Computational modeling of neural circuits is increasingly essential to systems neuroscience, as the ability of researchers to construct sound working hypotheses on an ad hoc basis is becoming increasingly challenging as the complexity and diversity of datasets increase.  Models serve as proofs of concept, tests of sufficiency, and as quantitative embodiments of working hypotheses, and are increasingly essential tools for understanding and interpreting complex data sets. In the olfactory system, models have played a particularly prominent role in framing contemporary theories and presenting novel hypotheses, a role that will only grow as the complexity and intricacy of experimental data continue to increase.  Theoretical modeling in this context is not an alternative to experimental research, but an essential tool for its assessment and optimization.

We build computational models at various levels of analysis, from highly detailed modeling of the biophysical properties of neurons and networks to simpler models intended to distill and analyze particular attributes of a circuit or even to serve as proof of concept.  Each of these approaches has its place.