  This paper reveal the selective rotation in the CNNs' forward processing. It
elucidates the activation function as a discerning mechanism that unifies and
quantizes the rotational aspects of the input data. Experiments show how this
defined methodology reflects the progress network distinguish inputs based on
statistical indicators, which can be comprehended or analyzed by applying
structured mathematical tools. Our findings also unveil the consistency between
artificial neural networks and the human brain in their data processing
pattern.
