  The fruit fly's natural visual environment is often characterized by light
intensities ranging across several orders of magnitude and by rapidly varying
contrast across space and time. Fruit fly photoreceptors robustly transduce
and, in conjunction with amacrine cells, process visual scenes and provide the
resulting signal to downstream targets. Here we model the first step of visual
processing in the photoreceptor-amacrine cell layer. We propose a novel
divisive normalization processor (DNP) for modeling the computation taking
place in the photoreceptor-amacrine cell layer. The DNP explicitly models the
photoreceptor feedforward and temporal feedback processing paths and the
spatio-temporal feedback path of the amacrine cells. We then formally
characterize the contrast gain control of the DNP and provide sparse
identification algorithms that can efficiently identify each the feedforward
and feedback DNP components. The algorithms presented here are the first
demonstration of tractable and robust identification of the components of a
divisive normalization processor. The sparse identification algorithms can be
readily employed in experimental settings, and their effectiveness is
demonstrated with several examples.
