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Recent Additions: New and Updated Entries in the Past 60 Days
Displaying entries 16 through 30 of 67 found.


Ecosmomics: A Survey of Animate Complex Network Systems

Cosmic Code > nonlinear > networks

Siew, Cynthia, et al. Cognitive Network Science: A Review of Research on Cognition through the Lens of Network Representations, Processes, and Dynamics. Complexity. Art. 2108423, 2019. Within a special issue on this subject, University of Warwick. Basel, Wisconsin, and Pennsylvania including Nicole Beckage contribute to this 2010s revolution (see Barabasi 2012) by an observance of how a common, representative form and utility, as if a natural anatomy and physiology, is now well in place. By so doing (second quote) it can then join the prior pieces altogether. As a topical example, their occasion even in literary syntactic and informational content is confirmed. Again by turns, we note that an independent, universal network reality would be deeply textual in kind. See also From Topic Networks to Distributed Cognitive Maps by Akexander Mehler, et al in this issue.

Network science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a network perspective, the application of network methodologies to quantitatively study cognition has so far been limited. This review shows how such approaches have been applied to the study of human cognition and can uniquely provide novel insight on important questions related to the complexity of cognitive systems. Drawing on the literature in cognitive network science, with a focus on semantic and lexical networks, we argue three points. (i) Network science provides aquantitative approach to represent cognitive systems. (ii) This method enables cognitive scientists to achieve a deeper understanding of how the neural network processes interact to produce behavioral phenomena. (iii) Network science provides a quantitative framework to model the dynamics of cognitive systems as structural changes on different timescales and resolutions. (Abstract)
Networks are composed of two elements: nodes that represent the conceptual entities of interest (e.g., persons, websites, or words) and edges that represent the relationships among those units (e.g., friendship, hyperlinks, or semantic similarity). While additional aspects can be considered as is done in bipartite and multiplex networks, identifying these two basic elements in the system of study is sufficient to formalize the system as a network and to employ the powerful tools provided by network science. Network science approaches often capitalize on the fact that relationships between nodes (i.e., edges) can be as important as the nodes themselves. A challenge in studying cognitive systems as networks is to represent these systems in a meaningful way in terms of nodes and edges. (2)

Cosmic Code > nonlinear > Algorithms

Molina, Daniel, et al. Comprehensive Taxonomies of Nature-and Bio-inspired Optimization. Cognitive Computation. 12/897, 2020. Five University of Granada, University of the Basque Country, and King Abdulaziz University Saudi Arabia informatics scientists achieve a comprehensive survey to date of iterative search methods across categories such as Physical, Evolutionary, Organism Breeding, Plants, Social and Swarm Intelligence. Examples are Big Bang Big Crunch, Cuttlefish Algorithm, Moth Flame Optimization, Galaxy Based Search, Bus Transport Behavior onto Soccer League Games and many more. Altogether they imply how much this Ecosmos to Earthling scenario in which we find ourselves is involved with reaching and achieving a “good enough” result or resolve at every phase. Whom then are we international inquirers just coming to learn this? What quality or feature is a genesis nature trying to optimize and select for?

In the last years the number of bio-inspired optimization approaches have so grown in number that they compromise this vital field. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies to help organize existing and future developments into two criteria: the source of inspiration and the behavior of each algorithm. In regard, we review more than 300 publications dealing with nature-inspired and bio-inspired algorithms leading to a critical summary of design trends and similarities, between them. We show that more than one-third of the bio-inspired solvers are versions of classical algorithms. We close with recommendations for better methodological practices. (Abstract excerpt, edits)

Cosmic Code > nonlinear > Rosetta Cosmos

Linzen, Tal and Marco Baroni. Syntactic Structure from Deep Learning. Annual Review of Linguistics. Volume 7, January, 2021. We are interested in this contribution by NYU and Facebook AI Research, Paris linguists because it seems to infer that many, widely removed, natural realms under study have a deep language-like essential character and content.

Modern deep neural networks achieve impressive performance in engineering applications that require linguistic skills, such as machine translation. This success has sparked interest in probing whether these models are inducing human-like grammatical knowledge from the raw data they are exposed to and, consequently, whether they can shed light on long-standing debates concerning the innate structure necessary for language acquisition. In this article, we survey the syntactic abilities of deep networks and discuss broader implications that this work has for theoretical linguistics. (Abstract)

Cosmic Code > nonlinear > 2015 universal

Braccini, Michele, et al. Online Adaptation in Robots as Biological Development Provides Phenotypic Plasticity. arXiv:2006.02367. MB and Andrea Roli, University of Bologna and Stuart Kauffman, Institute for Systems Biology, Seattle consider how this responsive organismic feature, re the Abstract, could be availed for better android designs. By so doing, a concept, due much to SK decades ago (search), is advanced that this condition is effective because it resides at an active critical poise between conserve and create states. See also Emergence of Organisms by Andrea Roli and Stuart Kauffman in Entropy (22/10, 2020), re third quote, and Self-organization toward Criticality by Synaptic Plasticity by Roxana Zeraati, et al at arXiv:2010.07888.

The ability to respond to environmental stimuli with appropriate actions is a property shared by living organisms, and it is sought in the design of robotic systems. Phenotypic plasticity provides a way for achieving this as it qualifies those organisms that, from one genotype, can express different phenotypes in response to changing environments, without genetic modifications. In this work we study phenotypic plasticity in robots that are equipped with online sensor adaptation. We also show that the dynamical regime necessary for the best performance is the critical one, bringing further evidence that natural and artificial systems capable of optimally balancing robustness and adaptivity are in a critical state. (Abstract excerpt)

We start from known and relevant properties of organisms and check whether they can provide general principles that can explain their phenotypic plasticity and can then bring us to link development and evolution. We believe that one of these principles can be found in criticality. A long-standing conjecture in complex system science — the criticality hypothesis — emphasizes the optimal balance between robustness and adaptiveness of those systems in a dynamical regime between order and chaos. (3)

Criticality: The organisms in the evolving biosphere are very likely to be critical, i.e., their dynamical regime is at the boundary between order and disorder. This conjecture has found strong support in biology, neuroscience, as well as computer science, and it can be expressed by these statements: (1) critical systems are more evolvable than systems in other dynamical conditions as they attain an optimal trade-off between mutational robustness and phenotypic innovation and (2) critical systems have advantages over ordered or disordered ones, because they optimally balance information storage, modification and transfer. (Entropy paper, 3)

Cosmic Code > nonlinear > 2015 universal

Pavithran, Induja, et al. Universality in Spectral Condensation. Nature Scientific Reports. 10/17405, 2020. As the Abstract says, by an advanced technical finesse nine scientists from the Indian Institute of Technology, Madras, UC San Diego, and the Potsdam Institute for Climate Impact Research including Jurgen Kurths uncover a constant presence of this manifest physical phenomena. The article number means that it is amongst thousands each year, millions more if eprint sites are added. Whenever might we be able to perceive our worldwise endeavor as a vital work of ecosmic self-quantification and ultimate discovery?

Self-organization is the spontaneous formation of spatial, temporal, or spatiotemporal patterns in complex systems far from equilibrium. During such self-organization, energy distributed in a broadband of frequencies gets condensed into a dominant mode, analogous to a condensation phenomenon. We call this phenomenon spectral condensation and study its occurrence in fluid mechanical, optical and electronic systems. We define a set of spectral measures to quantify this condensation spanning several dynamical systems. Further, we uncover an inverse power law behaviour of spectral measures with the power corresponding to the dominant peak in the power spectrum in all the aforementioned systems. (Abstract)

Cosmic Code > nonlinear > 2015 universal

Zeraati, Roxana, et al. Self-organization toward Criticality by Synaptic Plasticity. arXiv:2010.07888. RZ, MPI Biological Cybernetics, Viola Priesemann, MPI Dynamics and Self-organization, and Anna Levina, University of Tubingen theorists add to a flow of timely papers which identify and affirm that life’s evolution and development seems to prefer and tend to this malleable condition for both brains and bodies because it can achieve an optimum responsiveness by being critically poised between relatively closed, fixed and open, fluid states. As this section and elsewhere documents, into the later 2010s and 2020, so many various studies are coming to and realizing nature’s universal preference for this optimum balance. See also Online Adaptation in Robots as Biological Development Provides Phenotypic Plasticity by Michele Braccini, et al at 2006.02367.

Self-organized criticality has been proposed as a universal mechanism for the emergence of scale-free dynamics in many complex systems, and in the brain. While such scale-free patterns appear many neural recordings, the biological principles behind their presence remained unknown. By way of network models and experimental observations, synaptic plasticity was proposed as a mechanism to drive self-organized brain dynamics towards a critical point. We discuss how biological plasticity rules operate across timescales and how they alter the network's dynamical state through modification of the connections between neurons. Overall, the concept of criticality helps to shed light on brain function and self-organization, whence living neural networks also avail their criticality for computation. (Abstract excerpt)

Synaptic plasticity is the biological process by which specific patterns of synaptic activity result in changes in synaptic strength and is thought to contribute to learning and memory. (Nature Research) In neuroscience, synaptic plasticity is the ability of synapses to strengthen or weaken over time, in response to increases or decreases in their activity. (Wikipedia)

Thus instead of self-organizing precisely to criticality, the brain could make use of the divergence of processing capabilities around the critical point. Thereby, each brain area might optimize its computational properties by tuning itself towards and away from criticality in a flexible, adaptive manner. In the past decades, the community has revealed the local plasticity rules that would enable such a tuning and adaption of the working point. Criticality has been very inspiring to understand brain dynamics and function. We assume that being perfectly critical is not an optimal solution for many brain areas, during different task epochs. (16)

Cosmic Code > Genetic Info

Misteli, Tom. The Self-Organizing Genome: Principles of Genome Architecture and Function. Cell. 183/1, 2020. The veteran Swiss-American systems biologist (search) is director of the NIH Center for Cancer Research. This paper describes a confirmation of his collegial 21st century project to reconceive life’s genetic and cellular phases by way of a primary self-organization. As the quotes say, this intrinsic developmental process is not random happenstance but a guided process which results in a reliable array of forms, units and features. A further significant finding is that even genetic phenomena can be seen to reach and take on a critical balance of conserved, stable states along with creative responses to external changes. We add several quotes for this consummate achievement.


Genomes have complex three-dimensional architectures. The recent convergence of genetic, biochemical, biophysical, and cell biological methods has uncovered several fundamental principles of genome organization. They highlight that genome function is a major driver of genome architecture and that structural features of chromatin act as modulators, rather than binary determinants, of genome activity. The interplay of these principles in the context of self-organization can account for the emergence of structural chromatin features, the diversity and single-cell heterogeneity of nuclear architecture in cell types and tissues, and explains evolutionarily conserved functional features of genomes, including plasticity and robustness. (Abstract)

An important realization from these studies has been that the organization of genomes is characterized by a high degree of order and non-randomness. An overt example is the physical segregation of transcriptionally active euchromatin from repressed heterochromatin into distinct regions in the cell nucleus of most eukaryotic cells. Other non-random features of genomes include the formation of chromatin domains and the positioning of genes to preferred locations within the nuclear space. In addition to the genetic material, many proteins are non-randomly distributed in the nucleus and are concentrated in sub-nuclear bodies. These observations highlight a considerable degree of order and non-randomness in genome organization. (28)

As outlined above, genomes are characterized by a high degree of order represented by ubiquitously conserved architectural features, such as chromatin loops, domains, and nuclear bodies, as well as by non-random patterns, such as the location of genes and chromosomes in 3D space. In addition, the transcriptional program of a given cell is stable and defines its overall state. (35)

At the same time, genome organization and gene expression are also highly dynamic, variable, and stochastic. How can these two apparently conflicting aspects of genome organization — steady-state stability and intrinsic variability — be reconciled? One hint comes from the realization that the major characteristics of genome organization, including a dynamic, stable steady state and a high degree of heterogeneity and variability, are hallmarks of a self-organizing system. The principle of self-organization is ubiquitous in nature and, when applied to the genome, provides a unifying mechanism to account for many of its structural and functional features. (35)

With the realization that genome architecture is an emergent property of a self-organizing system, the next phase of studying the genome is now upon us. (42)

Cosmic Code > Genetic Info > Paleo/Cosmo

Frantz, Laurent, et al. Animal Domestication in the Era of Ancient Genomics. Nature Reviews Genetics. 21/8, 2020. Queen Mary University of London, Trinity College, Dublin, Oxford University, and University of Toulouse (Ludovic Orlando) paleogeneticists apply the latest advances in nucleotide recovery and sequencing ability to reconstruct, in this instance, the historic occasions by which many feral, native creatures were enjoined as beneficial hominid and human co-inhabitants. This long process, as readers know, led to much evolutionary modification, as cited and described in this paper.

The domestication of animals led to a major shift in human subsistence patterns from hunter–gatherers to a sedentary agricultural lifestyle. Over the past 15,000 years, the phenotype and genotype of multiple animal species, such as dogs, pigs, sheep, goats, cattle and horses, have been substantially altered during their adaptation to the human niche. Recent innovations such as improved ancient DNA extraction methods and next-generation sequencing, have enabled whole ancient genomes to be read. These genomes have helped reconstruct how animals entered into domestic relationships with humans and were subjected to selection pressures. Here, we discuss and update key concepts in animal domestication in light of these novel contributions. (Abstract)

Cosmic Code > Genetic Info > Paleo/Cosmo

Gokeumen, Omer and Michael Frachetti. The Impact of Ancient Genome Studies in Archaeology. Annual Review of Anthropology. 49/277, 2020. SUNY Buffalo and Washington University, St. Louis researchers provide a latest, wide-ranging tutorial on these revolutionary collection and sequencing techniques as they result in a whole scale revision and expansion of past skeletal and artifact paleontological studies.

The study of ancient genomes has proceeded at an incredible rate in the last decade. The result is a shift in archaeological narratives and a fierce debate on the place of genetics in anthropological research with regard to human origins, movement of ancient and modern populations, the role of social organization in shaping material culture, and the relationship between culture, language, and ancestry. Further concerns involve indigenous rights, ownership of ancient materials, inclusion in the scientific process, and even the meaning of what it is to be a human. (Abstract)

Systems Evolution: A 21st Century Genesis Synthesis

Quickening Evolution

Aria, Cedric. Macroevolutionary Patterns of Body Plan Canalization in Euarthropods. Paleobiology. October, 2020. A University of Toronto biologist meticulously analyzes datasets for these diverse invertebrates as they appeared in the prolific Cambrian era (~540 my ago). He concludes that their swift rise was mostly due to the buildup of genetic regulatory networks. See also Early Fossil Record of Euarthropoda and the Cambrian Explosion by Allison Daley, et al in PNAS (115/5325, 2018.)

Quickening Evolution

Cohen, Irun and Assaf Marron. The Evolution of Universal Adaptations of Life is Driven by Universal Properties of Matter: Energy, Entropy and Interaction. F1000Research. July 30, 2020. While the olden neoDarwinian version of selection alone persists, Weizmann Institute of Science, Israel biomathematicans (search IC) contribute to a revolutionary genesis synthesis by viewing life’s oriented emergence as a complex dynamical process which involves not only objects, be they genes or animals, but equally real cooperative relations between them. I log this in along with a brain research paper (Harang Ju) which emphasizes a similar emphasis of neural interactions, and a symbiosis report (F. Prosdocimi) as another example of this pervasive entity/group mutuality. As a result, in each and all cases a whole, composite genome, connectome and regnant organism in community is thus achieved.

The evolution of multicellular eukaryotes expresses two sorts of adaptations: local aspects like fur or feathers, which serve species in bioregions, and universal adaptations like microbiomes or sexual reproduction, which distinguish multicellularity in any environment. We reason that the mechanisms which drive them should be universal, and based on properties of matter and systems: energy, entropy, and interaction. Energy from the sun creates complex arrangements while metabolic networks channel it to form cooperative interactions. Entropy, a term for disorder, acts as a selective force.

Dynamic Interactions restrain entropy and enable survival and propagation of integrated living systems. The “unit” of evolution is not a discrete entity what evolves are related interactions at multiple scales. Our “survival-of-the-fitted” can explain universal adaptations, including microbiomes, reproduction, diversification, altruism, environmental niches and more. We propose ways to test our theories, and implications for the wellbeing of humans and the biosphere. (Abstract excerpts)

Cooperative interactions are pervasive and central to life: We define an interaction as a relationship between two or more entities involving a transfer or exchange of matter, information and/or energy. Interactions include both struggle and cooperation: in a struggle, the participants each strive to win and dominate the others – who become the losers. In a cooperative interaction, there are no losers; the participants each gain some benefit, or at the least suffer no loss. (5)

Quickening Evolution

Schwartzman, David. Biological Evolution is Coarsely Deterministic. Journal of Big History. 4/2, 2020. This paper was presented by the veteran Howard University biologist at the Life in the Universe: Big History, SETI and the Future conference in Milan in July 2019. In essence, it continues his insightful views that “playing the tape over again” would necessarily lead to life’s development into human-like beings and cognitive capabilities because biochemical and thermal properties of the geobiosphere would again impel and channel it that way.

Starting with the origin of life, I argue that the general pattern of the tightly coupled evolution of biota and climate on Earth has been the very probable outcome from a relatively small number of possible histories at the macroscale, given the same initial conditions. Thus, the evolution of the biosphere self-selects a pattern of biotic evolution that is coarsely deterministic, with critical constraints likely including surface temperature as well as oxygen and carbon dioxide levels in the atmosphere. Environmental physics and chemistry drive the major events in biotic evolution, including photosynthesis and oxygenic photosynthesis, the emergence of new cell types (eucaryotes) from the merging of complementary metabolisms, multicellularity and even encephalization. (Abstract)

Quickening Evolution > major

Rafiqi, Matteen, et al. Origin and Elaboration of a Major Evolutionary Transition in Individuality. Nature. 585/239, 2020. As the abstract cites, McGill University, Montreal and Bezmialem Vakif University, Istanbul biologists discuss how the latest detailed studies of morphogenetic forms and processes are revealing the innate, persistent ways that a natural genesis proceeds toward further scalar levels of organismic complexities. An elaborate graphic display depicts a course for bacterial symbiotic integration.

Obligate endosymbiosis, in which distantly related species integrate to form a single replicating individual, represents a major evolutionary transition in individuality. Although such transitions are thought to increase biological complexity, the evolutionary and developmental steps that lead to integration remain poorly understood. Here, we show that obligate endosymbiosis between the bacteria Blochmannia and the hyperdiverse ant tribe Camponotini originated and elaborated through radical alterations in embryonic development, as compared to other insects. By this example and others, we find that the convergence of pre-existing molecular capacities and ecological interactions—as well as the rewiring of highly conserved gene networks—may be a general feature that facilitates the origin and elaboration of major transitions in individuality. (Abstract excerpts)

We therefore propose that other major transitions in individuality may originate and also elaborate through the rewiring of highly conserved gene regulatory networks, as well as by exploiting pre-existing molecular or developmental capacities and ecological interactions. (243)

Quickening Evolution > major

Warrell, Jonathan and Mark Gerstein. Cyclical and Multilevel Causation in Evolutionary Processes. Biology & Philosophy. 35/Art.50, 2020. Yale University computational biophysicists and geneticists (search MG) post a 36 page careful consideration of how novel machine learning techniques and models seem able to gain deeper insights about into life’s structured developmental advance, better ways to understand and mitigate diseases and a sense of identities.

We develop here a general theoretical framework for analyzing evolutionary processes drawing on recent approaches to causal modeling developed in the machine-learning literature, which have extended Pearls do-calculus to incorporate cyclic causal interactions and multilevel causation. We show how our causal framework helps to clarify conceptual issues in the contexts of complex trait analysis and cancer genetics, including assigning variation in an observed trait to genetic, epigenetic and environmental sources in the presence of epigenetic and environmental feedback processes, and variation in fitness to mutation processes in cancer using a multilevel causal model respectively. Finally, we consider the potential relevance of our framework to biology and evolution, including supervenience, multilevel selection and individuality. (Abstract excerpt)

Quickening Evolution > Systems Biology

Gilpin, William, et al. Learning Dynamics from Large Biological Data Sets: Machine Learning Meets Systems Biology. Current Opinion in Systems Biology. July 30, 2020. As is the current case for many scientific fields, Harvard and Dartmouth researchers scope out ways by which a suitable apply of AI deep neural net techniques can effectively interface with life studies so to enhance research methods and results.

In the past few decades, mathematical models based on dynamical systems theory have provided new insight into diverse biological systems. In this review, we ask whether the recent success of machine learning techniques for large-scale biological data analysis can provide a complementary, beneficial approach to traditional modeling. Recent applications of machine learning have been used to study biological dynamics in diverse systems from neuroscience to animal behavior. We propose several avenues for bridging dynamical systems theory with large-scale analysis enabled by machine learning. (Abstract excerpt)

Taken together, these results introduce the question of whether universal mathematical constraints determine certain aspects of large biological systems whose interacting units spontaneously collapse onto a low-dimensional manifold. Has evolution driven complex biological networks toward these emergent motifs, and do they confer adaptive benefits such as stabilizing an ecosystem against an invasive predator, or suppressing unwanted fluctuations in a genetic circuit? We hope that further development of models at the intersection of machine learning and dynamical theory will provide unified insight into these questions. (6)

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