His architecture first decomposed input pictures into multiple feature maps, like V1 seemed to do. Each feature map was a grid that signaled the location of a feature—such as vertical or horizontal lines—within the input picture. This process is called a convolution, hence the name applied to the type of network that Fukushima had invented: convolutional neural networks.* After these feature maps identified certain features, their output was compressed and passed to another set of feature maps that could combine them into higher-level features across a wider area of the picture, merging lines
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