David Marr: A Pioneer in Computational Neuroscience

  • Terrence J. Sejnowski


David Man advocated and exemplified an approach to brain modeling that is based on computational sophistication together with a thorough knowledge of the biological facts. The pioneering papers in this collection demonstrate that a combination of computational analysis and biological constraints can lead to interesting neural algorithms. The recent developments in computational models of neural information processing systems is an extension of this seminal research: Man has influenced the latest generation of network models through both his models and his emphasis on the computational level of analysis (Man, 1975, 1982). Progress has been made by adopting an integrated approach in which constraints from all three of Man’s levels of analysis—the computational, algorithmic and the implementational—are applied at many different levels of investigation (Sejnowski and Churchland, 1989).


Neural Information Processing System Stereo Vision Computational Neuroscience Computational Level Local Energy Minimum 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ballard D, Hinton G, Sejnowski T (1983): Parallel visual computation. Nature 306: 21–26CrossRefGoogle Scholar
  2. Churchland PS, Sejnowski TJ (1988): Perspectives on cognitive neuroscience. Science, 242: 741–745CrossRefGoogle Scholar
  3. Dev P (1975): Perception of depth surfaces in random-dot stereograms: a neural model. Int J Man-Machine Stud 7: 511–528CrossRefGoogle Scholar
  4. Grimson E (1981): From Images to Surfaces. Cambridge, MA: MIT PressGoogle Scholar
  5. Grossberg S (1976): Adaptive pattern classification and universal recoding: I: Parallel development and coding of neural feature detectors. Biol Cybern 23: 121–134CrossRefGoogle Scholar
  6. Hinton GE, Becker S (1990): An unsupervised learning procedure that discovers sur-faces in random-dot stereograms. New Jersey: Lawrence Erlbaum Associates, pp 218–222Google Scholar
  7. Hopfield JJ, (1982): Neural networks and physical systems with emergent collective computational abilities. Natl Acad Sci USA 79: 2554–2558CrossRefGoogle Scholar
  8. Hopfield JJ, Tank DW (1986): Computing with neural circuits: A model. Science 233: 625–633CrossRefGoogle Scholar
  9. Hurlbert AC, Poggio TA (1988): Synthesizing a color algorithm from examples. Science 239: 482–485CrossRefGoogle Scholar
  10. Julesz B (1971): Foundations of Cyclopean Vision. Chicago: University of Chicago PressGoogle Scholar
  11. Kandel ER, Klein M, Hochner B, Shuster M, Siegelbaum SA, Hawkins RD, Glanzman DL, Castellucci VF (1987): Synaptic modulation and learning: new insights into synaptic transmission from the study of behavior. In Synaptic Function, Edelman GM, Gall WE, Cowan WM, eds, New York: John Wiley and Sons, pp 471–518Google Scholar
  12. Kienker PK, Sejnowski TJ, Hinton GE, Schumacher LE (1986): Separating figure from ground with a parallel network. Perception 15: 197–216CrossRefGoogle Scholar
  13. Kohonen T (1984): Self-Organization and Associative Memory. New York: Springer VerlagGoogle Scholar
  14. Land EH (1986): An alternative technique for the computation of the designator in the retinex theory of color vision. Proc Natl Acad Sci USA 83: 3078–3080CrossRefGoogle Scholar
  15. Linsker R (1990): Perceptual neural organization: some approaches based on network models and information theory. Annu Rev Neurosci 13: 257–281CrossRefGoogle Scholar
  16. Llinas RR (1988): The intrinsic electrophysiological properties of mammalian neurons:Insights into central nervous system function. Science 242:1654–1664Google Scholar
  17. Marr D (1969): A theory of cerebellar cortex. J Physiol Lond 202: 437–470Google Scholar
  18. Marr D (1970): A theory for cerebral neocortex. Proc R Soc Lond, B 176:161–234 Marr D (1971): Simple memory: a theory for archicortex. Phil Trans Roy Soc B 262: 23–81CrossRefGoogle Scholar
  19. Marr D (1974): The computation of lightness by the primate retina. Science 14: 1377–1387Google Scholar
  20. Marr D (1975): Approaches to biological information processing. Science 190: 875–876Google Scholar
  21. Marr D (1982): Vision. San Francisco: FreemanGoogle Scholar
  22. Marr D, Palm G, Poggio T (1978): Analysis of a cooperative stereo algorithm. Biol Cybern 28: 223–239CrossRefGoogle Scholar
  23. Marr D, Poggio T (1976): Cooperative computation of stereo disparity. Science 194::283–287Google Scholar
  24. Marr D, Poggio T (1979): A computational theory of human stereo vision. Proc R Soc Lond Ser. B 204: 301–28CrossRefGoogle Scholar
  25. Nelson, JI (1975): Globality and stereoscopic fusion in binocular vision. J Theor Biology 49: 1–88CrossRefGoogle Scholar
  26. Poggio G, Poggio T (1984): The analysis of stereopsis. Anna Revs Neurosci 7:379–412 Poggio T, Gamble EB, Little JJ (1988): Parallel integration of vision modules. Nature 242: 436–439Google Scholar
  27. Qian N, Sejnowski TJ (1988): Learning to solve random-dot stereograms of dense transparent surfaces with recurrent backpropagation. Pittsburgh, PA: Morgan-Kaufmann Publishers, pp 235–443Google Scholar
  28. Rumelhart D, Zipser D (1985): Feature discovery by competitive learning. Cogn Sci 9: 75–112CrossRefGoogle Scholar
  29. Rumelhart DE, Hinton GE, Williams RJ (1986): Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Vol. 1: Foundations Cambridge, MA: MIT PressGoogle Scholar
  30. Sejnowski T, Koch C, Churchland P (1988): Computational neuroscience. Science 241: 1299–1306CrossRefGoogle Scholar
  31. Sejnowski TJ, Churchland PS (1989): Brain and cognition. In: Foundations of Cognitive Science, Posner Ml, ed. Cambridge, MA: MIT PressGoogle Scholar
  32. Zeki S (1983): Colour coding in the cerebral cortex: the reaction of cells in monkey visual cortex to wavelengths and colors. Neuroscience 9: 741–765CrossRefGoogle Scholar

Copyright information

© Birkhäuser Boston 1991

Authors and Affiliations

  • Terrence J. Sejnowski
    • 1
  1. 1.The Salk InstituteUniversity of California, San DiegoLa JollaUSA

Personalised recommendations