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János Végh

Publications and source records attributed to János Végh.

At least 19 recordsLinked to original sources

Is K+ current in Action Potential real?

The correct description of ion traffic during an Action Potential in neurons has fundamental importance from its theoretical description to practical clinical applications. The classical Hodgkin-Huxley theory hypothesized that a delayed K+ current causes the hyperpolarization of the Action Potential. The paper shows that the membrane's capacitive current was misinterpreted as K+ current, and that the direct evidence for that current is an experimental artifact, both arising from an incomplete understanding of the electrical model. After introducing the Axon Initial Segment (discovered decades after constructing the classical model) and using the correct cross-disciplinary physical laws for living nature (instead of using laws valid for inanimate nature), one can construct the correct model for neuronal operation, which provides the perfect description of neuronal operation, including the Action Potential, in full accordance with the laws of science.

physics.bio-ph↗

Do ions have a coating in neuronal electrolytes under an electric field?

Ions in electrolytes can have coatings and can combine into different complexes that significantly affects their size, mass, and transport features. Measuring such coatings of ions traveling in narrow, limited spaces inside biological objects is challenging. We assumed that the coating changes the size of the ion and that the speed of the complex may influence its composition. The original Stokes-Einstein relation for diffusion assumes a simple, spherical particle in a homogeneous Newtonian fluid. It was modified to describe an electric-field-driven drift. One possible application of the result is describing the propagation of axonal impulses, especially since soliton theory models it as a mechanical vibration, i.e., its speed changes rapidly. Hodgkin and Huxley measured the membrane current (due to axonal current) as a function of the clamping voltage, and they confirmed the linear dependence, that is, the independence of the ions' coating state from their speed.

physics.bio-ph↗

The unified cross-disciplinary model of the operation of neurons

Physics perfectly describes neuronal operation, provided that we take into account that biology uses slow, positively charged ions rather than electrons as charge carriers and remove untested ad hoc hypotheses that contradict science's first principles. We also incorporate recent experimental discoveries into the outdated classic theoretical description. Lipid mechanisms are really very important for cellular biology, but they are certainly not suitable for describing the phenomena we discuss. We introduce the correct physical model, significantly enhancing the classic \gls{HH} model; furthermore, the fundamentally bio-electrically triggered operation leads to changes in the electrical, mechanical, and thermodynamic properties of living matter. We derive the resting potential from first principles of science, showing that it is unrelated to an ad hoc linear combination of mobilities or reversal potentials, as the \gls{GHK} equation claims. Furthermore, we derive an "equivalent thermodynamic electric field" that enables discussion of, among others, the operation of ion channels, their ion selectivity, and voltage sensing. We demonstrate that a simple electrical-thermodynamic control circuit regulates neuronal operation, setting and maintaining a stable resting potential and handling an unstable transient process known as the \gls{AP}. Its setpoint entirely defines the resting potential, explaining its robustness during growth and evolution. Our cross-disciplinary approach naturally fuses the electrical and mechanical/thermodynamic description of neuronal operation, resolves the decades-old mystery of "heat absorption" and "leakage current" (with their far-reaching consequences), and derives the thermodynamic description of neural computing. We defy that science cannot describe life.

physics.bio-ph↗

How cross-disciplinary science can describe living matter

Experience shows that disciplinary science cannot describe life without contradictions. We show that one of the fundamental reasons is the disciplinarity itself: the disciplines deal with a limited set of quantities. This way some 'outlaw' quantities are not measured and the discipline does not have laws about them. All laws of science are based on approximations and the approximations must be different for inanimate and life sciences. Studying ions is special because ions belong simultaneously to thermodynamics and electricity, but neither of those disciplines alone can describe biological processes. One needs a cross-disciplinary discussion and maybe a new scientific discipline. We provide a method for handling the different interaction speeds characterizing the ion transport. Electrolytes in living matter introduce further peculiarities with their closed volumes, internal structure, and slow processes. Their meticulous analysis led to the appropriate approximations, leading to the correct scientific description. As a success story, the cross-disciplinary theory of neuronal operation has been developed.

physics.bio-ph↗

Finally, how many efficiencies supercomputers have? And, what do they measure?

Using an extremely large number of processing elements in computing systems leads to unexpected phenomena, such as different efficiencies of the same system for different tasks, that cannot be explained in the frame of classical computing paradigm. The simple non-technical (but considering the temporal behavior of the components) model, introduced here, enables us to set up a frame and formalism, needed to explain those unexpected experiences around supercomputing. Introducing temporal behavior into computer science also explains why only the extreme scale computing enabled us to reveal the experienced limitations. The paper shows, that degradation of efficiency of parallelized sequential systems is a natural consequence of the classical computing paradigm, instead of being an engineering imperfectness. The workload, that supercomputers run, is much responsible for wasting energy, as well as limiting the size and type of tasks. Case studies provide insight, how different contributions compete for dominating the resulting payload performance of a computing system, and how enhancing the interconnection technology made computing+communication to dominate in defining the efficiency of supercomputers. Our model also enables to derive predictions about supercomputer performance limitations for the near future, as well as it provides hints for enhancing supercomputer components. Phenomena experienced in large-scale computing show interesting parallels with phenomena experienced in science, more than a century ago, and through their studying a modern science was developed.

cs.PF↗

Which scaling rule applies to Artificial Neural Networks

The experience shows that cooperating and communicating computing systems, comprising segregated single processors, have severe performance limitations. In his classic "First Draft" von Neumann warned that using a "too fast processor" vitiates his simple "procedure" (but not his computing model!); furthermore, that using the classic computing paradigm for imitating neuronal operations, is unsound. Amdahl added that large machines, comprising many processors, have an inherent disadvantage. Given that ANN's components are heavily communicating with each other, they are built from a large number of components designed/fabricated for use in conventional computing, furthermore they attempt to mimic biological operation using improper technological solutions, their achievable payload computing performance is conceptually modest. The type of workload that AI-based systems generate leads to an exceptionally low payload computational performance, and their design/technology limits their size to just above the "toy" level systems: the scaling of processor-based ANN systems is strongly nonlinear. Given the proliferation and growing size of ANN systems, we suggest ideas to estimate in advance the efficiency of the device or application. Through analyzing published measurements we provide evidence that the role of data transfer time drastically influences both ANNs performance and feasibility. It is discussed how some major theoretical limiting factors, ANN's layer structure and their methods of technical implementation of communication affect their efficiency. The paper starts from von Neumann's original model, without neglecting the transfer time apart from processing time; derives an appropriate interpretation and handling for Amdahl's law. It shows that, in that interpretation, Amdahl's Law correctly describes ANNs.

cs.DC↗

Revising the classic computing paradigm and its technological implementations

Today's computing is told to be based on the classic paradigm, proposed by von Neumann, a three-quarter century ago. However, that paradigm was justified (for the timing relations of) vacuum tubes only. The technological development invalidated the classic paradigm (but not the model!) and led to catastrophic performance losses in computing systems, from operating gate level to large networks, including the neuromorphic ones. The paper reviews the critical points of the classic paradigm and scrutinizes the confusion made around it. It discusses some of the consequences of improper technological implementation, from the shared media to the parallelized operation. The model is perfect, but it is applied outside of its range of validity. The paradigm is extended by providing the "procedure" that enables computing science to work with cases where the transfer time is not negligible apart from processing time.

cs.AR↗

von Neumann's missing "Second Draft": what it should contain

Computing science is based on a computing paradigm that is not valid anymore for today's technological conditions. The reason is that the transmission time even inside the processor chip, but especially between the components of the system, is not negligible anymore. The paper introduces a quantitative measure for dispersion, which is vital for both computing performance and energy consumption, and demonstrates how its value increased with the changing technology. The temporal behavior (including the dispersion of the commonly used synchronization clock time) of computing components has a critical impact on the system's performance at all levels, as demonstrated from gate-level operation to supercomputing. The same effect limits the utility of the researched new materials/effects if the related transfer time cannot be proportionally mitigated. von Neumann's model is perfect, but now it is used outside of its range of validity. The correct procedure to consider the transfer time for the present technological background is also derived.

cs.AR↗

Introducing temporal behavior to computing science

The abstraction introduced by von Neumann correctly reflected the state of the art 70 years ago. Although it omitted data transmission time between components of the computer, it served as an excellent base for classic computing for decades. Modern computer components and architectures, however, require to consider their temporal behavior: data transmission time in contemporary systems may be higher than their processing time. Using the classic paradigm leaves some issues unexplained, from enormously high power consumption to days-long training of artificial neural networks to failures of some cutting-edge supercomputer projects. The paper introduces the up to now missing timely behavior (a temporal logic) into computing, while keeps the solid computing science base. The careful analysis discovers that with considering the timely behavior of components and architectural principles, the mystic issues have a trivial explanation. Some classic design principles must be revised, and the temporal logic enables us to design a more powerful and efficient computing.

cs.DC↗

On the spatiotemporal behavior in biology-mimicking computing systems

The payload performance of conventional computing systems, from single processors to supercomputers, reached its limits the nature enables. Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems from inexpensive conventional components and build different ("neuromorphic") computing systems. On one side, those systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling. On the other side, the neuronal operation drastically differs from the conventional workloads. The conventional computing (including both its mathematical background and physical implementation) is based on assuming instant interaction, while the biological neuronal systems have a "spatiotemporal" behavior. This difference alone makes imitating biological behavior in technical implementation hard. Besides, the recent issues in computing called the attention to that the temporal behavior is a general feature of computing systems, too. Some of their effects in both biological and technical systems were already noticed. Nevertheless, handling of those issues is incomplete/improper. Introducing temporal logic, based on the Minkowski transform, gives quantitative insight into the operation of both kinds of computing systems, furthermore provides a natural explanation of decades-old empirical phenomena. Without considering their temporal behavior correctly, neither effective implementation nor a true imitation of biological neural systems are possible.

cs.ET↗

How to extend the Single-Processor Paradigm to the Explicitly Many-Processor Approach

The computing paradigm invented for processing a small amount of data on a single segregated processor cannot meet the challenges set by the present-day computing demands. The paper proposes a new computing paradigm (extending the old one to use several processors explicitly) and discusses some questions of its possible implementation. Some advantages of the implemented approach, illustrated with the results of a loosely-timed simulator, are presented.

cs.AR↗

Do we know the operating principles of our computers better than those of our brain?

The increasing interest in understanding the behavior of the biological neural networks, and the increasing utilization of artificial neural networks in different fields and scales, both require a thorough understanding of how neuromorphic computing works. On the one side, the need to program those artificial neuron-like elements, and, on the other side, the necessity for a large number of such elements to cooperate, communicate and compute during tasks, need to be scrutinized to determine how efficiently conventional computing can assist in implementing such systems. Some electronic components bear a surprising resemblance to some biological structures. However, combining them with components that work using different principles can result in systems with very poor efficacy. The paper discusses how the conventional principles, components and thinking about computing limit mimicking the biological systems. We describe what changes will be necessary in the computing paradigms to get closer to the marvelously efficient operation of biological neural networks.

cs.DC↗

How deep the machine learning can be

Today we live in the age of artificial intelligence and machine learning; from small startups to HW or SW giants, everyone wants to build machine intelligence chips, applications. The task, however, is hard: not only because of the size of the problem: the technology one can utilize (and the paradigm it is based upon) strongly degrades the chances to succeed efficiently. Today the single-processor performance practically reached the limits the laws of nature enable. The only feasible way to achieve the needed high computing performance seems to be parallelizing many sequentially working units. The laws of the (massively) parallelized computing, however, are different from those experienced in connection with assembling and utilizing systems comprising just-a-few single processors. As machine learning is mostly based on the conventional computing (processors), we scrutinize the (known, but somewhat faded) laws of the parallel computing, concerning AI. This paper attempts to review some of the caveats, especially concerning scaling the computing performance of the AI solutions.

cs.DC↗

Re-evaluating scaling methods for distributed parallel systems

The paper explains why Amdahl's Law shall be interpreted specifically for distributed parallel systems and why it generated so many debates, discussions, and abuses. We set up a general model and list many of the terms affecting parallel processing. We scrutinize the validity of neglecting certain terms in different approximations, with special emphasis on the famous scaling laws of parallel processing. We clarify that when using the right interpretation of terms, Amdahl's Law is the governing law of all kinds of parallel processing. Amdahl's Law describes among others the history of supercomputing, the inherent performance limitation of the different kinds of parallel processing and it is the basic Law of the 'modern computing' paradigm, that the computing systems working under extreme computing conditions are desperately needed.

cs.PF↗

The need for modern computing paradigm: Science applied to computing

More than hundred years ago the 'classic physics' was it in its full power, with just a few unexplained phenomena; which however led to a revolution and the development of the 'modern physics'. Today the computing is in a similar position: computing is a sound success story, with exponentially growing utilization, but with a growing number of difficulties and unexpected issues as moving towards extreme utilization conditions. In physics studying the nature under extreme conditions has lead to the understanding of the relativistic and quantal behavior. Quite similarly in computing some phenomena, acquired in connection with extreme (computing) conditions, cannot be understood based on of the 'classic computing paradigm'. The paper draws the attention that under extreme conditions qualitatively different behaviors may be encountered in both physics and computing, and pinpointing that certain, formerly unnoticed or neglected aspects enable to explain new phenomena as well as to enhance computing features. Moreover, an idea of modern computing paradigm implementation is proposed.

cs.GL↗

The performance wall of parallelized sequential computing: the dark performance and the roofline of performance gain

The computing performance today is developing mainly using parallelized sequential computing, in many forms. The paper scrutinizes whether the performance of that type of computing has an upper limit. The simple considerations point out that the theoretically possible upper bound is practically achieved, and that the main obstacle to step further is the presently used computing paradigm and implementation technology. In addition to the former "walls", also the "performance wall" must be considered. As the paper points out, similarly to the "dark silicon", also the "dark performance" is always present in the parallelized many-processor systems.

cs.DC↗

Limitations of performance of Exascale Applications and supercomputers they are running on

The paper highlights that the cooperation of the components of the computing systems receives even more focus in the coming age of exascale computing. It discovers that inherent performance limitations exist and identifies the major critical contributions of the performance on many-many processor systems. The extended and reinterpreted simple Amdahl model describes the behavior of the existing supercomputers surprisingly well, and explains some mystical happenings around high-performance computing. It is pointed out that using the present technology and paradigm only marginal development of performance is possible, and that the major obstacle towards higher performance applications is the 70-years old computing paradigm itself. A way to step forward is also suggested

cs.DC↗

Statistical considerations on limitations of supercomputers

Supercomputer building is a many sceene, many authors game, comprising a lot of different technologies, manufacturers and ideas. Checking data available in the public database in a systematic way, some general tendencies and limitations can be concluded, both for the past and the future. The feasibility of building exa-scale computers as well as their limitations and utilization are also discussed. The statistical considerations provide a strong support for the conclusions.

cs.DC↗