Neuron models are typically built by measuring individually for each membrane conductance its parameters (e.g., half-maximal voltages) and maximal conductance value (g_max). However, neurons have extended morphologies with non-uniform conductance distributions whereas models generally contain at most a few compartments. Both the original conductance measurements and the models therefore unavoidably contain error due to the electrical filtering of neurons and the differential placement of conductances on them. Model parameters (typically g_max's) are therefore generally altered by hand or brute force to match model and neuron activity. We propose an alternative method in which complicated, rapidly changing driving input is used to optimize model parameters. This method also ensures that neuron and model dynamics match across a wide dynamic range, a test not performed in most modeling. We tested this concept using leech heartbeat and generic tonically firing models and lobster stomatogastric and generic bursting models as targets and g_max's as optimized parameters. In all four cases optimization solutions excellently matched target activity. Complicated, wide dynamic range driving thus appears to be an excellent method to characterize neuron properties in detail and to build highly accurate models. In these completely defined targets, the method found each target's 8-13 g_max values with high accuracy, and may therefore also provide an alternative, functionally-based, method of defining neuron g_max values. The method uses only standard experimental and computational techniques, could be easily extended to optimize conductance parameters other than g_max, and should be readily applicable to real neurons.