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On Reverse Engineering in the Cognitive and Brain Sciences

Schierwagen, Prof. Andreas (2012) On Reverse Engineering in the Cognitive and Brain Sciences. [Journal (Paginated)]

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Abstract

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.

Item Type:Journal (Paginated)
Keywords: Brain Cognition Capacity Decompositional analysis Localization Linearity Modularization Column concept Reverse engineering Complex systems Modeling relation
Subjects:Psychology > Cognitive Psychology
Neuroscience > Computational Neuroscience
Philosophy > Philosophy of Science
ID Code:8729
Deposited By: Schierwagen, Professor Andreas
Deposited On:25 Nov 2012 12:34
Last Modified:18 Feb 2013 15:12

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. Arbib M, Érdi P, Szenthágothai J (1997) Neural organization: structure, function and dynamics. MIT Press, Cambridge

2. Atkinson AP (1998) Persons, systems and subsystems: the explanatory scope of cognitive psychology. Acta Analytica 20:43–60

3. Bechtel W, Richardson RC (1993) Discovering complexity: decomposition and localization as strategies in scientific research. Princeton University Press, Princeton

4. Bechtel W (2002) Decomposing the brain: a long term pursuit. Brain and Mind 3:229–242 CrossRef

5. Bressler SL, Tognoli E (2006) Operational principles of neurocognitive networks. Intern J Psychophysiol 60:139–148 CrossRef

6. Brodkin J (2009) IBM cat brain simulation dismissed as ‘hoax’ by rival scientist. Network World, Framingham

7. Brooks R (2001) The relationship between matter and life. Nature 409:409–410 CrossRef

8. Cummins R (1983) The nature of psychological explanation. MIT Press, Cambridge

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 117–145

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 679–689

11. Dennett DC (1991) Consciousness explained. Little, Brown and Co, Boston

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.: 09-203

14. Forrest S (1990) Emergent computation: self-organizing, collective, and cooperative phenomena in natural and artificial computing networks. Physica D 42:1–11 CrossRef

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, Cambridge

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 205:581–598 CrossRef

17. Grillner S, Markram H, De Schutter E, Silberberg G, LeBeau FEN (2005) Microcircuits in action from CPGs to neocortex. Trends Neurosci 28:525–533 CrossRef

18. Gurney K (2009) Reverse engineering the vertebrate brain: methodological principles for a biologically grounded programme of cognitive modelling. Cognit Computat 1:29–41 CrossRef

19. Henson R (2005) What can functional neuroimaging tell the experimental psychologist?. Quart J Exper Psychol 58:193–233 CrossRef

20. Herculano-Housel S, Collins CE, Wang P, Kaas J (2008) The basic nonuniformity of the cerebral cortex. Proc Natl Acad Sci USA 105:12593–12598 CrossRef

21. Horton JC, Adams DL (2005) The cortical column: a structure without a function. Phil Trans R Soc B 360:386–362 CrossRef

22. Hubel DH, Wiesel TN (1963) Shape and arrangement of columns in cats striate cortex. J Physiol 165:559–568

23. Hubel DH, Wiesel TN (1977) Ferrier lecture: functional architecture of Macaque Monkey visual cortex. Proc R Soc Lond B 198:1–59 CrossRef

24. Levins R (1970) Complex systems. In: Waddington CH (eds) Towards a theoretical biology, University of Edinburgh Press, Edinburgh, pp 73–88

25. Le Novere N (2007) The long journey to a systems biology of neuronal function. BMC Syst Biol. 1–28

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 371–390

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 7:153–160 CrossRef

29. Mountcastle VB (1997) The columnar organization of the neocortex. Brain 120:701–722 CrossRef

30. Price CJ, Friston KJ (2005) Functional ontologies for cognition: the systematic definition of structure and function. Cogn Neuropsychol 22:262–275 CrossRef

31. Rakic P (2008) Confusing cortical columns. Proc Natl Acad Sci USA 105:12099–12100 CrossRef

32. Rockel AJ, Hiorns RW, Powell TPS (1980) The basic uniformity in structure of the neocortex. Brain 103:221–244 CrossRef

33. Rosen R (1991) Life itself: a comprehensive inquiry into the nature, origin, and fabrication of life. Columbia University Press, New York

34. Rosen R (2000) Essays on life itself. Columbia University Press, New York

35. Ross ED (2010) Cerebral localization of functions and the neurology of language: fact versus fiction or is it something else?. Neuroscientist 16:222–243 CrossRef

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 31–40

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 195–204

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), Eberswalde

39. Simon H (1969) The sciences of the artificial. MIT Press, Cambridge

40. Suykens JAK, Vandewalle JPL, Moor BL de (1996) Artificial neural networks for modelling and control of non-linear systems. Kluwer Academic Publishers, Dordrecht

41. Systems of neuromorphic adaptive plastic scalable electronics (SyNAPSE). DARPA/IBM (2008)

42. Szenthágothai J (1983) The modular architectonic principle of neural centers. Rev Physiol Bioche Pharmacol 98:11–61 CrossRef

43. Uttal WR (2001) The new phrenology. The limits of localizing cognitive processes in the brain. MIT Press, Cambridge

44. von Eckardt B, Poland JS (2004) Mechanism and explanation in cognitive neuroscience. Philos Sci 71:972–984 CrossRef

45. Wimsatt W (1986) Forms of aggregativity. In: Donagan A, Perovich AN, Wedin MV (eds) Human nature and natural knowledge, D. Reidel, Dordrecht, pp 259–291 CrossRef

46. Wimsatt WC (1972) Complexity and organization. Proc Biennial Meeting Philos Sci Ass 1972:67–86

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