We apply an adaptive approach to optimal experimental design in the context of estimating the unknown parameters of a model of a neuron's response. We present an algorithm to choose the optimal (most informative) stimulus on each trial; this algorithm can be implemented efficiently even for high-dimensional stimulus and parameter spaces (in particular, no high-dimensional numerical optimizations or integrations are required). Our simulation results show that model parameters can be estimated much more efficiently using this adaptive algorithm rather than random sampling. We also show that this adaptive approach leads to superior performance in the case that the model parameters are nonstationary, as would be expected in real experiments.