Hurri Jarmo, Hyvärinen Aapo
Neural Networks Research Centre, Helsinki University of Technology, 02015 HUT, Finland.
Neural Comput. 2003 Mar;15(3):663-91. doi: 10.1162/089976603321192121.
Recently, statistical models of natural images have shown the emergence of several properties of the visual cortex. Most models have considered the nongaussian properties of static image patches, leading to sparse coding or independent component analysis. Here we consider the basic time dependencies of image sequences instead of their nongaussianity. We show that simple-cell-type receptive fields emerge when temporal response strength correlation is maximized for natural image sequences. Thus, temporal response strength correlation, which is a nonlinear measure of temporal coherence, provides an alternative to sparseness in modeling simple-cell receptive field properties. Our results also suggest an interpretation of simple cells in terms of invariant coding principles, which have previously been used to explain complex-cell receptive fields.
最近,自然图像的统计模型已经显示出视觉皮层的几种特性。大多数模型考虑了静态图像块的非高斯特性,从而导致了稀疏编码或独立成分分析。在这里,我们考虑图像序列的基本时间依赖性,而不是它们的非高斯性。我们表明,当自然图像序列的时间响应强度相关性最大化时,简单细胞类型的感受野就会出现。因此,时间响应强度相关性作为时间相干性的非线性度量,为简单细胞感受野特性建模中的稀疏性提供了一种替代方法。我们的结果还提出了一种基于不变编码原理对简单细胞的解释,该原理以前曾被用于解释复杂细胞的感受野。