Normalization is a fundamental operation in image processing. Convolutional nets have evolved to include a large number of normalizations (Ioffe and Szegedy 2015; Ulyanov, Vedaldi, and Lempitsky 2016; Wu and He 2018), and this architectural shift has proved essential for robust computer vision (He et al. 2015; Bjorck et al. 2018; Santurkar, Tsipras, and Ilyas 2018). Studies of biological vision, in contrast, have invoked just one or a few normalizations to model psychophysical (Mach 1868; Furman 1965; Sperling 1970) and physiological (Carandini and Heeger 2011; Shin and Adesnik 2024) observations that have accumulated for over a century. Here connectomic information (Matsliah et al. 2023) is used to argue that interneurons of the fly visual system support a large number of normalizations with unprecedented specificity. Ten interneuron types in the distal medulla (Dm) of the fly optic lobe, for example, appear to support chiefly spatial normalizations, each of which is specific to a single cell type and length scale. Another Dm type supports normalization over features as well as space. Two outlier types do not appear to support normalization at all. Interneuron types likely to be normalizers are identified not only in Dm but also in all other interneuron families of the optic lobe. For fly vision, the diversity of interneurons appears to be an inevitable consequence of the specificity of normalizations.

中文翻译：

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视觉系统中的中间神经元多样性和标准化特异性

归一化是图像处理中的基本操作。卷积网络已经发展到包含大量标准化（Ioffe 和 Szegedy 2015；Ulyanov、Vedaldi 和 Lempitsky 2016；Wu 和 He 2018），并且这种架构转变已被证明对于稳健的计算机视觉至关重要（He 等人 2015；Bjorck）等人 2018；桑图尔卡、齐普拉斯和伊利亚斯 2018）。相比之下，生物视觉研究仅引用了一个或几个标准化来模拟心理物理学（Mach 1868；Furman 1965；Sperling 1970）和生理学（Carandini 和 Heeger 2011；Shin 和 Adesnik 2024）积累了一个多世纪的观察结果。 。这里使用连接组信息（Matsliah et al. 2023）来论证果蝇视觉系统的中间神经元支持大量具有前所未有的特异性的标准化。例如，果蝇视叶远端髓质（Dm）中的十种中间神经元类型似乎主要支持空间标准化，每种类型都特定于单个细胞类型和长度尺度。另一种 Dm 类型支持特征和空间的标准化。两种异常值类型似乎根本不支持标准化。可能是标准化的中间神经元类型不仅在 Dm 中被识别，而且在视叶的所有其他中间神经元家族中也被识别。对于果蝇视觉来说，中间神经元的多样性似乎是标准化特异性的必然结果。