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Xu, Yifan, et al. Sleep restores an optimal computational regime in cortical networks.. Nature Neuroscience.. 27/328, 2024. Washington University, St. Louis biologists including Ralf Wessel and Keith Hengen add another instance of the brain’s propensity to more or less reside in a preferred self-organized state. After a long, tiring day, they find that our a good night’s rest then serves to restore this optimum condition.

Sleep is assumed to subserve homeostatic processes in the brain; however, the set point around which sleep tunes circuit computations is unknown. Slow-wave activity (SWA) is used to reflect the homeostatic aspects; it does not explain why animals need sleep. This study aimed to assess whether criticality may be the set point of sleep. By recording cortical neuron activity in freely behaving rats, we show that normal waking experience can disrupt this poise and that sleep functions to restore critical dynamics. Our results demonstrate that perturbation and recovery of criticality is a network homeostatic mechanism consistent with the core, restorative function of sleep. (Excerpt)

Planatural Genesis: A 21st Century Revolutionary, PhiloSophia, Earthumanity, Organismic Edition

Cech, Thomas. The Catalyst: RNA and the Quest to Unlock Life's Deepest Secrets. New York: Norton, 2024. The 1989 Nobel chemist has been at the center of a revolutionary shift in genetic science from an original emphasis on DNA to its companion biomolecule ribonucleic acid RNA. The thematic story is about realizations of a wide array of features which have given it a significant role in many metabolic functions. A recent merit is to provide a vaccine base during the COVID pandemic. But our interest also lies with the title concept which can identify life’s deep propensity to consistently initiate and perpetuate its own procreative course, seemingly within a catalytic ecosmos.

As an example of current advances on this frontier, we cite BEACON: Benchmark for Comprehensive RNA Tasks and Language Models by Yuchen Ren, et al at arXiv:2406.10391 and RNAFlow: RNA Structure & Sequence Design via Inverse Folding-Based Flow Matching by Divya Nori and Wengong Jin at arXiv:2405.18768.

For over half a century, DNA has reigned in the popular imagination as the “secret of life.” But over the past decades, a quiet revolution has taken place. The biochemist Thomas Cech and a diverse cast of scientists have revealed that RNA, long overlooked as the passive servant of DNA, sits at the center of biology. In The Catalyst, Cech finally brings together years of research to demonstrate that RNA is the true key to understanding life on Earth, from its very origins to our future in the twenty-first century. The Catalyst moves from the early experiments to Cech’s own findings that it can catalyze cellular reactions and on to novel biotechnologies. The work glimpses how RNA-powered therapies such as CRISPR that can RNA rewrite the code of life and mRNA vaccines that have saved millions during the pandemic. BOOK

I tell the story of RNA in two parts: the first is how RNA revealed itself as life’s great catalyst. We begin in the 1950s with the experiments that un covered how RNA orchestrates the construction of the proteins that perform most of the essential functions in living organisms. Then we see how RNA is responsible for helping us humans do so much with our DNA jnformation. From there, the story takes a personal turn. I recount how my team discovered catalyric RNA called riboenzynes. (13)

Thomas Robert Cech (born December 8, 1947) is an American chemist who shared the 1989 Nobel Prize in Chemistry with Sidney Altman, for their discovery of the catalytic properties of RNA. He is also the president of Howard Hughes Medical Institute.

Sole, Ricard, et al.. Fundamental Constraints to the Logic of Living Systems.. C:/Users/Author/Downloads/preprints202406.0891.v1.pdf.. his June 13, 2024 preprint is so notable that it merits the full list of coauthors who are RS, Christopher Kempes, Bernat Corominas-Murtra, Manlio De Domenico, Artemy Kolchinsky, Michael Lachmann, Eric Libby, Serguei Saavedra, Eric Smith and David Wolpert (search each). Their postings include Universitat Pompeu Fabra, Barcelona, MIT, the Santa Fe Institute, University of Graz, Earth-Life Science Institute, Tokyo, Umea University, Sweden and the University of Padua.

Akin to the early 2020s recognition of a teleological course because its presence became so strong, herein it is said that evidence for an insistent evolutionary convergence is so profound that it is suggestive of an inherent, independent source which repeats in kind wherever it can. As the quotes say, this admitted observance can reveal companion properties such as universal recurrences across emergent life’s milieu from proteins to cells, metabolisms and onto ecosystems. With these common patterns in place, a further notice of complex self-organized criticalities everywhere can be achieved. As I write in mid July, a growing number of entries are beginning to consider an independent, mathematic, exemplary realm in effect, which altogether can imply something phenomenal going on by itself. May we begin to glimpse the very idea of an imminent revolutionary scientific discovery of a greater, abiding genesis universe, with our Earth once again be its temporal center

It has been said that the historical nature of evolution makes it a path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is much evidence that convergence and constraints limit what life’s evolution can achieve. Here, we argue that such fundamental domain occasions are associated with the logic of animate matter. We illustrate this by thermodynamic properties, molecular information, multicellularity and development, computational cognitive systems, and viable ecosystems. In all these examples, we present past and recent evidential proof and suggest pathways towards a well-defined theoretical formulation. (Abstract)

Nonetheless, there are several major reasons to expect convergence. First, all evolutionary trajectories occur under generic selective margins due to the laws of mathematics, physics, and chemistry, which should lead to universal features. Second, many of these spaces are not explored randomly. The percolating nature of genotype spaces favours success in evolutionary search. Moreover, some intrinsic properties of chemical and physical nature channel the repertoires of molecular structures because only a small fraction of protein folds are realizable. (4)

Here, we discuss some fundamental limits to the space of evolutionary outcomes. We start with some core thermodynamic vectors along with molecular information carriers, cellular reproduction, cognitive architectures, and ecosystems. Finally, we consider the concept of phase transitions as a paradigm for the emergence of living complexity. In our view, such predictions strongly infer a universality which fits within the broader goal of finding general theories that transcend the specifics of life on Earth. (5) Moreover, despite the enormous variation of climate and resource conditions, all ecosystems essentially display the same properties from Antarctica to the Sahel. We can also reconstruct fossil phases, the so-called “paleo food webs” and again find universal patterns shared with modern ecological networks. (25)

Some of our case studies are deeply connected with the emergence of innovations in evolution. These major transitions refer to critical points in the history of life where new levels of biological organisation (cells, groups or language) and complexity emerged. It has been suggested that these events as a marked shift from a qualitative level of organization to a new one, can be mapped into the concept of phase transitions from statistical physics. (28)

Along with evolutionary innovations and phase transitions, critical points play another role. Criticality refers to the state of a system poised between dual ordered and disordered states. The idea that criticality has desirable properties has been advanced within the context of nonlinearity and chaos, computation, genetic codes, neuroscience and ecology. There are good reasons because critical states often enhance information transfer and sensitivity to external signals. (30)

Pedia Sapiens: A Planetary Progeny Comes to Her/His Own Actual Factual Knowledge

IAUS 397: Exploring the Universe with Artificial Intelligence. iau.org/science/meetings/future/symposia/2788.. We note as an example of this latest frontier as our Earthuman prodigy proceeds apace to learn, quantify and achieve an innate representation of a genesis universe to itself.

Computer science is an ever-increasing part of astrophysics and cosmology. The upcoming big-data multimessenger era will provide both epochal opportunities for exploration, analysis and discovery. Here, we introduce the first IAU interdisciplinary symposium in Athens, Greece in May 2025 which will cover a wide range of topics related to AI, deep learning, astronomical databases, current and upcoming large-scale surveys, and pressing research issues.

Collins, Katherine, et al. Building Machines that Learn and Think with People.. arXiv:2408.03943.. Thirteen concerned scholars at University, Princeton, NYU, Alan Turing Institute, MIT and Microsoft Research including Umang Bhatt, Mina Lee and Thomas Griffiths enter a latest proposal and plan toward a considerate, reciprocal assimilation of personal discourse with more amenable computational resources.

What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. In this Perspective, we show how the science of collaborative cognition can be put to work to engineer systems that really can be called “thought partners.'' Drawing on motifs from computational cognitive science, we motivate an alternative scaling path through a Bayesian lens, whereby the partners we actively build and reason over models of the human and world. (Excerpt)

Tosato, Tommaso, et al. Lost in Translation: The Algorithmic Gap Between LMs and the Brain\. . . University of Montreal and Strungmann Institute for Neuroscience, Frankfurt researchers propose a series of parsed programs to better cross-align computational text with our intricate vernaculars. See also Building Artificial Intelligence with Creative Agency by Liane Gabora and Joscha Bach at 2407.10978 for a similar endeavor by way of autocatalic networks.

Language Models (LMs) have achieved impressive performance on linguistic tasks, but their relation to human processing remains unclear. This paper examines pros and con between LMs and the brain at different levels to compare their internal efficacy. We discuss how insights from neuroscience such as sparsity, modularity, internal states, and interactive learning can inform the development of more biologically plausible language models. The role of scaling laws is seen as an analogous way to bridge these loquacious systems. By developing LMs that more closely align with brain function, we aim to advance both artificial intelligence and our understanding of human cognition. (Abstract)

Tuckute, Greta, et al. Language in Brains, Minds, and Machines.. Annual Review of Neuroscience.. Volume 47, 2024. MIT neurolinguists including Evelina Fedorenko provide a range of current insights and concerns appreciations as Large Language version come into our knowsphere content. See also Elements of World Knowledge (EWOK): A cognition-inspired framework for evaluating basic world knowledge in language models by this group at arXiv:2405.09605.

It has long been argued that only humans could produce and understand language. But now, for the first time, artificial language models (LMs) achieve this feat. Here we survey the new purchase LMs are providing on the question of how language is implemented in the brain. We summarize evidence that LMs represent linguistic information similarly enough to humans to enable relatively accurate brain encoding and decoding during language processing. Finally, we examine which LM properties—their architecture, task performance, or training—are critical for capturing human neural responses to language.

Rabb, Nathaniel and Steven Sloman. Radical Collective Intelligence and the Reimagining of Cognitive Science.. Topics in Cognitive Science.. 16/2, 2024. As the quote says, MIT and Brown University introduce a special issue along with a novel perspective for the endeavor. Among the papers we note What Makes Us Smart? by Joseph Henrich and Michael Muthukrishna (see below) and The Wisdom of the Crowd is not a Forgone Conclusion. Effects of Self-Selection on (Collaborative) Knowledge Construction by Marie-Christin Krebs, et al.

Our special issue How Minds Work: The Collective in the Individual proposes a “radical CI” as a new paradigm for for this emergent collaborative facility. Radical CI posits that the representations and processes necessary to perform the cognitive functions that humans perform are collective entities, not encapsulated by any individual. This concept clarifies how the volume's contributions either rethink long-studied cognitive processes (memory, metacognition, reasoning) or contemplate how radical CI can arise.

Human creativity does not solely rely on our individual cognitive abilities, but instead emerges from the recombination of ideas, practices, and approaches that result from social interactions and idea exchanges in large, diverse populations. These population-level processes, which operate over generations, influence not only on our tools, technologies, and languages, but also key aspects of our culturally-evolved cognition, such as our epistemologies and ontologies. (JH & MM)

Ecosmos: A Revolutionary Fertile, Habitable, Solar-Bioplanet, Incubator Lifescape

Scholes, Gregory. Quantum-like states on complex synchronized networks.. Proceedings of the Royal Society A. June, 2024. The Princeton chemist with his lab group (scholes.princeton.edu) is a pioneer researcher for a beneficial integration of macro/micro, classical and quantum chemical reactivities. This entry is a latest progress report, search the eprint arXiv site for more work such as Foundations of Quantum Information for Physical Chemistry at 2311.12238.

3. Recent work suggests that interesting quantum-like probability laws, including interference effects, can be manifest in classical systems. Here we propose a model for quantum-like (QL) states and bits. We propose a way that complex systems can host robust states to process information in a QL fashion. It is shown that QL states are networks based on k-regular random graphs which can encode information for QL like processing. Although the emergent cases are classical, they have properties analogous to quantum states. The possibility of a QL advantage for computer operations and new kinds of function in the brain are discussed as open questions. (Abstract)

Tian, Yu, et al. Tian, Yu, et al. Quantum Networks: from Multipartite Entanglement to Hypergraph Immersion.. arXiv:2406.13452.. We record this entry by Stockholm University, KTH Royal Institute of Technology and Northwestern University researchers to show how readily this once arcane domain has become amenable and assimilated with nature’s nonlinear transitional ascent. See also Topologically Robust Quantum Network Nonlocality by Sadra Boreiri, et al at arXiv:2406.09510 for a version that includes Bell nonlocality.

Multipartite entanglement, a higher-order interaction unique to quantum information, offers advantages in quantum network (QN) applications. Here, we address the question of whether a QN can be topologically transformed into another via entanglement routing. Our key result is an exact mapping from multipartite entanglement routing to Nash-Williams's graph immersion problem, extended to hypergraphs. This generalized solution introduces a partial order between QN topologies, permitting transformations which offer insights into the design and manipulation of higher-order network topologies. (Abstract)

Kadmom, Jonathon. Efficient coding with chaotic neural networks: A journey from neuroscience to physics and back. arXiv:2408.01949. A Center for Brain Sciences, Hebrew University, Jerusalem polythreorist posts his workshop paper as this broadest, integral unification gains credence and comes together into these 2020s. A robust legitimacy is evident as many clarified aspects in both domains are found to readily be assimilated across this widest human to universe to Earthuman expanse.

This essay is derived from my lecture at "The Physics Modeling of Thought" workshop in Berlin in winter 2023. In regard, it explores a mutually beneficial relationship between theoretical neuroscience and statistical physics through the lens of computation in cortical circuits. It highlights how the study of neural networks has enhanced our understanding of complex, nonequilibrium, and disordered systems and how brain research has led to developments in physics such as phase transitions and critical phenomena. (Excerpt)

Workshop on Physics Modeling of Thought This is the first of a series within a four-year program at the MPI History of Science dedicated to this subject. For some years now, the Institute has carried out a historical-critical investigation of the theory and practices of modeling in different scientific realms from fundamental physics to earth systems. The general themes of the workshop include: The Neural Network Paradigm: The Complex and Dynamic Brain, Macro vs. Micro and Space-time Representations.

Kaneko, Kunihiko. Constructing universal phenomenology for biological cellular systems by evolutionary dimensional reduction.. Journal of Statistical Mechanics. February, 2024. A veteran biophysicist with postings at the Niels Bohr Institute, Copenhagen and the University of Tokyo contributes a paper to the STATPHYS 28 meeting held in August 2023 in Tokyo which can serve as another instance of current expansive integral rootings of life’s organismic and development in this conducive, many-body ground. See also Evolutionary accessibility of random and structured fitness landscapes by Joachim Krug and Daniel Oros.

The possibility of a macroscopic phenomenological theory for biological systems, akin to a thermodynamic framework is reviewed. Weround. introduce the concept of an evolutionary fluctuation–response relationship, which highlights the variance between phenotypic traits caused by genetic mutations. The universality of evolutionary dimensional reduction is presented along with theoretical formulations. We conclude with the prospects of a macroscopic basis that conveys biological robustness and irreversibility in cell differentiation. (Excerpt)

Kruse, Karsten, et al. Acto-myosin clusters as active units shaping living matter. arXiv:2408.05119.. arXiv:2408.05119. University of Geneva and University of Strasbourg biologists including Daniel Riveline provide an exercise whereby these title entities are treated as a self-assembling form of mobile matter.

Stress generation by the actin cytoskeleton shapes cells and tissues. Despite progress in live imaging and quantitative descriptions of cytoskeletal network dynamics, the connection between molecular scales and cell-scale spatio-temporal patterns is still unclear. Here we review studies of acto-myosin clusters at micrometer size and with lifetimes of several minutes in organisms from fission yeast to humans. We propose that tracking these clusters can serve as a simple readout for living matter such as morphogenetic processes that play similar roles in diverse organisms. (Abstract)

We have reviewed experimental and theoretical studies showing that self-organised acto-myosin clusters in a wide range of species behave locally and globally according to common rules. Apart from their biological significance, we speculate that acto-myosin clusters can also be applied to physical parameters. As such, we propose that acto-myosin clusters might act as appropriate quasi-particles on which general principles underlying morphogenesis can be built. It will be interesting to test these ideas in embryos while outlining the mechanisms securing robust morphogenesis with outstanding precisions over time and space. (9, 10)

Kulkarni, Suman and Dani Bassett.. Towards principles of brain network organization and function. arXiv:2408.02640l. As many fields this year seek and gain a deeper substantial ground in a conducive nature, here University of Pennsylvania prolific neuroscientists (search both) proceed to connect cerebral topologies and cognitive behaviors with a meld of many-body physics (organics), multiplex nets as they actively process knowledge content.

Understanding patterns of complex interactions and how they support collective neural activity and function is vital to parse human and animal behavior, treat mental illness, and develop artificial intelligence. Here, we take stock of recent progress in statistical physics, network geometry and information theory. Our discussion scales from individual neurons to mappings across brain regions. We examine the organizing principles and constraints that shape the biological structure and function of neural circuits and close with a look ahead at further integrities.

Newbolt, Joel and Nickolas Lewis. Flow interactions lead to self-organized flight formations disrupted by self-amplifying waves. Nature Communications. 15/ 3462, 2024. We cite this entry by NYU Courant Institute and Institut Polytechnique de Paris mathematicians for new findings about entities in motion and also for its deeper exemplary attribution to generic active physical sources.

Collectively locomoting animals are often seen akin to states of matter whereby group phenomena emerge from individuals. Motivated by linear formations, we show that pairwise flow interactions tend to promote crystalline or lattice-like arrangements. Force measurements and perturbations inform a wake model that views self-ordering as mediated by the self-amplification of disturbances as a resonance cascade. These results derive from generic features, and hence may arise more generally in macroscale, flow-mediated collectives. (Excerpt)

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