Baddeley R
Department of Experimental Psychology and Department of Physiology, University of Oxford, South Parks Road, Oxford, OX1 3UD, UK.
Network. 1996 May;7(2):409-21. doi: 10.1088/0954-898X/7/2/021.
It has been independently proposed, by Barlow, Field, Intrator and co-workers, that the receptive fields of neurons in V1 are optimized to generate 'sparse', Kurtotic, or 'interesting' output probability distributions. We investigate the empirical evidence for this further and argue that filters can produce 'interesting' output distributions simply because natural images have variable local intensity variance. If the proposed filters have zero DC, then the probability distribution of filter outputs (and hence the output Kurtosis) is well predicted simply from these effects of variable local variance. This suggests that finding filters with high output Kurtosis does not necessarily signal interesting image structure. It is then argued that finding filters that maximize output Kurtosis generates filters that are incompatible with observed physiology. In particular the optimal difference-of-Gaussian (DOG) filter should have the smallest possible scale, an on-centre off-surround cell should have a negative DC, and that the ratio of centre width to surround width should approach unity. This is incompatible with the physiology. Further, it is also predicted that oriented filters should always be oriented in the vertical direction, and of all the filters tested, the filter with the highest output Kurtosis has the lowest signal-to-noise ratio (the filter is simply the difference of two neighbouring pixels). Whilst these observations are not incompatible with the brain using a sparse representation, it does argue that little significance should be placed on finding filters with highly Kurtotic output distributions. It is therefore argued that other constraints are required in order to understand the development of visual receptive fields.
巴洛、菲尔德、英特拉托及其同事独立提出，V1区神经元的感受野经过优化，以生成“稀疏”、峰态或“有趣”的输出概率分布。我们进一步研究了这方面的经验证据，并认为滤波器能够产生“有趣”的输出分布，仅仅是因为自然图像具有可变的局部强度方差。如果所提出的滤波器直流分量为零，那么仅根据可变局部方差的这些效应，就能很好地预测滤波器输出的概率分布（进而预测输出峰度）。这表明，找到具有高输出峰度的滤波器并不一定意味着存在有趣的图像结构。接着有人认为，寻找使输出峰度最大化的滤波器会产生与观察到的生理学不相符的滤波器。特别是，最优的高斯差分（DOG）滤波器应该具有尽可能小的尺度，中心-外周细胞应该具有负的直流分量，并且中心宽度与外周宽度的比值应该接近1。这与生理学不相符。此外，还预测定向滤波器应该总是垂直定向，并且在所有测试的滤波器中，输出峰度最高的滤波器信噪比最低（该滤波器仅仅是两个相邻像素的差值）。虽然这些观察结果与大脑使用稀疏表示并不矛盾，但确实表明，对于找到具有高输出峰度分布的滤波器不应赋予太大意义。因此有人认为，为了理解视觉感受野的发育，还需要其他约束条件。
