Advertisement

Large-Scale Simulations of the Brain: Is There a “Right” Level of Detail?

  • Edoardo DatteriEmail author
Chapter
Part of the Philosophical Studies Series book series (PSSP, volume 134)

Abstract

A number of research projects have recently taken up the challenge of formulating large-scale models of brain mechanisms at unprecedented levels of detail. These research enterprises have raised lively debates in the press and in the scientific and philosophical literature, some of them revolving around the question whether the incorporation of so many details in a theoretical model and in a computer simulations of it is really needed for the model to be explanatory. Is there a “right” level of detail? In this article I analyse the claim, made by two leading neuroscientists, according to which the content of the why-question addressed and the amount of computational resources available constrains the choice of the most appropriate level of detail in brain modelling. Based on the recent philosophical literature on (neuro)scientific explanation, I distinguish between two kinds of details, called here mechanistic decomposition and property details, and argue that the nature of the why-question provides only partial constraints to the choice of the most appropriate level of detail under the two interpretations of the term considered here.

Keywords

Neuroscience Computer simulation Neural modeling Brain project Mechanistic decomposition Levels of analysis 

References

  1. Ananthanarayanan, R., S.K. Esser, H.D. Simon, and D.S. Modha. 2009. The cat is out of the bag: Cortical simulations with 109 neurons, 1013 synapses. In High performance computing networking, storage and analysis, proceedings of the conference on, (c), 1–12.  https://doi.org/10.1145/1654059.1654124.CrossRefGoogle Scholar
  2. Braitenberg, V. 1986. Vehicles. Experiments in synthetic psychology. Cambridge, MA: The MIT Press.Google Scholar
  3. Bromberger, S. 1966. Why-questions. In Mind and Cosmos: Essays in contemporary science and philosophy, ed. R. Colodny, 68–111. Pittsburgh: University of Pittsburgh Press.Google Scholar
  4. Brooks, R.A. 1991. New approaches to robotics. Science 253 (5025): 1227–1232.  https://doi.org/10.1126/science.253.5025.1227.CrossRefGoogle Scholar
  5. Cordeschi, R. 2002. The discovery of the artificial. Behavior, mind and machines before and beyond cybernetics. Dordrecht: Springer.  https://doi.org/10.1007/978-94-015-9870-5.CrossRefGoogle Scholar
  6. Craver, C.F. 2002. Interlevel experiments and multilevel mechanisms in the neuroscience of memory. Philosophy of Science 69: September), 83–September), 97.CrossRefGoogle Scholar
  7. Craver, C. 2007. Explaining the brain: Mechanisms and the mosaic unity of neuroscience. New York: Clarendon Press.CrossRefGoogle Scholar
  8. Cummins, R. 1975. Functional analysis. Journal of Philosophy 72 (20): 741–765.CrossRefGoogle Scholar
  9. Datteri, E., and F. Laudisa. 2014. Box-and-arrow explanations need not be more abstract than neuroscientific mechanism descriptions. Frontiers in Psychology 5 (MAY): 1–10.  https://doi.org/10.3389/fpsyg.2014.00464.CrossRefGoogle Scholar
  10. ———. 2016. Large-scale simulations of brain mechanisms: Beyond the synthetic method. Paradigmi 3: 23–46.  https://doi.org/10.3280/PARA2015-003003.CrossRefGoogle Scholar
  11. Eliasmith, C., and O. Trujillo. 2014. The use and abuse of large-scale brain models. Current Opinion in Neurobiology 25: 1–6.  https://doi.org/10.1016/j.conb.2013.09.009.CrossRefGoogle Scholar
  12. Eliasmith, C., T.C. Stewart, X. Choo, T. Bekolay, T. DeWolf, C. Tang, and D. Rasmussen. 2012. A large-scale model of the functioning brain. Science 338 (6111): 1202–1205.  https://doi.org/10.1126/science.1225266.CrossRefGoogle Scholar
  13. Glennan, S. 2002. Rethinking mechanistic explanation. Philosophy of Science 69 (S3): S342–S353.  https://doi.org/10.1086/341857.CrossRefGoogle Scholar
  14. Grey Walter, W. 1950. An imitation of life. Scientific American 182 (5): 42–45.CrossRefGoogle Scholar
  15. Grillner, S., N. Ip, C. Koch, W. Koroshetz, H. Okano, M. Polachek, and M. Poo. 2016. Worldwide initiatives to advance brain research. Nature 19 (9): 1118–1122.  https://doi.org/10.1038/nn.4371.CrossRefGoogle Scholar
  16. Hintikka, J., and I. Halonen. 1995. Semantics and pragmatics for why-questions. Journal of Philosophy 92 (12): 636–657.MathSciNetCrossRefGoogle Scholar
  17. Komer, B., and C. Eliasmith. 2016. A unified theoretical approach for biological cognition and learning. Current Opinion in Behavioral Sciences 11: 14–20.  https://doi.org/10.1016/j.cobeha.2016.03.006.CrossRefGoogle Scholar
  18. Levy, A., and W. Bechtel. 2013. Abstraction and the organization of mechanisms. Philosophy of Science 80: 241–261.  https://doi.org/10.1086/670300.CrossRefGoogle Scholar
  19. Markram, H. 2006. The blue brain project. Nature Reviews. Neuroscience 7 (2): 153–160.  https://doi.org/10.1038/nrn1848.MathSciNetCrossRefGoogle Scholar
  20. Markram, H., K. Meier, T. Lippert, S. Grillner, R. Frackowiak, S. Dehaene, A. Knoll, H. Sompolinsky, K. Verstreken, J. DeFelipe, S. Grant, J.P. Changeux, and A. Sariam. 2011. Introducing the human brain project. Procedia Computer Science 7: 39–42.  https://doi.org/10.1016/j.procs.2011.12.015.CrossRefGoogle Scholar
  21. Miłkowski, M. 2015. Explanatory completeness and idealization in large brain simulations: A mechanistic perspective. Synthese 193: 1457–1478.  https://doi.org/10.1007/s11229-015-0731-3.CrossRefGoogle Scholar
  22. Pfeifer, R., and J. Bongard. 2006. How the body shapes the way we think. A new view of intelligence. Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
  23. Pfeifer, R., and C. Scheier. 1999. Understanding Intelligence. Cambridge, MA: The MIT Press.Google Scholar
  24. Piccinini, G., and C. Craver. 2011. Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese 183: 283–311.CrossRefGoogle Scholar
  25. Rosenblueth, A., and N. Wiener. 1945. The role of models in science. Philosophy of Science 12 (4): 316–321.  https://doi.org/10.1086/286874.CrossRefGoogle Scholar
  26. Simon, H.A. 1996. The sciences of the artificial. Cambridge, MA: The MIT Press.Google Scholar
  27. Tamburrini, G., and E. Datteri. 2005. Machine experiments and theoretical modelling: From cybernetic methodology to neuro-robotics. Minds and Machines 15 (3–4): 335–358.  https://doi.org/10.1007/s11023-005-2924-x.CrossRefGoogle Scholar
  28. Van Fraassen, B. 1980. The scientific image. Oxford: Clarendon Press.CrossRefGoogle Scholar
  29. Woodward, J. 2002. What is a mechanism? A counterfactual account. Philosophy of Science 69: S366–S377 JOUR.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Human Sciences for Education, RobotiCSS Lab – Laboratory of Robotics for the Cognitive and Social SciencesUniversity of Milano-BicoccaMilanItaly

Personalised recommendations