Toward Reference-Grade neuron models

Computational neuroscience increasingly produces detailed, data-driven neuron models, yet compressing biological variability and measurement uncertainty into single-parameter sets can blur the line between data-supported claims and modeling choices. Here, a reference-grade culture is proposed, adapting the traceable standards, explicit uncertainty budgets, and ensemble predictions of analytical chemistry to computational neuroscience. As neuron models grow more detailed, the uncertainty behind them often collapses into single best-fit parameters, obscuring what the data support. This Comment argues for reference-grade modeling standards, with traceable provenance, declared uncertainty, and ensemble predictions, adapted from the hierarchy of standards used in analytical chemistry.