In this essay I will discuss the central question: how can a large network of neurons acquire consciousness or intuition? Furthermore I will present a model for the activity inside of the neural network which based on the principles of quantum physics. This model has similar features known from the theory of random surfaces and spin networks. Here we will analyze the network by considering the interaction of neurons by feedback loops. If one takes the signal amplitudes and frequency into account where high signals with high frequency representing learned features then the signal network degenerates to a tree. But then, the set of learned information (or the sample of examples to learn the network) can be divided into a finite number of classes. Then we are able to describe the learning process itself. Furthermore we will discuss what is intuition by introducing new relations between the feedback loops. Then we will obtain a simple criteria that a network has classified the information of the learning sample: the classes forming a fractal. Finally we will end up with the statement: Mindless rules (mathematics) are able to generate mind.
Torsten Asselmeyer-Maluga