On the Role of Theory and Modeling in Neuroscience
Daniel Levenstein, Veronica A. Alvarez, Asohan Amarasingham, Habiba Azab, Zhe Chen, Richard C. Gerkin, Andrea R. Hasenstaub, Ramakrishnan Iyer, Renaud Jolivet, Sarah Marzen, Joseph D. Monaco, Astrid A. Prinz, Salma Quraishi, Fidel Santamarı́a, Sabyasachi Shivkumar, Matthew F. Singh, Roger D. Traub, Farzan Nadim, Horacio G. Rotstein, A. David Redish
Abstract
In recent years, the field of neuroscience has gone through rapid experimental advances and a significant increase in the use of quantitative and computational methods. This growth has created a need for clearer analyses of the theory and modeling approaches used in the field. This issue is particularly complex in neuroscience because the field studies phenomena that cross a wide range of scales and often require consideration at varying degrees of abstraction, from precise biophysical interactions to the computations they implement. We argue that a pragmatic perspective of science, in which descriptive, mechanistic, and normative models and theories each play a distinct role in defining and bridging levels of abstraction, will facilitate neuroscientific practice. This analysis leads to methodological suggestions, including selecting a level of abstraction that is appropriate for a given problem, identifying transfer functions to connect models and data, and the use of models themselves as a form of experiment.