On the Role of Theory and Modeling in Neuroscience

• Type of Material: Article
• Authors: Daniel Levenstein, Institut-Hôpital Neurologique de Montréal; Veronica A. Alvarez, National Institute on Alcohol Abuse and Alcoholism (NIAAA); Asohan Amarasingham, City College of New York; Habiba Azab, University of Minnesota; Zhe S. Chen, New York University; Richard C. Gerkin, School of Life Sciences; Andrea Hasenstaub, University of California San Francisco; Ramakrishnan Iyer, Allen Institute for Brain Science; Renaud B. Jolivet, Universiteit Maastricht; Sarah Marzen, Keck Graduate Institute; Joseph D. Monaco, Johns Hopkins School of Medicine; Astrid Prinz, Emory University; Salma Quraishi, University of Texas San Antonio; Fidel Santamaria, University of Texas San Antonio; Sabyasachi Shivkumar, University of Rochester; Matthew F. Singh, Washington University in St. Louis; Roger Traub, IBM Thomas J. Watson Research Center; Farzan Nadim, New Jersey Institute of Technology; Horacio G. Rotstein, New Jersey Institute of Technology; A. David Redish, University of Minnesota Twin Cities
• Language: English
• Date: 2023-02-15
• Publisher: Society for Neuroscience
• Publication Version: Final Published Version
• Copyright Statement: © 2023 the authors
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1523/JNEUROSCI.1179-22.2022
• Title of Journal or Parent Work: Journal of Neuroscience
• Volume: 43
• Issue: 7
• Start Page: 1074
• End Page: 1088
• Grant/Funding Information: This paper is the result of discussions as part of the workshop “Theoretical and Future Theoretical Frameworks in Neuroscience” (San Antonio, Feb 4-8, 2019) supported by the National Science Foundation Grants DBI-1820631 to H.G.R. and IOS-1516648 to F.S. This work was supported by National Institutes of Health Grant T90DA043219 and the Samuel J. and Joan B. Williamson Fellowship to D.L.; IRP-National Institutes of Health ZIA-AA000421 and DDIR Innovation Award, National Institutes of Health to V.A.A.; MH118928 to Z.S.C.; National Institute of Neurological Disorders and Stroke 1U19NS112953, National Institute on Deafness and Other Communication Disorders 1R01DC018455, National Institute of Mental Health 1R01MH106674, and NIBIB 1R01EB021711 to R.C.G.; National Institute on Deafness and Other Communication Disorders R01DC014101 and Hearing Research Incorporated, Sandler Foundation to A.H.; H2020 GAMMA-MRI (964644) and H2020 IN-FET (862882) to R.B.J.; National Institute of Neurological Disorders and Stroke 1R03NS109923 and National Science Foundation/NCS-FO 1835279 to J.D.M.; National Institute of Mental Health-NIBIB BRAIN Theories 1R01EB026939 to F.S.; IBM Exploratory Research Councils to R.T.; DOD ARO W911F-15-1-0426 to A.A.; National Science Foundation CRCNS-DMS-1608077 and National Science Foundation-IOS 2002863 to H.G.R.; National Institutes of Health MH060605 to F.N.; and National Institutes of Health MH080318, MH119569, and MH112688 to A.D.R. The authors further acknowledge the University of Texas at San Antonio Neuroscience Institute and the New Jersey Institute of Technology Department of Biological Sciences and Institute for Brain and Neuroscience Research for technical support in the organization of the workshop, as well as all of the participants in the workshop.
• Keywords: Neurosciences; Biophysics
• Research Categories: Biophysics, General; Biology, Neuroscience

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.