The Brain, A Decoded Enigma | Page 4

any fundamental term. Just think, for instance, how one can explain what is "time". The only possibility to explain what is "time" is to use examples that already use the term "time". In fact it is impossible to define terms as "mass", "time", "space", "information" or "entity".
Let's introduce two new terms: "harmony" and "logic".
Once a model is given, it is possible to make simulations on the model, as it has already been explained. By simulation, it is necessary to change an element or a relation. The model goes into a temporary unstable situation because all the elements are connected between them. The model will evolve to a new stable situation. For an image model, the evolution to stability is based on harmony laws. For a symbolic model, the evolution to stability is based on logic. Thus, a stable model is a harmonic or logic model and, after a perturbation, the model will regain the stability based on the laws of harmony (image models) or logic (symbolic models). The evolution of any model toward stability (to become harmonic or logic) is also a basic hardware facility of the brain.
Because some situations from external reality can be associated, sometimes, with both types of models, there can be a corespondence between harmony and logic.
Thus, the implicit definitions of the terms "harmony" and "logic" are associated with the methods to regain the stability of an image model (harmony) or symbolic model (logic). An "implicit definition" means that we are able to recognize the effect of harmony or logic in an informational structure.
We are now in the situation to present the basic hardware function of any brain, based on the terms, which have already been defined.
The basic hardware function of any brain (human or animal) is to make models associated to external reality and to predict, by simulation, the possible evolutions of the model. Because the model is associated with external reality, it is possible to predict by simulation some probable evolutions of the external reality.
We already used the term "external reality" which is not defined yet. This fundamental term is considered as a source of information, which is not localized in the structure of models of the brain. I want to emphasize that the external reality is not a source of information, but is just considered so by any brain.
Thus, one of the main hardware functions of the brain is to make models of the external reality and to predict, by simulation on the model, the possible evolution of the associated external reality.
We already defined the reality as all the information which is or could be generated by a model. This means that we understand the external reality by the reality, which is generated by a model, which is associated with the external reality.
Example: For a given external reality, any person makes an associated model. Any person has his/her own model associated to the same external reality. We think and act based on our own reality and not based directly on the external reality.
In fact, external reality is rather an invention of the brain to explain its structure of models.

THE BASIC HARDWARE ELEMENT
Let's see what is the basic hardware element of a brain (human or animal). There are some image-type models called M-models, which are associated with the sense organs (eyes, ears and so on). M-models work in association with some YM-models, which already exist in the brain. YM-models are concept models. A concept-model is a simplified model which, in this way, fits a large class of similar models.
Example of YM models: "dog", "table" and so on.
M-models have to discover as many as possible entities in the external reality and to associate a YM model to any entity. Once an entity was firstly associated with a YM, M-models will predict its evolution based also on that YM.
Example: if an entity was associated with a YM-dog, the M-model is able to predict how this YM performs in connection with all the other YMs of it.
Any prediction of M with that YM included is compared with the information obtained by M from external reality. The information obtained by a M-model from outside during the comparison process, is called "input reality" (IR).
We just introduced a new term as "input reality" or IR. IR is the information obtained by an M-model from outside (from external reality or from other models) to improve its predictions.
If the prediction meets IR, then M will try another prediction to improve its quality. If one or more predictions do not meet IR, then M will replace that YR with another, and the process will continue. This process will continue so that all the entities which are discovered by M-models will be associated with some YMs and all the predictions of M must confirm the M-model, unchanged. Such a model is, thus, a stable