The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

2020 ◽  
Author(s):  
Colin Hales
Keyword(s):  
Artificial Intelligence ◽  
Brain Tissue ◽  
General Purpose ◽  
Empirical Science ◽  
Background Information ◽  
Cell Tissue ◽  
Artificial Brain ◽  
Extended Analysis ◽  
General Purpose Computer ◽  
Theoretical Science

The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue. Instead of the physics of a general-purpose computer or the physics of abstract models on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. The chip has a functionally relevant endogenous quasistatic electric and magnetic field system of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like signature. This article does an extended analysis to understand an observed generalized lack of the physics replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes the necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<br>

scholarly journals The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

2020 ◽  
Author(s):  
Colin Hales
Keyword(s):  
Artificial Intelligence ◽  
Brain Tissue ◽  
General Purpose ◽  
Empirical Science ◽  
Background Information ◽  
Cell Tissue ◽  
Artificial Brain ◽  
Extended Analysis ◽  
Electromagnetic Signature ◽  
Theoretical Science

<p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has a functionally relevant endogenous quasistatic electric and magnetic field system of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes the necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br>

scholarly journals The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

2021 ◽  
Author(s):  
Colin Hales
Keyword(s):  
Artificial Intelligence ◽  
Brain Tissue ◽  
General Purpose ◽  
Empirical Science ◽  
Background Information ◽  
Cell Tissue ◽  
Artificial Brain ◽  
Extended Analysis ◽  
Electromagnetic Signature ◽  
Theoretical Science

<p></p><p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models of the brain on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has functionally relevant endogenous quasistatic electric and magnetic field systems of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics-replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br><p></p>

scholarly journals The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

2021 ◽  
Author(s):  
Colin Hales
Keyword(s):  
Artificial Intelligence ◽  
Brain Tissue ◽  
General Purpose ◽  
Empirical Science ◽  
Background Information ◽  
Cell Tissue ◽  
Artificial Brain ◽  
Extended Analysis ◽  
Electromagnetic Signature ◽  
Theoretical Science

<p></p><p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models of the brain on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has functionally relevant endogenous quasistatic electric and magnetic field systems of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics-replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br><p></p>

scholarly journals The Model-less Neuromimetic Chip and its Normalization of Neuroscience and Artificial Intelligence

2021 ◽  
Author(s):  
Colin Hales
Keyword(s):  
Artificial Intelligence ◽  
Brain Tissue ◽  
General Purpose ◽  
Empirical Science ◽  
Background Information ◽  
Cell Tissue ◽  
Artificial Brain ◽  
Extended Analysis ◽  
Electromagnetic Signature ◽  
Theoretical Science

<p></p><p>The conceptual basis of a novel neuromimetic chip is described. Based on an existing computational bioelectrodynamics study and adaptive brain signaling biophysics knowledge from neuroscience, the chip is, in effect, a form of inorganic artificial brain tissue. This ‘physics replication’ approach involves no abstract models of brain tissue physics or function. Instead of the physics of a general-purpose computer or the physics of abstract models of the brain on the chip (analogue or digital), this neuromimetic chip has an inorganic version of natural adaptive brain signaling physics. As a result of using the native brain physics, the chip has functionally relevant endogenous quasistatic electric and magnetic field systems of the form known to be expressed by excitable cell tissue. Fully developed at macroscopic scales it can be expected to produce an EEG/MEG-like electromagnetic signature. This article does an extended analysis to understand an observed generalized lack of the physics-replication approach and its implications for the neuroscience of natural and artificial intelligence. This is achieved through a technical comparison with the neuromimetic chip’s closest relative, the neuromorphic chip (of the class of general-purpose computers). The results indicate that the physics-replication approach is a possible but neglected option. It also reveals that the neuromimetic chip contributes empirical science, in contrast to the theoretical science conducted using general-purpose computers. Because of the chip’s novelty and proximity to foundational issues, the article contributes necessary background information in anticipation of the arrival of the first prototyping results over the coming years.<b></b></p><br><p></p>

scholarly journals Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 312
Author(s):  
Naruki Hagiwara ◽  
Shoma Sekizaki ◽  
Yuji Kuwahara ◽  
Tetsuya Asai ◽  
Megumi Akai-Kasaya
Keyword(s):  
Artificial Intelligence ◽  
Human Brain ◽  
High Speed ◽  
Conductive Polymer ◽  
Long Term Potentiation ◽  
Short Term ◽  
Synaptic Functions ◽  
Term Potentiation ◽  
Artificial Brain ◽  
External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

scholarly journals Lessons from the COVID-19 Pandemic on the Use of Artificial Intelligence in Digital Radiology: The Submission of a Survey to Investigate the Opinion of Insiders

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 331
Author(s):  
Daniele Giansanti ◽  
Ivano Rossi ◽  
Lisa Monoscalco
Keyword(s):  
Artificial Intelligence ◽  
General Purpose ◽  
Point Of View ◽  
General Point ◽  
Thoracic Radiography ◽  
Digital Radiology ◽  
Electronic Survey ◽  
Radiology Technicians ◽  
Medical Radiology ◽  
Great Development

The development of artificial intelligence (AI) during the COVID-19 pandemic is there for all to see, and has undoubtedly mainly concerned the activities of digital radiology. Nevertheless, the strong perception in the research and clinical application environment is that AI in radiology is like a hammer in search of a nail. Notable developments and opportunities do not seem to be combined, now, in the time of the COVID-19 pandemic, with a stable, effective, and concrete use in clinical routine; the use of AI often seems limited to use in research applications. This study considers the future perceived integration of AI with digital radiology after the COVID-19 pandemic and proposes a methodology that, by means of a wide interaction of the involved actors, allows a positioning exercise for acceptance evaluation using a general purpose electronic survey. The methodology was tested on a first category of professionals, the medical radiology technicians (MRT), and allowed to (i) collect their impressions on the issue in a structured way, and (ii) collect their suggestions and their comments in order to create a specific tool for this professional figure to be used in scientific societies. This study is useful for the stakeholders in the field, and yielded several noteworthy observations, among them (iii) the perception of great development in thoracic radiography and CT, but a loss of opportunity in integration with non-radiological technologies; (iv) the belief that it is appropriate to invest in training and infrastructure dedicated to AI; and (v) the widespread idea that AI can become a strong complementary tool to human activity. From a general point of view, the study is a clear invitation to face the last yard of AI in digital radiology, a last yard that depends a lot on the opinion and the ability to accept these technologies by the operators of digital radiology.

An IBM PC Based Computer Program for the Analysis of Infrared Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1739-1745
Author(s):  
Min J. Yang ◽  
Paul W. Yang
Keyword(s):  
Artificial Intelligence ◽  
Operating System ◽  
Computer Program ◽  
Infrared Spectra ◽  
Personal Computer ◽  
Research Laboratory ◽  
General Purpose ◽  
Disk Operating System ◽  
Interpretation System ◽  
Ibm Pc

A computerized infrared interpreter has been developed on an IBM personal computer (PC) running under the Microsoft disk operating system (DOS). Based on the original Merck Sharp & Dhome Research Laboratory Program for the Analysis of InfRared Spectra (PAIRS), this infrared interpreter, PC PAIRS+, is capable of analyzing infrared spectra measured from a wide variety of spectrophotometers. Modifications to PAIRS now allow the application of both artificial intelligence and library searching techniques in the program. A new algorithm has been devised to combine the results from the library searching and the PAIRS program to enhance the dependability of interpretational data. The increased capability of this infrared interpreter along with its applicability on a personal computer results in a powerful, general-purpose, and easy-to-use infrared interpretation system. Applications of PC PAIRS+ on petrochemical samples are described.

An Efficient Method for Calculating Damped Critical Speeds of Rotor-Bearing Systems

1991 ◽  
Author(s):  
D. Z. Wang ◽  
D. L. Taylor
Keyword(s):  
Element Model ◽  
General Purpose ◽  
Critical Speeds ◽  
Rotor Bearing ◽  
General Purpose Computer ◽  
System Equations ◽  
Newton Raphson ◽  
Reported Data ◽  
Derivatives Of ◽  
Purpose Computer

Abstract This paper describes an analytical approach for calculating the damped critical speeds of multi-degree-of-freedom rotor-bearing systems. It is shown that to calculate the critical speeds is equivalent to finding the roots of a proposed matrix algebraic equation. The technique employes a Newton-Raphson scheme and the derivatives of eigenvalues. The system left eigenvectors are used to simplify the calculations. Based on this approach, a general-purpose computer program was developed with a finite element model of rotor-bearing systems. The program automatically generates system equations and finds the critical speeds. The program is applied to analyze a turbomachine supported by two cylindrical oil-film Journal bearings. The results are compared with reported data and the agreements are very good.

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