Skip to main content
SpringerLink
Log in

Reverse Engineering for Biologically Inspired Cognitive Architectures: A Critical Analysis

  • Conference paper
From Brains to Systems

Part of the book series: Advances in Experimental Medicine and Biology ((AEMB,volume 718))

Abstract

Research initiatives on both sides of the Atlantic try to utilize the operational principles of organisms and brains to develop biologically inspired, artificial cognitive systems. This paper describes the standard way bio-inspiration is gained, 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. Using Robert Rosen’s modeling relation, 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 are decomposable, must be abandoned. Implications for investigations of organisms and behavior as well as for engineering artificial cognitive systems are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Only recently, differences between proponents of reverse engineering on how it is appropriately to be accomplished became public. The prominent heads of two reverse engineering projects, Markram [2] and Modha [8], disputed publicly as to what granularity of the modeling is needed to reach a valid simulation of the brain. Markram questioned the authenticity of Modha’s respective claims [9].

  2. 2.

    A corresponding class of models in mathematics is characterized by a theorem stating that for homogeneous linear differential equations, the sum of any two solutions is itself a solution.

  3. 3.

    We must differentiate between the natural, complex system and its description using modeling techniques from linear system theory or nonlinear mathematics.

  4. 4.

    See [7] for discussion of the computational approaches (including the neurocomputational one) to brain function, and their shortcomings.

References

  1. Biologically-Inspired Cognitive Architectures, Proposer Information Pamphlet (PIP) for Broad Agency Announcement 05-18. DARPA Information Processing Technology Office, Arlington, VA (2005)

    Google Scholar 

  2. Markram, H.: The blue brain project. Nat. Rev., Neurosci. 7, 153–160 (2006)

    Article  CAS  Google Scholar 

  3. Albus, J.S., Bekey, G.A., Holland, J.H., Kanwisher, N.G., Krichmar, J.L., Mishkin, M., Modha, D.S., Raichle, M.E., Shepherd, G.M., Tononi, G.: A proposal for a decade of the mind initiative. Science 317, 1321 (2007)

    Article  PubMed  CAS  Google Scholar 

  4. Perry, W., Broers, A., El-Baz, F., Harris, W., Healy, B., Hillis, W.D., et al.: Grand challenges for engineering. National Academy of Engineering, Washington (2008)

    Google Scholar 

  5. Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE). DARPA/IBM (2008)

    Google Scholar 

  6. European Commission, ICT Call 6 of the 7th Framework Programme, Objective 2.1: Cognitive Systems and Robotics (2009)

    Google Scholar 

  7. Schierwagen, A.: Brain organization and computation. In: Mira, J., Alvarez, J.R. (eds.) IWINAC 2007, Part I: Bio-inspired Modeling of Cognitive Tasks. LNCS, vol. 4527, pp. 31–40 (2007)

    Chapter  Google Scholar 

  8. Ananthanarayanan, R., Esser, S.K., Simon, H.D., Modha, D.S.: The cat is out of the bag: cortical simulations with 109 neurons and 1013 synapses. Supercomputing 09. In: Proc. ACM/IEEE SC2009 Conference on High Performance Networking and Computing, Nov. 14–20, 2009, Portland, OR (2009)

    Google Scholar 

  9. Brodkin, J.: IBM cat brain simulation dismissed as ‘hoax’ by rival scientist. Network World November, 24 (2009)

    Google Scholar 

  10. Dennett, D.C.: Cognitive science as reverse engineering: several meanings of ‘top down’ and ‘bottom up’. In: Prawitz, D., Skyrms, B., Westerståhl, D. (eds.) Logic, Methodology and Philosophy of Science IX, pp. 679–689. Elsevier, Amsterdam (1994)

    Google Scholar 

  11. Marom, S., Meir, R., Braun, E., Gal, A., Kermany, E., Eytan, D.: On the precarious path of reverse neuro-engineering. Front. Comput. Neurosci. 3 (2009). doi:10.3389/neuro.10.005

  12. Gurney, K.: Reverse engineering the vertebrate brain: methodological principles for a biologically grounded programme of cognitive modelling. Cogn. Comput. 1, 29–41 (2009)

    Article  Google Scholar 

  13. Rosen, R.: Anticipatory Systems: Philosophical, Mathematical and Methodological Foundations. Pergamon, Oxford (1985)

    Google Scholar 

  14. Rosen, R.: Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life. Columbia University Press, New York (1991)

    Google Scholar 

  15. Rosen, R.: Essays on Life Itself. Columbia University Press, New York (2000)

    Google Scholar 

  16. Keyser, S.J., Miller, G.A., Walker, E.: Cognitive Science in 1978. An unpublished report submitted to the Alfred P. Sloan Foundation, New York (1978)

    Google Scholar 

  17. Simon, H.: The Sciences of the Artificial. MIT Press, Cambridge (1969)

    Google Scholar 

  18. Wimsatt, W.: Forms of aggregativity. In: Donagan, A., Perovich, A.N., Wedin, M.V. (eds.) Human Nature and Natural Knowledge, pp. 259–291. D. Reidel, Dordrecht (1986)

    Google Scholar 

  19. Bechtel, W., Richardson, R.C.: Discovering complexity: Decomposition and localization as strategies in scientific research. Princeton University Press, Princeton (1993)

    Google Scholar 

  20. Cummins, R.: The Nature of Psychological Explanation. MIT Press, Cambridge (1983)

    Google Scholar 

  21. Cummins, R.: “How does it work” versus “What are the laws?”: two conceptions of psychological explanation. In: Keil, F., Wilson, R.A. (eds.) Explanation and Cognition, pp. 117–145. MIT Press, Cambridge (2000)

    Google Scholar 

  22. Atkinson, A.P.: Persons systems and subsystems: the explanatory scope of cognitive psychology. Acta Anal. 20, 43–60 (1998)

    Google Scholar 

  23. Rosen, R.: The mind-brain problem and the physics of reductionism. In: Rosen, R. (ed.) Life Itself: A Comprehensive Inquiry into the Nature, Origin, and Fabrication of Life, pp. 126–140. Columbia University Press, New York (1991)

    Google Scholar 

  24. Dennett, D.C.: Consciousness Explained. Brown, Boston (1991)

    Google Scholar 

  25. Price, C.J., Friston, K.J.: Functional ontologies for cognition: the systematic definition of structure and function. Cogn. Neuropsychol. 22, 262–275 (2005)

    Article  PubMed  Google Scholar 

  26. Uttal, W.R.: The New Phrenology. The Limits of Localizing Cognitive Processes in the Brain. MIT Press, Cambridge (2001)

    Google Scholar 

  27. Henson, R.: What can functional neuroimaging tell the experimental psychologist? Q. J. Exp. Psychol. A 58, 193–233 (2005)

    PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  29. Schierwagen, A.: 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 (1989)

    Google Scholar 

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

    Article  Google Scholar 

  31. Levins, R.: Complex Systems. In: Waddington, C.H. (ed.) Towards a Theoretical Biology, vol. 3, pp. 73–88. University of Edinburgh Press, Edinburgh (1970)

    Google Scholar 

  32. Hubel, D.H., Wiesel, T.N.: Shape and arrangement of columns in cat’s striate cortex. J. Physiol. 165, 559–568 (1963)

    PubMed  CAS  Google Scholar 

  33. Mountcastle, V.B.: The columnar organization of the neocortex. Brain 120, 701–722 (1997)

    Article  PubMed  Google Scholar 

  34. Szenthágothai, J.: The modular architectonic principle of neural centers. Rev. Physiol., Biochem. Pharmacol. 98, 11–61 (1983)

    Article  Google Scholar 

  35. Maass, W., Markram, H.: Theory of the computational function of microcircuit dynamics. In: Grillner, S., Graybiel, A.M. (eds.) The Interface between Neurons and Global Brain Function, Dahlem Workshop Report 93, pp. 371–390. MIT Press, Cambridge (2006)

    Google Scholar 

  36. Arbib, M., Érdi, P., Szenthágothai, J.: Neural Organization: Structure, Function and Dynamics. MIT Press, Cambridge (1997)

    Google Scholar 

  37. Rockel, A.J., Hiorns, R.W., Powell, T.P.S.: The basic uniformity in structure of the neocortex. Brain 103, 221–244 (1980)

    Article  PubMed  CAS  Google Scholar 

  38. Bressler, S.L., Tognoli, E.: Operational principles of neurocognitive networks. Int. J. Psychophysiol. 60, 139–148 (2006)

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  40. Horton, J.C., Adams, D.L.: The cortical column: a structure without a function. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 360, 386–462 (2005)

    Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  43. de Garis, H., Shuo, C., Goertzel, B., Ruiting, L.: A world survey of artificial brain projects Part I: Large-scale brain simulations. Neurocomputing. (2010). doi:10.1016/j.neucom.2010.08.004

    Google Scholar 

  44. Gould, S.J., Lewontin, R.C.: The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc. R. Soc. Lond. B, Biol. Sci. 205, 581–598 (1979)

    Article  CAS  Google Scholar 

  45. Destexhe, A., Marder, E.: Plasticity in single neuron and circuit computations. Nature 431, 789–795 (2004)

    Article  PubMed  CAS  Google Scholar 

  46. Frégnac, Y., et al.: Ups and downs in the genesis of cortical computation. In: Grillner, S., Graybiel, A.M. (eds.) Microcircuits: The Interface between Neurons and Global Brain Function, Dahlem Workshop Report 93, pp. 397–437. MIT Press, Cambridge (2006)

    Google Scholar 

  47. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev., Neurosci. 10, 186–198 (2009)

    Article  CAS  Google Scholar 

  48. Bechtel, W.: Dynamics and decomposition: are they compatible? In: Proceedings of the Australasian Cognitive Science Society (1997)

    Google Scholar 

  49. Bechtel, W.: Decomposing the brain: A long term pursuit. Brain and Mind 3, 229–242 (2002)

    Article  Google Scholar 

  50. Poirier, P.: Be there, or be square! On the importance of being there. Semiotica 130, 151–176 (2000)

    Google Scholar 

  51. van Vreeswijk, C.: What is the neural code. In: van Hemmen, J.L., Sejnowski, T. Jr. (eds.): 23 Problems in System Neuroscience, pp. 143–159. Oxford University Press, Oxford (2006)

    Chapter  Google Scholar 

  52. Mikulecky, D.C.: Robert Rosen: the well posed question and its answer—why are organisms different from machines? Syst. Res. 17, 419–432 (2000)

    Article  Google Scholar 

  53. Wimsatt, W.C.: Complexity and organization. Proc. Biennial Meet. Philos. Sci. Ass. 1972, 67–86 (1972)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute for Computer Science, Intelligent Systems Department, University of Leipzig, Leipzig, Germany

    Andreas Schierwagen

Authors
  1. Andreas Schierwagen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Schierwagen .

Editor information

Editors and Affiliations

  1. , Departamento de Automática, Universidad Politécnica de Madrid, Jose Gutierrez Abascal 2, Madrid, 28006, Spain

    Carlos Hernández

  2. , Departamento de Automática, Universidad Politecnica Madrid, Jose Gutierrez Abascal 2, Madrid, 28006, Spain

    Ricardo Sanz

  3. , Departamento de Automática, Universidad Politécnica de Madrid, C/ Jose Gutierrez Abascal 2, Madrid, 28006, Spain

    Jaime Gómez-Ramirez

  4. Dept. Computing Science, University of Stirling, Stirling, FK9 4LA, United Kingdom

    Leslie S. Smith

  5. Dept. Computing Science, University of Stirling, Stirling, FK9 4LA, United Kingdom

    Amir Hussain

  6. Dipto. Ingegneria Automatica e, Informatica, Università Palermo, Corso Pizasi 106, Palermo, 90144, Italy

    Antonio Chella

  7. Technology & Medicine, Dept. Electrical & Electronic, Imperial College of Science,, Exhibition Rd., London, SW7 2BT, United Kingdom

    Igor Aleksander

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer Science+Business Media, LLC

About this paper

Cite this paper

Schierwagen, A. (2011). Reverse Engineering for Biologically Inspired Cognitive Architectures: A Critical Analysis. In: Hernández, C., et al. From Brains to Systems. Advances in Experimental Medicine and Biology, vol 718. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0164-3_10

Download citation

Publish with us

Policies and ethics

Search

Navigation