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.

中文翻译：

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在果蝇感光细胞和无长突细胞层中对比控制的稀疏识别。

果蝇的自然视觉环境通常具有跨数个数量级的光强度和随时间和空间变化迅速变化的特征。果蝇光感受器能可靠地进行转导，并与无长突细胞结合，处理视觉场景并将产生的信号提供给下游目标。在这里，我们对感光受体-amacrine细胞层中视觉处理的第一步进行建模。我们提出了一种新颖的除法归一化处理器（DNP），用于对在光感受器-amacrine细胞层中发生的计算进行建模。DNP显式地建模了无长突细胞的感光器前馈和时间反馈处理路径以及时空反馈路径。然后，我们正式表征DNP的对比度增益控制，并提供可有效识别每个前馈和反馈DNP分量的稀疏识别算法。这里介绍的算法是对除法归一化处理器组件的易处理且鲁棒性标识的首次演示。稀疏识别算法可以很容易地在实验环境中使用，并通过几个示例证明了其有效性。