, Volume 16, Issue 1–2, pp 137–154 | Cite as

The Memory Evolutive Systems as a Model of Rosen’s Organisms – (Metabolic, Replication) Systems

  • Andrée C. Ehresmann
  • Jean-Paul Vanbremeersch


Robert Rosen has proposed several characteristics to distinguish “simple” physical systems (or “mechanisms”) from “complex” systems, such as living systems, which he calls “organisms”. The Memory Evolutive Systems (MES) introduced by the authors in preceding papers are shown to provide a mathematical model, based on category theory, which satisfies his characteristics of organisms, in particular the merger of the Aristotelian causes. Moreover they identify the condition for the emergence of objects and systems of increasing complexity. As an application, the cognitive system of an animal is modeled by the “MES of cat-neurons” obtained by successive complexifications of his neural system, in which the emergence of higher order cognitive processes gives support to Mario Bunge’s “emergentist monism.”


category cognition complexity emergence hierarchy organism 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abramsky, S., Coecke, B. 2005‘Abstract Physical Traces’Theory and Applications of Categories14111124Google Scholar
  2. Baianu, I. C. 1971‘Organismic Supercategories and Qualitative Dynamics of Systems’Bulletin of Mathematical Biophysics33339354Google Scholar
  3. Baianu, I. C., Marinescu, M. 1968‘Organismic Supercategories: Towards a Unified Theory of Systems’Bulletin of Mathematical Biophysics30148165Google Scholar
  4. Baianu, I. C., Marinescu, M. 1974‘A Functorial Construction of (M, R)-Systems’Revue Roumaine Mathematiques Pures Et Appliquees19388391Google Scholar
  5. Barr, M. and C. Wells: 1984, Toposes, Triples and Theories, SpringerGoogle Scholar
  6. Bunge, M.: 1967, Scientific Research, 1 and 2, SpringerGoogle Scholar
  7. Changeux, J. -P. 1983L’homme NeuronalFayardParisGoogle Scholar
  8. Duskin, J.: 1966, ‘Pro-objects (d’après Verdier)’, Séminaire Heidelberg-Strasbourg, Exposé 6Google Scholar
  9. Edelman, G. M. 1989The Remembered PresentBasic BooksNew YorkGoogle Scholar
  10. Edelman, G. M., Gally, J. A. 2001‘Degeneracy and Complexity in Biological Systems’Proceedings of the National Academy of Sciences981376313768CrossRefGoogle Scholar
  11. Ehresmann, A. C., Vanbremeersch, J. P. 1987‘Hierarchical Evolutive Systems: A Mathematical Model for Complex Systems’Bulletin of Mathematical Biology491350CrossRefGoogle Scholar
  12. Ehresmann, A. C., Vanbremeersch, J. P. 1990

    Hierarchical Evolutive Systems

    Manikopoulos,  eds. Proceedings of 8th International Conference of Cybernetics and SystemsThe NIJT PressNewark320327New York, Vol. 1.
    Google Scholar
  13. Ehresmann, A. C., Vanbremeersch, J. P. 1996‘Multiplicity Principle and Emergence in MES’Journal of Systems Analysis, Modelling, Simulation2681117Google Scholar
  14. Ehresmann, A. C. and J. P. Vanbremeersch: 2002, ‘Emergence Processes up to Consciousness Using the Multiplicity Principle and Quantum Physics’, A.I.P. Conference Proceedings (CASYS, 2001, Ed. D. Dubois) 627, 221–233Google Scholar
  15. Ehresmann, A. C. and J. P. Vanbremeersch: 2005, Online. URL: (developed in a book in print at Elsevier)
  16. Eilenberg, S., Mac Lane, S. 1945‘General Theory of Natural Equivalences’Transactions of the American Mathematical Society58231294Google Scholar
  17. Gray, J. W. 1989

    ‘The Category of Sketches as a Model for Algebraic Semantics’

    Gray, Scedrov,  eds. Categories in Computer Science and LogicAmerican Mathematical SocietyProvidence R.I.
    Google Scholar
  18. Hebb, D. O. 1949The Organization of BehaviourWileyNew YorkGoogle Scholar
  19. Kainen, P. C.: 1990, ‘Functorial Cybernetics of Attention’, in Holden and Kryukov (eds.), Neurocomputers and Attention II, Chap. 57, Manchester University PressGoogle Scholar
  20. Kan, D. M. 1958‘Adjoint Functors’Transactions of the American Mathematical Society89294329Google Scholar
  21. Lawvere, F. W. and S. H. Schanuel (eds.): 1980, Categories in Continuum Physics, Lecture Notes in Mathematics 1174, SpringerGoogle Scholar
  22. Mac Lane, S.: 1971, Categories for the Working Mathematician, SpringerGoogle Scholar
  23. Malsburg, C. 1995‘Binding in Models of Perception and Brain Function’Current Opinions in Neurobiology5520526CrossRefGoogle Scholar
  24. Malsburg, C. (von der) and E. Bienenstock: 1986, ‘Statistical Coding and Short-Term Synaptic Plasticity’, in Disordered Systems and Biological Organization, NATO ASI Series 20, Springer, 247–272Google Scholar
  25. Pribram, K. H. 1971Languages of the BrainPrentice HallEnglewood Cliffs, NJGoogle Scholar
  26. Pribram, K. H. 2000

    ‘Proposal for a Quantum Physical Basis for Selective Learning’

    Farre,  eds. Proceedings ECHO IVPreprint Georgetown UniversityWashington14
    Google Scholar
  27. Rashevsky, N. 1967‘Organismic Sets. Outline of a General Theory of Biological and Sociological Organisms’Bulletin of Mathematical Biophysics29139152Google Scholar
  28. Rosen, R. 1958‘The Representation of Biological Systems from the Standpoint of the Theory of Categories’Bulletin of Mathematical Biophysics20245260Google Scholar
  29. Rosen, R.: 1985a, ‘Organisms as Causal Systems Which are not Mechanisms’, in Theoretical Biology and Complexity, New York: Acad. Press, 165–203Google Scholar
  30. Rosen, R. 1985bAnticipatory SystemsPergamonNew York1985Google Scholar
  31. Rosen, R.: 1986, Theoretical Biology and complexity, Academic PressGoogle Scholar
  32. Thom, R. 1974Modèles Mathématiques de la MorphogenèseUnion Générale d’EditionParisColl. 10/18Google Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • Andrée C. Ehresmann
  • Jean-Paul Vanbremeersch

There are no affiliations available

Personalised recommendations