We analyze the loss of information and the loss of learning capability inside an arrangement of neural networks. Our method is new and based on the formulation of non-unitary Bogoliubov transformations in order to connect the information between different points of the arrangement. This can be done after expanding the activation function in a Fourier series and then assuming that its information is stored inside a Quantum scalar field.

中文翻译：

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关于神经网络排列中学习能力的损失

我们分析了神经网络排列中的信息损失和学习能力的损失。我们的方法是新的，并且基于非幺正 Bogoliubov 变换的公式，以连接不同排列点之间的信息。这可以在将激活函数展开为傅立叶级数之后，然后假设其信息存储在量子标量场中来完成。