On the Loss of Learning Capability Inside an Arrangement of Neural Networks: The Bottleneck Effect in Black-Holes

Abstract

We analyze the loss of information and the loss of learning capability inside an arrangement of neural networks. Our method is based on the formulation of the Bogoliubov transformations in order to connect the information between different points of the arrangement. Similar methods translated to the physics of black-holes, reproduce the Hawking radiation effect. From this perspective we can conclude that the black-holes are objects reproducing naturally the bottleneck effect, which is fundamental in neural networks in order to perceive the useful information, eliminating in this way the noise.