Neurotoxic effects are an important non-cancer endpoint in health risk assessment and environmental regulation. Neurotoxicity tests such as neurobehavioral screenings using a functional observational battery generate longitudinal dose–response data to profile neurological effects over time. Analyses of longitudinal neurotoxicological data have mostly relied on analysis of variance; explicit dose–time–response modeling has not been reported in the literature. As dose–response modeling has become an increasingly indispensible component in risk assessment as required by the use of benchmark doses, there are strong interests in and needs for appropriate dose–response models, effective model-fitting techniques, and computation methods for benchmark dose estimation. In this article we propose a family of dose–time–response models, illustrate statistical inference of these models in conjunction with random-effects to quantify inter-subject variation, and describe a procedure to profile benchmark dose across time. We illustrate the methods through a dataset from a US/EPA experiment involving the FOB tests on rats administered to a single dose of triethyl tin (TET). The results indicate that the existing functional observational battery data can be utilized for dose–response and benchmark dose analyses and the methods can be applied in general settings of neurotoxicity risk assessment. Copyright © 2005 John Wiley & Sons, Ltd.