Biology & Philosophy

, Volume 30, Issue 6, pp 757–786 | Cite as

Moving parts: the natural alliance between dynamical and mechanistic modeling approaches

  • David Michael KaplanEmail author


Recently, it has been provocatively claimed that dynamical modeling approaches signal the emergence of a new explanatory framework distinct from that of mechanistic explanation. This paper rejects this proposal and argues that dynamical explanations are fully compatible with, even naturally construed as, instances of mechanistic explanations. Specifically, it is argued that the mathematical framework of dynamics provides a powerful descriptive scheme for revealing temporal features of activities in mechanisms and plays an explanatory role to the extent it is deployed for this purpose. It is also suggested that more attention should be paid to the distinctive methodological contributions of the dynamical framework including its usefulness as a heuristic for mechanism discovery and hypothesis generation in contemporary neuroscience and biology.


Mechanism Dynamics Explanation Models Neuroscience 


  1. Abraham R, Shaw CD (1992) Dynamics: the geometry of behavior. Addison-Wesley, Redwood CityGoogle Scholar
  2. Abrahamsen A, Bechtel W (2006) Phenomena and mechanisms: putting the symbolic, connectionist, and dynamical systems debate in broader perspective. In Stainton R (ed) Contemporary debates in cognitive science. Basil Blackwell, Oxford  Google Scholar
  3. Ahrens MB, Li JM, Orger MB, Robson DN, Schier AF, Engert F, Portugues R (2012) Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485(7399):471–477Google Scholar
  4. Amit DJ (1992) Modeling brain function: the world of attractor neural networks. Cambridge University Press, CambridgeGoogle Scholar
  5. Bechtel W (1998a) Dynamicists versus computationalists: whither mechanists? Behav Brain Sci 21(05):629CrossRefGoogle Scholar
  6. Bechtel W (1998b) Representations and cognitive explanations: assessing the dynamicist’s challenge in cognitive science. Cogn Sci 22(3):295–318CrossRefGoogle Scholar
  7. Bechtel W (2008) Mental mechanisms: philosophical perspectives on cognitive neuroscience. Lawrence Erlbaum, Routledge, MahwahGoogle Scholar
  8. Bechtel W, Abrahamsen A (2002) Connectionism and the mind. Parallel processing, dynamics, and evolution in networks. Blackwell, OxfordGoogle Scholar
  9. Bechtel W, Abrahamsen A (2010) Dynamic mechanistic explanation: computational modeling of circadian rhythms as an exemplar for cognitive science. Stud Hist Philos Sci Part A 41(3):321–333CrossRefGoogle Scholar
  10. Bechtel W, Richardson RC (1993/2010) Discovering complexity: decomposition and localization as strategies in scientific research. Reprinted MIT Press, CambridgeGoogle Scholar
  11. Beek PJ, Peper CE, Daffertshofer A (2002) Modeling Rhythmic Interlimb Coordination: beyond the Haken–Kelso–Bunz Model. Brain Cogn 48(1):149–165CrossRefGoogle Scholar
  12. Beer RD (2000) Dynamical approaches to cognitive science. Trends Cogn Sci 4(3):91–99CrossRefGoogle Scholar
  13. Bogen J (2005) Regularities and causality; generalizations and causal explanations. Stud Hist Philos Sci Part C 36(2):397–420CrossRefGoogle Scholar
  14. Bressler SL, Kelso JAS (2001) Cortical coordination dynamics and cognition. Trends Cogn Sci 5(1):26–36CrossRefGoogle Scholar
  15. Brown EN, Kass RE, Mitra PP (2004) Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat Neurosci 7(5):456–461CrossRefGoogle Scholar
  16. Chemero A (2011) Radical embodied cognitive science. MIT Press, CambridgeGoogle Scholar
  17. Chemero A, Silberstein M (2008) After the philosophy of mind: replacing scholasticism with science. Philos Sci 75(1):1–27CrossRefGoogle Scholar
  18. Choe S (2002) Potassium channel structures. Nat Rev Neurosci 3(2):115–121CrossRefGoogle Scholar
  19. Churchland MM, Yu BM, Sahani M, Shenoy KV (2007) Techniques for extracting single-trial activity patterns from large-scale neural recordings. Curr Opin Neurobiol 17(5):609–618CrossRefGoogle Scholar
  20. Churchland MM, Cunningham JP, Kaufman MT, Foster JD, Nuyujukian P, Ryu SI, Shenoy KV (2012) Neural population dynamics during reaching. Nature 487(7405):51–56Google Scholar
  21. Clark A (1997) Being there: putting brain, body, and world together again. MIT press, CambridgeGoogle Scholar
  22. Compte A, Brunel N, Goldman-Rakic PS, Wang XJ (2000) Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cerebral Cortex (New York, N.Y.: 1991) 10(9):910–923CrossRefGoogle Scholar
  23. Craver CF (2006) When mechanistic models explain. Synthese 153(3):355–376CrossRefGoogle Scholar
  24. Craver CF (2007) Explaining the brain: mechanisms and the mosaic unity of neuroscience. Oxford University Press, New YorkCrossRefGoogle Scholar
  25. Craver CF (2008) Physical law and mechanistic explanation in the Hodgkin and Huxley model of the action potential. Philos Sci 75(5):1022–1033CrossRefGoogle Scholar
  26. Craver CF, Darden L (2013) In search of biological mechanisms: discoveries across the life sciences. University of Chicago Press, ChicagoCrossRefGoogle Scholar
  27. Cunningham JP, Yu BM (2014) Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17(11):1500–1509CrossRefGoogle Scholar
  28. Dayan P, Abbott LF (2001) Theoretical neuroscience: computational and mathematical modeling of neural systems. MIT Press, CambridgeGoogle Scholar
  29. Doyle DA, Morais Cabral J, Pfuetzner RA, Kuo A, Gulbis JM, Cohen SL, Chait BT, MacKinnon R (1998) The structure of the potassium channel: molecular basis of K + conduction and selectivity. Science 280(5360):69–77CrossRefGoogle Scholar
  30. Dupré J (2013) Living Causes. Aristot Soc Suppl Vol 87(1):19–37Google Scholar
  31. Earman J, Roberts JT (1999) Ceteris paribus, there is no problem of provisos. Synthese 118(3):439–478CrossRefGoogle Scholar
  32. Earman J, Roberts JT, Smith S (2002) Ceteris paribus lost. Erkenntnis 57(3):281–301CrossRefGoogle Scholar
  33. FitzHugh R (1955) Mathematical models of threshold phenomena in the nerve membrane. Bull Math Biophys 17:257–278CrossRefGoogle Scholar
  34. Fodor JA (1991) You can fool some of the people all of the time, everything else being equal; hedged laws and psychological explanations. Mind 100(397):19–34CrossRefGoogle Scholar
  35. Fuchs A (2013) Nonlinear dynamics in complex systems: theory and applications for the life-, neuro- and natural sciences. Springer, BerlinGoogle Scholar
  36. Gervais R, Weber E (2011) The covering law model applied to dynamical cognitive science: a comment on Joel Walmsley. Minds Mach 21(1):33–39CrossRefGoogle Scholar
  37. Haken H, Kelso JA, Bunz H (1985) A theoretical model of phase transitions in human hand movements. Biol Cybern 51(5):347–356CrossRefGoogle Scholar
  38. Hempel CG (1965) Aspects of scientific explanation. The Free Press, New YorkGoogle Scholar
  39. Hille B (2001) Ion channels of excitable membranes, 3rd edn. Sinauer Associates, SunderlandGoogle Scholar
  40. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117(4):500–544CrossRefGoogle Scholar
  41. Izhikevich EM (2007) Dynamical systems in neuroscience. MIT press, CambridgeGoogle Scholar
  42. Jantzen KJ, Steinberg FL, Scott Kelso JAS (2009) Coordination dynamics of large-scale neural circuitry underlying rhythmic sensorimotor behavior. J Cogn Neurosci 21(12):2420–2433CrossRefGoogle Scholar
  43. Jeffrey KJ (2011) Place Cells, grid cells, attractors, and remapping. Neural Plast 2011:182602Google Scholar
  44. Jirsa VK, Fuchs A, Kelso JAS (1998) Connecting cortical and behavioral dynamics: bimanual coordination. Neural Comput 10(8):2019–2045CrossRefGoogle Scholar
  45. Kaplan DM (2011) Explanation and description in computational neuroscience. Synthese 183(3):339–373CrossRefGoogle Scholar
  46. Kaplan DM, Bechtel W (2011) Dynamical models: an alternative or complement to mechanistic explanations? Top Cogn Sci 3(2):438–444CrossRefGoogle Scholar
  47. Kaplan DM, Craver CF (2011) The explanatory force of dynamical and mathematical models in neuroscience: a mechanistic perspective. Philos Sci 78(4):601–627CrossRefGoogle Scholar
  48. Kauffman SA (1970) Articulation of parts explanation in biology and the rational search for them. Philos Sci 1970:257–272Google Scholar
  49. Kelso JAS (1995) Dynamic patterns: the self-organization of brain and behavior. MIT press, CambridgeGoogle Scholar
  50. Kelso JAS, Fuchs A, Lancaster R, Holroyd T, Cheyne D, Weinberg H (1998) Dynamic cortical activity in the human brain reveals motor equivalence. Nature 392(6678):814–818CrossRefGoogle Scholar
  51. Lappi O, Rusanen A-M (2011) Turing machines and causal mechanisms in cognitive science. In: McKay Illari P, Russo F, Williamson J (eds) Causality in the sciences. Oxford University Press, Oxford, pp 224–239Google Scholar
  52. Laurent G (2002) Olfactory network dynamics and the coding of multidimensional signals. Nat Rev Neurosci 3(11):884–895CrossRefGoogle Scholar
  53. Levy A (2014) What was Hodgkin and Huxley’s achievement? Br J Philos Sci 65(3):469–492CrossRefGoogle Scholar
  54. Levy A, Bechtel W (2013) Abstraction and the organization of mechanisms. Philos Sci 80(2):241–261CrossRefGoogle Scholar
  55. Machamer P, Darden L, Craver CF (2000) Thinking about mechanisms. Philos Sci 67(1):1–25Google Scholar
  56. Mascagni MV, Sherman AS (1989) Numerical methods for neuronal modeling. In: Segev I, Koch C (eds) Methods in neuronal modeling. The MIT Press, Cambridge, pp 569–606Google Scholar
  57. McCormick DA, Shu Y, Yu Y (2007) Neurophysiology: Hodgkin and Huxley model–still standing? Nature 445(7123):E1–E2 (discussion E2–3) CrossRefGoogle Scholar
  58. Naundorf B, Wolf F, Volgushev M (2006) Unique features of action potential initiation in cortical neurons. Nature 440(7087):1060–1063CrossRefGoogle Scholar
  59. Naundorf B, Wolf F, Volgushev M (2007) Neurophysiology: Hodgkin and Huxley model-still standing?(Reply). Nature 445:2–3CrossRefGoogle Scholar
  60. Oullier O, de Guzman GC, Jantzen KJ, Lagarde J, Kelso JAS (2008) Social coordination dynamics: measuring human bonding. Social Neurosci 3(2):178–192CrossRefGoogle Scholar
  61. Peper CLE, Ridderikhoff A, Daffertshofer A, Beek PJ (2004) Explanatory limitations of the HKB model: incentives for a two-tiered model of rhythmic interlimb coordination. Hum Movement Sci 23(5):673–697CrossRefGoogle Scholar
  62. Pietroski P, Rey G (1995) When other things aren’t equal: saving ceteris paribus laws from vacuity. Br J Philos Sci 46(1):81–110CrossRefGoogle Scholar
  63. Port RF, Van Gelder T (1995) Mind as motion: explorations in the dynamics of cognition. MIT press, CambridgeGoogle Scholar
  64. Reutlinger A, Unterhuber M (2014) Thinking about non-universal laws. Erkenntnis 79(10):1703–1713CrossRefGoogle Scholar
  65. Rosenbaum DA (1998) Is dynamical systems modeling just curve fitting? Mot Control 2(2):101–104Google Scholar
  66. Rusanen A-M, Lappi O (2007) The limits of mechanistic explanation in neurocognitive sciences. In: Proceedings of the european cognitive science conferenceGoogle Scholar
  67. Salmon WC (1984) Scientific explanation and the causal structure of the world. Princeton University Press, PrincetonGoogle Scholar
  68. Salmon WC (1989/2006) Four decades of scientific explanation. Reprinted University of Pittsburgh Press, PittsburghGoogle Scholar
  69. Schmidt RC, Carello C, Turvey MT (1990) Phase transitions and critical fluctuations in the visual coordination of rhythmic movements between people. J Exp Psychol Hum Percep Perform 16(2):227–247CrossRefGoogle Scholar
  70. Scholz JP, Kelso JAS (1989) A quantitative approach to understanding the formation and change of coordinated movement patterns. J Motor Behav 21(2):122–144CrossRefGoogle Scholar
  71. Schöner G, Kelso JAS (1988) Dynamic pattern generation in behavioral and neural systems. Science 239(4847):1513–1520CrossRefGoogle Scholar
  72. Shenoy KV, Sahani M, Churchland MM (2013) Cortical control of arm movements: a dynamical systems perspective. Ann Rev Neurosci 36:337–359CrossRefGoogle Scholar
  73. Sporns O (2011) Networks of the Brain. MIT Press, CambridgeGoogle Scholar
  74. Stepp N, Chemero A, Turvey MT (2011) Philosophy for the rest of cognitive science. Top Cogn Sci 3(2):425–437   CrossRefGoogle Scholar
  75. Stevenson IH, Körding KP (2011) How advances in neural recording affect data analysis. Nat Neurosci 14(2):139–142CrossRefGoogle Scholar
  76. Strogatz SH (2014) Nonlinear dynamics and chaos: with application to physics, biology, chemistry, and engineering, 2nd edn. Westview Press, CambridgeGoogle Scholar
  77. Swinnen SP (2002) Intermanual coordination: from behavioural principles to neural-network interactions. Nat Rev Neurosci 3(5):348–359CrossRefGoogle Scholar
  78. Tank DW, Hopfield JJ (1987) Collective computation in neuronlike circuits. Sci Am 257(6):104–114CrossRefGoogle Scholar
  79. van Gelder T (1995) What might cognition be, if not computation? J Philos 92(7):345–381CrossRefGoogle Scholar
  80. van Gelder T (1998) The dynamical hypothesis in cognitive science. Behav Brain Sci 21(05):615–628Google Scholar
  81. van Gelder T, Port RF (1995) It’s about time: an overview of the dynamical approach to cognition. In: Port, van Gelder (eds) Explorations in the dynamics of cognition: mind as motion. MIT Press, Cambridge, pp 1–43Google Scholar
  82. Von Eckardt B, Poland JS (2004) Mechanism and explanation in cognitive neuroscience. Philos Sci 71(5):972–984CrossRefGoogle Scholar
  83. Walmsley J (2008) Explanation in dynamical cognitive science. Minds Mach 18(3):331–348CrossRefGoogle Scholar
  84. Wang X-J (2009) Attractor network models. In: Squire LR (ed) Encyclopedia of neuroscience, vol 1. Academic Press, Oxford, pp 667–679CrossRefGoogle Scholar
  85. Weber M (2008) Causes without mechanisms: experimental regularities, physical laws, and neuroscientific explanation. Philos Sci 75(5):995–1007CrossRefGoogle Scholar
  86. Weiskopf DA (2011) Models and mechanisms in psychological explanation. Synthese 183(3):313–338CrossRefGoogle Scholar
  87. Wills TJ, Lever C, Cacucci F, Burgess N, O’Keefe J (2005) Attractor dynamics in the hippocampal representation of the local environment. Science 308(5723):873–876CrossRefGoogle Scholar
  88. Wong K-F, Wang X-J (2006) A recurrent network mechanism of time integration in perceptual decisions. J Neurosci 26(4):1314–1328CrossRefGoogle Scholar
  89. Woodward J (2000) Explanation and invariance in the special sciences. Br J Philos Sci 51(2):197–254CrossRefGoogle Scholar
  90. Woodward J (2002) There is no such thing as a ceteris paribus law. Erkenntnis 57(3):303–328CrossRefGoogle Scholar
  91. Woodward J (2003) Making things happen: a theory of causal explanation. Oxford University Press, OxfordGoogle Scholar
  92. Woodward J (2013) Mechanistic explanation: its scope and limits. Aristot Soc Suppl 87:39–65CrossRefGoogle Scholar
  93. Yu BM, Afshar A, Santhanam G, Ryu SI, Sheynoy KV, Sahani M (2006) Extracting dynamical structure embedded in neural activity. Adv Neural Inform Process Syst 18:1545–1552Google Scholar
  94. Zednik C (2011) The nature of dynamical explanation. Philos Sci 78(2):238–263CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Department of Cognitive Science, ARC Centre of Excellence in Cognition and its Disorders (CCD), Perception in Action Research Centre (PARC)Macquarie UniversitySydneyAustralia

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