Realistic Neuronal Modeling Revisited

Biophysically detailed neuron models are essential, but they are usually built from a single best-fit parameter set, obscuring a fundamental problem. Much of the model is set by the modeler's choices, not by the data, and many different parameter sets fit the data equally well. Thus, a single best-fit model represents only one point in a much larger space of models the data allow. For claims about robustness, transferability across cell types or species, natural variability, or prediction, we argue that a family of models is needed, one that openly carries its uncertainty. We review recent advances that may make this practical: a clearer understanding of parameter degeneracy; new single-cell methods that measure a neuron's activity, shape, and gene expression together; and new tools that estimate uncertainty, identify the parameters that matter, and accelerate fitting. Finally, we propose sharing reusable model components, qualified reference components, that come with their uncertainty, tested limits, and origin, so that larger brain models can stay trustworthy.