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Difference between revisions of "Encrypted code: Ephaptic transmission"
Encrypted code: Ephaptic transmission (view source)
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The term 'Cognitive Neural Network' abbreviated to 'RNC' is a dynamic cognitive intellectual process of the clinician who interrogates the network for self-training. The 'RNC' is not a 'Machine Learning' because while the latter must be trained by the clinician, with statistical and prediction adjustments, the 'RNC' trains the clinician or rather directs the clinician to the diagnosis while always being questioned following a logical human, hence the term 'cognitive'. As demonstrated, the definition of 'Emasticatory spasm' in our patient Mary Poppins was not a clinically simple process, however, considering the themes presented in the previous chapters of Masticationpedia we have at least three supporting elements available: a vision of 'Quantum Probability' of physical-chemical phenomena in complex biological systems which will be discussed extensively in the specific chapters; a more formal and less vague language than the natural language which directs the diagnostic analysis to the first input node of the 'RNC' through the '<math>\tau</math> Coherence Demarcator' described in the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]'; the 'RNC' process which, being managed and guided exclusively by the clinician, becomes an essential means for the definitive diagnosis. The 'RNC', in fact, is the result of a profound cognitive process that is performed on each step of the analysis in which the clinician weighs his intuitions, clarifies his doubts, evaluates the reports, considers the contexts and advances step by step confronting the result of the answer coming from the database which in our case is Pubmed and which substantially represents the current level of basic knowledge <math>KB_t</math> at the time of the query and <math>KB_c</math> in the broader specialist contexts. | |||
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== | == Introduction == | ||
In the chapter '[[1° Clinical case: Hemimasticatory spasm - en|1st Clinical case: Hemimasticatory spasm]]' we immediately reached a conclusion bypassing all the cognitive, clinical and scientific process which underlies the diagnostic definition but it is not that simple otherwise our poor patient Mary Poppins would not have had to wait 10 years for the correct diagnosis.<blockquote>It should be emphasized that it is not a question of negligence on the part of clinicians rather of the complexity of 'biological complex systems' and above all of a mindset still anchored to a 'classical probability' which categorizes healthy and diseased phenotypes according to symptoms and signs sampled clinicians instead of probing the 'State' of the system in the temporal evolution. This concept, anticipated in the chapter '[[Logic of medical language: Introduction to quantum-like probability in the masticatory system]]' and in '[[Conclusions on the status quo in the logic of medical language regarding the masticatory system]]' has laid the foundations for a medical language more articulated and less deterministic, mainly focused on the 'State' of the 'Mesoscopic System' whose purpose is, essentially, to decrypt the message in machine language generated by the Central Nervous System as we will assist in the description of other clinical cases that will be reported in the next Masticationpedia chapters. </blockquote>This model, which we propose with the term 'Cognitive Neural Network' abbreviated as 'RNC' is a dynamic cognitive intellectual process of the clinician who interrogates the network for self-training. The 'RNC' is not a 'Machine Learning' because while the latter must be trained by the clinician, with statistical and prediction adjustments, the 'RNC' trains the clinician or rather directs the clinician to the diagnosis while always being questioned following a logical human, hence the term 'cognitive'. | |||
In fact, some classic machine learning models, whose training in the laboratory gives positive results, fail when applied to the real context. This is typically attributed to a mismatch between the datasets the machine was trained with and the data it encounters in the real world. A practical example of this can be represented by the conflict of assertions encountered in the diagnostic process of our patient Mary Poppins between the dental and neurological context which only the support of the coherence demarcator <math>\tau</math>(cognitive process) managed to solve. | |||
One of the limits of machine learning, therefore, is known as "data shift",<ref>Jérôme Dockès, Gaël Varoquaux, Jean-Baptiste Poline. Preventing dataset shift from breaking machine-learning biomarkers.GigaScience, Volume 10, Issue 9, September 2021, giab055,</ref> or "data movement" and another underlying cause of the failure of some models outside the laboratory, is the "subspecification"<ref>Alexander D’Amour et al. Underspecification Presents Challenges for Credibility in Modern Machine Learning. Journal of Machine Learning Research 23 (2022) 1-61,Submitted 11/20; Revised 12/21; Published 08/22</ref><ref>Damien Teney, Maxime Peyrard, Ehsan Abbasnejad. Predicting Is Not Understanding: Recognizing and Addressing Underspecification in Machine Learning.ECCV 2022: Computer Vision – ECCV 2022 pp 458–476Cite as</ref> so much so that the attempt to build an algorithm-enhanced electronic medical record (EMR) system designed specifically for use in a cancer center, was a notable failure at an estimated cost of $39,000,000 USD. This effort was a 2012 partnership between M.D. Anderson Partners and IBM Watson in Houston, Texas.<ref>Herper M. MD Anderson benches IBM Watson in setback for artificial intelligence in medicine. Forbes. 2017 February 19. [Ref list]</ref> Early promotional news describing the project stated that the plan was to combine genetic data, pathology reports with doctors' notes and relevant journal articles to help doctors come up with diagnoses and treatments. However, five years later, in February 2017, M.D. Anderson announced that he had closed the project because, after several years of trying, he hadn't produced a tool for use with patients that was ready to move beyond pilot testing.{{q2|Fascinating and provocative, explain to me in detail|... the model is essentially simple in its cognitive complexity}} | |||
In essence, the encrypted machine language message sent out by the Central Nervous System in the 10 years of illness of our patient Mary Poppins was interpreted through verbal language as Orofacial Pain from Temporomandibular Disorders'. We have remarked several times, however, that human verbal language is distorted by vagueness and ambiguity therefore, not being a formal language, such as mathematical language, it can generate diagnostic errors. The message in machine language sent out by the Central Nervous System to be searched for is not pain (pain is a verbal language) but the anomaly of 'System State' in which the organism was in that time period. Hence the shift from the semiotics of the symptom and the clinical sign to the '[[System logic|System Logic]]' which, through 'Systems Theory' models, quantify the system's responses to incoming stimuli, even in healthy subjects. | |||
All this is replicated in the proposed 'RNC' model by dividing the process into incoming triggers (Input) and outgoing data (Output) to then be reiterated in a loop managed cognitively by the clinician up to the generation of a single node useful for the definitive diagnosis. The model basically breaks down as follows: | |||
* '''Input:''' By incoming trigger we mean the cognitive process that the clinician implements as a function of the considerations received from previous statements, as has been pointed out in the chapters concerning the 'Medical language logic'. In our case, through the 'Consistency Demarcator <math>\tau</math>, the neurological context was defined as suitable instead of the dental one pursuing a clinical diagnostic explanation of TMDs. This trigger is of essential importance because it allows the clinician to center the network analysis initiation command which will connect a large sample of data corresponding to the set trigger. To this essential initial command, as an algorithmic decryption key, is added the last closing command which is equally important as it depends on the intuition of the clinician who will consider the decryption process finished. In Figure 1, the structure of the 'RNC' 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 'RNC' 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. | |||
[[File:Immagine 17-12-22 alle 11.34.jpeg|center|500x500px|'''Figura 1:''' Rappresentazione grafica della 'RNC' proposto da Masticationpedia|thumb]] | |||
[[File:Immagine 17-12-22 alle 11.34.jpeg|center|500x500px|'''Figura 1:''' Rappresentazione grafica della 'RNC' proposto da Masticationpedia| | |||
<center></center> | <center></center> | ||
* '''Output:''' | * '''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: | |||
<blockquote><math>+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) | <blockquote><math>+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)</math> | P(B=\beta|a=\alpha_2)</math> 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 testo di una formula matematica. Notare il comando <nowiki><math> di inizializzazione ed il comando </math></nowiki> di chiusura dello script]] | ||
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> | |||
<blockquote>'''00101011 00110010 01011100 01110011 01110101 01101101''' 01011111 01111011 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 00111100 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110010 01111101 01011100 01100011 01101111 01110011 01011100 01110100 01101000 01100101 01110100 01100001 01011111 01111011 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110010 01111101 01011100 01110011 01110001 01110010 01110100 01111011 01010000 00101000 01000001 00111101 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 00101001 01010000 00101000 01000010 00111101 01011100 01100010 01100101 01110100 01100001 01111100 01000001 00111101 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 00101001 01111101 00100000 01010000 00101000 01000001 00111101 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110010 00101001 00001010 01010000 00101000 01000010 00111101 01011100 01100010 01100101 01110100 01100001 01111100 01100001 00111101 01011100 01100001 '''01101100 01110000 01101000 01100001 01011111 00110010 00101001'''</blockquote><blockquote> | <blockquote>'''00101011 00110010 01011100 01110011 01110101 01101101''' 01011111 01111011 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 00111100 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110010 01111101 01011100 01100011 01101111 01110011 01011100 01110100 01101000 01100101 01110100 01100001 01011111 01111011 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110010 01111101 01011100 01110011 01110001 01110010 01110100 01111011 01010000 00101000 01000001 00111101 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 00101001 01010000 00101000 01000010 00111101 01011100 01100010 01100101 01110100 01100001 01111100 01000001 00111101 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110001 00101001 01111101 00100000 01010000 00101000 01000001 00111101 01011100 01100001 01101100 01110000 01101000 01100001 01011111 00110010 00101001 00001010 01010000 00101000 01000010 00111101 01011100 01100010 01100101 01110100 01100001 01111100 01100001 00111101 01011100 01100001 '''01101100 01110000 01101000 01100001 01011111 00110010 00101001'''</blockquote><blockquote>But what if the following string 00101011 00110010 01011100 01110011 01110101 01101101 which corresponds to the <nowiki><math> command is not present in this code? </nowiki></blockquote>The message would be corrupted and the formula would not be generated due to lack of the most important step that of 'Initialization of the command code', as well as if we eliminated the last part of the code 01101100 01110000 01101000 01100001 01011111 00110010 00101001, corresponding to the closure of the script < /math> the formula would remain corrupted and indeterminate. | ||
In | In practice, without the initial and final command, the formula is well described in the following form that is understandable to us: | ||
<math>+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) | <math>+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)</math> | P(B=\beta|a=\alpha_2)</math> | ||
it would present itself in a way incomprehensible to most people. | |||
+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) | +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) | ||
Just as the lack of part of the binary code corrupts the representation of the formula, similarly the decryption of the machine language of the CNS is a source of vagueness and ambiguity of the verbal language and contextually of diagnostic error. | |||
=== | === Cognitive process === | ||
----Il cuore del modello 'RNC' sta nel processo cognitivo riferito esclusivamente al clinico che ha in mano il timone mentre la rete rimane sostanzialmente la bussola che avverte del fuori rotta e/o suggerire altre rotte alternative ma la responsabilità decisionale è sempre riferita al clinico ( mente umana). In questo semplice definizione, lo percepiremo meglio a fine del capitolo, il sinergismo 'Rete neurale' e 'Processo cognitivo umano' del clinico sarà auto-implementante perchè da un lato il clinico viene addestrato o meglio guidato dalla rete neurale ( database) e quest'ultima verrà addestrata all'ultimo evento scientifico-clinico aggiornato. In sostanza la diagnosi definitiva aggiungerà un dato informativo in più alla conoscenza di base temporale <math>Kb_t</math>. Questo modello differisce sostanzialmente dal 'machine Learning' già solo osservando i due modelli nella loro configurazione strutturale ( Figura 1 e 3). | ----Il cuore del modello 'RNC' sta nel processo cognitivo riferito esclusivamente al clinico che ha in mano il timone mentre la rete rimane sostanzialmente la bussola che avverte del fuori rotta e/o suggerire altre rotte alternative ma la responsabilità decisionale è sempre riferita al clinico ( mente umana). In questo semplice definizione, lo percepiremo meglio a fine del capitolo, il sinergismo 'Rete neurale' e 'Processo cognitivo umano' del clinico sarà auto-implementante perchè da un lato il clinico viene addestrato o meglio guidato dalla rete neurale ( database) e quest'ultima verrà addestrata all'ultimo evento scientifico-clinico aggiornato. In sostanza la diagnosi definitiva aggiungerà un dato informativo in più alla conoscenza di base temporale <math>Kb_t</math>. Questo modello differisce sostanzialmente dal 'machine Learning' già solo osservando i due modelli nella loro configurazione strutturale ( Figura 1 e 3). | ||
[[File:Joim12822-fig-0004-m.jpeg|alt=|left|thumb|200x200px|'''Figura 3:''' Rappresentazione grafica di una RNA archetipica in cui si può notare nel primo stadio di inizializzazione dove sono presenti cinque nodi di input<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> mentre nel modello 'RNC' il primo stadio è composto da un solo nodo. Segui testo. ]]Nella figura 3 viene rappresentato una tipica rete neurale, note anche come NN artificiali. Queste NN artificiali tentano di utilizzare più livelli di calcoli per imitare il concetto di come il cervello umano interpreta e trae conclusioni dalle informazioni.<ref name=":1" /> NN sono essenzialmente modelli matematici progettati per gestire informazioni complesse e disparate e la nomenclatura di questo algoritmo deriva dal suo uso di "nodi" simili alle sinapsi nel cervello.<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> Il processo di apprendimento di un NN può essere supervisionato o non supervisionato. Si dice che una rete neurale apprenda in modo supervisionato se l'output desiderato è già mirato e introdotto nella rete mediante l'addestramento dei dati mentre NN non supervisionato non ha tali output target preidentificati e l'obiettivo è raggruppare unità simili vicine in determinate aree del intervallo di valori. Il modulo supervisionato prende i dati (ad es. sintomi, fattori di rischio, risultati di laboratorio e di imaging) per l'addestramento su esiti noti e cerca diverse combinazioni per trovare la combinazione più predittiva di variabili. NN assegna più o meno peso a determinate combinazioni di nodi per ottimizzare le prestazioni predittive del modello addestrato.<ref>Abdi H. A neural network primer. J Biol Syst 1994; 02: 247–81.</ref> | [[File:Joim12822-fig-0004-m.jpeg|alt=|left|thumb|200x200px|'''Figura 3:''' Rappresentazione grafica di una RNA archetipica in cui si può notare nel primo stadio di inizializzazione dove sono presenti cinque nodi di input<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> mentre nel modello 'RNC' il primo stadio è composto da un solo nodo. Segui testo. ]]Nella figura 3 viene rappresentato una tipica rete neurale, note anche come NN artificiali. Queste NN artificiali tentano di utilizzare più livelli di calcoli per imitare il concetto di come il cervello umano interpreta e trae conclusioni dalle informazioni.<ref name=":1" /> NN sono essenzialmente modelli matematici progettati per gestire informazioni complesse e disparate e la nomenclatura di questo algoritmo deriva dal suo uso di "nodi" simili alle sinapsi nel cervello.<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> Il processo di apprendimento di un NN può essere supervisionato o non supervisionato. Si dice che una rete neurale apprenda in modo supervisionato se l'output desiderato è già mirato e introdotto nella rete mediante l'addestramento dei dati mentre NN non supervisionato non ha tali output target preidentificati e l'obiettivo è raggruppare unità simili vicine in determinate aree del intervallo di valori. Il modulo supervisionato prende i dati (ad es. sintomi, fattori di rischio, risultati di laboratorio e di imaging) per l'addestramento su esiti noti e cerca diverse combinazioni per trovare la combinazione più predittiva di variabili. NN assegna più o meno peso a determinate combinazioni di nodi per ottimizzare le prestazioni predittive del modello addestrato.<ref>Abdi H. A neural network primer. J Biol Syst 1994; 02: 247–81.</ref> | ||