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From Brain Theory to Future Generations Computer Systems

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Nature, Cognition and System I

Part of the book series: Theory and Decision Library ((TDLD,volume 2))

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Abstract

According to the old metaphor of classical cybernetics the brain can be considered as a computer. The question could be reversed: what neurobiology could offer to engineers of near-future generation computer systems.

Principles of the brain organization, ontogenetic neural development, plastic behavior and learning are interpreted in the spirit of the theory of dynamic systems. Neural pattern formation, pattern recognition and action can be treated by unified conceptual framework. Fault-tolerant, parallel structures capable of exhibiting “intelligent”, behaviour are hoped to be designed utilizing knowledges about biological information processing.

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References

  • Ackley, D.H., Hinton, G.E. & Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cognitive Sci. 9(ll47–169) 1985.

    Google Scholar 

  • Amari, S.: A method of statistical neurodynamics Kybernetik 14 (201–215) 1974.

    Google Scholar 

  • Amari, S.: Field theory of self-organizing neural nets. IEEE Trans. SMC-13 (741–748) 1983.

    Google Scholar 

  • Anninos, P.A., Beek, B., Csermely, T.J., Harth, E. &Perile, G.: Dynamics of neural structures. J. Theor. Biol. 26 (201–218) 1970.

    Article  Google Scholar 

  • Arbib, M.A.: Brain theory and cooperative computation. Human Neurobiol. 4 (201–218) 1985.

    Google Scholar 

  • Arbib, M.A. &Amari, S.: Sensori-motor transformations in the brain -with a critique of the tensor theory of cerebellum -J. Theor. Biol. 112(123–155) 1985.

    Article  Google Scholar 

  • Arbib, M.A., Overton, K.J. &Lawton, D.T.: Perceptual systems for robots. Interdiscipl. Sci. Rev. 9(31–46) 1984.

    Google Scholar 

  • Arneodo, A., Argoul, F., Richetti, P. &Roux, J.C.: The Belousov-Zhabotinskii reaction: a paradigm for theoretical studies of dynamical systems (manuscript).

    Google Scholar 

  • Atkinson, J.: Human visual development over the first 6 month of life. A review and a hypothesis. Human Neurobiol. 3(61–74) 1984.

    Google Scholar 

  • Ballard, D.H.: Cortical connections and parallel processing: Structure and function. Behav. Brain Sci. 9(67–120) 1986.

    Article  Google Scholar 

  • Ballard, D.H., Hinton, G.E. &Sejnowski, T.J.: Parallel visual computation. Nature 306(21–26) 1983.

    Article  Google Scholar 

  • Barna, G. &Erdi, P.: Pattern formation in neural systems II Noise-induced selective mechanism for the formation of ocular dominance columns. In: Cybernetics and Systems ‘86, Trappl. R. (ed), pp. 343–350, D. Reidel Publ. Company, 1986.

    Chapter  Google Scholar 

  • Barna, G. &Erdi, P.: ‘Normal’ and ‘abnormal’ dynamic behaviour during synaptic transmission. In: Computer Simulation in Brain Science. Cotteril, R.M.J. (ed.), Cambridge Universit Press (in press).

    Google Scholar 

  • Bienenstock, E.: Dynamics of the central nervous system. In: Dynamics of Macrosystems. (Aubin, J.-P., Saari, D. &Sigmund, K. (eds.), Lect. Notes in Econ. &Math. Systems, pp. 3–20, Springer–Verlag, 1985.

    Google Scholar 

  • Carter, F.L.: The molecular device computer: point of departure for large scale cellular automata. Physica 10D(175–194) 1984.

    Google Scholar 

  • Clarke, P.G.H.: Chance, repetition, and error in the development of normal nervous system. Perspect. Biol. Med. 25.(2–19) 1981.

    Google Scholar 

  • Conrad, M.:Microscopic-macroscopic interface in biological information processing.BioSystems 16(345–363)1984.

    Article  Google Scholar 

  • Conrad, M.: Microscopic-macroscopic interface in biological information processing. BioSystems 16(345–363) 1984.

    Article  Google Scholar 

  • Conrad, M.:On design principles for a molecular computer.Coram. ACM 28464–4801985.

    Article  Google Scholar 

  • Conrad, M.: On design principles for a molecular computer. Coram. ACM 28(464–480) 1985.

    Article  Google Scholar 

  • Changeux, J.-P.Couregge, P.Danchin, A.:A theory of the epigenesis of neural networks by selective stabilization of synapses.Proc. Natl. Acad. Sci. USA 70(2974–2978)1973.

    Article  Google Scholar 

  • Changeux, J.-P., Couregge, P. &Danchin, A.: A theory of the epigenesis of neural networks by selective stabilization of synapses. Proc. Natl. Acad. Sci. USA 70(2974–2978) 1973.

    Article  Google Scholar 

  • Changeux, J.-P.Heidmann, T.Patee, P.:Learning by selection.In: The biology of learning.Marler, P.,Terrace, H.S.(eds.) Dahlem Konferenzen 1984, pp. 115–133. Springer Verlag.

    Google Scholar 

  • Changeux, J.-P., Heidmann, T., Patee, P.: Learning by selection. In: The biology of learning. Marler, P., &Terrace, H.S. (eds.) Dahlem Konferenzen 1984, pp. 115–133. Springer Verlag.

    Google Scholar 

  • Choi, M.Y.Huberman, B.A.:Dynamic behaviour of nonlinear networks.Phys. Rev. A 28(1204–1206)1983.

    Article  Google Scholar 

  • Choi, M.Y. &Huberman, B.A.: Dynamic behaviour of nonlinear networks. Phys. Rev. A 28(1204–1206) 1983.

    Google Scholar 

  • Cottrell, M.Fort, J.C.:A stochastic model of retinotopy: a self organizing process.Biol. Cybernetics 53(405–411)1986.

    Article  Google Scholar 

  • Cottrell, M. &Fort, J.C.: A stochastic model of retinotopy: a self organizing process. Biol. Cybernetics 53(405–411) 1986.

    Article  Google Scholar 

  • Edelman, G.M.Finkel, L.H.:Neuronal group selection in the cerebral cortex.In: Dynamic aspects of neocortical function,Edelman, G.M.,Gall, W.E.Cowan, W.M.(eds.), Wiley 1984.

    Google Scholar 

  • Edelman, G.M. &Finkel, L.H.: Neuronal group selection in the cerebral cortex. In: Dynamic aspects of neocortical function, Edelman, G.M., Gall, W.E. &Cowan, W.M. (eds.), Wiley 1984.

    Google Scholar 

  • Erdi, P.:Hierarchical thermodynamic approach to the brain.Intern. J. Neurosci.20(193–216)1983.

    Article  Google Scholar 

  • Erdi, P.: Hierarchical thermodynamic approach to the brain. Intern. J. Neurosci. 20(193–216) 1983.

    Google Scholar 

  • Erdi, P. & Barna, G.: Self-organizing mechanism for the formation of ordered neural map**s. Biol. Cybernetics 51(93–101)1984.

    Article  Google Scholar 

  • Erdi, P. &Barna, G.: Self-organization of neural networks: noise-induced transition. Phys. Lett 107A(287–290)1985.

    Google Scholar 

  • Erdi, P. &Szentagothai, J.: Neural connectivities: between determinism and randomness. In: Dynamics of Macrosystems. Lect. Notes in Econ. Math. Systems. (Aubin, J.-P., Saari, D. &Sigmund, K. (eds.). Springer Verlag, Berlin-Heidelberg-New York-Tokyo, 1985, pp. 21–29.

    Google Scholar 

  • Feldman, J.A. &Ballard, D.H.: Connctionist models and their properties. Cognitive Sci. 6(205–254)1982.

    Article  Google Scholar 

  • Fukushima, K.: A hierarchical neural network model for associative memory. Biol. Cybernetics 50(105–113)1984.

    Article  Google Scholar 

  • Glansdorff, P. &Prigogine, I.: Thermodynamics of structure, stability and fluctuations. New York, Wiley-Interscience, 1971.

    Google Scholar 

  • Glünder, H. : On functional concepts for the explanation of visual pattern recognition. Human. Neurobiol. 5(37–47)1986.

    Google Scholar 

  • Goldman, P.S. &Nauta, W.J.H.: Columnar distribution of cortico-cortical fibers in frontal association, limbic and motor cortex of the develo** Rhesus monkey. Brain Res. 122(393–413)1977.

    Article  Google Scholar 

  • Goldman -Rakic, P.: Modular organization of the prefrontal cortex. Trends in Neurosciences 7.(419–424)1984.

    Article  Google Scholar 

  • Guevara, M.R., Glass, L., Mackey, C. &Schrier, A.: Chaos in neurobiology. IEEE Trans. Systems, Man and Cybernetics, SMC-13(790–797)1983.

    Google Scholar 

  • Hebb, D.O.: The organization of the behaviour. Wiley, New York, 1949.

    Google Scholar 

  • Hillis, W.D.: The connection machine. MIT Press, 1986.

    Google Scholar 

  • Hogg, T. &Hubermann, B.A.: Parallel computing structures capable of flexible associations and recognition of fuzzy inputs. J. Stat. Phys. 41(115–123)1985.

    Article  Google Scholar 

  • Holden, A.V. &Muhamed, M.A.: Chaotic activity in neural systems. Cybernetics and System Research 2, Trappl, R. (ed.), North-Holland, Amsterdam, pp. 245–250, 1984.

    Google Scholar 

  • Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79(2254–2258)1982.

    Article  Google Scholar 

  • Hopfield, J.J. &Tank, D.W.: “Neural” computation of decisions in optimization problems. Biol. Cybernetics 52(141–152)1985.

    Google Scholar 

  • Horsthemke, W. &Lefever, R.: Noise-induced transition. Theory and applications in physics, chemistry and biology. Springer: Berlin-Heidelberg-Tokyo, 1984.

    Google Scholar 

  • Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurons in the cat’s striate cortex. J. Physiol. 148(574–591)1959.

    Google Scholar 

  • Kinzel, W.: Learning and pattern recognition in spin glass models. Z. Phys. B. 60(205–213)1985.

    Article  Google Scholar 

  • Kohonen, T.: Self-organization and associative memory. Springer, 1984.

    Google Scholar 

  • Kohonen, T.: Representation of sensory information in self-organizing feature map, and relation of these maps to distributed memory networks. (manuscript) 1986.

    Google Scholar 

  • Kossylin, J.M.: Externalizing mental images: a computational neuropsychological approach. Workshop on language for automatation, cognitive aspects of onfromation processing. IEEE 1985, pp. 110–115.

    Google Scholar 

  • Loeb, G.: Finding common ground between robotics and physiology. Trends in Neurosciences 6(203–204)1983.

    Article  Google Scholar 

  • MacKay, D.M.: Cerebral organization and the conscious control of action. In: Brain and conscious experience, Eccles, J.C. (ed.): pp. 422–445 and 566–574, Springer 1966.

    Google Scholar 

  • MacKay, D.M.: Mind Talk and Brain Talk. In: Handbook of cognitive neuroscience, Gazzaniga, M.S. (ed.), Plenum, New York, pp. 293–317, 1983.

    Google Scholar 

  • Maturana, H.R. &Varela, F.J.: Autopoiesis and cognition. Reidel, Boston, 1980.

    Book  Google Scholar 

  • Mountcastle, V.B.: Modality and topographic properties of single neurons of cat’s somatic sensory cortex. J. Neurophysiol. 20(408–434)1957.

    Google Scholar 

  • Nicolis, J.S.: Chaotic dynamics of information processing with relevance to cognitiv brain functions. Kybernetes 14(167–172)1985.

    Article  Google Scholar 

  • Nicolis, J.S., Tsuda, I.: Chaotic dynamics of information processing: the ‘magic number seven plus-minus two’. Bull. Math. Biol. 47(343–365)1985.

    Google Scholar 

  • Pellionisz, A.: Brain theory: connecting neurobiology to robotics. Tensor analysis: utilizing intrinsic coordinates to describe, understand and engineer functional geometries of intelligent organisms. J. Theor. Neurobiol. 2(185–211)1983.

    Google Scholar 

  • Pellionisz, A. &Llinas, R.: Brain modeling by tensor network theory and computer simulation. The cerebellum: distributed processor for predictive coordination. Neurosci. 4(323–348)1979.

    Article  Google Scholar 

  • Peretto, P.: Collective properties of neural networks. A statistical physics approach. Biol. Cybern. 50(51–62)1984.

    Article  Google Scholar 

  • Peretto, P. &Niez, J.: Stochastic dynamics of neural networks. IEEE Trans. SMC-16(73–83)1986.

    Google Scholar 

  • Ritten, H. &Schulten, K.: On the stationary state of Kohonen’s self-organizing sensory map**. Biol. Cybernetics 54(99–106)1986.

    Article  Google Scholar 

  • Rosen, R.: Pattern generation in networks. Progr. Theor. Biol. 6(161–209)1981.

    Google Scholar 

  • Rosenblatt, F. : Principle of neurodynamics. Washington D.C.: Spaston Books (1962).

    Google Scholar 

  • Sagi, D. &Julesz, B.: “Where” and “what” in vision. Science 228(1217–1219)1985.

    Article  Google Scholar 

  • Saridis, G.N.: An integrated theory of intelligent machines by expressing the control performance entropy. Control-Theory and Advanced Technology 1(l25–138)1985.

    Google Scholar 

  • Scheibel, M.E. &Sceibel, A.B.: Structural substrates for integrative patterns in the brain stem reticular core. In: Reticular formation in the brain. Jasper, H.H. et al (eds.), Little, Brown &Co., Boston, pp. 31–68, 1958.

    Google Scholar 

  • Shepherd, G.M.: Neurobiology, Oxford Univ. Press, New York-Oxford, 1983.

    Google Scholar 

  • Shimizu, H., Yamaguchi, Y., Tsuda, I. &Yano, M.: Pattern recognition on holonic information dynamics. In: Compleex systems -Operational approach. Haken, H. (ed.). Springer-Verlag, Berlin -Heidelberg -New York -Tokyo, 1985, pp. 225–233.

    Google Scholar 

  • Stent, G.S.: Strength and weakness of the genetic approach to the development of the nervous system. Ann. Rev. Neurosci. 4(163–194)1981.

    Article  Google Scholar 

  • Szentagothai, J.: The local neuronal apparatus of the cerebral cortex. In: Buser, P.A. &Rougeul-Buser, A. (eds.): Cerebral correlates of conscious experience. North Holland, Amsterdam -New York -Oxford 1978, pp. 131–138.

    Google Scholar 

  • Szentagothai, J.: The modular architectonic principle of neural centers. Rev. Physiol. Biochem. Pharmacol. 98(11–61)1983.

    Article  Google Scholar 

  • Szentagothai, J.: The neuronal architectonic principle of the neocortex. An. Acad, brasil. Cienc. 57(249–259)1985.

    Google Scholar 

  • Szentagothai, J. &Erdi, P.: Outline of a general brain theory. Techn. Report, Central Res. Inst. Physics, Hung. Acad. Sci. 1983.

    Google Scholar 

  • Tsuda, I.: A hermeneutic process of the brain. Prog. Theor. Phys. Suppl. 79(241–259)1984.

    Article  Google Scholar 

  • Uhr, L.: Massively parallel multi-computer hardware = software structures for learning. In: Complex systems -Operational approaches. Haken, H. (ed.), Springer Verlag, Berlin -Heidelberg -New York -Tokyo, 1985, pp. 212–224.

    Google Scholar 

  • Ventriglia, F.: Kinetic approach to neural systems. I. Bull. Math. Biol. 36(535–544)1974.

    Google Scholar 

  • Ventriglia, F.: Kinetic theory of neural systems: an overview. In: Dynamic phenomena in neurochemistry and neurophysics: theoretical aspects. (Erdi, P. ed). Central. Res. Inst. Physics, Budapest, 1985, pp. 39–43.

    Google Scholar 

  • Ventriglia, F. &Erdi, P.: Statistical approach to the dynamics of cerebral cortex: some learning aspects (in preparation).

    Google Scholar 

  • Von der Malsburg, Ch.: The correlation theory of brain function. Internal Report 81–2, Dept. Neurobiol. Max Planck Inst. f. Biophys. Chem. 1981.

    Google Scholar 

  • Von der Malsburg, Ch. Nervous structures with dynamical links. Ber. Bunsen Ges. Phys. Chem. 89(700–709)1985.

    Google Scholar 

  • Wilson, H.R. &Cowan, J.D.: A mathematical theory of the functional dynamics of cortical and thalamic nervous tissue. Kybernetik 13(55–80)1973.

    Article  Google Scholar 

  • Waddington, CH. : The strategy of the genes. Allen &Unwin, 1957.

    Google Scholar 

  • Wolfram, S.: Cellular automata as models of complexity. Nature 311(419–424)1984.

    Article  Google Scholar 

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  1. Central Research Institute for Physics, Hungarian Academy of Sciences, H-1525, Budapest, Hungary

    P. Erdi

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  1. P. Erdi
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  1. State University of Groningen, The Netherlands

    Marc E. Carvallo

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© 1988 Kluwer Academic Publishers

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Erdi, P. (1988). From Brain Theory to Future Generations Computer Systems. In: Carvallo, M.E. (eds) Nature, Cognition and System I. Theory and Decision Library, vol 2. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2991-3_4

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  • DOI: https://doi.org/10.1007/978-94-009-2991-3_4

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