Learning Invariance from Transformation Sequences
Learning Invariance from Transformation Sequences
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DOI：
10.1162/neco.1991.3.2.194
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发表时间：
1991-06-01
影响因子：
2.9
通讯作者：
Foldiak, Peter
中科院分区：
文献类型：
--
作者：
Foldiak, Peter
视觉系统可以可靠地识别物体，即使当视网膜图像由于环境中通常发生的变化而发生相当大的变化时。提出了一种局部学习规则，它允许网络学习在这种变换中泛化。在学习阶段，网络暴露于经历变换的模式的时间序列。该算法的一个应用，其中网络学习不变性，以转移视网膜位置。这一原理可能与初级视皮层复杂细胞特有的移位不变性特性的发展有关，也可能与高级视区神经元更为复杂的不变性特性的发展有关。
The visual system can reliably identify objects even when the retinal image is transformed considerably by commonly occurring changes in the environment. A local learning rule is proposed, which allows a network to learn to generalize across such transformations. During the learning phase, the network is exposed to temporal sequences of patterns undergoing the transformation. An application of the algorithm is presented in which the network learns invariance to shift in retinal position. Such a principle may be involved in the development of the characteristic shift invariance property of complex cells in the primary visual cortex, and also in the development of more complicated invariance properties of neurons in higher visual areas.