Structures, dynamics and mechanisms in neuroscience: an integrative account
- 50 Downloads
Proponents of mechanistic explanations have recently proclaimed that all explanations in the neurosciences appeal to mechanisms. The purpose of the paper is to critically assess this statement and to develop an integrative account that connects a large range of both mechanistic and dynamical explanations. I develop and defend four theses about the relationship between dynamical and mechanistic explanations: that dynamical explanations are structurally grounded, that they are multiply realizable, possess realizing mechanisms and provide a powerful top-down heuristic. Four examples shall support my points: the harmonic oscillator, the Haken–Kelso–Bunz model of bimanual coordination, the Watt governor and the Gierer–Meinhardt model of biological pattern formation. I also develop the picture of “horizontal” and “vertical” directions of explanations to illustrate the different perspectives of the dynamical and mechanistic approach as well as their potential integration by means of intersection points.
KeywordsDynamical explanations Mechanisms Structures Multi-realizability Generalizability Harmonic oscillator HKB model Watt governor Gierer–Meinhardt model Horizontal versus vertical explanations
Many thanks to Carlos Zednik and two anonymous reviewers for valuable inputs and comments that helped to improve the paper.
- Brauer, F., & Kribs, C. (2016). Dynamical systems for biological modeling: An introduction. Boca Raton, FL: CRC Press.Google Scholar
- Craver, C. & Kaplan, D. M. (2011). Towards a mechanistic philosophy of neuroscience: A Mechanistic Approach. In: S. French, & J. Saatsi (eds.) The continuum companion to the philosophy of science, Continuum.Google Scholar
- Felline, L. (2015). Mechanisms meet structural explanation. Synthese. https://doi.org/10.1007/s11229-015-0746-9.
- Izhikevich, E. M. (2007). Dynamical systems in neuroscience: The geometry of excitability and bursting. Cambridge, MA: MIT Press.Google Scholar
- Lyre, H. (2009). The “Multirealization” of multiple realizability. In A. Hieke & H. Leitgeb (Eds.), Reduction–abstraction–analysis (pp. 79–94). Frankfurt: Ontos.Google Scholar
- Meinhardt, H. (1982). Models of biological pattern formation. London: Academic Press.Google Scholar
- Schuster, P. (2011). Physical principles of evolution. In H. Meyer-Ortmanns & S. Thurner (Eds.), Principles of evolution. Berlin: Springer.Google Scholar