Abstract
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).
Chapter PDF
References
Ballard D, Hinton G, Sejnowski T (1983): Parallel visual computation. Nature 306: 21–26
Churchland PS, Sejnowski TJ (1988): Perspectives on cognitive neuroscience. Science, 242: 741–745
Dev P (1975): Perception of depth surfaces in random-dot stereograms: a neural model. Int J Man-Machine Stud 7: 511–528
Grimson E (1981): From Images to Surfaces. Cambridge, MA: MIT Press
Grossberg S (1976): Adaptive pattern classification and universal recoding: I: Parallel development and coding of neural feature detectors. Biol Cybern 23: 121–134
Hinton GE, Becker S (1990): An unsupervised learning procedure that discovers sur-faces in random-dot stereograms. New Jersey: Lawrence Erlbaum Associates, pp 218–222
Hopfield JJ, (1982): Neural networks and physical systems with emergent collective computational abilities. Natl Acad Sci USA 79: 2554–2558
Hopfield JJ, Tank DW (1986): Computing with neural circuits: A model. Science 233: 625–633
Hurlbert AC, Poggio TA (1988): Synthesizing a color algorithm from examples. Science 239: 482–485
Julesz B (1971): Foundations of Cyclopean Vision. Chicago: University of Chicago Press
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 471–518
Kienker PK, Sejnowski TJ, Hinton GE, Schumacher LE (1986): Separating figure from ground with a parallel network. Perception 15: 197–216
Kohonen T (1984): Self-Organization and Associative Memory. New York: Springer Verlag
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: 3078–3080
Linsker R (1990): Perceptual neural organization: some approaches based on network models and information theory. Annu Rev Neurosci 13: 257–281
Llinas RR (1988): The intrinsic electrophysiological properties of mammalian neurons:Insights into central nervous system function. Science 242:1654–1664
Marr D (1969): A theory of cerebellar cortex. J Physiol Lond 202: 437–470
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: 23–81
Marr D (1974): The computation of lightness by the primate retina. Science 14: 1377–1387
Marr D (1975): Approaches to biological information processing. Science 190: 875–876
Marr D (1982): Vision. San Francisco: Freeman
Marr D, Palm G, Poggio T (1978): Analysis of a cooperative stereo algorithm. Biol Cybern 28: 223–239
Marr D, Poggio T (1976): Cooperative computation of stereo disparity. Science 194::283–287
Marr D, Poggio T (1979): A computational theory of human stereo vision. Proc R Soc Lond Ser. B 204: 301–28
Nelson, JI (1975): Globality and stereoscopic fusion in binocular vision. J Theor Biology 49: 1–88
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: 436–439
Qian N, Sejnowski TJ (1988): Learning to solve random-dot stereograms of dense transparent surfaces with recurrent backpropagation. Pittsburgh, PA: Morgan-Kaufmann Publishers, pp 235–443
Rumelhart D, Zipser D (1985): Feature discovery by competitive learning. Cogn Sci 9: 75–112
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 Press
Sejnowski T, Koch C, Churchland P (1988): Computational neuroscience. Science 241: 1299–1306
Sejnowski TJ, Churchland PS (1989): Brain and cognition. In: Foundations of Cognitive Science, Posner Ml, ed. Cambridge, MA: MIT Press
Zeki S (1983): Colour coding in the cerebral cortex: the reaction of cells in monkey visual cortex to wavelengths and colors. Neuroscience 9: 741–765
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1991 Birkhäuser Boston
About this chapter
Cite this chapter
Sejnowski, T.J. (1991). David Marr: A Pioneer in Computational Neuroscience. In: Vaina, L. (eds) From the Retina to the Neocortex. Birkhäuser Boston. https://doi.org/10.1007/978-1-4684-6775-8_12
Download citation
DOI: https://doi.org/10.1007/978-1-4684-6775-8_12
Publisher Name: Birkhäuser Boston
Print ISBN: 978-1-4684-6777-2
Online ISBN: 978-1-4684-6775-8
eBook Packages: Springer Book Archive