PT  - JOURNAL ARTICLE
AU  - Daniel Levenstein
AU  - Veronica A. Alvarez
AU  - Asohan Amarasingham
AU  - Habiba Azab
AU  - Zhe S. Chen
AU  - Richard C. Gerkin
AU  - Andrea Hasenstaub
AU  - Ramakrishnan Iyer
AU  - Renaud B. Jolivet
AU  - Sarah Marzen
AU  - Joseph D. Monaco
AU  - Astrid A. Prinz
AU  - Salma Quraishi
AU  - Fidel Santamaria
AU  - Sabyasachi Shivkumar
AU  - Matthew F. Singh
AU  - Roger Traub
AU  - Farzan Nadim
AU  - Horacio G. Rotstein
AU  - A. David Redish
TI  - On the Role of Theory and Modeling in Neuroscience
AID  - 10.1523/JNEUROSCI.1179-22.2022
DP  - 2023 Feb 15
TA  - The Journal of Neuroscience
PG  - 1074--1088
VI  - 43
IP  - 7
4099  - http://www.jneurosci.org/content/43/7/1074.short
4100  - http://www.jneurosci.org/content/43/7/1074.full
SO  - J. Neurosci.2023 Feb 15; 43
AB  - 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.