Sparse Identification of Contrast Gain Control in the Fruit Fly Photoreceptor and Amacrine Cell Layer

TL;DR

This paper introduces a novel divisive normalization processor model for the fruit fly's visual layer, and presents sparse identification algorithms to efficiently and robustly identify its components, aiding experimental analysis.

Contribution

It proposes a new divisive normalization model for the photoreceptor-amacrine layer and develops the first tractable sparse algorithms for identifying its components.

Findings

The DNP effectively models contrast gain control in fruit fly vision.

Sparse algorithms accurately identify feedforward and feedback components.

Algorithms are demonstrated to be robust and suitable for experimental use.

Abstract

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.