M-models are just preliminary models based on YM-models. A ZM model will take any information from any other M and ZM models of the brain, to improve it. Example: an M-model is associated with a bus that transports people. A ZM- model takes this information and tries to see if this bus transports tourists or is a public transport vehicle. To do this, it will use information taken from any other ZM-models and M-models. The aim is to make a ZM-model, which reflects as well as possible a section of the external reality. Because ZM is a model, it is stable and because this model is integrated in a structure of other ZM-models, the structure of ZM-models is stable too. This problem will be treated later in details. ZM-models are long-range models. This term will be explained later. Here, the "long-range model" is understood as a model, which already developed its elements as self standing models. ZM models are the main models, which reflect the external reality. We define now two very important terms: knowledge and consciousness. Knowledge is associated with the facility to predict the evolution of the external reality based on a structure of harmonic/logic models. This structure was made by a large number of interactions with many sections of the external reality and so it already generated a large number of good predictions. This means that the only guarantee of the correctness of the knowledge is the confidence in that structure of models. This issue will be developed in details later in the book. The consciousness is the facility to make and operate a model, associated with the external reality, where the person itself is an element of that model. When such a model is activated, it will also find the position of the person in the model and so it will predict the position of the person in the external reality. This issue will also be developed in detail in another part of the book. We will now develop some issues associated with the term "knowledge". We already defined knowledge as the capacity to predict in a correct way the evolution of the external reality. Here we use the term "correct". Let's see what it means. This term has two definitions. One situation is when a model makes a prediction and the prediction is compared with IR. If the prediction meets IR, then the prediction is "correct". Unfortunately, there are very few situations when the comparison between prediction and IR is possible. For instance, building a bridge. A problem is, for instance, if the bridge will be stable or not in case of an earthquake. Here we need a guarantee that the bridge is properly built and there is no possibility to verify this based on IR.