QUANTITATIVE STUDIES OF FLY VISUAL SUSTAINED NEURONS

We present a quantitative study of a neural network model [1] proposed for the sustained neurons in the fly visual system. Electrophysiologi cal recordings of sustained neurons [2] are digitized and transferred to a computer. A numerical ordinary differential equation solver is us ed to simulate the model. In order to obtain an initial set of paramet ers, we introduce approximations to the model and obtain fits to parts of the response characteristics. These initial parameter values are t hen refined by optimization routines. The model is compared to data in LC different experimental paradigms and in general is in good agreeme nt with data. We conclude that the simplified versions of temporal and spatial adaptation mechanisms of the model capture the essential feat ures of the dynamics of sustained neurons and that the refinement of t he model requires further experimental studies to elucidate the number of stages involved in temporal adaptation as well as the precise shap e of the relationship between the membrane potentials and the spike fr equency for the sustained neurons.