Natural image model features and likelihood estimates.

The natural image models we tested along with the neural response properties they mimic: “BF” is bandpass filtering, “OS” is orientation selectivity, “DN” is divisive normalization, and “CP” is complex cell pooling. We also show cited likelihood estimates. MEC is the mixture of elliptically contoured distributions model [33]. All models are described in detail in the “Models Tested” section. Higher likelihood indicates that a model captures more of the regularities present in natural images than a model with lower likelihood.我们所测试的自然图像模型及其所模拟的神经响应特性如下：「BF」为带通滤波（bandpass filtering），「OS」为方向选择性（orientation selectivity），「DN」为除法归一化（divisive normalization），「CP」为复杂细胞池化（complex cell pooling）。我们同时展示了引用的似然估计结果。MEC为椭圆等高分布混合模型（mixture of elliptically contoured distributions model）[33]。所有模型的详细说明均见于「测试模型」章节。似然值越高，代表该模型相较于似然值更低的模型，能够捕捉到更多自然图像中蕴含的固有规律。