Synthese

, 183:339

Explanation and description in computational neuroscience

Article

Abstract

The central aim of this paper is to shed light on the nature of explanation in computational neuroscience. I argue that computational models in this domain possess explanatory force to the extent that they describe the mechanisms responsible for producing a given phenomenon—paralleling how other mechanistic models explain. Conceiving computational explanation as a species of mechanistic explanation affords an important distinction between computational models that play genuine explanatory roles and those that merely provide accurate descriptions or predictions of phenomena. It also serves to clarify the pattern of model refinement and elaboration undertaken by computational neuroscientists.

Keywords

Explanation Mechanism Computational models Computational neuroscience 

References

  1. Aizawa K. (2010) Computation in cognitive science: It is not all about Turing-equivalent computation. Studies in History and Philosophy of Science Part A 41(3): 227–236CrossRefGoogle Scholar
  2. Andersen R. A., Essick G. K., Siegel R. M. (1985) Encoding of spatial location by posterior parietal neurons. Science 230: 450–458CrossRefGoogle Scholar
  3. Andersen R. A., Mountcastle V. B. (1983) The influence of the angle of gaze upon the excitability of light-sensitive neurons of the posterior parietal cortex. Journal of Neuroscience 3: 532–548Google Scholar
  4. Batterman R. (2002) Asymptotics and the role of minimal models. The British Journal for the Philosophy of Science 53(1): 21–38CrossRefGoogle Scholar
  5. Batterman R. (2009) Idealization and modeling. Synthese 169: 427–446CrossRefGoogle Scholar
  6. Bechtel W. (2002) Decomposing the brain: A long term pursuit. Brain and Mind 3: 229–242CrossRefGoogle Scholar
  7. Bechtel W. (2008) Mental mechanisms: Philosophical perspectives on cognitive neuroscience. Routledge, LondonGoogle Scholar
  8. Bechtel W., Abrahamsen A. (2005) Explanation: A mechanistic alternative. Studies in History and Philosophy of the Biological and Biomedical Sciences 36: 421–441CrossRefGoogle Scholar
  9. Bechtel W., Mundale J. (1999) Multiple realizability revisited: Linking cognitive and neural states. Philosophy of Science 66: 175–207CrossRefGoogle Scholar
  10. Bechtel W., Richardson R. C. (1993) Discovering complexity: Decomposition and localization as strategies in scientific research. Princeton University Press, Princeton, NJGoogle Scholar
  11. Bickle J. (1998) Psychoneural reduction: The new wave. Bradford/MIT Press, Cambridge, MAGoogle Scholar
  12. Bogen J. (2005) Regularities and causality; generalizations and causal explanations. Studies in History and Philosophy of Science Part C 36(2): 397–420CrossRefGoogle Scholar
  13. Bogen J. (2008) The Hodgkin–Huxley equations and the concrete model: Comments on Craver, Schaffner, and Weber. Philosophy of Science 75(5): 1034–1046CrossRefGoogle Scholar
  14. Brozović M., Abbott L. F., Andersen R. A. (2008) Mechanism of gain modulation at single neuron and network levels. Journal of Computational Neuroscience 25: 158–168CrossRefGoogle Scholar
  15. Bunge M. (1964) Phenomenological theories. In: Bunge M. (eds) The critical approach: In honor of Karl Popper. Free Press, New YorkGoogle Scholar
  16. Catterall W. A. (2000) From ionic currents to molecular mechanisms: The structure and function of voltage-gated sodium channels. Neuron 26(1): 13–25CrossRefGoogle Scholar
  17. Chance F. S., Abbott L. F., Reyes A. D. (2002) Gain modulation from background synaptic input. Neuron 35: 773–782CrossRefGoogle Scholar
  18. Chemero A., Silberstein M. (2008) After the philosophy of mind: Replacing scholasticism with science. Philosophy of Science 75: 1–27CrossRefGoogle Scholar
  19. Churchland P. S. (1986) Neurophilosophy. MIT Press, Cambridge, MAGoogle Scholar
  20. Churchland P. M. (2005) Functionalism at forty: A critical retrospective. Journal of Philosophy 102(1): 33–50Google Scholar
  21. Churchland P. S., Koch C., Sejnowski T. J. (1988) Computational neuroscience. Science 241: 1299–1306CrossRefGoogle Scholar
  22. Cole K. S., Moore J. W. (1960) Potassium ion current in the squid giant axon: Dynamic characteristic. Biophysical Journal 1: 1–14CrossRefGoogle Scholar
  23. Craver C. F. (2001) Role functions, mechanisms, and hierarchy. Philosophy of Science 68(1): 53–74CrossRefGoogle Scholar
  24. Craver C. F. (2006) When mechanistic models explain. Synthese 153: 355–376CrossRefGoogle Scholar
  25. Craver C. F. (2007) Explaining the brain. Oxford University Press, OxfordCrossRefGoogle Scholar
  26. Craver C. F. (2008) Physical law and mechanistic explanation in the Hodgkin and Huxley model of the action potential. Philosophy of Science 75: 1022–1033CrossRefGoogle Scholar
  27. Crick F. (1989) The recent excitement about neural networks. Nature 337: 129–132CrossRefGoogle Scholar
  28. Cummins R. (1975) Functional analysis. Journal of Philosophy 72: 741–765CrossRefGoogle Scholar
  29. Cummins R. (1983) The nature of psychological explanation. Bradford/MIT Press, Cambridge, MAGoogle Scholar
  30. Cummins R. (2000) “How does it work?” vs. “What are the laws?” Two conceptions of psychological explanation. In: Keil F., Wilson R. (eds) Explanation and cognition. MIT Press, Cambridge, MA, pp 117–145Google Scholar
  31. Dawson M. R. W. (1998) Understanding cognitive science. Wiley-Blackwell, OxfordGoogle Scholar
  32. Dayan P., Abbott L. F. (2001) Theoretical neuroscience: Computational and mathematical modeling of neural systems. MIT Press, Cambridge, MAGoogle Scholar
  33. Downes, S. M. (1992) The importance of models in theorizing: A deflationary semantic approach. In D. Hull, M. Forbes, & K. Okrulik (Eds.), Proceedings of the Philosophy of Science Association (Vol. 1, pp. 142–153). East Lansing.Google Scholar
  34. Doyle D. A., Cabral J., Pfuetzner R. A., Kuo A., Gulbis J. M., Cohen S. L., Chait B. T., MacKinnon R. (1998) The structure of the potassium channel: Molecular basis of K + conduction and selectivity. Science 280: 69–77CrossRefGoogle Scholar
  35. Dray W. H. (1993) Philosophy of history. (2nd ed.). Prentice Hall, Englewood, NJGoogle Scholar
  36. Dror I. E., Gallogly D. P. (1999) Computational analyses in cognitive neuroscience: In defense of biological implausibility. Psychonomic Bulletin & Review 6(2): 173–182CrossRefGoogle Scholar
  37. Einevoll G.T. (2006) Mathematical modeling of neural activity. In: Skjeltorp A., Belushkin A.V. (eds) Dynamics of complex interconnected systems: Networks and bioprocesses NATO Science Series II: Mathematics, Physics & Chemistry. Kluwer, AmsterdamGoogle Scholar
  38. Einevoll G. T., Heggelund P. (2000) Mathematical models for the spatial receptive-field organization of nonlagged X-cells in dorsal lateral geniculate nucleus of cat. Visual Neuroscience 17(6): 871–885CrossRefGoogle Scholar
  39. Einevoll G. T., Plesser H. E. (2002) Linear mechanistic models for the dorsal lateral geniculate nucleus of cat probed using drifting-grating stimuli. Network: Computation in Neural Systems 13: 503–530CrossRefGoogle Scholar
  40. Einevoll G. T., Plesser H. E. (2005) Responses of the difference-of-Gaussians model to circular drifting-grating patches. Visual Neuroscience 22(4): 437–446CrossRefGoogle Scholar
  41. Enroth-Cugell C., Robson J. G. (1966) The contrast sensitivity of retinal ganglion cells of the cat. Journal of Physiology 187(3): 517–552Google Scholar
  42. Ermentrout G. B., Terman D. H. (2010) Mathematical foundations of neuroscience. Springer, New YorkCrossRefGoogle Scholar
  43. Fodor J. A. (1974) Special sciences (Or: The disunity of science as a working hypothesis). Synthese 28: 97–115CrossRefGoogle Scholar
  44. Fodor J. A. (1975) The language of thought. Harvard University Press, Cambridge, MAGoogle Scholar
  45. Forber P. (2010) Confirmation and explaining how possible. Studies in the History and Philosophy of Science Part C: Studies in the History and Philosophy of Biological and Biomedical Sciences 41(1): 32–40CrossRefGoogle Scholar
  46. Haugeland J. (1978) The nature and plausibility of cognitivism. Behavioral and Brain Sciences 1: 215–226CrossRefGoogle Scholar
  47. Hempel C. G. (1965) Aspects of scientific explanation and other essays in the philosophy of science. Free Press, New YorkGoogle Scholar
  48. Hille B. (2001) Ion channels of excitable membranes. (3rd ed.). Sinauer, Sunderland, MAGoogle Scholar
  49. Hille B., Armstrong C. M., MacKinnon R. (1999) Ion channels: From idea to reality. Nature Medicine 5(10): 1105–1109CrossRefGoogle Scholar
  50. Hodgkin A. L., Huxley A. F. (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology 117: 500–544Google Scholar
  51. Johnson-Laird P. N. (1983) Mental models: Towards a cognitive science of language, inference and consciousness. Cambridge University Press, New YorkGoogle Scholar
  52. Kaplan, D. M. & Craver, C. F. (forthcoming): The explanatory force of dynamical and mathematical models in neuroscience: A mechanistic perspective. Philosophy of Science.Google Scholar
  53. Kuffler S. (1953) Discharge patterns and functional organization of mammalian retina. Journal of Neurophysiology 16(1): 37–68Google Scholar
  54. Machamer P., Darden L., Craver C. F. (2000) Thinking about mechanisms. Philosophy of Science 67: 1–25CrossRefGoogle Scholar
  55. MacKinnon R. (2004) Nobel lecture: Potassium channels and the atomic basis of selective ion conduction. Bioscience Reports 24(2): 75–100CrossRefGoogle Scholar
  56. Marr D. (1982) Vision: A computational investigation into the human representation and processing of visual information. W.H.Freeman & Co. Ltd, San FranciscoGoogle Scholar
  57. Marr D., Hildreth E. (1980) Theory of edge detection. Proceedings of the Royal Society of London Series B, Biological Sciences 207(1167): 187–217CrossRefGoogle Scholar
  58. Marr D., Ullman S., Poggio T. (1979) Bandpass channels, zero-crossings, and early visual information processing. Journal of the Optical Society of America 69(6): 914–916CrossRefGoogle Scholar
  59. Mastronarde D. N. (1992) Nonlagged relay cells and interneurons in the cat lateral geniculate nucleus: Receptive-field properties and retinal inputs. Visual Neuroscience 8(5): 407–441CrossRefGoogle Scholar
  60. Mauk M. D. (2000) The potential effectiveness of simulations versus phenomenological models. Nature Neuroscience 3(7): 649–651CrossRefGoogle Scholar
  61. Mazzoni P., Andersen R. A., Jordan M. I. (1991) A more biologically plausible learning rule applied to a network model of cortical area 7a. Cerebral Cortex 1: 293–307CrossRefGoogle Scholar
  62. Mel B. W. (1993) Synaptic integration in an excitable dendritic tree. Journal of Neurophysiology 70: 1086–1101Google Scholar
  63. Neher E. (1992) Nobel lecture: Ion channels for communication between and within cells. Neuron 8(4): 605–612CrossRefGoogle Scholar
  64. Newell A., Simon H. A. (1976) Computer science as an empirical enquiry: Symbols and search. Communications of the ACM 19: 113–126CrossRefGoogle Scholar
  65. Piccinini G. (2006) Computational explanation in neuroscience. Synthese 153: 343–353CrossRefGoogle Scholar
  66. Piccinini G. (2007) Computing mechanisms. Philosophy of Science 74: 501–526CrossRefGoogle Scholar
  67. Piccinini G. (2010) The mind as neural software? Revisiting functionalism, computationalism, and computational functionalism. Philosophy and Phenomenological Research 81(2): 269–311CrossRefGoogle Scholar
  68. Piccinini, G., & Craver, C. F. (2011). Integrating psychology and neuroscience: Functional analyses as mechanism sketches. Synthese. doi:10.1007/s11229-011-9898-4.
  69. Pouget A., Sejnowski T. J. (1997) Spatial transformations in the parietal cortex using basis functions. Journal of Cognitive Neuroscience 9(2): 222–237CrossRefGoogle Scholar
  70. Pouget A., Snyder L. H. (2000) Computational approaches to sensorimotor transformations. Nature Neuroscience 3(Suppl): 1192–1198CrossRefGoogle Scholar
  71. Putnam, H. (1967). The nature of mental states (Reprinted from Mind, language, and reality, by Putnam, Ed., 1975, Cambridge: Cambridge University Press.)Google Scholar
  72. Pylyshyn Z. W. (1984) Computation and cognition. MIT Press, Cambridge, MAGoogle Scholar
  73. Revonsuo A. (2001) On the nature of explanation in the neurosciences. In: Machamer P., Grush R., McLaughlin P. (eds) Theory and method in the neurosciences. University of Pittsburgh Press, Pittsburgh, PA, pp 45–69Google Scholar
  74. Rodieck R. W. (1965) Quantitative analysis of cat retinal ganglion cell response to visual stimuli. Vision Research 5(11): 583–601CrossRefGoogle Scholar
  75. Rusanen A.M., Lappi O. (2007) The limits of mechanistic explanation in neurocognitive sciences. In: Vosniadou S., Kayser D., Protopapas A. (eds) Proceedings of the European cognitive science conference. Francis and Taylor, LondonGoogle Scholar
  76. Salinas E., Abbott L. F. (1996) A model of multiplicative neural responses in parietal cortex. Proceedings of the National Academy of Sciences 90: 11956–11961CrossRefGoogle Scholar
  77. Salinas E., Thier P. (2000) Gain modulation: A major computational principle of the central nervous system. Neuron 27: 15–21CrossRefGoogle Scholar
  78. Salmon W. (1984) Scientific explanation and the causal structure of the world. Princeton University Press, Princeton, NJGoogle Scholar
  79. Salmon W. (1989) Four decades of scientific explanation. University of Pittsburg Press, Pittsburg, PAGoogle Scholar
  80. Schaffner K. F. (2008) Theories, models, and equations in biology: The heuristic search for emergent simplifications in neurobiology. Philosophy of Science 75(5): 1008–1021CrossRefGoogle Scholar
  81. Sejnowski T. J. (2001) Computational neuroscience. In: Smelser N. J., Baltes P. B. (eds) Encyclopedia of the social and behavioral sciences. Elsevier, Oxford, pp 2460–2465CrossRefGoogle Scholar
  82. Shadmehr R., Wise S. (2005) Computational neurobiology of reaching and pointing: A foundation for motor learning. MIT Press, Cambridge, MAGoogle Scholar
  83. Shagrir O. (1998) Multiple realization, computation and the taxonomy of psychological states. Synthese 114: 445–461CrossRefGoogle Scholar
  84. Shagrir O. (2006) Why we view the brain as a computer. Synthese 153: 393–416CrossRefGoogle Scholar
  85. Shapiro L. A. (2000) Multiple realizations. Journal of Philosophy 97: 635–654CrossRefGoogle Scholar
  86. Smolensky P. (1988) On the proper treatment of connectionism. Behavioral and Brain Sciences 11: 1–23CrossRefGoogle Scholar
  87. Von Eckart B., Poland J. S. (2004) Mechanism and explanation in cognitive neuroscience. Philosophy of Science 71: 972–984CrossRefGoogle Scholar
  88. Weber M. (2008) Causes without mechanisms: Experimental regularities, physical laws, and neuroscientific explanation. Philosophy of Science 75(5): 995–1007CrossRefGoogle Scholar
  89. Weisberg M. (2007) Three kinds of idealization. Journal of Philosophy 104(12): 639–659Google Scholar
  90. Woodward J. (2003) Making things happen. Oxford University Press, New YorkGoogle Scholar
  91. Zipser D., Andersen R. A. (1988) A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature 331: 679–684CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Department of Anatomy and NeurobiologyWashington University School of MedicineSaint LouisUSA

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