On the role of theory and modeling in neuroscience

TL;DR

This paper discusses the importance of integrating theory and modeling at appropriate levels of abstraction to advance neuroscience, emphasizing pragmatic approaches and methodological strategies for effective scientific practice.

Contribution

It advocates for a pragmatic perspective that combines descriptive, mechanistic, and normative approaches to improve modeling and analysis in neuroscience.

Findings

A multi-level approach enhances understanding of neural phenomena.

Selecting appropriate abstraction levels improves model-data integration.

Using models as experimental tools advances neuroscientific research.

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 across a wide range of scales and often requires consideration of these phenomena 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 approaches 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.