Read Ebook: The Brain A Decoded Enigma by Moisa Dorin Teodor

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Ebook has 1165 lines and 60682 words, and 24 pages

An image model contains an unspecified number of elements and an unspecified number of relations between the elements. An image model is just given as it is. It is not possible to specify in explicit and precise ways which are the elements and which are the relations.

Examples of image models: maps, models of an object of any type, an assembly of such models including any material elements , any representation in any form of such elements.

A symbolic model uses as elements letters, numbers or words. The relations are of logical or mathematical type.

The most important symbolic model is the General Communications Language . The elements are usually nouns and the relations are usually verbs.

Warning: GCL is not really a symbolic model. The GCL just contains all the elements and all the relations. When a symbolic model is made , elements and relations from GCL are used. Thus, because there is no available word, I decided to consider, by extension, the GCL as a symbolic model. In this frame, GCL has to be considered as "symbolic model".

Once a model given, it is possible to simulate some situations on it. For simulation, a change must be made to the model. After that, the entire model will be changed because all the elements have some relations between them.

Any implicit or explicit information which is generated by simulation by a model, is called "truth". Any truth must be associated with the model, which generated it. This is the definition of the term "truth" in the MDT theory.

All the information, which is or could be generated by a model by simulation, is called "reality" associated to that model. This is the definition of the term "reality" in the MDT theory. We also see here that before declaring the reality, one needs to declare the model which generated it.

We already used the term "information". This term is a fundamental term. It has no normal definition. MDT accepts the descriptive definition from common life and from science. The same situation is for the term "entity".

Warning: in connection with the term "information", something is considered as information after that "something" is processed somehow by a device which takes and processes that "something".

This somehow confuse situation is normal for 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".

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 or symbolic model . 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 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 . There are some image-type models called M-models, which are associated with the sense organs . 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" .

We just introduced a new term as "input reality" or IR. IR is the information obtained by an M-model from outside 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 model. When M is stable, all YMs are integrated in M in a harmonic way.

The main function of M-models is to make a preliminary harmonic model associated with an external reality.

Conclusion: a M-model interacts with a section of the external reality. M will be a model made in an informational way by analogy with that section of the external reality. Because M is a model, all the elements are connected between them in a harmonic way, so that the model is stable. This stability is verified on and on in an automatic way, as long as a specific external reality is in interaction with the specific M-model.

M-models interact with some other type models, called ZM-models. ZM-models take some information from one or more M-models and continue the construction of models associated with the corresponding external reality. To do this, ZM- models interact with the other ZM-models of the brain to improve M-models.

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.

The second definition of the term "correct" is: the brain will consider as "correct" any prediction based on a harmonic/logic structure of models. To be harmonic, the structure was already verified, based on IR in many other situations. So, the only guarantee of a "correct" prediction is the confidence in that structure of models.

MDT is associated with the basic hardware functions of the brain. Once we described the hardware structure, everything what the MDT predicts is based on what the hardware is able to do. What MDT says about knowledge is not another theory on knowledge but what the hardware is able to do.

Any experiment is based on a model. That model tells us what we are doing and the same model tells us what we get and what we see. Any model that makes the experiment just improves itself. An improved model will make better predictions and that is all. There is no guarantee associated with the knowledge except the confidence in our own structure of models.

Let's see another aspect. We saw that any experiment is based on a model. The model tells us what we did and what we get and see. If there are many persons who participate in an experiment, everyone will make his/her own model based on his/her own structure of models. What everyone gets and sees depends on one's own structure of models.

Example: up to around year 1500 everybody knew that the Earth was the center of the Universe. This idea was supported by direct observation of the sky but also by a powerful structure of models. So, in that period, the astronomers were able to calculate Sun and Moon eclipses, understand and calculate many parameters associated with the movement of the Moon, Sun and stars. Even the Holy Book supported this idea, at least in an implicit way. In that period, the idea that Earth is the center of the Universe was correct.

I want to emphasize again that the situation is generated by the work principle of the brain. It does not matter if we like or not this situation! The situation will be the same forever. For instance, Newton's Mechanics considers that there is a fundamental field of forces called "gravity". Everybody considers that the gravity exists. But Einstein says that there is no such a field of forces; what we see is just an effect of the distortion of the space due to mass. If Einstein is right, the idea that there is gravity is not correct anymore. See also the applications.

So, in every moment, the brain will consider as correct everything which is generated by its structure of stable models.

Some scientists could consider these assertions as unacceptable, but regardless of the fact that we like or not such a situation, the brain is able to do only what the hardware structure is able to do.

There is another term that has some associated problems. This term is "wrong". If a model makes wrong predictions, this usually does not mean that the model is wrong. It means just that the model is not suitable to the given external reality.

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