Natural Computing

, Volume 11, Issue 1, pp 141–150 | Cite as

On reverse engineering in the cognitive and brain sciences

  • Andreas Schierwagen


Various research initiatives try to utilize the operational principles of organisms and brains to develop alternative, biologically inspired computing paradigms and artificial cognitive systems. This article reviews key features of the standard method applied to complexity in the cognitive and brain sciences, i.e. decompositional analysis or reverse engineering. The indisputable complexity of brain and mind raise the issue of whether they can be understood by applying the standard method. Actually, recent findings in the experimental and theoretical fields, question central assumptions and hypotheses made for reverse engineering. Using the modeling relation as analyzed by Robert Rosen, the scientific analysis method itself is made a subject of discussion. It is concluded that the fundamental assumption of cognitive science, i.e. complex cognitive systems can be analyzed, understood and duplicated by reverse engineering, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.


Brain Cognition Capacity Decompositional analysis Localization Linearity Modularization Column concept Reverse engineering Complex systems Modeling relation 


  1. Arbib M, Érdi P, Szenthágothai J (1997) Neural organization: structure, function and dynamics. MIT Press, CambridgezbMATHGoogle Scholar
  2. Atkinson AP (1998) Persons, systems and subsystems: the explanatory scope of cognitive psychology. Acta Analytica 20:43–60Google Scholar
  3. Bechtel W, Richardson RC (1993) Discovering complexity: decomposition and localization as strategies in scientific research. Princeton University Press, PrincetonGoogle Scholar
  4. Bechtel W (2002) Decomposing the brain: a long term pursuit. Brain and Mind 3:229–242CrossRefGoogle Scholar
  5. Bressler SL, Tognoli E (2006) Operational principles of neurocognitive networks. Intern J Psychophysiol 60:139–148CrossRefGoogle Scholar
  6. Brodkin J (2009) IBM cat brain simulation dismissed as ‘hoax’ by rival scientist. Network World, FraminghamGoogle Scholar
  7. Brooks R (2001) The relationship between matter and life. Nature 409:409–410CrossRefGoogle Scholar
  8. Cummins R (1983) The nature of psychological explanation. MIT Press, CambridgeGoogle Scholar
  9. Cummins R (2000) “How does it work” versus “What are the laws?”: two conceptions of psychological explanation. In: Keil F, Wilson RA (eds) Explanation and cognition, MIT Press, Cambridge, pp 117–145Google Scholar
  10. Dennett DC (1994) Cognitive science as reverse engineering: several meanings of ‘top down’ and ‘bottom up’. In: Prawitz D, Skyrms B, Westersthl D (eds) Logic, methodology and philosophy of science IX, Elsevier Science, Amsterdam, pp 679–689Google Scholar
  11. Dennett DC (1991) Consciousness explained. Little, Brown and Co, BostonGoogle Scholar
  12. de Garis H, Shuo C, Goertzel B, Ruiting L (2010) A world survey of artificial brain projects PartI: large-scale brain simulations. Neurocomputing. doi: 10.1016/j.neucom.2010.08.004
  13. Edmonds B (2009) Understanding observed complex systems the hard complexity problem. CPM Report No.: 09-203Google Scholar
  14. Forrest S (1990) Emergent computation: self-organizing, collective, and cooperative phenomena in natural and artificial computing networks. Physica D 42:1–11CrossRefMathSciNetGoogle Scholar
  15. Frégnac Y et al. (2006) Ups and downs in the genesis of cortical computation. In: Grillner S, Graybiel AM (eds) Microcircuits: the interface between neurons and global brain function, Dahlem Workshop Report 93. MIT Press, CambridgeGoogle Scholar
  16. Gould SJ, Lewontin RC (1979) The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc R Soc London B 205:581–598CrossRefGoogle Scholar
  17. Grillner S, Markram H, De Schutter E, Silberberg G, LeBeau FEN (2005) Microcircuits in action from CPGs to neocortex. Trends Neurosci 28:525–533CrossRefGoogle Scholar
  18. Gurney K (2009) Reverse engineering the vertebrate brain: methodological principles for a biologically grounded programme of cognitive modelling. Cognit Computat 1:29–41CrossRefGoogle Scholar
  19. Henson R (2005) What can functional neuroimaging tell the experimental psychologist?. Quart J Exper Psychol 58:193–233CrossRefGoogle Scholar
  20. Herculano-Housel S, Collins CE, Wang P, Kaas J (2008) The basic nonuniformity of the cerebral cortex. Proc Natl Acad Sci USA 105:12593–12598CrossRefGoogle Scholar
  21. Horton JC, Adams DL (2005) The cortical column: a structure without a function. Phil Trans R Soc B 360:386–362CrossRefGoogle Scholar
  22. Hubel DH, Wiesel TN (1963) Shape and arrangement of columns in cats striate cortex. J Physiol 165:559–568Google Scholar
  23. Hubel DH, Wiesel TN (1977) Ferrier lecture: functional architecture of Macaque Monkey visual cortex. Proc R Soc Lond B 198:1–59CrossRefGoogle Scholar
  24. Levins R (1970) Complex systems. In: Waddington CH (eds) Towards a theoretical biology, University of Edinburgh Press, Edinburgh, pp 73–88Google Scholar
  25. Le Novere N (2007) The long journey to a systems biology of neuronal function. BMC Syst Biol. 1–28Google Scholar
  26. Maass W, Markram H (2006) Theory of the computational function of microcircuit dynamics. In: Grillner S, Graybiel AM (eds) The interface between neurons and global brain function, Dahlem Workshop Report 93. MIT Press, Cambridge, pp 371–390Google Scholar
  27. Marom S, Meir R, Braun E, Gal A, Kermany E, Eytan D (2009) On the precarious path of reverse neuro-engineering. Front Comput Neurosci 3. doi: 10.3389/neuro.10.005
  28. Markram H (2006) The blue brain project. Nat Rev Neurosci 7:153–160CrossRefGoogle Scholar
  29. Mountcastle VB (1997) The columnar organization of the neocortex. Brain 120:701–722CrossRefGoogle Scholar
  30. Price CJ, Friston KJ (2005) Functional ontologies for cognition: the systematic definition of structure and function. Cogn Neuropsychol 22:262–275CrossRefGoogle Scholar
  31. Rakic P (2008) Confusing cortical columns. Proc Natl Acad Sci USA 105:12099–12100CrossRefGoogle Scholar
  32. Rockel AJ, Hiorns RW, Powell TPS (1980) The basic uniformity in structure of the neocortex. Brain 103:221–244CrossRefGoogle Scholar
  33. Rosen R (1991) Life itself: a comprehensive inquiry into the nature, origin, and fabrication of life. Columbia University Press, New YorkGoogle Scholar
  34. Rosen R (2000) Essays on life itself. Columbia University Press, New YorkGoogle Scholar
  35. Ross ED (2010) Cerebral localization of functions and the neurology of language: fact versus fiction or is it something else?. Neuroscientist 16:222–243CrossRefGoogle Scholar
  36. Schierwagen A (2007) Brain organization and computation. In: Mira J, Alvarez JR (eds) IWINAC 2007, Part I: Bio-inspired modeling of cognitive tasks. Lecture notes in computer science, Springer, Heidelberg 4527, pp 31–40Google Scholar
  37. Schierwagen A (2009) Brain complexity: analysis, models and limits of understanding. In: Mira J et al. (eds) IWINAC 2009, Part I, Lecture notes in computer science, Springer, Heidelberg 5601, pp 195–204Google Scholar
  38. Schierwagen A (1989) Real neurons and their circuitry: Implications for brain theory. iir–reporte, pp. 17–20. Akademie der Wissenschaften der DDR, Institut für Informatik und Rechentechnik), EberswaldeGoogle Scholar
  39. Simon H (1969) The sciences of the artificial. MIT Press, CambridgeGoogle Scholar
  40. Suykens JAK, Vandewalle JPL, Moor BL de (1996) Artificial neural networks for modelling and control of non-linear systems. Kluwer Academic Publishers, DordrechtGoogle Scholar
  41. Systems of neuromorphic adaptive plastic scalable electronics (SyNAPSE). DARPA/IBM (2008)Google Scholar
  42. Szenthágothai J (1983) The modular architectonic principle of neural centers. Rev Physiol Bioche Pharmacol 98:11–61CrossRefGoogle Scholar
  43. Uttal WR (2001) The new phrenology. The limits of localizing cognitive processes in the brain. MIT Press, CambridgeGoogle Scholar
  44. von Eckardt B, Poland JS (2004) Mechanism and explanation in cognitive neuroscience. Philos Sci 71:972–984CrossRefGoogle Scholar
  45. Wimsatt W (1986) Forms of aggregativity. In: Donagan A, Perovich AN, Wedin MV (eds) Human nature and natural knowledge, D. Reidel, Dordrecht, pp 259–291CrossRefGoogle Scholar
  46. Wimsatt WC (1972) Complexity and organization. Proc Biennial Meeting Philos Sci Ass 1972:67–86Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Intelligent Systems Department, Institute for Computer ScienceUniversity of LeipzigLeipzigGermany

Personalised recommendations