Slow feature analysis yields a rich repertoire of complex cell properties
Slow feature analysis yields a rich repertoire of complex cell properties
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DOI：
10.1167/5.6.9
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发表时间：
2005-01-01
影响因子：
1.8
通讯作者：
Wiskott, L
中科院分区：
文献类型：
--
作者：
Berkes, P;Wiskott, L
在这项研究中，我们调查的时间慢作为一个学习原则，感受野使用缓慢的特征分析，一种新的算法来确定功能，从输入数据中提取缓慢变化的信号。我们发现在图像序列上训练的学习函数集与初级视觉皮层（V1）中的复杂细胞群之间存在良好的定性和定量匹配。这些函数具有方向选择性、非正交抑制、端抑制和侧抑制等性质，在复杂细胞中也有实验发现。我们的研究结果表明，一个单一的无监督学习原则可以解释这样一个丰富的剧目的感受野属性。
In this study we investigate temporal slowness as a learning principle for receptive fields using slow feature analysis, a new algorithm to determine functions that extract slowly varying signals from the input data. We find a good qualitative and quantitative match between the set of learned functions trained on image sequences and the population of complex cells in the primary visual cortex (V1). The functions show many properties found also experimentally in complex cells, such as direction selectivity, non-orthogonal inhibition, end-inhibition, and side-inhibition. Our results demonstrate that a single unsupervised learning principle can account for such a rich repertoire of receptive field properties.