In a neural network, as we know it, the flow of information can be
represented with a flood of information from the neurons of a set of points to another set. As
the information propagate, it will at the end only affects the output
nodes having no direct impact on another network. Even more, the network
will be supposed to function as we believe it should - and to achieve this
goal, we train the network intentionally to adapt its internal
connection filter (weights of the links) until the solution provided by the output nodes satisfy
the designed scheme. This learning procedure or initiation procedure is to
alter the young interconnection definition in such a way that it matches the
desired result.
A neuron Entity is a pool of neurons. Are they connected or not is
not our concern as we assume they are not connected to serve our answer. The
entity is the concept we will start with in order to define a neural
process. the trivial definition of a neuron entity is a simple vector of
neuron and you already have the entity - not quite practical form I guess.
For a specific processing, we will start to talk of a neural entity
when we are working a solution. We do not concern of a predefined set of neurons,
the amount can even be randomize between a defined boundary - neurons may be
dynamically added or even removed from the entity; The only that matter is
we have pointed out some input and output neurons. We know that the
input will be modified and we monitor the output neurons. In the case an
output does not satisfy the reality, we will adapt and learn to balance
correctly our pool of interaction.
A neural process could be seen as a neural network but I I
prefer to see it as a function interface of the world: in fact our input
neurons are or immediately connected to external sensors or collect the
result of another neural process. It provides a none/have answer as the
result of an interconnectivity - like the result of a mathematical operation
of the conclusion of report. A neural process is linked to one and only one
neural patterns but but execute and evaluate several profiles.
We also need to make a clear distinction between a
neural pattern and the neural profile.
Indeed, the pattern describe a set on neuron and their interconnection as
the neural profile specifies the weights of the dendrites. Meaning we can
easily apply a well known pattern to a new situation, create the
missing connection is necessary then adjust the weights to fit our
situation. In a way, a neural pattern will be used by several profiles.
We will also be confronted with two different kind of modification of a
neural process: when we learn and when we adapt. The learning
procedure of a neural process has for aim to modify/alter the
connectivity in order to satisfy and answer - we even may create a new
connection to reach our goal. To adapt a neural process if more to
adjust the weights of a neural process without altering the interconnection.
An error is the difference between a computed result and an
expected one. Reducing the error will mainly be the tasks of the learning
process.
The none/have values of a neuron is somewhat quite special in this
environment. We will process information but the absence of information has
a bigger meaning than the dual answer (this is why I do not call this as
true/false). Indeed, we see the neuron as followed: it is an organism that
propagates a signal (modifying the signal strength) - how can we then see
the absence of a signal? Well in fact we should not concern ourselves with
this as we will only be concerned of the signal. The absence of information
could not be propagated and therefore we cannot propagate a none as
we cannot differentiate in our concept from the rest state (doing nothing).
In order to avoid this kind of ambiguity, we have to formulate our sentence
differently: we will only use positive sentences (we make the
difference between 'not good and bad' between 'nothing and not anything' etc
etc....).
For example what does mean the information 'not good'? It surely does not
mean bad, maybe we do not know yet. Anyway what kind of information does it
gives us? in fact it provides us with no information at all - only the
absence of information associated to good and this does not help. The same
information is processed in a neural entity only the presence of information
but thi8s does not mean that the absence is not to be taken into account in
a neural process....
