  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.
