Abstract Realistic single-neuron modeling organizes and clarifies physiological hypotheses. It extends the experimenter's intuition and leads to testable predictions. A powerful new algorithm, several user-friendly software packages and the advent of fast, cheap computers have together made this tool accessible to a broad range of neurobiologists. Equally dramatic advances in experimental findings have increased the level of sophistication of the models. Here we provide a guide to single-neuron modeling, illustrate its power with a few examples and speculate on possible future directions for this rapidly growing field.