The Brain, A Decoded Enigma | Page 4

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".
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