, Volume 193, Issue 12, pp 3741–3762 | Cite as

Computational neuroscience and localized neural function

  • Daniel C. BurnstonEmail author
S.I.: Neuroscience and Its Philosophy


In this paper I criticize a view of functional localization in neuroscience, which I call “computational absolutism” (CA). “Absolutism” in general is the view that each part of the brain should be given a single, univocal function ascription. Traditional varieties of absolutism posit that each part of the brain processes a particular type of information and/or performs a specific task. These function attributions are currently beset by physiological evidence which seems to suggest that brain areas are multifunctional—that they process distinct information and perform different tasks depending on context. Many theorists take this contextual variation as inimical to successful localization, and claim that we can avoid it by changing our functional descriptions to computational descriptions. The idea is that we can have highly generalizable and predictive functional theories if we can discover a single computation performed by each area regardless of the specific context in which it operates. I argue, drawing on computational models of perceptual area MT, that this computational version of absolutism fails to come through on its promises. In MT, the modeling field has not produced a univocal computational description, but instead a plurality of models analyzing different aspects of MT function. Moreover, CA cannot appeal to theoretical unification to solve this problem, since highly general models, on their own, neither explain nor predict what MT does in any particular context. I close by offering a perspective on neural modeling inspired by Nancy Cartwright’s and Margaret Morrison’s views of modeling in the physical sciences.


Absolutism Computational neuroscience Explanation Functional localization Models Perceptual neuroscience 



I would like to thank Thomas Albright, William Bechtel, Jonathan Cohen, Rick Grush, John Serences, and Ben Sheredos for extremely helpful discussion and comments on earlier versions of this paper. Distant cousins of this material were presented as posters at the Methodology in Neuroscience Workshop at Pitt HPS (November 2013) and the 2014 Society for Philosophy and Psychology Conference in Vancouver, and I benefitted from discussion with audiences at each.


  1. Adelson, E. H., & Bergen, J. R. (1985). Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 2(2), 284–299.CrossRefGoogle Scholar
  2. Anderson, M. L. (2010). Neural reuse: A fundamental organizational principle of the brain. The Behavioral and Brain Sciences, 33(4), 245–266; discussion 266–313. doi: 10.1017/S0140525X10000853.
  3. Anderson, M. L. (2014). After phrenology: Neural reuse and the interactive brain. Cambridge, MA: MIT Press.Google Scholar
  4. Bergeron, V. (2007). Anatomical and functional modularity in cognitive science: Shifting the focus. Philosophical Psychology, 20(2), 175–195.CrossRefGoogle Scholar
  5. Bradley, D. C., & Goyal, M. S. (2008). Velocity computation in the primate visual system. Nature Reviews Neuroscience, 9(9), 686–695.CrossRefGoogle Scholar
  6. Britten, K. H., Newsome, W. T., Shadlen, M. N., Celebrini, S., & Movshon, J. A. (1996). A relationship between behavioral choice and the visual responses of neurons in macaque MT. Visual Neuroscience, 13, 87–100.CrossRefGoogle Scholar
  7. Burnston, D. C. (2015). Perceptual context and the nature of neural function. Doctoral dissertation, University of California, San Diego.Google Scholar
  8. Burnston, D. C. (forthcoming). A contextualist approach to functional localization in the brain. Biology & Philosophy. doi: 10.1007/s10539-016-9526-2.
  9. Carandini, M., & Heeger, D. J. (2012). Normalization as a canonical neural computation. Nature Reviews Neuroscience, 13(1), 51–62. doi: 10.1038/nrn3136.CrossRefGoogle Scholar
  10. Cartwright, N. (1983). How the laws of physics lie. Cambridge: Cambridge University Press.Google Scholar
  11. Cartwright, N. (1999). The dappled world: A study of the boundaries of science. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  12. Chirimuuta, M. (2014). Minimal models and canonical neural computations: The distinctness of computational explanation in neuroscience. Synthese, 191(2), 127–153. doi: 10.1007/s11229-013-0369-y.CrossRefGoogle Scholar
  13. Chirimuuta, M., & Gold, I. (2009). The embedded neuron, the enactive field? In J. Bickle (Ed.), The Oxford handbook of philosophy and neuroscience. New York: Oxford University Press.Google Scholar
  14. Cummins, R. C. (1975). Functional analysis. Journal of Philosophy, 72(20), 741–765.CrossRefGoogle Scholar
  15. DeAngelis, G. C., Cumming, B. G., & Newsome, W. T. (1998). Cortical area MT and the perception of stereoscopic depth. Nature, 394(6694), 677–680.CrossRefGoogle Scholar
  16. DeAngelis, G. C., & Newsome, W. T. (1999). Organization of disparity-selective neurons in macaque area MT. The Journal of Neuroscience, 19(4), 1398–1415.Google Scholar
  17. Dodd, J. V., Krug, K., Cumming, B. G., & Parker, A. J. (2001). Perceptually bistable three-dimensional figures evoke high choice probabilities in cortical area MT. The Journal of Neuroscience: The Official Journal of the Society for Neuroscience, 21(13), 4809–4821.Google Scholar
  18. Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138.CrossRefGoogle Scholar
  19. Grunewald, A., Bradley, D. C., & Andersen, R. A. (2002). Neural correlates of structure-from-motion perception in macaque V1 and MT. The Journal of Neuroscience, 22(14), 6195–6207.Google Scholar
  20. Klein, C. (2012). Cognitive ontology and region- versus network-oriented analyses. Philosophy of Science, 79(5), 952–960.CrossRefGoogle Scholar
  21. Koechlin, E., Anton, J. L., & Burnod, Y. (1999). Bayesian inference in populations of cortical neurons: A model of motion integration and segmentation in area MT. Biological Cybernetics, 80(1), 25–44. doi: 10.1007/s004220050502.CrossRefGoogle Scholar
  22. Krekelberg, B., & Albright, T. D. (2005). Motion mechanisms in macaque MT. Journal of Neurophysiology, 93(5), 2908–2921. doi: 10.1152/jn.00473.2004.CrossRefGoogle Scholar
  23. Levy, W. B., Hocking, A. B., & Wu, X. (2005). Interpreting hippocampal function as recoding and forecasting. Neural Networks, 18(9), 1242–1264.CrossRefGoogle Scholar
  24. Livingstone, M., & Hubel, D. (1988). Segregation of form, color, movement, and depth: Anatomy, physiology, and perception. Science, 240(4853), 740–749.CrossRefGoogle Scholar
  25. Maunsell, J. H., & Van Essen, D. C. (1983). Functional properties of neurons in middle temporal visual area of the macaque monkey. II. Binocular interactions and sensitivity to binocular disparity. Journal of Neurophysiology, 49(5), 1148–1167.Google Scholar
  26. McIntosh, A. R. (2004). Contexts and catalysts: A resolution of the localization and integration of function in the brain. Neuroinformatics, 2(2), 175–182.CrossRefGoogle Scholar
  27. Morrison, M. (2000). Unifying scientific theories: Physical concepts and mathematical structures. Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  28. Nishimoto, S., & Gallant, J. L. (2011). A three-dimensional spatiotemporal receptive field model explains responses of area MT neurons to naturalistic movies. The Journal of Neuroscience, 31(41), 14551–14564.CrossRefGoogle Scholar
  29. Nowlan, S. J., & Sejnowski, T. J. (1995). A selection model for motion processing in area MT of primates. The Journal of Neuroscience, 15(2), 1195–1214.Google Scholar
  30. Olshausen, B. A., & Field, D. J. (1997). Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23), 3311–3325.CrossRefGoogle Scholar
  31. Palanca, B. J. A., & DeAngelis, G. C. (2003). Macaque middle temporal neurons signal depth in the absence of motion. The Journal of Neuroscience, 23(20), 7647–7658.Google Scholar
  32. Piccinini, G. (2008). Computation without representation. Philosophical Studies, 137(2), 205–241.CrossRefGoogle Scholar
  33. Price, C. J., & Friston, K. J. (2005). Functional ontologies for cognition: The systematic definition of structure and function. Cognitive Neuropsychology, 22(3), 262–275. doi: 10.1080/02643290442000095.CrossRefGoogle Scholar
  34. Rathkopf, C. A. (2013). Localization and intrinsic function. Philosophy of Science, 80(1), 1–21.CrossRefGoogle Scholar
  35. Rust, N. C., & Movshon, J. A. (2005). In praise of artifice. Nature Neuroscience, 8(12), 1647–1650. doi: 10.1038/nn1606.CrossRefGoogle Scholar
  36. Shadlen, M. N., Britten, K. H., Newsome, W. T., & Movshon, J. A. (1996). A computational analysis of the relationship between neuronal and behavioral responses to visual motion. The Journal of Neuroscience, 16(4), 1486–1510.Google Scholar
  37. Shagrir, O. (2001). Content, computation and externalism. Mind, 110(438), 369–400.CrossRefGoogle Scholar
  38. Simoncelli, E. P., & Heeger, D. J. (1998). A model of neuronal responses in visual area MT. Vision Research, 38(5), 743–761.CrossRefGoogle Scholar
  39. Snowden, R. J., Treue, S., Erickson, R. G., & Andersen, R. A. (1991). The response of area MT and V1 neurons to transparent motion. The Journal of Neuroscience, 11(9), 2768–2785.Google Scholar
  40. Uka, T., & DeAngelis, G. C. (2003). Contribution of middle temporal area to coarse depth discrimination: Comparison of neuronal and psychophysical sensitivity. The Journal of Neuroscience, 23(8), 3515–3530.Google Scholar
  41. Van Essen, D. C., & Gallant, J. L. (1994). Neural mechanisms of form and motion processing in the primate visual system. Neuron, 13(1), 1–10.CrossRefGoogle Scholar
  42. Zeki, S. M. (1978). Functional specialisation in the visual cortex of the rhesus monkey. Nature, 274(5670), 423–428.CrossRefGoogle Scholar

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© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Department of PhilosophyTulane UniversityNew OrleansUSA

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