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On Some Fresh Air in Neural Modeling

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Cybernetics and Systems ’86

Abstract

Neural modeling is becoming a purely mathematical exercise due mainly to some methodological faults in the steps of modeling. We consider as weak points in the basic strategy of neurocibernetic modeling the wrong selection of description levels, significance tables and formal tools suited to describe the neuronal dynamics.

The antropomorphycal viewpoint is considered and a new theoretical frame (anastomotic net) is included. This frame is general enough to embody non trivial neural processes like co-opertivity. The software description level leads to neuronal computational frames and algorithms. The hardware level consider that among the composition rules used to describe neuronal events also are included instructions of high level programming languages and pragmatic aspects of natural languages.

In caming from anatomy to function we propose the speculative assumption that the topological structure we meet in neural nets can be associated to the control structures in the Bohn-Jacopini sense. As we are drawing a net we are printing the embodied function.

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References

  1. Ballard D. A. et al. (1983). Nature, 306, pp. 21–26.

    Article  Google Scholar 

  2. Braitemberg V. (1976). Progress in Brain Research, 45, pp. 197–204.

    Article  Google Scholar 

  3. Caianello E. R. (1965). Cybernetics of neural processes, pp. 1–27

    Google Scholar 

  4. Caianello E. R. et al. (1967). Kybernetik, 4, pp. 10–18.

    Article  Google Scholar 

  5. Eccles J. C. (1984). Cerebral Cortex, ed. by Jones amp; Peters, 2, pp. 1–36

    Google Scholar 

  6. Koch C. and Poggio T. (1985). New insights into synaptic function.

    Google Scholar 

  7. Lorente de No M. D. (1933). J. für Psychol, un Neurol., 45, 6, pp. 381–438

    Google Scholar 

  8. Marr D. C. and Poggio T. (1977). Neurosci. Res. Prog. Bull. 15, 3, 470–488

    Google Scholar 

  9. Marr D. (1982). Vision. V. H. Freeman, San Francisco.

    Google Scholar 

  10. McCulloch W. S. and Pitts W. H. (1943). Bull. of Math. Bioph. 5, 115–133

    Article  MathSciNet  MATH  Google Scholar 

  11. McCulloch W. S. (about 1950 ): Lekton. The Res. Lab, of Elect. MIT Mass.

    Google Scholar 

  12. McCulloch W. S. (1959). Mechanisation of Thought Proc. II, 10, 611–634.

    Google Scholar 

  13. McCulloch W. S. and Moreno-Díaz R. (1968). Neural Networks, 78–86.

    Google Scholar 

  14. Mira J. and Simoes da Fonseca (1975). If life, 1, 7, 32–44.

    Google Scholar 

  15. Mira J. and Delgado A. E. (1981). Proc. Fifth Cong. Cyb. Mexico. 2–24

    Google Scholar 

  16. Mira J. et al. (1983). Rel. between M. W. P. and S. Learning, II, 687–690

    Google Scholar 

  17. Mira J. et al. (1984.a). Proc. of 10th Int. Cong. Cyb. Namur, 17–29.

    Google Scholar 

  18. Mira J. et al. (1984.b). Proc. 6th Int. Cong. Cyb and S., París 2,819–24

    Google Scholar 

  19. Mira J. et al. (1985). Proc. First IFSA Cong., I, P. de Mallorca.

    Google Scholar 

  20. Poggio T.A.I (1982). Memory, 683, MIT, Mass.

    Google Scholar 

  21. Rakic P. (1975). Neuroscience R.P.B, on L.C.N., 13, 3, 299–314. Boston.

    Google Scholar 

  22. Ramón, Cajal S. (1911). Histologie du S.N.,…, II, Maloine. París.

    Google Scholar 

  23. Schmitt F. O. and Worden F. G. (1979). The neurosci.: fourth study prog.

    Google Scholar 

  24. Shepherd G. M. (1975). Neurosci. R.P.B, on L.C.N., 13, 3, 344–352.

    Google Scholar 

  25. Simões da Fonseca J. and Mira J. (1970). Signif. and Intention, 5–11.

    Google Scholar 

  26. Szetágothai J. (1975). Brain Research, 95, 475–496.

    Article  Google Scholar 

  27. Sutro L. (1985.a). Personal communication on McCulloch viewpoints.

    Google Scholar 

  28. Sutro L. (1985.b). Personal communication on McCulloch concept of Anastomotic Net. also included in McCulloch’s paper: “Anastomotic nets combating noise” in Information storage and neural control (1963).

    Google Scholar 

  29. Winograd T. (1983). Lang. as a cognitive process, I. Addison-Wesley.

    Google Scholar 

  30. Young J. Z. (1978). Programs of the Brain. Oxford Univ. Press.

    Google Scholar 

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© 1986 D. Reidel Publishing Company

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Mira, J., Delgado, A.E. (1986). On Some Fresh Air in Neural Modeling. In: Trappl, R. (eds) Cybernetics and Systems ’86. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-4634-7_40

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  • DOI: https://doi.org/10.1007/978-94-009-4634-7_40

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-8560-1

  • Online ISBN: 978-94-009-4634-7

  • eBook Packages: Springer Book Archive

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