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.

翻译：本文揭示了卷积神经网络（CNN）前向处理中的选择性旋转机制。我们阐释了激活函数作为一种判别机制，能够统一并量化输入数据的旋转特征。实验表明，这种定义的方法反映了网络如何基于统计指标区分输入，这一过程可通过结构化数学工具进行理解与分析。我们的发现还揭示了人工神经网络与人脑在数据处理模式上的一致性。