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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 1 through 15 of 38 found.


> Geonativity

Autorino, Camilla and Nicoletta Petridou. Critical Phenomenon in Embryonic Organization. Critical Phenomenon in Embryonic Organization. Vol. 31, September, 2022. European Molecular Biology Laboratory, Heidelberg University biochemists describe an historic conceptual advance in this regard by a novel ability to perceive and quantify self-organized criticalities in effect during prenatal development. As the quotes say, even life’s earliest formative phases can be traced to a deep physical source. See also Programmed and Self-Organized Flow of Information during Morphogenesis by Claudio Collinet and Thomas Lecuit in Nature Reviews Molecular Cell Biology (22/245, 2021).

For a concurrent paper (these insights seem just now possible) see Animal Synchrony and Agent’s Segregation by Laura Schaposnik, et al (arXiv:2212.07505). We also note a 1995 paper Evolution as a Self-Organized Critical Phenomena by Per Bak, et al (PNAS, 92/5219) in response to initial their notices of SOC theories. Some three decades later might it be again considerable, by way of our global collaborations, to understand how we all arose from a conducive ecosmos.

The physics of critical points lies behind the organization of various complex systems, from molecules to ecosystems. Several functional benefits emerge when operating at a “criticality” state. Here, we propose that introducing this dynamic concept in developmental biology may explain remarkable features of embryonic development, such as collective behavior and fitness. Recent interdisciplinary work has studied embryogenesis within statistical physics frameworks and found that biochemical and biomechanical processes do indeed resemble critical phenomena. In regard we discuss gene expression, cell differentiation, and tissue mechanics whereby a critical balance can foster an optimum organization. (Abstract)

Many natural phenomena are considered to operate via similar mechanisms. Evolutionary systems, bird flocks, epidemic spreading, immune systems, intelligent behavior, neuronal activity, and gene expression are only a subset of biological phenomena that although display differences in their physical characteristics, such as size, shape, and material composition, also express remarkable similarities in their mode of organization and function. A major commonality is that they can be described using general frameworks of statistical physics by way of critical points in phase transitions (PTs). Criticality is traced from unique mathematical traits using PTs theories which can be studied via dynamical systems frameworks or statistical mechanics. Given that these traits are universal and largely independent, it is considered that such a deep theoretical basis can somehow wholly serve to explain the behavior of various critical occasions across nature. (1)

This search for a unifying theory of natural complexity an active research direction in physics. Why are biologists becoming more and more interested in it? Living systems appear to “know” how to reproducibly develop, as well as evolve and adapt to environments. One hypothesis is that their fitness is gained at criticality, since operating close to a critical point can balancing between functional regimes, so the system may efficiently shift between them. (2) The natural physics to seek and reside at a critical juncture can even be seen, by our work, to underlie embryo development, ranging from gene expression and cell differentiation, to tissue mechanics and morphogenesis. (5)

In conclusion, the developing embryo exhibits complex physical traits, which biologists attribute to the numerous mechanochemical interactions. Biology, however, shows that development needs to be approached as a whole, as a critical system. By utilizing statistical physics, scientists will be able to bridge theory and experiments to address the functional role of criticality in embryo development. (6)

> Geonativity

Chen, Luyao, et al. AI of Brain and Cognitive Sciences: From the Perspective of First Principles. arXiv:2301.08382. Sixteen Chinese length scholars under the auspices of the AI of Brain and Cognitive Sciences Research Group, Beijing Academy of Artificial Intelligence, and Beijing University post an array of chapters, per the first quote, in an effort to advance AI abilities by better appreciations of how our own cerebral faculties have actually formed and well function. Our main interest is Criticality: Bringing New Perspectives to the Brain and AI, which gathers and presents an extensive, latest survey as this realization lately gains a wide, quantified acceptance. The two longer quotes are from this section.

This paper collects six such first principles summarized by the research team, “AI of Brain and Cognitive Sciences”, in the Beijing Academy of Artificial Intelligence (BAAI). They are attractor network, criticality, random network, sparse coding, relational memory, and perceptual learning. On each topic, we review its biological background, fundamental property, potential application to AI, and future development.

The framework of criticality is a powerful tool to understand and analyze complex systems because many systems in physics and nature are in a critical state. In the past 20 years, researchers found that biological neural networks in the brain operate close to a critical state, which provides a new perspective on studying brain dynamics. It is known that the critical state is important for brain activities/functions because it optimizes numerous aspects of information transmission, storage, and processing. In addition, some brain diseases are believed to be related to the deviation from the critical state, which also opens a new window for diagnosing and treating these diseases. In the field of artificial intelligence, the framework of the critical state is used to analyze and guide both the structural design and weight initialization of deep neural networks, suggesting that operating close to a critical state may be considered one of the fundamental principles governing computations in neural networks. (9)

The critical state gives us a new perspective to study biological and artificial neural networks. At present, the framework of criticality has not only been used to understand neural dynamics and brain diseases but also to analyze the operation of deep neural networks and guide the design for further improvement. Through theoretical analysis and numerical simulations, we know that the critical state of the network can be controlled by some simple control parameters, such as branching ratio, spectral radius, and input-output Jacobian singular values. This makes it possible to analyze or tune the overall behavior of complex networks through statistics that are easy to observe. We believe that the framework of criticality will play an even more important role in helping us better understand the constraints applied to artificial neural networks and to design better architectures as well as dynamical rules to improve its performance in complex information processing. (14)

> Geonativity

Cheraghalizadeh, J., et al. Simulating Cumulus Clouds Based on Self-Organized Criticality. arXiv:2211.06111. University of Mohaghech Ardabili, Iran and ETH Zurich physicists advance recent findings that even such weather conditions can be found to take on and exhibit this intrinsic phenomenal preference. In regard, this widespread, exemplary occasion of a SOC viability strongly indicates an independent source which is universally inl manifest effect.

Recently it was shown that self-organized criticality is an important ingredient of the dynamics of cumulus clouds (Physical Review E, 103(5), 2021). Here we introduce a new algorithm to simulate cumulus clouds in two-dimensional square lattices, based on the cohesive energy of wet air parcels and a sandpile-type diffusion of cloud segments. We observed that the cloud fields that we obtain from our model are fractal, with the outer perimeter having a fractal dimension. (Excerpt)

> Geonativity

Frolov, Nikita and Alexander Hramov. Self-Organized Bistability on Scale-Free Networks. arXiv:2211.06111. In this litany of SOC occurrences, Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, scientists identify still another instance by way of extreme human cerebral states which takes on this double bilateral dynamic mode. (by whatever lights then might such endemic findings be applied to cease insane warfare)

A dynamical system approaching the first-order transition can exhibit a critical behavior known as self-organized bistability SOB which can switch between oexisting states under self-tuning of a control parameter. Here, we theoretically explore an extension of the SOB concept on the scale-free network which originates from facilitated criticality macro- and mesoscopic levels. The spatial self-organization and temporal self-similarity of the critical dynamics then replicates epileptic seizure recurrences. Thus our proposed conceptual model can deepen the understanding of emergent collective behavior behind neurological diseases. (Excerpt)

> Geonativity

Kagaya, Katsushi, et al.. Self-organized Criticality of Dendritic Readiness Potential. arXiv:2209.09075. University of Tokyo neuroresearchers report the latest sophisticated experimental proofs of nature’s widely ubiquitous avail and preference for this best balance optimum condition.

Self-organized criticality is a principle explaining avalanche-like phenomena obeying power-laws in integrate-and-fire type dynamical systems. Here, we demonstrate that the behaviorally relevant brain neurons, mediating voluntary and reflexive behaviors in crayfish show signatures of self-organized criticality. The dendritic activities reside at critical states with power-laws and scaling functions, in line with the extracellular neuronal avalanches in vertebrate species which provide similar evidence of the critical brain. Our intracellular data extend the "from crayfish to human" universality of the hypothesis. Thus the nervous systems can exploit the universal dynamics for volition across the phylogenetic tree. (Abstract)

> Geonativity

Kauffman, Stuart, et al, eds.. The Principle of Dynamical Criticality.. Entropy. December, 2022. This is a special issue to collect a current flow of evident findings about nature’s deep propensity across the universe to seek and reside at this optimum resolve. It is edited by SK, Roberto Serra, University of Modena, Italy, and Ilya Shmulevich and Sui Huang, Institute of Systems Biology, Seattle. Among the six papers so far are Emergent Criticality in Coupled Boolean Networks by Chris Kang, et al, and Robustness and Flexibility of Neural Function through Dynamical Criticality by Marcel Magnasco (see review herein).

While life, as Darwin noted, displays “endless forms most beautiful” at a macroscopic scale, it appears much more uniform at a microscopic level, where living systems share many common structural and functional features. There are, however, few “operating principles” at a macroscale that seem to hold for large classes of organisms. A promising candidate is the “criticality” principle, whereby evolution would have driven living beings towards critical states, since they are should be favorably selected over those that are chaotic or ordered. Moreover, since dynamically critical states are endowed with computational properties, they are interesting outside the domain of biology, such as artificial designs. (Excerpt)

> Geonativity

Magnasco, Marcelo. Robustness and Flexibility of Neural Function through Dynamical Criticality. Entropy. 24/5, 2022. In a special issue on this title subject into the 2020s, a Rockefeller University integrative neuroscientist (view his RU website) writes one of the strongest theoretical confirmations of this common phenomena to date. After an extensive review of earlier notices back to Stuart Kauffman in 1970s and Leo Szilard before, as this site section reports, it has lately become well known that this preferred poise actually occurs from celestial, quantum and material phases all through life’s cellular and communal development. A natural propensity thus seems to seek, prefer and reside at an optimum middle way via a reciprocity of more or less order, conserve or create, tradition and innovation options, and so on. Into 2023, after decades and years of complexity studies, by a combined virtue of structural and vitality features our Earthuman acumen may at last have reached a true discovery which can be affirmed and announced. As a horrific war rage close to the lands of Mendel and Galileo, this grand fulfillment need take on a guise as an epochal, crucial, revolutionary realization.

In theoretical biology, robustness refers to the ability of a biological system to function even under a stress of basic parameters (temperature or pH); flexibility refers to the ability of a system to switch functions or behaviors easily when necessary. While there are extensive explorations of the concept of robustness and what it requires mathematically, understanding flexibility has proven elusive, as well as also elucidating the opposite mathematical models for either mode. In this paper we consider a numberof neuroscience theories that show both robustness and flexibility can be attained by systems that poise themselves at the onset of dynamical bifurcations, which can influence the integration of information processing and function. (Abstract)

Long-term survival requires surviving many short terms. Thus species need to do “well enough” in the short term, but able ability to change when the niche shifts. In physiology, these two conflicting demands are identified with “robustness”, the ability of a physiological system to perform the same task correctly, and “flexibility”, so to adjust when as conditions change. While studies have explored robustness most often in molecular cell biology, the theoretical bases of biological flexibility are still obscure. One of the most striking forms of flexibility in neural function is integration. This phenomenon occurs at various scales, from the input-dependent changes in the range of intracortical functional connectivity, all the way up to entire brain areas working together to form associations. (1, 2)

Conclusion A large number of biological systems have shown dynamics that are linked to a system state which spontaneously poises itself at the boundary of dynamical transitions. We have reviewed the evolution of these ideas over many years and their firm rootings in experimental evidence. We have derived from this a family of models, the critically coupled map lattices. We have here shown the direct similarity and many connections to a related notion of criticality, that of “edge of chaos” dynamics, which altogether become a cellular automaton Turing universality. (15)

Marcelo Magnasco’s neuropsychology group uses living beings as a source of inspiration for creating new mathematical descriptions of nature. The lab’s focus is on computational and experimental methods to model the complexity, organization, and information-processing properties of living organisms. A primary interest is auditory function along with studies of vision, memory, olfaction, and sensory processing. Human beings are the main subject but dolphin communication in aquaria and the wild is also undertaken. (RU synopsis)

> Geonativity

Mondal, Suman, et al. Self-Organized Criticality of Magnetic Avalanches in Disordered Ferrimagentic Material. arXiv:2210.05183. Indian Association for the Cultivation of Science researchers posted in Kolkata and Bangalore proceed to report the universal presence of this “golden mean” optimum state even across these substantial domains. (Once again, our intent is to document SOC occurrences everywhere across a genesis nature so that public politics might finally also become healed by a bicameral complementarity.)

We observe multiple step-like jumps in a Dy-Fe-Ga-based ferrimagnetic alloy in its magnetic hysteresis curve. The observed jumps have a stochastic character with respect to their magnitude and critical field of occurrence.. The jump size distribution follows a power law indicating the scale invariance nature of the jumps. The flipping of coupled Dy and Fe clusters is responsible for the observed discrete avalanche-like features in the hysteresis loop. These characteristics indicate that the present phenomenon can be well described within the realm of self-organized criticality. (Abstract)

> Geonativity

Pereverzev, Sergey. Dark Matter Searches and Energy Accumulation and Release in Materials. arXiv:2212.13964. This presentation at the 14th International Conference on Identification of Dark Matter (Vienna 2022) by a Lawrence Livermore experimental physicist describes highly complex, diverse experiments which show how self-organized critical phenomena is inherently pervasive all manner of material and energetic phases.

This presentation at the 14th International Conference on Identification of Dark Matter (Vienna 2022) by a Lawrence Livermore experimental physicist describes highly complex, diverse experiments which show how self-organized critical phenomena is inherently pervasive all manner of material and energetic phases.

> Geonativity

Provata, Astero.. From Turing Patterns to Chimera States in the 2D Brusselator Model. arXiv:2212.01297. A National Center for Scientific Research, Athens (bio below) physicist contributes a latest explanation about these spatial and temporal conditions which appear across a widening array of life’s natural and social phenomena. A novel contribution is their aptness to shift from a morphogenetic phase to a self-organized critical activity. On a personal note, I had lunch with Ilya Prigogine in a group in 1987 at a conference. Some 35 years on, into these 2020s the complexity sciences have come to and settled on this common non-equilibrium occasion, a true universality in kind.

The Brusselator has been used as a prototype model for autocatalytic reactions, and for the Belouzov-Zhabotinsky reaction. When coupled at the diffusive limit, the it undergoes a bifurcation resulting in the formation of classical Turing patterns such as spots, stripes and spirals. In the present study we use generic nonlocally coupled Brusselators and show that in the diffuse limit of the coupling range, the Turing effects are recovered, while for intermediate coupling ranges and appropriate parameter values chimera states are produced. This study demonstrates how the parameters of a typical nonlinear oscillator can be tuned so that the coupled system passes from spatially stable structures to dynamical spatiotemporal chimera modes. (Abstract)

A decade after the first theoretical prediction of chimera states, experimental evidence has been reported in mechanical, physical and chemical lsystems consisting of interacting oscillatory units. Some examples are optical systems, in electronic circuits, in mechanics, in biomedicine, and reaction diffusion systems. Beyond experimental findings in the laboratory, chimera states have been associated with the uni-hemispheric sleep in mammals and birds, with the onset of epileptic seizures and other biomedical conditions. (2)
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The Brusselator is a theoretical model for a type of autocatalytic reaction. It was proposed by Ilya Prigogine and collaborators at the Université Libre de Bruxelle A Belousov–Zhabotinsky reaction servse as a classic example of non-equilibrium thermodynamics, resulting in the establishment of a nonlinear chemical oscillator.

Astero Provata received her B.Sc. degree in physics from the University of Athens in 1985 and Ph.D. degree from Boston University, USA, in 1991. She is currently Research Director of Nanoscience and Nanotechnology, Greece. Her interests include neural networks, nonlinear dynamical systems, statistical physics, fractals, and complex systems.

> Geonativity

Proverbio, Daniele, et al. Buffering in Cell Regulation Motifs Close to Criticality. arXiv:2212.08600. As late 2022 scientific studies proceed to find a widening propensity across life’s metabolic, and cerebral phases to achieve and benefit from dynamic sensitivities, University of Luxembourg theorists show how these a these nuanced responses can also foster a ecological resilience.

Bistable biological regulatory systems need to cope with stochastic noise to fine-tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer noise-induced regime shifts. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality. (Abstract)

Overall, our study characterised fundamental dynamical mechanisms to buffer systems’ variability in critical regimes. We determined parameter ranges, corresponding to plausible cooperativity values for the positive feedback loop motif, where both variance and autocorrelation display low relative sensitivity to additive noise. (5)

> Geonativity

Rao, Ankit, et al. Self-Assembled Meuromorphic Networks at Self-Organized Criticality in Ag-hBN Platform. arXiv:2301.01619. Eight India Institute of Science and University of Groningen researchers describe an involved physical material method by which to attain, demonstrate and explain this optimum performance condition.

Networks and systems which exhibit brain-like behavior can analyze information from noisy data with low power consumption due to the critical nature and complex interconnectivity of their neuronal-like network. We show that a system comprised of hexagonal Boron Nitride (hBN) films contacted with Silver (Ag), that can uniquely host two different self-assembled networks, which are self-organized at criticality (SOC). This system shows bipolar resistive switching between high resistance (HRS) and low resistance states (LRS). The temporal avalanche dynamics in both these states exhibit power-law scaling,
The temporal avalanche dynamics in both these states exhibit power-law scaling, long-range temporal correlation, and SOC. (Abstract excerpt)

> Geonativity

Schaposnik, Laura, et al. Animal Synchrony and Agent’s Segregation. arXiv:2212.07505. Into late 2022, a paper by University of Chicago, Illinois and Oxford University (Robin Dunbar) biobehavior researchers to appear in the Proceedings A of the Royal Society proceeds to add a a further causal mathematic basis whereby creaturely activities in diverse assemblies can be well modeled as reciprocal, self-organized critical, relations. By our notice, this is the first time (on schedule) that Kuramoto (cited), chimera-like oscillations have been applied to and seen in formative effect across to life’s multi-organism phase. See also Relating Size and Functionality in Human Social Networks through Complexity by Bruce West, Robin Dunbar, et al in PNAS (117/31, 2022) for another approach which can perceive and quantify critical behaviors of active groups.

In recent years it has become evident that a lack of coordination imposes constraints on the size of stable groups that highly social mammals can live in. Here we examine the forces that keep animals together as a herd and others that drive them apart. For example, different phenotypes (e.g. genders) have various rates of gut fill, causing them to spend more or less time performing activities. By modeling a group as a set of semi-coupled oscillators, we show that its members may become decoupled until the group breaks apart. We show that when social bonding creates a stickiness, or gravitational pull, between pairs of individuals, fragmentation is reduced. (Abstract)

> Geonativity

Shpurov, Ivan and Tom Froese. Evidence of Critical Dynamics in Movements of Bees inside a Hive. Entropy. 24/12, 2022. As scientific realizations in later 2022 report an increasing notice of a vital self-organized critically from quantum to neural and social realms, as this new section reports, for a Statistical Physics of Collective Behavior issue edited by Bryan Daniels (see below), Okinawa Institute of Science and Technology cognitive theorists (search Froese) even perceive and report how this optimum behavioral phenomena is present in insect activities.

Social insects such as honey bees exhibit complex behavioral patterns whose distributed coordination enables decision-making at the colony level. It has been proposed that a high-level description of their collective behavior might share commonalities with the dynamics of neural processes in brains. Here, we investigated this proposal by focusing on how brains are poised at the edge of a critical phase transition which fosters increased computational power and adaptability. We found that certain characteristics of the activity of the bee hive system are consistent with the Ising model when it operates at a critical temperature, and that the system’s behavioral dynamics share features with the human brain in the resting state. (Abstract)

Understanding how the adaptive behavior of groups is controlled by the individuals within them is a major challenge for 21st century science. From proteins in a cell to neurons in a brain, and from fish in a school to people in society, we know how most entities perform and interact, but mapping this to adaptive behavior at the aggregate scale is difficult. Statistical physics has long approached similar problems in non-living systems, connecting macroscopic theories to the microscopic details. This Special Issue will explore these themes using concepts such as coarse graining, renormalization, scaling, phase transitions, collective instabilities, broken symmetries, dynamical modes, free energy, critical phenomena, and more. Our aim is to build predictive theories o describe the collective behavior of proteins, bacteria, neurons, insects, mammals, fish, robots, computers, artificial neural networks, species, people, societies, and ideas. (Byron Daniels Entropy)

> Geonativity

Tian, Yang, et al. Theoretical Foundations of Studying Criticality in the Brain. Network Neuroscience. 6/4, 2022. For a special Connectivity, Cognition and Consciousness issue, Tsinghua University, Beijing, University of Paris, and Chinese Academy of Science researchers theoretically explain, clarify and advance how the dynamic presence of self-organized phenomena is being found to play a central creative role.

The brain criticality hypothesis is one of the most active topics in neuroscience and biophysics. This work develops a unified framework to reformulate the physics theories of four basic types of brain criticality, ordinary criticality (OC), quasi-criticality (qC), self-organized criticality (SOC), and self-organized quasi-criticality (SOqC), into more accessible and neuroscience-related forms. This framework may help resolve potential controversies in studying the brain criticality hypothesis, especially those arising from the misconceptions about the theoretical foundations of brain criticality. (Author)

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