Modeling neural activity using the generalized inverse Gaussian distribution

Spike trains from neurons are often used to make inferences about the underlying processes that generate the spikes. Random walks or diffusions are commonly used to model these processes; in such models, a spike corresponds to the first passage of the diffusion to a boundary, or firing threshold. An important first step in such a study is to fit families of densities to the trains' interspike interval histograms; the estimated parameters, and the families' goodness of fit can then provide information about the process leading to the spikes. In this paper, we propose the generalized inverse Gaussian family because its members arise as first passage time distributions of certain diffusions to a constant boundary. We provide some theoretical support for the use of these diffusions in neural firing models. We compare this family with the lognormal family, using spike trains from retinal ganglion cells of goldfish, and simulations from an integrate-and-fire and a dynamical model for generating spikes. We show that the generalized inverse Gaussian family is closer to the true model in all these cases. Received: 16 September 1996 / Accepted in revised form: 2 July 1997     

Parental Body Mass as a Determinant of Egg Size and Egg Output in an Arctiid Moth (Utetheisa ornatrix)

Females of the moth Utetheisa ornatrix (family Arctiidae) mate preferentially with larger males. As a consequence, females have larger sons and daughters, which have been shown to be more acceptable in courtship and more fecund, respectively. We demonstrate that mating with larger males results in accelerated oviposition by the female on the day after mating and that females are intrinsically prone to lay larger eggs in the first days after mating. Both these additional size-dependent effects have potential adaptive consequences.