ZM-models: they are the main long-range models of the brain. They generate knowledge and consciousness. Also they make YMs, ZAMs and AZMs. They are able to take any information from any other model of the brain. ZMs can replace a YM-model with another if something is not OK after an advance prediction and simulation based on any available data. They also control ZAM-models during their activity.
ZAM-models: they are artificial and invariant models. An artificial model is not generated by direct interaction with the external reality. An invariant model is a model, which cannot be changed by direct interaction with the external reality. ZAMs are models, which act on the external reality. Once a ZAM was made and activated by a ZM, it will simulate the activity, using any information from any model of the brain. By one or more simulations, the ZAM will find the right solution. If it fails to find a solution, then the ZM will make another ZAM and the process continues.
AZM-models: they are associated in a direct way to the organs which can act on external reality. They are ready-made when a being is born, but, to be used, they have to be dynamically calibrated by the activity of the ZAMs. That is, a ZAM has to know everything is association with the external organs of a body (e.g. hands, legs for a human). When a ZAM has to make a simulation, it has to know all the parameters of the muscles, for instance. An AZM has to know and transmit such parameters. To do this, AZMs keep a model of any external organ of that being.
All these models are associated with the hardware implementation of the brain. We will see later some others types of models which are associated with the software implementation of the brain.
SOME PRINCIPIAL PROBLEMS
When an M-model is activated it does not know how many entities are in the external reality. Even more, it does not know which are these entities. The device will try to find them based on the facilities of the sense organs, but there is no guarantee that M-models have found all the entities and no guarantee that the right YMs are associated to such entities. This is a basic deficiency.
The camouflage and dissimulation are methods which use this deficiency. By camouflage an entity is not discovered and by dissimulation M-models associate a wrong YM to an entity.
Let's see another basic problem. Any model evolves to be harmonic with itself and so, to be stable. This means that, after any change in the model, it has to regain its stability. If a model has a disharmony, it has to correct itself based on IR or based on an internal change (IR is not available in any situation). Thus the model regains its stability, but in some cases the model could be not suitable anymore to reflect the external reality. There are many cases when a model is stable but its predictions associated with the external reality are wrong.
We already defined reality as all the information that is or could be generated by a model by simulation. The guarantee of a correct reality is the stability of the model but the stability of the model is not a guarantee that the model is capable to accurately reflect the associated external reality.
That is, there is no guarantee that all the entities of a given external reality are discovered, there is no guarantee that the right YMs are associated with these entities and so on. The stability of a model is just a guarantee that all the available information is correlated in the right way.