Journal of Computational Neuroscience

, Volume 2, Issue 1, pp 45–62 | Cite as

A multiscale dynamic routing circuit for forming size- and position-invariant object representations

  • Bruno A. Olshausen
  • Charles H. Anderson
  • David C. Van Essen
Article

Abstract

We describe a neural model for forming size- and position-invariant representations of visual objects. The model is based on a previously proposed dynamic routing circuit that remaps selected portions of an input array into an object-centered reference frame. Here, we show how a multiscale representation may be incorporated at the input stage of the model, and we describe the control architecture and dynamics for a hierarchical, multistage routing circuit. Specific neurobiological substrates and mechanisms for the model are proposed, and a number of testable predictions are described.

Keywords

recognition invariance routing multiscale attention 

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References

  1. Ahmad S (1992) VISIT: A neural model of covert visual attention. In: JE Moody, SJ Hanson, and RP Lippman, eds. Advances in Neural Information Processing Systems 4. Kaufmann, San Mateo, CA, pp. 420–427.Google Scholar
  2. Anderson CH, Burt PJ, and van der Wall GS (1985) Change detection and tracking. In:SPIE Vol. 579-Intelligent Robots and Computer Vision, pp. 72–78.Google Scholar
  3. Anderson CH and Van Essen DC (1987) Shifter circuits: A computational strategy for dynamic aspects of visual processing.Proceedings of the National Academy of Sciences, USA 84:6297–6301.Google Scholar
  4. Atick JJ (1992) Could information theory provide an ecological theory of sensory processing?Network 3:213–251.Google Scholar
  5. Baron RJ (1987) The Cerebral Computer. Erlbaum.Google Scholar
  6. Buhmann J, Lades M, and von der Malsburg C (1990) Size and distortion invariant object recognition by hierarchical graph matching. In:Proceedings of the International Joint Conference on Neural Networks, San Diego pp. 411–416.Google Scholar
  7. Cherniak C (1990) The bounded brain: Toward quantitative neuroanatomy.Journal of Cognitive Neuroscience 2(1):58–68.Google Scholar
  8. Cohen MA and Grossberg S (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks.IEEE Transactions on Systems, Man and Cybernetics 13(5):815–826.Google Scholar
  9. Connor CE, Gallant JL, and Van Essen DC (1993) Effects of focal attention on receptive field profiles in area V4.Soc. Neurosci. Abstr. 19, p. 974.Google Scholar
  10. Connor CE, Gallant JL, and Van Essen DC (1994a) Modulation of receptive field profiles in area V4 by shifts in focal attention.Invest. Opthal. Vis. Sci. 35, p. 2147.Google Scholar
  11. Connor CE, Gallant JL, and Van Essen DC (1994b) Dynamic modulation of receptive field profiles in area V4. Submitted for publication.Google Scholar
  12. Desimone R (1992) Neural circuits for visual attention in the primate brain. In: GA Carpenter and S Grossberg, eds. Neural Networks for Vision and Image Processing. MIT Press, Cambridge, Mass, pp. 343–364.Google Scholar
  13. De Valois RL, Albrecht DG, and Thorell LG (1982) Spatial frequency selectivity of cells in macaque visual cortex.Vision Res. 22:545–559.Google Scholar
  14. DeYoe EA, Felleman DJ, Van Essen DC, and McClendon E (1994) Multiple processing streams in occipito-temporal visual cortex.Nature 371:151–154.Google Scholar
  15. Douglas RJ and Martin KAC (1990a) Neocortex. In: GM Shepard, ed. Synaptic Organization of the Brain. Oxford UP, New York, pp. 389–438.Google Scholar
  16. Drasdo N (1977) The neural representation of visual space.Nature 266:554–556.Google Scholar
  17. Eriksen CW and St. James JD (1986) Visual attention within and around the field of focal attention: A zoom lens model.Perception and Psychophysics 40(4):225–240.Google Scholar
  18. Felleman DJ and McClendon E (1991) Modular connections between area V4 and temporal lobe area PITv in macaque monkeys.Society for Neuroscience Abstracts 17:1282.Google Scholar
  19. Field DJ (1989) What the statistics of natural images tell us about visual coding. SPIE Vol. 1077 Human Vision, Visual Processing, and Digital Displays, pp. 269–273.Google Scholar
  20. Field DJ (1994) What is the goal of sensory coding?Neural Computation 6:559–601.Google Scholar
  21. Foldiak P (1991) Learning invariance from transformation sequences.Neural Computation 3:194–200.Google Scholar
  22. Freeman WT (1992) Steerable filters and local analysis of image structure, Ph.D. thesis, MIT Media Laboratory.Google Scholar
  23. Fukushima K (1980) Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position.Biological Cybernetics 36:193–202.Google Scholar
  24. Hinton GE (1981) A parallel computation that assigns canonical object-based frames of reference. In:Proceedings of the Seventh International Joint Conference on Artificial Intelligence 2, Vancouver B.C., Canada.Google Scholar
  25. Hinton GE and Lang KJ (1985) Shape recognition and illusory conjunctions. In:Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles.Google Scholar
  26. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons.Proc. Natl. Acad. Sci. USA 81:3088–3092.Google Scholar
  27. Koch C and Ullman S (1985) Shifts in selective visual attention: towards the underlying neural circuitry.Human Neurobiology 4:219–227.Google Scholar
  28. Koenderink JJ and van Doom AJ (1978) Visual detection of spatial contrast; Influence of location in the visual field, target extent and illuminance level.Biological Cybernetics 30:157–167.Google Scholar
  29. LaBerge D, Carter M, and Brown V (1992) A network simulation of thalamic circuit operations in selective attention.Neural Computation 4:318–331.Google Scholar
  30. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, and Jackel LD (1990) Backpropagation applied to handwritten Zip code recognition.Neural Computation 1:541–551.Google Scholar
  31. Lee CW and Olshausen BA (1994) A nonlinear hebbian network that learns to detect disparity in random-dot stereograms. Technical Report 94-20, Philosophy, Neuroscience and Psychology program, Washington University, St. Louis, MO. In review.Google Scholar
  32. Lowe DG (1985) Perceptual organization and visual recognition. Boston: Kluwer.Google Scholar
  33. Marr D and Poggio T (1976) Cooperative computation of stereo disparity.Science 194:283–287.Google Scholar
  34. Milanese R (1993) Detecting salient regions in an image: From biological evidence to computer implementation. Ph.D. thesis, Computer Science Dept., University of Geneva.Google Scholar
  35. Moran J and Desimone R (1985) Selective attention gates visual processing in the extrastriate cortex.Science 229:782–784.Google Scholar
  36. Niebur E and Koch C (1994) A model for the neuronal implementation of selective visual attention based on temporal correlation among neurons.Journal of Computational Neuroscience 1:141–158.Google Scholar
  37. Nowlan SJ and Sejnowski TJ (1993) Filter selection model for generating visual motion signals. In: SJ Hanson, JD Cowan, and CL Giles, eds.Advances in Neural Information Processing Systems, 5. Morgan-Kaufmann, San Mateo, CA, pp. 369–376.Google Scholar
  38. Olshausen BA (1994) Neural routing circuits for forming invariant representations of visual objects. Ph.D. thesis, Computation and Neural Systems Program, California Institute of Technology.Google Scholar
  39. Olshausen BA, Anderson CH, and Van Essen DC (1993) A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information.The Journal of Neuroscience 13:4700–4719.Google Scholar
  40. Olshausen BA and Anderson CH (1994) A model of the spatial frequency organization in primate visual cortex. Paper presented at CNS*94, Monterey, CA, (proceedings in press).Google Scholar
  41. Parker AJ and Hawken MJ (1988) Two-dimensional spatial structure of receptive fields in monkey striate cortex.Journal of the Optical Society of America A 5:598–605.Google Scholar
  42. Pettet MW and Gilbert CD (1992) Dynamic changes in receptive-field size in cat primary visual cortex.Proc. Natl. Acad. Sci. USA 89:8366–8370.Google Scholar
  43. Pitts W and McCulloch WS (1947) How we know universals: The perception of auditory and visual forms.Bulletin of Mathematical Biophysics 9:127–147.Google Scholar
  44. Postma EO, van den Herik HJ, and Hudson PTW (1992) The gating lattice: A neural substrate for dynamic gating. In:CNS'92 Proceedings, July 26–29, San Francisco, California. Kluwer Academic Publishers.Google Scholar
  45. Postma EO (1994) SCAN: A neural model of covert attention. Ph.D. thesis, Computer Science Dept., University of Limburg, Maastricht, The Netherlands.Google Scholar
  46. Rockland KS (1992) Configuration, in serial reconstruction, of individual axons projecting from area V2 to V4 in the macaque monkey.Cerebral Cortex 2:353–374.Google Scholar
  47. Sajda P and Finkel LH (1994) Dual mechanisms for neural binding and segmentation. In: JD Cowan, G Tesauro, and J Alspector, eds.Advances in Neural Information Processing Systems, 6. Morgan-Kaufmann, San Francisco, CA, pp. 993–1000.Google Scholar
  48. Sandon PA and Uhr LM (1988) An adaptive model for viewpoint-invariant object recognition.Proceedings of the 10th Annual Conference of the Cognitive Science Society, Montreal, Canada, August, pp. 209–215.Google Scholar
  49. Schwartz EL (1977) Spatial mapping in the primate sensory projection: Analytic structure and relevance to perception.Biological Cybernetics 25:181–194.Google Scholar
  50. Thau R (1994) Visual segmentation and feature binding without synchronization. Paper presented at CNS*94, Monterey, CA, (proceedings in press).Google Scholar
  51. Tootell BH, Silverman MS, Hamilton SL, Switkes E, and De Valois RL (1988) Functional anatomy of macaque striate cortex. V. Spatial Frequency.The Journal of Neuroscience 8:1610–1624.Google Scholar
  52. Trehub A (1977) Neuronal models for cognitive processes: Networks for learning, perception and imagination.J Theor. Biol. 65:141–169.Google Scholar
  53. Tsotsos JK (1994) Towards a computational model of visual attention. In: T Papathomas, ed. Early Vision and Beyond. MIT Press, Cambridge, Mass.Google Scholar
  54. Van Essen DC and Anderson CH (1990) Information processing strategies and pathways in the primate retina and visual cortex. In: SF Zornetzer, JL Davis, C Lau, ed. An Introduction to Neural and Electronic Networks. Academic, New York, pp. 43–72. (2nd Edition in press).Google Scholar
  55. Van Essen DC and DeYoe EA (1994) Concurrent processing in the primate visual cortex. In: MS Gazzaniga, ed. The Cognitive Neurosciences. MIT Press, Cambridge, MA, pp. 383–400.Google Scholar
  56. Van Essen DC, Newsome WT, and Maunsell JHR (1984) The visual field representation in striate cortex of the macaque monkey: Asymmetries, anisotropies, and individual variability.Vision Research 24(5):429–448.Google Scholar
  57. Van Essen DC, Newsome WT, Maunsell JHR, and Bixby JL (1986) The projections from striate cortex (VI) to areas V2 and V3 in the macaque monkey: asymmetries, areal boundaries, and patchy connections.J. Comp. Neurol. 244:451–480.Google Scholar
  58. Van Essen DC, Olshausen B, Anderson CH, and Gallant JL (1991) Pattern recognition, attention, and information bottlenecks in the primate visual system. In: BP Mathur, C Koch, eds. Proc. SPIE Conf. on Visual Information Processing: From Neurons to Chips, Vol. 1473. SPIE, Bellingham, WA, pp. 17–28.Google Scholar
  59. Verghese P and Pelli DG (1992) The information capacity of visual attention.Vision Research 32(5):983–995.Google Scholar
  60. von der Malsburg C and Bienenstock E (1986) Statistical coding and short-term synaptic plasticity: A scheme for knowledge representation in the brain. In: E Bienenstock, Soulie F Fogelman, G Weisbuch, eds. Disordered Systems and Biological Organization (NATO ASI Series, Vol. F20). Springer, Berlin, pp. 247–272.Google Scholar
  61. Wilson MA and McNaughton BL (1993) Dynamics of the hippocampal ensemble code for space.Science 261:1055–1058.Google Scholar
  62. Yukie M and Iwai E (1985) Laminar origin of direct projection from cortex area VI to V4 in the rhesus monkey.Brain Research 346:383–386.Google Scholar

Copyright information

© Kluwer Academic Publishers 1995

Authors and Affiliations

  • Bruno A. Olshausen
    • 1
    • 2
  • Charles H. Anderson
    • 3
  • David C. Van Essen
    • 3
  1. 1.Dept. of Anatomy and NeurobiologyWashington University School of MedicineSt. Louis
  2. 2.Computation and Neural Systems ProgramCalifornia Institute of TechnologyPasadena
  3. 3.Dept. of Anatomy and NeurobiologyWashington University School of MedicineSt. Louis

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