Difference between revisions of "Encrypted code: Ephaptic transmission"

**In Figure 1, the structure of the 'CNN' is represented in which the difference between the more common neural network structures in which the first stage is structured with a high number of input variables can be noted. In our 'CNN' the first stage corresponds only to a node and precisely to the network analysis initialization command called 'Consistency Demarcator <math>\tau</math>', the subsequent loops of the network, which allow the clinician to terminate or to reiterate the network, (1st loop open, 2st loop open,...... nst loop open) are decisive for concluding the decryption process ( Decrypted Code ). This step will be explained in more detail later in the chapter.

* '''Output:''' The outgoing data from the network, which substantially correspond to a precise cognitive trigger request, returns a large number of data classified and correlated to the requested keyword. The clinician will have to devote time and concentration to continue decrypting the machine code. In fact, we have witnessed how, following the indications dictated by research criteria such as the 'Research Diagnostic Criteria' (RDC), our patient Mary Poppins was immediately categorized as 'TMDs' and we have also suggested a way to expand diagnostic capabilities in dentistry through a 'fuzzy' model that would allow to range in contexts other than one's own. This shows the complexity in making differential diagnoses and the difficulties in following a classical semiotic roadmap because we are anchored too much to verbal language and too little to a quantum culture of biological systems. This borders on the concept of machine language and initial decryption command that we will briefly explain in the next paragraph.

=== Initiation command ===

For a moment let's imagine that the brain speaks the language of a computer and not vice versa as happens in engineering, to distinguish the aforementioned difference between machine language and human verbal language. To write a sentence, a word or a formula, the computer does not use the classic verbal mode (alphabet) or the decimal mode (numbers) with which we write mathematical formulas but its own 'writing' language code called html code for the web . Let's take as an example the writing of a fairly complex formula, it is presented to our brain in the verbal language with which we have learned to read a mathematical equation, in the following form:  

and let us imagine, letting our minds wander, that this formula corresponds to the message of the Central Nervous System, as we have anticipated, and in particular in the 'Ephaptic Transmission' still to be decrypted</blockquote>The computer and therefore the brain, for our metaphorical example, does not know verbal language or rather it is only a convention generated to simplify natural communication, rather it has its own one with which to write the formula mentioned and in the wiki text language (with extension .php) looks like this, represented in figure 2:                <blockquote>[[File:Codice mod.png|alt=|center|frame|'''Figura 2:''' Wiki text of a mathematical formula. Note the initialization <nowiki><math> command and the script exit </math></nowiki> command]]

as you can see it has nothing to do with verbal language and in fact, the brain has its own machine language made up not of vowels, consonants and numbers but of action potentials, wave packets, frequencies and amplitudes, electric populations , etc. what we simply observe in an electroencephalographic tracing (EEG) and which represents, precisely, the electromagnetic fields on the scalp of the activity of the dipoles and the cerebral ionic currents that propagate in the encephalic volume. </blockquote>

The story, however, does not end here because this is a writing language that has nothing to do with the interpreter of computer hardware and therefore with the organic structure of the brain made up of Centers with specialized functions, synaptic, polysynaptic circuitry and other other. This writing language, therefore, derives from a machine language that is not modeled in the command '<nowiki><math>' rather than '+2\sum_{\alpha_1'} but derives from a binary language subsequently converted into html writing code. This is referred to as 'machine language' for both the computer and the brain and can be simulated as follows</nowiki>

+2\sum_{\alpha_1<\alpha_2}\cos\theta_{\alpha_1\alpha_2}\sqrt{P(A=\alpha_1)P(B=\beta|A=\alpha_1)} P(A=\alpha_2) P(B=\beta|a=\alpha_2)

----The heart of the 'CNN' model lies in the cognitive process referred exclusively to the clinician who is at the helm while the network essentially remains the compass that warns of off course and/or suggests other alternative routes but the decision-making responsibility always refers to the clinician ( human mind). In this simple definition, we will perceive it better at the end of the chapter, the synergism 'Neural network' and 'Human cognitive process' of the clinician will be self-implementing because on the one hand the clinician is trained or better guided by the neural network (database) and this The last one will be trained on the latest updated scientific-clinical event. Basically, the definitive diagnosis will add an additional piece of information to the temporal base knowledge <math>Kb_t</math>. This model differs substantially from 'machine learning' just by observing the two models in their structural configuration (Figures 1 and 3).

[[File:Joim12822-fig-0004-m.jpeg|alt=|left|thumb|200x200px|'''Figure 3:''' Graphic representation of an archetypal ANN in which it can be seen in the first stage of initialization where there are five input nodes<ref name=":1">G S Handelman, H K Kok, R V Chandra, A H Razavi, M J Lee, H Asadi. eDoctor: machine learning and the future of medicine.J Intern Med.2018 Dec;284(6):603-619.doi: 10.1111/joim.12822. Epub 2018 Sep 3.</ref> while in the 'CNN' model the first stage is composed of only one node. Follow text. ]]Figure 3 shows a typical neural network, also known as artificial NNs. These artificial NNs attempt to use multiple layers of calculations to mimic the concept of how the human brain interprets and draws conclusions from information.<ref name=":1" /> NNs are essentially mathematical models designed to handle complex and disparate information, and this algorithm's nomenclature comes from its use of synapse-like "nodes" in the brain.<ref>Schwarzer G, Vach W, Schumacher M. On the misuses of artificial neural networks for prognostic and diagnostic classification in oncology. Stat Med 2000; 19: 541–61.</ref> The learning process of a NN can be supervised or unsupervised. A neural network is said to learn in a supervised manner if the desired output is already targeted and introduced into the network by data training while unsupervised NN has no such pre-identified target outputs and the goal is to group similar units close together in certain areas of the range of values. The supervised module takes data (e.g., symptoms, risk factors, imaging and laboratory findings) for training on known outcomes and searches for different combinations to find the most predictive combination of variables. NN assigns more or less weight to certain combinations of nodes to optimize the predictive performance of the trained model.<ref>Abdi H. A neural network primer. J Biol Syst 1994; 02: 247–81.</ref>