Advertisement

Why Cortices? Neural Networks for Visual Information Processing

  • Hanspeter A. Mallot
  • Werner Von Seelen
Chapter

Abstract

Neural networks for the processing of sensory information show remarkable similarities between different species and across different sensory modalities. As an example, cortical organization found in the mamalian neopallium and in the optic tecta of most vertebrates appears to be equally appropriate as a substrate for visual, auditory, and somatosensory information processing. In this paper, we formulate three structural principles of the vertebrate visual cortex that allow to analyze structure and function of these neural networks on an intermediate level of complexity. Computational applications are taken from the field of early vision. The proposed principles are: (a) Average anatomy, i e, lamination, average axonal and dendritic domains, and intrinsic feedback, determines the spatiotemporal interactions in cortical processing. Possible applications of the resulting filters include continuous motion perception and the direct measurement of high-level parameters of image flow. (b) Retinotopic mapping is an emergent property of massively parallel connections. With a local intrinsic operation in the target area, mapping combines to a space-variant image processing system as would be useful in the analysis of optical flow. (c) Further space-variance is brought about by both, discrete or patchy connections between areas and periodic or columnar arrangement of specialized neurons within the areas. We present preliminary results on the significance of these principles for neural computation.

Keywords

Visual Cortex Receptive Field Optical Flow Optic Tectum Visual Information Processing 
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.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adelson EH, Bergen JR (1985) Spatiotemporal energy models for the perception of motion. J Opt Soc Am A2: 322–342CrossRefGoogle Scholar
  2. Barlow HB (1985) Cerebral cortex as a model builder. In: Rose D, Dobson VG (eds) Models of the visual cortex. John Wiley & Sons, Chichester, New York, pp 37–46Google Scholar
  3. Barlow HB (1986) Why have multiple cortical areas? Vision Res26: 81–90Google Scholar
  4. Braccini C, Gambardella G, Sandini G (1981) A signal theory approach to the space and frequency variant filtering performed by the human visual system. Signal Processing3: 231–240Google Scholar
  5. Braitenberg V (1985) Charting the visual cortex. In: Peters A, Jones EG (eds) Cerebral cortex Vol 3: Visual cortex. Plenum Press, New York, pp 379–410Google Scholar
  6. Creutzfeldt OD, Sakmann B, Scheich H, Korn A (1970) Sensitivity distribution and spatial summation within receptive-field center of retinal on-center ganglion cells and transfer function of the retina. J Neurophysio133: 654–671Google Scholar
  7. Dinse HRO, Best J, Krüger K, Mallot HA, Seelen W v (1988) The dynamics of receptive field organization in the cat visual cortex (areas 17, 18, 19, and PMLS): oscillations and non-separability. (in Preparation)Google Scholar
  8. Enroth-Cugell C, Robson JG, Schweitzer-Tong DE, Watson AB (1983) Spatiotemporal interactions in the cat retinal ganglion cells showing linear spatial summation. JPhysiol (London) 341: 279–307Google Scholar
  9. Epstein LI (1984) An attempt to explain the differences between the upper and lower halves of the striate cortical map of the cat’s field of view. Bio! Cybern 49: 175–177CrossRefGoogle Scholar
  10. Fischer B (1973) Overlap of receptive field centers and representation of the visual field in the cat’s optic tract. Vision Res 13: 2113–2120PubMedCrossRefGoogle Scholar
  11. Fischer B, May HU (1970) Invarianzen in der Katzenretina: GesätzmiiBige Beziehungen zwischen Empfindlichkeit, Größe und Lage receptiver Felder von Ganglienzellen. Exp Brain Res 11: 448–464PubMedCrossRefGoogle Scholar
  12. Götz KG (1988) Cortical templates for the self-organization of orientation-specific d-and 1-hypercolumns in monkeys and cats. Bio! Cybern 58: 213–223CrossRefGoogle Scholar
  13. Grimson WEL (1981) From images to surfaces. MIT Press, Cambridge, MA, LondonGoogle Scholar
  14. Heeger DJ (1987) Optical flow from spatiotemporal filters. Proc First Int Conf Comp Vision, London, pp 181–190Google Scholar
  15. Horn BKP (1986) Robot vision. MIT Press, Cambridge, MA, LondonGoogle Scholar
  16. Jain R, Barlett SL, O’Brien N (1987) Motion stereo using ego-motion complex logarithmic mapping. IEEE Trans Pattern Anal Machine Intel (PAMI) 9: 356–369CrossRefGoogle Scholar
  17. Knudsen EI, du Lac S, Esterly SD (1987) Computational maps in the brain. Ann Rev Neurosci 10: 41–65PubMedCrossRefGoogle Scholar
  18. Koenderink JJ, Van Doom AJ (1976) Local structure of movement parallax of the plane. J Opt Soc Amer 66: 717–723CrossRefGoogle Scholar
  19. Korn A, von Seelen W (1972) Dynamische Eigenschaften von Nervennetzen im visuellen System. Kybernetik 10: 64–77PubMedCrossRefGoogle Scholar
  20. Krone G, Mallot HA, Palm G, Schüz A (1986) Spatiotemporal receptive fields: a dynamical model derived from cortical architectonics. Proc Roy Soc (Lond) B 226: 421–444CrossRefGoogle Scholar
  21. Little JJ, Bülthoff HH, Poggio T (1987) Parallel optical flow computation. In: Baumann, L (ed) Proc Image Understanding Workshop Scientific Applications International Corp, Los Altos, CA, pp 915–920Google Scholar
  22. Löwel S, Freeman B, Singer W (1987) Topographic organization of the orientation column system in large flat-mounts of the cat visual cortex: a 2-deoxyglucose study. J Comp Neurol 255: 401–415PubMedCrossRefGoogle Scholar
  23. Mallot HA (1985) An overall description of retinotopic mapping in the cat’s visual cortex areas 17, 18, and 19. Biol Cybern 52: 45–51PubMedCrossRefGoogle Scholar
  24. Mallot HA (1987) Point images, receptive fields, and retinotopic mapping. TINS 10: 310–311Google Scholar
  25. Marr D (1982) Vision. Freeman, San FranciscoGoogle Scholar
  26. Marroquin JL, Mitter S, Poggio T (1987) Probabilistic solution of ill-posed problems in computational vision. JAmerStatAssoc82: 76–89Google Scholar
  27. Mitchison G, Durbin R (1986) Optimal numberings of an Nx N array. SIAM JAIg Disc Meth 7: 571–582CrossRefGoogle Scholar
  28. Peters A, Jones EG (1984) Cerebral cortex Vol 1: Cellular components of the cerebral cortex Plenum Press, New YorkGoogle Scholar
  29. Poggio T, Torre V, Koch C (1985) Computational vision and regularization theory. Nature 317: 314–319PubMedCrossRefGoogle Scholar
  30. Reitboeck HJ, Altmann J (1984) A model for size-and rotationinvariant pattern processing in the visual system. Biol Cybern 51: 113–121PubMedCrossRefGoogle Scholar
  31. Regan D, Beverly KI (1978) Looming detectors in the human visual pathway. Vision Res 18: 415–421PubMedCrossRefGoogle Scholar
  32. Sawchuk AA (1974) Space-variant image restoration by coordinate transforms. J Opt Soc Amer 64: 138144Google Scholar
  33. Schwartz EL (1980) Computational anatomy and functional architecture of striate cortex: a spatial mapping approach to perceptual coding. Vision Res 20: 645–669PubMedCrossRefGoogle Scholar
  34. Seelen W v, Mallot HA (1988) Parallelism and redundancy in neural networks. In: Eckmiller R, Malsburg C v d (eds) Neural computers. Springer-Verlag, Berlin Heidelberg New York, pp 51–60Google Scholar
  35. Seelen W v, Mallot HA, Giannakopoulos F (1987) Characteristics of neuronal systems in the visual cortex. Biol Cybern 56: 37–49CrossRefGoogle Scholar
  36. Shen CW, Lee RCT, Chin YH (1987) A parallel nonlinear mapping algorithm. Int J Pattern Recog Artif Intellig1: 53–69Google Scholar
  37. Sherk H. (1986) Coincidence of patchy inputs from the lateral geniculate complex and area 17 to the cat’s Clare-Bishop area. J Comp Neurol 253: 105–120PubMedCrossRefGoogle Scholar
  38. Swindale NV, Matsubara JA, Cynander MS (1987) Surface organization of orientation and direction selectiviy in cat area 18. JNeurosci7: 1414–1427Google Scholar
  39. Tusa RI, Rosenquist AC, Palmer LA (1979) Retinotopic organization of areas 18 and 19 in the cat. J Comp Neural 185: 657–678CrossRefGoogle Scholar
  40. Van Essen D (1985) Functional organization of primate visual cortex. In: Peters A, Jones EG (eds) Cerebral cortex, Vol 3. Plenum Press, New York, pp 259–329Google Scholar

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • Hanspeter A. Mallot
    • 1
  • Werner Von Seelen
    • 1
  1. 1.Institut für Zoologie III (Biophysik)Johannes Gutenberg-UniversitätMainzFR Germany

Personalised recommendations

Cite chapter

Buy options