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. Electrophysiological recordings of sustained neurons [2] are digitized and transferred to a computer. A numerical ordinary differential equation solver is used to simulate the model. In order to obtain an initial set of parameters, we introduce approximations to the model and obtain fits to parts of the response characteristics. These initial parameter values are then refined by optimization routines. The model is compared to data in 4 different experimental paradigms and in general is in good agreement with data. We conclude that the simplified versions of temporal and spatial adaptation mechanisms of the model capture the essential features of the dynamics of sustained neurons and that the refinement of the model requires further experimental studies to elucidate the number of stages involved in temporal adaptation as well as the precise shape of the relationship between the membrane potentials and the spike frequency for the sustained neurons.