Fitting intracranial self-stimulation data with growth models.

Abstract

Until now, the problem of fitting self-stimulation rate-frequency functions has been dealt with by using linear models applied to the linear portion of the empirical curve. In this article, an alternative procedure is presented, together with three sigmoid growth models that seem to accurately fit rate-frequency data. From any of these models, it is possible to compute the two indices of stimulation efficacy in use in the parametric study of brain stimulation reward (M50 and theta 0), in addition to the inflection point of the curve, which can be used as an alternative to M50. Important relations allowing initial estimation of each parameter are provided, allowing use of computer programs derived from the Gauss-Newton algorithm for nonlinear regression. The considerations relevant to the choice of a nonlinear model are discussed in terms of each efficacy index.