberger, T. L. (1995). Biologically motivated cross-modality sensory fusion system for automatic target recognition. Neural Networks 8(7/8), 1215–1226.CrossRefGoogle Scholar
  28. 28.
    Huntsberger, T. L. (1996). Hybrid teleoperated/cooperative multi-robot system for complex retireval operations. In P. S. Schenker and G. T. McKee (Eds.), Proc. SPIE Symposium on Sensor Fusion and Distributed Robotic Agents, Boston, MA, pp. 11–17.CrossRefGoogle Scholar
  29. 29.
    Huntsberger, T. L. (1997). Autonomous multirover system for complex planetary retrieval operations. In P. S. Schenker and G. T. McKee (Eds.), Proc. SPIE Symposium on Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, Pittsburgh, PA, pp. 221–227.Google Scholar
  30. 30.
    Huntsberger, T. L. (1998). Fault tolerant action selection for planetary rover control. In P. S. Schenker and G. T. McKee (Eds.), Proc. SPIE Symposium on Sensor Fusion and Decentralized Control in Autonomous Robotic Systems, Boston, MA. to appear.Google Scholar
  31. 31.
    Huntsberger, T. L. and P. Ajjimarangsee (1990). Parallel self-organizing feature maps for unsupervised pattern recognition. International Journal of General Systems16, 357–372. Also reprinted in Fuzzy Models for Pattern Recognition, (Eds. J. C. Bezdek, S. K. Pal), IEEE Press, Piscataway, NJ, 1992, pp. 483–495.Google Scholar
  32. 32.
    Huntsberger, T. L. and S. N. Jayaramamurthy (1987). Determination of the optic flow field using the spatiotemporal deformation of region properties. Pattern Recogition Letters6, 169–177.CrossRefGoogle Scholar
  33. 33.
    Huntsberger, T. L. and S. N. Jayaramamurthy (1988). Determination of the optic flow field in the presence of occlusion. Pattern Recognition Letters8, 325–333.CrossRefGoogle Scholar
  34. 34.
    Huntsberger, T. L. and J. Rose (1998). B ISM ARC: A Biologically Inspired System for Map-based Autonmous Rover Control. Neural Networks, in press.Google Scholar
  35. 35.
    Hutchinson, J., C. Koch, J. Luo, and C. Mead (1988). Computing motion using analog and binary resistive networks. IEEE Computer 21 (3), 52–64.CrossRefGoogle Scholar
  36. 36.
    Jain, R. (1983). Dynamic scene analysis. In Kanal and Rosenfeld (Eds.), Progress in Pattern Recognition. North Holland.Google Scholar
  37. 37.
    Jerian, C. and R. Jain (1990). Polynomial methods for structure from motion. IEEE Transactions on Pattern Analysis and Machine Intelligence12, 1150–1166.CrossRefGoogle Scholar
  38. 38.
    Jordan, M. J. (1986). Attractor dynamics and parallelism in a con-nectionist sequential machine. In Proc. Eighth Ann Conf. of Cognitive Science Society, pp. 531–546.Google Scholar
  39. 39.
    Katz, B. (1969). The Release of Neural Transmitter Substances. Springfield, IL: Charles C. Thomas.Google Scholar
  40. 40.
    Kernell, D. (1968). The repetitive impulse discharge of a simple neuron model compared to that of spinal motoneurons. Brain Research11, 685–687.CrossRefGoogle Scholar
  41. 41.
    Kikuchi, M. and K. Fukushima (1996). Neural network model of the visual system: Binding form and motion. Neural Networks9, 1417–1427.CrossRefMATHGoogle Scholar
  42. 42.
    Koch, C. and S. Ullman (1985). Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology4, 219–227.Google Scholar
  43. 43.
    Kolers, P. A. and M. von Grunau (1976). Shape and color in apparent motion. Vision Research16, 329–335.CrossRefGoogle Scholar
  44. 44.
    MacGregor, R. J. (1987). Neural and Brain Modeling. San Diego, CA: Academic Press, Inc.MATHGoogle Scholar
  45. 45.
    Marr, D. and S. Ullman (1981). Directional selectivity and its use in early visual processing. Proc. of the Royal Society of London (B)211, 151–180.CrossRefGoogle Scholar
  46. 46.
    Marshall, J. A. (1990). Self-organizing neural networks for perception of visual motion. Neural Networks3, 45–74.CrossRefGoogle Scholar
  47. 47.
    Mascagni, M. V. (1992). Numerical methods for neuronal modeling. In C. Koch and I. Segev (Eds.), Methods in Neuronal Modeling, pp. 439–484. Cambridge, MA: MIT Press.Google Scholar
  48. 48.
    McCulloch, W. S. and W. Pitts (1943). A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys.5, 115–133.MathSciNetCrossRefMATHGoogle Scholar
  49. 49.
    Minsky, M. (1967). Computation: Finite and Infinite Machines. En-glewood Cliffs, NJ: Prentice-Hall.MATHGoogle Scholar
  50. 50.
    Movshon, J. A., E. H. Adelson, M. S. Gizzi, and W. T. Newsome (1985). The analysis of moving visual patterns. In C. Chagas, R. Gat-tass, and C. Gross (Eds.), Pattern Recognition Mechanisms (Pontificiae Academiae Scientarum Scripta Varia, 4$), pp. 117–151. Vatican Press, Rome.Google Scholar
  51. 51.
    Nothdurft, H. C. and B. B. Lee (1982). Responses to colored patterns in the Macaque LGN: Pattern processing in single neurones. Experimental Brain Research48, 43–54.Google Scholar
  52. 52.
    Ohlander, R., K. Price, and D. R. Reddy (1978). Picture segmentation using a recursive region splitting method. Comput. Graphics Image Processing8, 313–333.CrossRefGoogle Scholar
  53. 53.
    Rail, W. (1969). Time constants and electrotonic length of membrane cylinders and neurons. Biophysical Journal9, 1483–1508.CrossRefGoogle Scholar
  54. 54.
    Rail, W. (1977). Core conductor theory and cable properties of neurons. In E. R. Kandel (Ed.), Handbook of Physiology (Sect. 1): The Nervous System I. Cellular Biology of Neurons, pp. 39–97. Baltimore, MD: American Physiological Society.Google Scholar
  55. 55.
    Seibert, M. and A. M. Waxman (1989). Spreading activation layers, visual saccades, and invariant representations for neural pattern recognition systems. Neural Networks 2(1), 9–27.CrossRefGoogle Scholar
  56. 56.
    Shariat, H. and K. E. Price (1990). Motion estimation with more than two images. IEEE Transactions on Pattern Analysis and Machine Intelligence12, 417–434.CrossRefGoogle Scholar
  57. 57.
    Shepherd, G. M. (1979). The Synaptic Organization of the Brain, 2nd Ed. New York: Oxford University Press.Google Scholar
  58. 58.
    Shepherd, G. M. (1992). Canonical neurons and their computational organization. In T. McKenna, J. Davis, and S. F. Zornetzer (Eds.), Single Neuron Computation, pp. 27–60. Boston, MA: Academic Press, Inc.Google Scholar
  59. 59.
    Simoncelli, E. P. (1993). Distributed representation and analysis of visual motion. Technical Report 209, MIT Media Lab, Cambridge, MA.Google Scholar
  60. 60.
    Soh, Y. S. (1989). Dynamic Scene Analysis. Ph. D. thesis, Department of Computer Science, University of South Carolina, Columbia, SC.Google Scholar
  61. 61.
    Terashima, S. and Y. Liang (1991). Temperature neurons in the Cro-taline trigeminal ganglia. Journal of Neurophysiology66, 623–634.Google Scholar
  62. 62.
    Traub, R. D. and R. Miles (1991). Neuronal Networks of the Hippocampus.New York: Cambridge University Press.CrossRefGoogle Scholar
  63. 63.
    Treisman, A. M. (1980). A feature-integration theory of attention. Cognitive Psychology12, 97–136.CrossRefGoogle Scholar
  64. 64.
    Tsao, T.-R., H.-J. Shyu, J. Libert, and V. Chen (1991). A Lie group approach to a neural system for three-dimensional interpretation of visual motion. IEEE Transactions on Neural Networks2, 149–155.CrossRefGoogle Scholar
  65. 65.
    Wiesel, T. N. and D. H. Hubel (1966). Spatial and chromatic interactions in the lateral geniculate body of the rhesus monkey. Journal Neurophysiology29, 1115–1156.Google Scholar
  66. 66.
    Wilson, C. J. (1992). Dendritic morphology, inward rectification, and the functional properties of neostriatal neurons. In T. McKenna, J. Davis, and S. F. Zornetzer (Eds.), Single Neuron Computation, pp. 141–172. Boston, MA: Academic Press, Inc.Google Scholar
  67. 67.
    Wilson, M. A. and J. M. Bower (1992). The simulation of large-scale neural networks. In C. Koch and I. Segev (Eds.), Methods in Neuronal Modeling, pp. 291–333. Cambridge, MA: MIT Press.Google Scholar
  68. 68.
    Zeki, S. (1993). A Vision of the Brain. London: Blackwell Scientific Publications.Google Scholar
  69. 69.
    Zeki, S. and S. Shipp (1988). The functional logic of cortical connections. Nature335, 311–317.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Terry L. Huntsberger
    • 1
  • John R. Rose
    • 1
  • Dudley Girard
    • 1
  1. 1.Intelligent Systems Laboratory, Department of Computer ScienceUniversity of South CarolinaColumbiaUSA

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