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Visuomotor Coordination: Neural Models and Perceptual Robotics

  • Michael A. Arbib

Abstract

This paper addresses two complementary questions: What is the appropriate set of tools for the study of the networks of animal and human brains, and what are the strategies for building computers with “intelligence”? We argue that there are overall architectural principles which unite both sides of this study, namely, that a computer no longer be thought of as a unitary system but rather as a network of more specialized devices, and that many of these devices be structured as highly parallel arrays of interacting neuron-like components. We illustrate this with a discussion of the architecture of the frog’s brain as revealed in studies of the mechanisms of visuomotor coordination, and of the design of vision and motor conrol systems for robots. The following topics are treated: (1) What is a schema? (2) Schemas for Rana computatrix. (3) Tectal columns (4) Depth perception. (5) Pathplanning and detours. (6) Schemas for hand control. (7) Schemas for vision. (8) Challenges for cooperation.

Keywords

Firing Rate Neural Model Optic Tectum Virtual Finger Perceptual Schema 
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.

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References

  1. Amari S-I, Arbib MA (1977) Competition and cooperation in neural nets. In: Metzler J (ed) Systems neuroscience. Academic Press London New York, pp 119–165Google Scholar
  2. Arbib MA (1975) Artificial intelligence and brain theory. unities and diversities. Ann Biomed Eng 3: 238–274PubMedCrossRefGoogle Scholar
  3. Arbib MA (1981) Perceptual structures and distributed motor control. In: Brooks VB (ed) Handbook of physiology–The nervous system II. Motor control. American Physiological Society Bethesda MD, pp 1449–1480Google Scholar
  4. Arbib MA (1987a) Levels of modeling of mechanisms of visually guided behavior. BehavBrain Sci 10: 407–465Google Scholar
  5. Arbib MA (1987b) Brains, machines, and mathematics, 2nd Edn, Springer-Verlag, New YorkGoogle Scholar
  6. Arbib MA (1989) The metaphorical brain 2: neural networks and beyond. Wiley Interscience, New York (in press)Google Scholar
  7. Arbib MA, Hanson AR (eds) (1987) Vision, brain, and cooperative computation. A Bradford Book/MIT Press Cambridge MAGoogle Scholar
  8. Arbib MA, House DH (1987) Depth and detours: an essay on visually-guided behavior. In: Arbib MA, Hanson AR (eds) Vision, brain, and cooperative computation. A Bradford Book/MIT Press Cambridge MA, pp 129–163Google Scholar
  9. Arbib MA, Iberall T, Lyons D (1985) Coordinated control programs for movements of the hand. In: Goodman AW, Darian-Smith I (eds) Hand function and the neocortex Springer-Verlag, New York, pp 135–170Google Scholar
  10. Arbib MA, Overton KJ, Lawton DT (1984) Perceptual systems for robots. Inteidiscipl Sci Rev9(1): 31–46 Barlow HB (1953) Summation and inhibition in the frog’s retina. JPhysiol (Lond) 119: 69–88Google Scholar
  11. Caine HS, Gruberg ER (1985) Ablation of nucleus isthmi leads to loss of specific visually guided behavior in the frog Rana pipiens. Neurosci Lett 54: 307–312CrossRefGoogle Scholar
  12. Cervantes-Pérez F (1985) Modelling and analysis of neural networks in the visuomotor system of anuran amphibia. PhD Thesis and COINS Techn Rep 85–27, Computer and Information Science Department, Univ of Massachusetts at Amherst MAGoogle Scholar
  13. Cervantes-Pérez F, Lara R, Arbib MA (1985) A neural model of interactions subserving prey-predator discrimination and size preference in anuran amphibia. J Theor Biol 113: 117–152PubMedCrossRefGoogle Scholar
  14. Chipalkatti R, Arbib MA (1987) The prey localization model: a stability analysis. Biol Cybern 57: 287–300PubMedCrossRefGoogle Scholar
  15. Chipalkatti R, Arbib MA (1988) The cue interaction model of depth perception: a stability analysis. JMath Biol 26: 235–262CrossRefGoogle Scholar
  16. Collett T (1982) Do toads plan routes? A study of the detour behaviour of Bufo viridis. J Comp Physiol 146: 261–271CrossRefGoogle Scholar
  17. Collett T, Udin S (1983) The role of the toad’s nucleus isthmi in prey-catching behaviour. In: Lara R, Arbib MA (eds) Proceedings of the second workshop on visuomotor coordination in frog and toad: models and experiments. COINS Techn Rep 83–19, Univ of Massachusetts at Amherst MAGoogle Scholar
  18. Dev P (1975) Perception of depth surfaces in random-dot stereograms: a neural model. Int J Man-Machine Studies 7: 511–528CrossRefGoogle Scholar
  19. Didday RL (1970) The simulation and modelling of distributed information processing in the frog visual system. PhD Thesis Stanford UniversityGoogle Scholar
  20. Didday R (1976) A model of visuomotor mechanisms in the frog optic tectum. Math Biosci 30: 169–180CrossRefGoogle Scholar
  21. Epstein S (1979) Vermin users manual. Unpublished MS Thesis, Department of Computer and Information Science, Univ of Massachusetts at Amherst MAGoogle Scholar
  22. Ewert J-P (1968) Der Einfluß von Zwischenhirndefekten auf die Visuomotorik im Beute-and Fluchtverhalten der Erdkröte (Bufo bufo L). Z Vergl Physiol 61: 41–70Google Scholar
  23. Ewert J-P (1974) The neural basis of visually guided behavior. Sci Amer230: 34–42Google Scholar
  24. Ewert J-P (1976) The visual system of the toad: behavioural and physiological studies on a pattern recognition system. In: Fite KV (ed) The amphibian visual system: a multidisciplinary approach. Academic Press, New York, pp 142–202Google Scholar
  25. Ewert J-P (1987) Neuroethology of releasing mechanisms: prey-catching in toads. Behav Brain Sci 10: 337–405CrossRefGoogle Scholar
  26. Ewert J-P, Seelen W v (1974) Neurobiologie and Systemtheorie eines visuellen Muster-Erkennungsmechanismus bei Kröten. Kybernetik 14: 167–183PubMedCrossRefGoogle Scholar
  27. Hanson AR, Riseman EM (1978) VISIONS: a computer system for interpreting scenes. In: Hanson ARGoogle Scholar
  28. Riseman EM (eds) Computervision systems,Academic Press, New York, pp 129–163Google Scholar
  29. Hirai Y, Fukushima K (1978) An inference upon the neural network finding binocular correspondence. Biol Cybern 31: 209–217PubMedCrossRefGoogle Scholar
  30. House DH (1982) The frog/toad depth perception system - A cooperative/competitive model. In: Arbib MA (ed) Proceedings of the workshop on visuomotor coordination in frog and toad: models and experiments. COINS Techn Rep 82–16, Univ of Massachusetts at Amherst MAGoogle Scholar
  31. House DH (1984) Neural models of depth perception in frog and toad. PhD Thesis, Department of Computer and Information Science, Univ of Massachusetts at Amherst MAGoogle Scholar
  32. House D (1988) A model of the visual localization of prey by frog and toad. Biol Cybern 58: 173–192PubMedCrossRefGoogle Scholar
  33. Iberall T (1987) A neural model of human prehension. PhD Thesis, Department of Computer and Information Science, Univ of Massuchusetts at Amherst MAGoogle Scholar
  34. Iberall T, Arbib MA (1988) Schemas for the control of hand movements: an essay on cortical localization. In: Goodale M (ed) Vision and action: the control of grasping (in press)Google Scholar
  35. Iberall T, Bingham G, Arbib MA (1986) Opposition space as a structuring concept for the analysis of skilled hand movements. EAT Brain Res Series 15: 158–173Google Scholar
  36. Ingle D (1975) Selective visual attention in frogs. Science 188: 1033–1035PubMedCrossRefGoogle Scholar
  37. Ingle D (1976) Spatial vision in anurans. In: Fite KV (ed) The amphibian visual system: a multidisciplinary approach. Academic Press, New York, pp 119–140Google Scholar
  38. Ingle DJ (1982) Organization of visuomotor behaviors in vertebrates. In: Ingle DJ, Mansfield RJW, Goodale MA (eds) Advances in the analysis of visual behavior. The MIT Press Cambridge MA, pp 67–109Google Scholar
  39. Ingle DJ (1983) Brain mechanisms of visual localization by frogs and toads. In: Ewert J-P, Capranica RR, Ingle DJ (eds) Advances in vertebrate neuroethology. Plenum Press, New York, pp 177–226CrossRefGoogle Scholar
  40. Jeannerod M, Biguer B (1982) Visuomotor mechanisms in reaching within extrapersonal space. In: Ingle DJ, Mansfield RJW, Goodale MA (eds) Advances in the analysis of visual behavior. The MIT Press Cambridge MA, pp 387–409Google Scholar
  41. Jeannerod M, Michel F, Prablanc C (1984) The control of hand movements in a case of hemianeaesthesia following a parietal lesion. Brain 107: 899–920PubMedCrossRefGoogle Scholar
  42. Lam R, Arbib MA (1982) A neural model of interaction between tectum and pretectum in prey selection. Cognition and Brain TheoryS: 149–171Google Scholar
  43. Lam R, Arbib MA, Cromarty AS (1982) The role of the tectal column in facilitation of amphibian prey-catching behaviour: a neural model. JNeurosci 2: 521–530Google Scholar
  44. Lam R, Carmona M, Daza F, Cruz A (1984) A global model of the neural mechanisms responsible for visuomotor coordination in toads. J Theor Bio! 110: 587–618CrossRefGoogle Scholar
  45. Lee Y (1986) A neural network model of frog retina: a discrete time-space approach. Computer Science Department, Univ of Southern Califonia, Los Angeles CA. Techn Report TR-86–219Google Scholar
  46. Lettvin JY, Maturana H, McCulloch WS, Pitts WH (1959) What the frog’s eye tells the frog’s brain. Proc IRE 47: 1940–1951CrossRefGoogle Scholar
  47. Lyons DM, Arbib MA (1988) A formal model of distributed computation for schema-based robot control. IEEE J Robotics and Automation (in press)Google Scholar
  48. Malsburg C vd, Schneider W (1986) A neural cocktail-party processor. Bio! Cybern 54: 29–40CrossRefGoogle Scholar
  49. Riseman EM, Hanson AR (1987) A methodology for the development of general knowledge-based vision systems. In: Arbib MA, Hanson AR (eds) Vision, brain and cooperative computation. A Bradford Book/ The MIT Press Cambridge MA, pp 285–328Google Scholar
  50. Robinson DA (1981) The use of control systems analysis in the neruophysiology of eye movements. Ann Rev Neurosci 4: 463–503PubMedCrossRefGoogle Scholar
  51. Székely G, Ldzir G (1976) Cellular and synaptic architecture of the optic tectum. In: Llinas R, Precht W (eds) Frog neurobiology. Springer-Verlag, Berlin Heidelberg New York, pp 407–434CrossRefGoogle Scholar
  52. Teeters J (1988) A simulation system for the amphibian retina. Techn Rep, Department of Computer Science, University of Southern CaliforniaGoogle Scholar
  53. Weymouth TE (1986) Using object descriptions in a schema network for machine vision. PhD Thesis and COINS Techn Rep 86–24, Department of Computer and Information Science, Univ of Massachusetts at Amherst MAGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1989

Authors and Affiliations

  • Michael A. Arbib
    • 1
  1. 1.Departments of Computer Science, Neurobiology, Physiology, Biomedical Engineering, Electrical Engineering, and PsychologyUniversity of Southern CaliforniaLos AngelesUSA

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