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Parallel in sequence — Towards the architecture of an elementary cortical processor

  • E. Koerner
  • I. Tsuda
  • H. Shimizu
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 269)

Abstract

First, a short evalution of the recent progress on the question of how the brain works is given and evidence submitted for the need of largescale computational approaches to neural computing which are as tightly as possible related to the known details of neural hardware and function. The crucial role of the firmware-level (modular) organization of neural mass for understanding the flexibility of neural computing is stressed. Based on both the anatomy of a neocortical column and an approach to the interpretation of complex scenes by the visual cortex, a functional architecture of a cortical processor is proposed with special emphasis on the dynamics of selfcontrol.

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References

  1. [1]
    Amari, S.I. "Neural theory of association and concept formation" Biol. Cyb. 26 (1977) 175–85CrossRefGoogle Scholar
  2. [2]
    Arbib, M.A. & S.I. Amari "Sensorimotor transformations in the brain (with a critique of the tensor theory of cerebellum" J. Theor. Biol. 112 (1985) 123–55PubMedGoogle Scholar
  3. [3]
    Churchland, P.M. "Cognitive neurobiology: a computational hypothesis for laminar cortex" Biology and Philosophy 1 (1986) 25–51CrossRefGoogle Scholar
  4. [4]
    Crick, F. The function of the thalamic reticular complex: the searchlight hypothesis" Proc. Natl. Acad. Sci. USA 81 (1984) 4586–90PubMedGoogle Scholar
  5. [5]
    Edelman, G.M. & L.H. Finkel "Neural group selection in the cerebral cortex" In: Dynamic Aspects of Neocortical Function, G.M. Edelman, W.E. Gall, W.M. Cowan (eds.) Wiley: NewYork, Chichester, Brisbane, Toronto, Singapore (1984)Google Scholar
  6. [6]
    Fukushima, K. "A hierarchical neural network model for associative memory" Biol. Cyb. 50 (1984) 105–13CrossRefGoogle Scholar
  7. [7]
    Fukushima, K. "A neural network model for selective attention in visual pattern recognition" Biol Cyb. 55 (1986) 5–15CrossRefGoogle Scholar
  8. [8]
    Hirai, Y. "A model of human associative processor (HASP)" IEEE Trans. Syst., Man and Cyb. SMC-13 (1983) 851–7Google Scholar
  9. [9]
    Hogg, T. & B.A. Huberman "Understanding biological computation: reliable learning and recognition" Proc. Natl. Acad. Sci. USA 81 (1984) 6871–5PubMedGoogle Scholar
  10. [10]
    Hopfield, J.J. & D.W. Tank "Neural computation of decisions in optimization problems" Biol. Cyb. 52 (1985) 141–52Google Scholar
  11. [11]
    Kohonen, T. "Selforganization and associative memory" Springer: Berlin, Heidelberg, NewYork, Tokyo (1984)Google Scholar
  12. [12]
    Koerner, E., I. Tsuda & H. Shimizu "Take-grant control, variable byte formation and processing parallel in sequence-characteristics of a new type of holonic processor" (subm. to publ.)Google Scholar
  13. [13]
    Malpeli, J.G. "Activity of cells in area 17 of the cat in absence of input from layer A of lateral geniculate nucleus" J. Neurophysiol. 49 (1983) 595–606PubMedGoogle Scholar
  14. [14]
    v.d.Malsburg, Ch. "Nervous structures with dynamical links" Ber. Bunsenges. Phys. Chem. 89 (1985) 703–10Google Scholar
  15. [15]
    v.d.Malsburg, Ch. & W. Schneider "A neural cocktail-party processor" Biol. Cybern. 54 (1986) 29–40PubMedGoogle Scholar
  16. [16]
    Marr, D. "Vision", Freeman: SanFrancisco (1982)Google Scholar
  17. [17]
    Mountcastle, V.B. "An organizing principle for cerebral function: the unit module and the distributed system" Group-Selective Theory of Higher Brain, Function, G.M. Edelman and V.B. Mountcastle (eds.) MIT Press: Cambridge, Mass. (1978)Google Scholar
  18. [18]
    Nicolis, J.S. "Dynamics of hierarchical systems" Springer: Berlin, Heidelberg, NewYork, Tokyo (1986)Google Scholar
  19. [19]
    Palm, G. "Associative Networks and Cell Assemblies" in: "Brain Theory" G. Palm & A. Aertsen (eds.), Springer: Berlin, Heidelberg, NewYork, Tokyo (1986)Google Scholar
  20. [20]
    Pellionisz, A. & R. Llinas "Tensor network theory of the metaorganization of functional geometries in the central nervous system", Neuroscience 16 (1985) 245–73PubMedGoogle Scholar
  21. [21]
    Orban, G.A. "Neuronal operations in the visual cortex" Springer: Berlin, Heidelberg, NewYork (1984)Google Scholar
  22. [22]
    Rumelhart, D., G. Hinton & J. McClelland (eds.) "Parallel distributed processing: explorations in the microstructure of cognition", MIT Press, Cambridge, MA (1986)Google Scholar
  23. [23]
    Singer, W. "Central control of developmental plasticity in the mammalian visual cortex", Vision Res. 25 (1985) 389–96PubMedGoogle Scholar
  24. [24]
    Silverman, D.J., G.L. Shaw & J.C. Pearson "Associative recall properties of the trion model of cortical organization", Biol. Cybern. 53 (1986) 259–271PubMedGoogle Scholar
  25. [25]
    Sutton, R.S. & A.G. Barto "Toward a modern theory of adaptive networks: expectation and prediction", Psychol. Rev. 88 (1981) 135–70PubMedGoogle Scholar
  26. [26]
    Szentagothai, J. "The modular architectonic principle of neural centers" Rev. Physiol. Biochem. Pharmac. 98 (1983) 11–61Google Scholar
  27. [27]
    Thagard, P. "Parallel computation and the mind-body problem" Cognitive Science 10 (1986) 301–18Google Scholar
  28. [28]
    Tsuda, I., E. Koerner & H. Shimizu "Memory dynamics in asynchronous neural networks" (subm. to publication)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • E. Koerner
    • 1
  • I. Tsuda
    • 1
  • H. Shimizu
    • 2
  1. 1.Bioholonics Research ProjectResearch Development Corporation of JapanBunkyo-ku, TokyoJapan
  2. 2.Faculty of Pharmaceutical Sciences, Department of BiophysicsUniversity of TokyoHongo, Bunkyo-ku, TokyoJapan

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