WHAT IS THE GOAL OF SENSORY CODING
WHAT IS THE GOAL OF SENSORY CODING
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DOI：
10.1162/neco.1994.6.4.559
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发表时间：
1994-07-01
影响因子：
2.9
通讯作者：
FIELD, DJ
中科院分区：
文献类型：
--
作者：
FIELD, DJ
最近的一些尝试已经作出了描述早期感觉编码的一般信息处理策略。在本文中，两种策略进行了对比。这两种策略都利用了环境中的冗余来产生更有效的表征。第一种被描述为“紧凑"编码方案。紧凑代码执行一种变换，该变换允许用具有最小RMS误差的减少数量的向量（单元）来表示输入。这种方法最近在神经网络文献中变得流行，并且与称为主成分分析（PCA）的过程有关。最近的一些论文表明，用于表示自然场景的最佳“紧凑”代码将具有与视网膜和初级视觉皮层中发现的感受野轮廓非常相似的单位。然而，在本文中，有人提出，紧凑的编码方案是不够的，以占哺乳动物视觉通路中的细胞的感受野特性。与此相反，它提出的视觉系统是接近最佳的表示自然场景，只有当最优性定义的“稀疏分布”编码。在稀疏分布式代码中，代码中的所有单元在图像类中具有相等的响应概率，但对于任何单个图像具有低响应概率。在这样的代码中，维度没有减少。相反，输入的冗余被转换成细胞的放电模式的冗余。提出了稀疏码的签名在响应分布的四阶矩中找到（即，峰度）。在55个校准的自然场景的测量中，当视觉代码的带宽与哺乳动物视觉皮层中的细胞的带宽相匹配时，峰度被发现达到峰值。提出类似于“小波变换”的代码是有效的，因为这种代码的响应直方图是稀疏的（即，显示高峰度）。提出了在图像的相位谱中发现允许稀疏编码的图像的结构。有人建议，自然场景，第一近似，可以被认为是一个自相似的本地功能（小波的逆）的总和。可能的原因，为什么感觉系统会演变成稀疏编码。
A number of recent attempts have been made to describe early sensory coding in terms of a general information processing strategy. In this paper, two strategies are contrasted. Both strategies take advantage of the redundancy in the environment to produce more effective representations. The first is described as a ''compact'' coding scheme. compact code performs a transform that allows the input to be represented with a reduced number of vectors (cells) with minimal RMS error. This approach has recently become popular in the neural network literature and is related to a process called Principal Components Analysis (PCA). A number of recent papers have suggested that the optimal ''compact'' code for representing natural scenes will have units with receptive field profiles much like those found in the retina and primary visual cortex. However, in this paper, it is proposed that compact coding schemes are insufficient to account for the receptive field properties of cells in the mammalian visual pathway. In contrast, it is proposed that the visual system is near to optimal in representing natural scenes only if optimality is defined in terms of ''sparse distributed'' coding. In a sparse distributed code, all cells in the code have an equal response probability across the class of images but have a low response probability for any single image. In such a code, the dimensionality is not reduced. Rather, the redundancy of the input is transformed into the redundancy of the firing pattern of cells. It is proposed that the signature for a sparse code is found in the fourth moment of the response distribution (i.e., the kurtosis). In measurements with 55 calibrated natural scenes, the kurtosis was found to peak when the bandwidths of the visual code matched those of cells in the mammalian visual cortex. Codes resembling ''wavelet transforms'' are proposed to be effective because the response histograms of such codes are sparse (i.e., show high kurtosis) when presented with natural scenes. It is proposed that the structure of the image that allows sparse coding is found in the phase spectrum of the image. It is suggested that natural scenes, to a first approximation, can be considered as a sum of self-similar local functions (the inverse of a wavelet). Possible reasons for why sensory systems would evolve toward sparse coding are presented.