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A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
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Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Recent Additions

Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 46 through 60 of 112 found.

Cosmomics: A Genomic Source Code in Procreative Effect

Cosmic Code > 2015 universal

Filatov, Denis and Alexey Lyubushin. Stochastic Dynamical Systems Always Undergo Trending Mechanisms of Transition to Criticality. Physica A. Volume 521, 2019. Sceptica Science, UK and Russian Academy of Sciences physicists post a theoretical affirmation as to why and how nature’s active phenomena has an intrinsic attraction to reach a critically poised state. See also A Method for Identification of Critical States of Open Stochastic Dynamical Systems by D. Filatov in Journal of Statistical Physics (165/4, 2016).

We study the transition of stochastic dynamical systems to critical states. We begin from employing two independent quantitative methods of time series analysis, first-order detrended fluctuation analysis and multivariate canonical coherence analysis. We find out that there are two different mechanisms of the transition to criticality: the first mechanism is consistent with that observed in some biological dynamical systems and associated with a growth of the energies at low frequencies in the power spectrum, whereas the second mechanism is new and governed by a decay of the energies at high frequencies. Despite this difference, we show that both mechanisms lead to a loss of chaoticity in the system’s behavior and result in a more deterministic evolution of the system as a whole. The obtained results allow hypothesis that in stochastic dynamical systems of any nature the transition to a critical state is always realized through a trending nonlinear process. (Abstract)

Cosmic Code > 2015 universal

Santos, Vagner, et al. Riddling: Chimera’s Dilemma. Chaos. 28/081105, 2018. State University of Ponta Grossa, Brazil, Potsdam Institute for Climate Impact Research, University of Aberdeen, Xian University of Technology, and Federal University of Paraná, Brazil researchers including Jurgen Kurths provide a general analysis of nature’s pervasive propensity to seek and reside in a dynamic duality of more and less orderly states at the same time. Life and mind increasingly seem to be attracted to and prefer this optimum condition in every case from quantum to cerebral phases.

We investigate the basin of attraction properties and its boundaries for chimera states in a circulant network of Hénon maps. It is known that coexisting basins of attraction lead to a hysteretic behaviour in the diagrams of the density of states as a function of a varying parameter. Chimera states, for which coherent and incoherent domains occur simultaneously, emerge as a consequence of the coexistence of basin of attractions for each state. Consequently, the distribution of chimera states can remain invariant by a parameter change, and it can also suffer subtle changes when one of the basins ceases to exist. A similar phenomenon is observed when perturbations are applied in the initial conditions. By means of the uncertainty exponent, we characterise the basin boundaries between the coherent and chimera states, and between the incoherent and chimera states. This way, we show that the density of chimera states can be not only moderately sensitive but also highly sensitive to initial conditions. This chimera’s dilemma is a consequence of the fractal and riddled nature of the basin boundaries. (Abstract)

Coupled dynamical systems have been used to describe the behaviour of real complex systems, such as power grids, neuronal networks, economics, and chemical reactions. Furthermore, these systems can exhibit various kinds of interesting nonlinear dynamics, e.g. synchronisation, chaotic oscillations, and chimera states. The chimera state is a spatio-temporal pattern characterised by the coexistence of coherent and incoherent dynamics. It has been observed in a great variety of systems, ranging from theoretical and experimental arrays of oscillators, to in phenomena such as the unihemispheric sleep of cetaceans. (3)

Cosmic Code > 2015 universal

Smyth, William, et al. Self-Organized Criticality in Geophysical Turbulence. Nature Scientific Reports. 9/3747, 2019. Into 2019, it is becoming strongly evident that a genesis universe evolves and develops by repetitions and iterations of the same dynamic phenomena in kind everywhere. Here Oregon State University oceanographers describe such a tendency to reach a critical balance even in these geologic and atmospheric phases.

Turbulence in geophysical flows tends to organize itself so that the mean flow remains close to a stability boundary in parameter space. That characteristic suggests self-organized criticality (SOC), a statistical property that has been identified in a range of complex phenomena including earthquakes, forest fires and solar flares. This note explores the relationship between forced, sheared, stratified turbulence in oceans, atmospheres and other geophysical fluids and those of SOC. Self-organization to the critical state is demonstrated in a wide range of ocean turbulence, which also follows a power-law distribution indicating self-similarity. (Abstract capsule)

Cosmic Code > 2015 universal

Wilting, Jens and Viola Priesemann. 25 Years of Criticality in Neuroscience. arXiv:1903.05129. MPI Dynamics and Self-Organization researchers begin with 1990s inklings that cerebral activity spontaneously seem to take on “dynamic reverberations” and ”power-law distributed avalanches” between reciprocal tighter or looser, more or less controlled, open or closed states. The survey is braced by some 90 references over the time span. See also Criticality Signatures in a Self-Organizing Recurrent Neural Network by Bruno Del Papa, et al in PLoS One (May 26, 2017) with Viola P. as a coauthor. We also note 25 Years of Self-Organized Criticality in Astrophysics in (Aschwanden, 2015) as this common propensity becomes known from universe to human.

Twenty-five years ago, Dunkelmann and Radons (1994) proposed that neural networks should self-organize to a critical state. In models, criticality offers a number of computational advantages. Thus this hypothesis, and in particular the experimental work by Beggs and Plenz (2003), has triggered an avalanche of research, with thousands of studies referring to it. Nonetheless, experimental results are still contradictory. How is it possible, that a hypothesis has attracted active research for decades, but nonetheless remains controversial? We discuss the experimental and conceptual controversy, and then present a parsimonious solution that (i) unifies the contradictory experimental results, (ii) avoids disadvantages of a critical state, and (iii) enables rapid, adaptive tuning of network properties to task requirements. (Abstract)

Cosmic Code > 2015 universal

Zarepour, Mahdi, et al. Universal and Non-Universal Neural Dynamics on Small World Connectomes. arXiv:1905.05280. Five Argentine complexity theorists including Dante Chialvo propose novel ways to quantify and understand nature’s propensity to seek and reside at a critically poised state. As the Abstract notes, this advance is achieved by joining active cerebral phenomena with common network topologies which serves to reveal optimal invariant behaviors. If to view altogether within this “connect-omic” motif, it well suggests that the uniVerse to us course is essentially genetic in kind.

Evidence of critical dynamics has been recently found in both experiments and models of large scale brain dynamics. The understanding of the nature and features of such critical regime is hampered by the relatively small size of the available connectome, which prevent among other things to determine its associated universality class. To circumvent that, here we study a neural model defined on a class of small-world network that share some topological features with the human connectome. We found that varying the topological parameters can give rise to a scale-invariant behavior belonging either to mean field percolation universality class or having non universal critical exponents. Overall these results shed light on the interplay of dynamical and topological roots of the complex brain dynamics. (Abstract)

Cosmic Code > networks

Mariani, Manuel, et al. Nestedness in Complex Networks: Observation, Emergence, and Implications. Physics Reports. Volume 813, 2019. Ecological theorists based in China and Switzerland including Jordi Bascompte post a 140 page, 400 reference affirmation of nature’s evolutionary developmental genesis, as long sensed, by way of combining smaller entities (biomolecules, cells, species) within larger, reciprocally beneficial, whole systems. Akin to the major transitions scale and others, life’s long recurrent emergence from microbes to meerkats to a metropolis gains its 2020 sophisticated mathematical quantification.

The observed architecture of ecological and socio-economic networks differs significantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks calls for an explanation of their emergence and systemic consequences. This article focuses on one of these patterns: nestedness. Given a network of interacting nodes, nestedness is a tendency for nodes to interact with subsets of the interaction partners of better-connected nodes. Nestedness has been found in diverse as ecological mutualistic organizations, world trade, inter-organizational relations, among many other areas. We survey results from variegated disciplines, including statistical physics, graph theory, ecology, and theoretical economics. (Abstract excerpt)

Perhaps one of the most intriguing features of real networks is the existence of common structural and dynamical patterns in a large number of systems from various domains of science, nature, and technology. The pervasiveness of structural patterns from various fields makes it possible to analyze them with a common set of tools. A popular example of such widespread patterns is the heavy-tailed degree distribution. Power-law degree distributions (often termed as ‘‘scale-free") have been reported in many different systems, ranging from social and information networks to protein–protein interaction networks. The ubiquity of heavy-tailed degree distributions has motivated studies aimed at unveiling plausible mechanisms that explain their emergence, understanding their implications for spreading, network robustness, synchronization phenomena, etc. (3)

Cosmic Code > networks

Serafino, Matteo, et al. Scale-Free Networks Revealed from Finite-Size Scaling. arXiv:1905.09512. Organic physicists based in Italy, the USA and UK including Amos Maritan and Guido Caldarelli describe a method to analyze common features of natural linkages such as protein interactions, technological computer hyperlinks, and informational citation and lexical networks. As a result, spontaneous self-organization to a critical-like state then becomes evident, which seems to hold across all manner of net topologies.

Cosmic Code > networks

Stone, Lewi, et al. Network Motifs and Their Origins. PLoS Computational Biology. April, 2019. As the Abstracts notes, LS, Tel Aviv University, Yael Artzy-Randrup, University of Amsterdam and the veteran ecologist Daniel Simberloff, University of Tennessee provide a once and present review of this common feature of life’s dynamic physiology and anatomy.

Modern network science is a new and exciting research field that has transformed the study of complex systems over the last 2 decades. Of much interest is the identification of small “network motifs” embedded in a larger network and that indicate the presence of evolutionary design principles or have an overly influential role on system-wide dynamics. Motifs are patterns of interconnections, or subgraphs that appear in an observed network more often than in compatible randomized networks. Here, we argue that the same concept and tools for the detection of motifs were well known in the ecological literature into the last century, a fact that is generally not recognized. We review the early history of network motifs, their evolution in the mathematics literature, and their recent rediscoveries. (Abstract)

Systems Evolution: A 21st Century Genesis Synthesis

Quickening Evolution

Ostachuk, Agustin. What is It Like to be a Crab? A Complex Network Analysis of Eucaridan Evolution. Evolutionary Biology. 46/2, 2019. In an entry which can exemplify a 2020s genesis synthesis, a National University of La Plata, Argentina biologist achieves a systems explanation of how this common crustacean came to form and develop. As the quotes say, a novel inclusion and application of nature’s pervasive topologies then provides a better explanation of how their skeletal carapace came to be. The result is seen as robust enough to be carried across Metazoan creatures because it implies an independent, generic anatomy and physiology. As the author noted in 2015 (search) new insights into an actual recapitulation process may also accrue. The entry illustrates a prime theoretical revision in our midst whence life’s long ascent from universe to us becomes braced by this vital, genetic-like mathematical source.

Eucaridan evolution involved a process starting from a body organization characterized by an elongate and cylindrical cephalothorax, a well-developed abdomen composed of swimming appendages, ending in a tail fan formed by flattened uropods and a telson. This process would lead to a body organization characterized by a shortened and depressed cephalothorax, and a reduced and ventrally folded abdomen. In this work, the evolution of the superorder Eucarida was studied using complex networks. A new definition of crab and its carcinization are given based on the results obtained. The evolution of the crab implied the formation of a triadic structure with high closeness centrality which represented a stable hierarchical core buried or enclosed in the topological structure of the network with its integrated and robust topology. (Abstract excerpts)

In this work, crustacean external morphology was abstracted as a network in which each individual morphological feature was considered as a node, and the edges among these nodes were established based on their physical connections. This representation and abstraction of the crab morphology as a network is considered to capture the evolutionary and developmental structural information of the whole organism. It represents the characteristic structure of a given organism, its architectural plan or Bauplan. The analysis of these different and successive plans yielded important results regarding the evolutionary trend of this group. In summary, this trend was involved an increase in complexity, integration and robustness. These models of crustaceans through complex network theory revealed important, unexpected and surprising features of their evolutionary process. (204)

Quickening Evolution > Biosemiotics

Barbieri, Marcello. Code Biology, Peircean Biosemiotics, and Rosen’s Relational Biology. Biological Theory. 14/1, 2019. This latest entry by the University of Ferrara biologist is a synoptic review of deepening perceptions that living, evolutionary nature is actually suffused by many generative source codes across all manner of phases and processes. By this insight, they can also each be seen to have a common affinity.

The classical theories of the genetic code claimed that its coding rules were determined by chemistry — either by stereochemical affinities or by metabolic reactions — but the evidence has revealed a different reality: any codon can be associated with any amino acid. The rules of the genetic code obey the laws of physics and chemistry but are not determined by them. In the past 20 years various discoveries have shown that many other organic codes exist in living systems. These experimental facts have this theoretical implication: in addition to the concept of information we must introduce in biology the concept of meaning, because we cannot have codes without meaning or meaning without codes. The problem is that at present we have two different frameworks for that purpose: one is Code Biology, where meaning is the result of coding, and the other is Peircean biosemiotics, where meaning is interpretation. Recently, however it has been proposed that Robert Rosen’s relational biology can provide a bridge between Code Biology and Peircean biosemiotics. (Abstract)

Quickening Evolution > Biosemiotics

Marijuan, Pedro, et al.. Fundamental, Quantitative Traits of the “Sociotype. Biosystems. Volume 180, 2019. Veteran researchers Pedro M., Raquel del Moral and Jorge Navarro, Aragon Health Research Institute, Sungchul Ji, Rutgers University, Marta Gil Lacruz and Juan Gomez-Quintero, University of Zaragoza (search names) press consider how such an emergent socio-genetic realm might be conceptually present in some working role akin to genotypes and phenotypes. As a result, it is advised that an optimum human grouping of social bonds seems to actually be 100 people, which is different from (Robin) Dunbar’s number of 150. See also The “Sociotype” Construct: Gauging the Structure and Dynamics of Human Sociality by this group in PLoS One (December 14, 2017).

In whatever domain of life from cells to organisms to societies, communicative exchanges underlie the formation and maintenance of the emerging collective structures. It can be clearly seen in the human social world. In the present work we have investigated the basic metrics of social bonds and communicative exchanges along the development of within our genotype-phenotype-sociotype conceptual triad. The sociotype means the relative constancy of the social world in which each individual life is developed. Other results about gender, age, and use of social Internet media highlight significant differences among the social segments, and particularly the diminished “sociotype” of the elderly. (Abstract excerpt)

Quickening Evolution > Intel Ev

Duran-Nebreda, Salva and George Bassel. Plant Behavior in Response to the Environment. Philosophical Transactions of the Royal Society B. 374/20190370, 2019. . In a special Liquid Brains, Solid Brains issue (search Forrest), University of Birmingham, UK botanists describe how even floral vegetation can be seen to embody and avail a faculty of cognitive intelligence for their benefit.

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. (Abstract)

Quickening Evolution > Intel Ev

Pinero, Jordi and Ricard Sole. Statistical Physics of Liquid Brains. Philosophical Transactions of the Royal Society B. 374/20180376, 2018. In a special Liquid Brains, Solid Brains issue (search Forrest), Institut de Biologia Evolutiva, Universitat Pompeu Fabra, Barcelona theorists consider a universal recurrence in kind of the same generic complex network system across natural and social domains. While akin to genomes and ecosystems, an apt model is cerebral cognition, broadly conceived, by way of agental neurons and synaptic links in multiplex arrays. A prime attribute is a cross-conveyance of intelligence and information, aka biological computation, which is how animal groupings from invertebrates to mammals to people achieve a collective decision-making.

Liquid neural networks (or ‘liquid brains’) are a widespread class of cognitive living networks characterized by a common feature: the agents move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. We review the generic properties of three large classes of systems, namely: standard neural networks (solid brains), ant colonies and the immune system. It is shown that, despite their intrinsic differences, these systems share key properties with neural systems in terms of formal descriptions, but depart in other ways. (Abstract excerpt)

Quickening Evolution > Intel Ev

Sole, Ricard, et al. Liquid Brains, Solid Brains: How Distributed Cognitive Architectures Process Information. Philosophical Transactions of the Royal Society B. 374/20190040, 2019. With Melanie Moses and Stephanie Moses, an introduction to papers from a December 2017 Santa Fe Institute seminar with this title, which represents how many domains across life’s evolution universe to human can actually be perceived as some manner of neural-like intelligent process. We note, for example, Metabolic Basis of Brain-like Electrical Signalling in Bacterial Communities, Plant Behavior in Response to the Environment (Duran-Nebreda & Bassel herein), Statistical Physics of Liquid Brains (Pinero & Sole), The Compositional Stance in Biology, and A Brief History of Liquid Computers. A tacit theme for this work is a further major evolutionary transition in individuality.

Cognitive networks have evolved a broad range of solutions to the problem of gathering, storing and responding to information. Some of these networks are describable as static sets of neurons linked in an adaptive web of connections. These are ‘solid’ networks, with a well-defined and physically persistent architecture. Other systems are formed by sets of agents that exchange, store and process information but without persistent connections or move relative to each other in physical space. We refer to these networks that lack stable connections and static elements as ‘liquid’ brains, a category that includes ant and termite colonies, immune systems and some microbiomes and slime moulds. What are the key differences between solid and liquid brains, particularly in their cognitive potential, ability to solve particular problems and environments, and information-processing strategies? To answer this question requires a new, integrative framework. (Abstract)

Earth Life Emergence: Development of Body, Brain, Selves and Societies

Earth Life > Common Code

Magliocca, Nicholas, et al. Modeling Cocaine Traffickers and Counterdrug Interdiction Forces as a Complex Adaptive System. Proceedings of the National Academy of Sciences. Early online April 1, 2019. Eight systems geographers posted in Alabama, Arizona, Wyoming, Texas, Oregon, and Ohio identify a common mathematical patterning that even criminal chaos seems to hold to and be constrained by. Our interest extends to a concurrent paper, Structure, Spatial Dynamics of Novel Seed Dispersal Mutualistic Networks in Hawaii (Visentin herein), which notes similar structuring dynamics across ecosystems. Within our 21st century scan, it is increasingly evident to a point of proof and discovery that an independent generative source is in exemplary presence everywhere.

The US government’s cocaine interdiction mission in the transit zone of Central America is now in its fifth decade despite its long-demonstrated ineffectiveness, both in cost and results. We developed a model that builds an interdisciplinary understanding of the structure and function of narco-trafficking networks and their coevolution with interdiction efforts as a complex adaptive system. The model produced realistic predictions of where and when narco-traffickers move in and around Central America in response to interdiction. The model demonstrated that narco-trafficking is as widespread and difficult to eradicate as it is because of interdiction, and increased interdiction will continue to spread traffickers into new areas, allowing them to continue to move drugs north. (Significance)

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