Neurotoxic effects are a non-cancer endpoint for health risk, and neurobehavioral screening tests can serve as a first tier investigation of neurotoxicity [US EPA, Federal Register 63 (1998) 26926]. Analysis of neurobehavioral screening data such as those of the functional observational battery (FOB) traditionally relies on analysis of variance (ANOVA). ANOVA is designed to detect whether there are dose-effects, but does not model the underlying dose-response relationship and subsequent risk assessment fails to utilize the shape of the underlying dose-response. In contrast, dose-response modeling interpolates toxic effects between experimental points, and permits prediction of toxic effects within the experimental range. Additionally it is also a prerequisite for estimating a benchmark dose. This paper discusses dose-time-response modeling of longitudinal neurotoxicity data and illustrates the methods using three continuous FOB outcomes from an EPA study involving acute exposure to triethyltin (TET). Several mathematical functions are presented as candidate dose-time-response models. The use of random effects is discussed to characterize inter-subject variation. The results indicate that it is feasible to use simple mathematical functions to model empirical dose-time-response observed in existing longitudinal neurotoxicological data. Further research is needed on the types of design and data required to reliably approximate the true underlying dose-time-response.