On the Use of the Computational Paradigm in Neurophysiology and Cognitive Science

  • José Mira Mira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3561)


Virtually from its origins, with Turing and McCulloch’s formulations, the use of the computational paradigm as a conceptual and theoretical framework to explain neurophysiology and cognition has aroused controversy. Some of the objections raised, relating to its constitutive and formal limitations, still prevail. We believe that others stem from the assumption that its objectives are different from those of a methodological approximation to the problem of knowing.

In this work we start from the hypothesis that it is useful to look at the neuronal circuits assuming that they are the neurophysiological support of a calculus, whose full description requires considering three nested levels of organization, one of circuits, other of neurophysiological symbols and another of knowledge, and two description domains, the intrinsic to each level and that of the external observer.


Knowledge Level Physical Machine Physical Level Computational Paradigm External Observer 
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.


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  1. 1.
    Clancey, W.J.: Situated cognition. On human knowledge and computer representation. Univ. Press, Cambridge (1997)Google Scholar
  2. 2.
    Craik, K.: The Nature of Explanation. Cambridge University Press, Cambridge (1943)Google Scholar
  3. 3.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience. The MIT Press, Cambridge (2001)MATHGoogle Scholar
  4. 4.
    Edelman, G.M.: Neural Darwinism. Basic Books, Inc., N. York (1987)Google Scholar
  5. 5.
    Fodor, J.A.: El Lenguaje del Pensamiento. Alianza Editorial, Madrid (1984)Google Scholar
  6. 6.
    Kleene, S.C.: Representation of events in nerve nets and finite automata. In: Shannon, McCarty, J. (eds.) Automata Studies, pp. 3–42. Princeton Univ. Press., Princenton (1956)Google Scholar
  7. 7.
    Kuhn, T.S.: La Estructura de las Revoluciones Científicas. Fondo de Cultura EconómicaFondo de Cultura Económica., México (1971)Google Scholar
  8. 8.
    Marr, D.: Vision. Freeman, New York (1982)Google Scholar
  9. 9.
    Maturana, H.: Ontology of observing. the biological foundations of self consciousness and the physical domain existence (2002), http://www.inteco.cl/biology/ontology/
  10. 10.
    Maturana, H.R.: The organization of the living: A theory of the living organization. Int. J. Man-Machine Studies 7, 313–332 (1975)CrossRefGoogle Scholar
  11. 11.
    Maturana, H.R., Varela, F.: El Árbol del Conocimiento Humano. Ed. Debate, Madrid (1990)Google Scholar
  12. 12.
    McCulloch, W.S.: Embodiments of Mind. The MIT Press, Cambridge, Mass (1965)Google Scholar
  13. 13.
    McCulloch, W.S., Pitts, W.: A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5, 115–133 (1943)MATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Mira, J.: Reverse neurophysiology: The embodiments of mind revisited. In: Moreno-Díaz, R., Mira-Mira, J. (eds.) Brain Processes, Theories and Models, pp. 37–49. The MIT Press, Massachusetts (1995)Google Scholar
  15. 15.
    Mira, J., Delgado, A.E.: Some comments on the antropocentric viewpoint in the neurocybernetic methodology. In: Proc of the Seventh International Congress of Cybernetics and Systems, pp. 891–895 (1987)Google Scholar
  16. 16.
    Mira, J., Delgado, A.E.: Neural modeling in cerebral dynamics. BioSystems 71, 133–144 (2003)CrossRefGoogle Scholar
  17. 17.
    Mira, J., Delgado, A.E.: Where is knowledge in robotics? some methodological issues on symbolic and connectionist perspectives of ai. In: Zhou, C., Maravall, D., Rua, D. (eds.) Autonomous robotic systems, p. 334. Physical-Verlag. Springer, Berlin (2003)Google Scholar
  18. 18.
    Mira, J., Delgado, A.E., Boticario, J.G., Díez, F.J.: Aspectos básicos de la inteligencia artificial. Sanz y Torres, SL, Madrid (1995)Google Scholar
  19. 19.
    Montserrat, J.: La Percepción Visual. Ed Biblioteca Nueva, Madrid (1998)Google Scholar
  20. 20.
    Newell, A.: The knowledge level. AI Magazine 120 (1981)Google Scholar
  21. 21.
    Newell, A., Simon, H.A.: Computer science as empirical inquiry: Symbols and search. Communications of ACM 19, 113–126 (1976)CrossRefMathSciNetGoogle Scholar
  22. 22.
    Pylyshyn, S.W.: Computation and Cognition. Towards a Foundation of Cognitive Science. The MIT Press, Cambridge (1986)Google Scholar
  23. 23.
    Moreno-Diaz, R.: Deterministic and probabilistic neural nets with loops. Mathematical Biosciences 11, 129–136 (1971)MATHCrossRefGoogle Scholar
  24. 24.
    Brooks, R.: Intelligence without reason. A.i. memo, MIT, N°. 1293 (1991)Google Scholar
  25. 25.
    Schreiber, G., Akkermans, H., de Anjo Anjewierden, R.: Engineering and Managing Knowledge: The CommonKADS Methodology. The MIT Press, Cambridge (1999)Google Scholar
  26. 26.
    Shepherd, G.M. (ed.): The Synaptic Organization of the Brain. Oxford University Press, Oxford (1990)Google Scholar
  27. 27.
    Varela, F.J.: Principles of Biological Autonomy. The North Holland Series in General Systems Research, New York (1979)Google Scholar
  28. 28.
    von Foester, H.: Understanding Understanding. Springer, Heidelberg (2003)Google Scholar
  29. 29.
    von Neumann, J.: Probabilistic logics and the synthesis of reliable organisms from unreliable components. In: Shannon, McCarthy (eds.) Automata Studies. Princenton Univ. Press., Princenton (1956)Google Scholar
  30. 30.
    Wiener, N.: Cybernetics. The Technology Press. J. Wiley & Sons, New York (1948)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • José Mira Mira
    • 1
  1. 1.Dpto. de Inteligencia Artificial, ETS Ing. InformáticaUNEDMadridSpain

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