  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.
