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Displaying entries 1 through 15 of 96 found.

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)

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

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)

Liu, Ziming, et al. KAN: Kolmogorov-Arnold Networks.. arXiv:2404.19756. MIT, Caltech and Northeastern University cognitive scholars including Max Tegmark draw on these companion mathematical theories to gin up a new, improved complementary version for the already capable artificial neural nets. See also Novel Architecture Makes Neural Networks More Understandable by Steve Nadis in Quanta for (September 11, 2024) for a good review article.

Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable functions on edges ("weights"). We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. Through examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs open opportunities for improving today's deep learning models. (Excerpt)

Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights") by which they can outperform in terms of accuracy and interpretability. Through two examples, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs. (S, Nadis)

Pfau, David, et al. Accurate computation of quantum excited states with neural networks. Science. Vol. 385/Iss. 6711, 2024. We cite this paper by Google DeepMind, London computational scientists as an example of how AI neural net procedures are being readily applied to quantum phenomena, which in turn implies that this fundamental realm has an innate, analytic affinity with cerebral structures and facilities. See also Understanding quantum machine learning also requires rethinking generalization by Elies Gil-Fuster, et al in Nature Communications (15/2277, 2024) for another instance.

xcited states are important in many areas of physics and chemistry; however, scalable, accurate, and robust calculations of their properties from first principles remain al theoretical challenge. Recent advances in computing molecular systems driven by deep learning show much promise. Pfau et al. present a parameter-free mathematics by directly generalizing variational quantum Monte Carlo to their ground states. The proposed method achieves accurate excited-state calculations on a number of atoms and molecule, and can be applied to various quantum systems. (Editor Summary)

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)

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)

Lian, Jianhui, et al. The broken-exponential radial structure and larger size of the Milky Way galaxy.. Nature Astronomy. June, 2024. We enter this work by Yunan University, University of Utah, and University of St Andrews for its content and in philoSophia wonder at the whole scenario whence at later date a minute, rare bioworld evolves a collective intellect which can then be able to retrospectively study, achieve and transcribe an extensive, integral galactic knowledge. See also, for example, The mass-metallicity relation as a ruler for galaxy evolution: insights from the James Webb Space Telescope at arXiv:2408.00061.

The radial structure of a galaxy is a fundamental property that reflects its growth and assembly history. Although it is straightforward to measure that of external galaxies, it is challenging for the Milky Way because of our inside perspective. The radial structure of the Milky Way has been assumed to be shaped by a single-exponential disk and a central bulge component. Here we report (1) a measurement of the age-resolved Galactic surface brightness profile and (2) the corresponding size of the Milky Way in terms of a half-light radius. Our results suggest that the Milky Way has a more complex radial structure and larger size than previously expected. (Excerpt)

McGaugh, Stacey, et al.. Accelerated Structure Formation: The Early Emergence of Massive Galaxies and Clusters of Galaxies. arXiv:2406.17930.. arXiv:2406.17930.. This entry by Western Reserve University, University of Oregon and INAF, Arcetri Astrophysical Observatory, Italy is a current example of the spatial breadth and spacetime duration of the observations that the James Webb Space Telescope is constantly achieving across the vast universe. How incredible is it that a minute biospheric collective sapience and capability can yet provide such vivid images and entire cosmic knowledge.

Galaxies in the early universe appear to have grown too big too fast into massive, monolithic objects in the hierarchical ΛCDM structure formation paradigm. The available data are consistent with a population that forms early and follows a star formation history to become full galaxies. Observations of the kinematics of spiral galaxies as a function of redshift show that massive disks and their scaling relations were in place at early times. (Excerpt)

Nadis, Steve. Diminishing Dark Energy May Evade the ‘Swampland’ of Impossible Universes.. Quanta. August 19, 2024. A science writer surveys these latest speculations as everything cosmic now seems in flux, open to question, and in need of revision due to the Dark Energy Spectroscopic Instrument (DESI) project 3D map findings of a variable dark energy. In this regard, an accelerating universe expansion may actually be receding. And may we again muse how incredible this whole scenario is whence a sentient collaborative bioworld is able to carry out such instrumental explorations, mathematic quantifications and successive iterations, by which, so it seems, some celestial reality is trying to represent, record and affirm itself.

But if the initial DESI finding is confirmed, it will tell us something crucial about dark energy and its future. “Even more importantly,” Vafa said, “we can deduce that this is marking the beginning of the end of the universe. By ‘end,’ I don’t mean nothing happens after that. I’m saying something else happens that is very different from what we have now.” Perhaps dark energy will fall until it settles into a stabler, possibly negative value. With that, a new universe, with new laws, particles and forces, would replace the current one.

The Dark Energy Spectroscopic Instrument (DESI) (search) is a scientific research instrument for conducting spectrographic astronomical surveys of distant galaxies. Its main components are a focal plane containing 5,000 fiber-positioning robots, and a bank of spectrographs. The instrument enables an experiment to probe the expansion history of the universe and the mysterious physics of dark energy.

Sawala, Till, et al. Distinct distributions of elliptical and disk galaxies across the Local Supercluster as a ΛCDM prediction. Nature Astronomy.. August, 2024. We enter this article by University of Helsinki and Durham University, UK astrophysicists including Carlos Frenk for its content and as an example of apparently limitless 21st century Earthuman stellar abilities to explore, instrument, quantify, compute and record by way of data, image, graph, equation, theory any celestial spacescape dimension and temporal dynamic animation.

Galaxies of different types are not equally distributed in the Local Universe. In particular, the supergalactic plane is prominent among the brightest ellipticals, but inconspicuous among the brightest disk galaxies. This striking difference provides a unique test for our understanding of galaxy and structure formation. Here we use the SIBELIUS DARK constrained simulation to confront the predictions of the standard Lambda Cold Dark Matter (ΛCDM) model and standard galaxy formation theory with these observations. We find that SIBELIUS DARK reproduces the spatial distributions of disks and ellipticals and, in particular, the observed excess of massive ellipticals near the supergalactic equator. (Excerpt).

Carlos S. Frenk is Director of the Institute for Computational Cosmology, Durham University's world-renowned theoretical cosmology research group. Along with collaborators from all over the world, he builds model universes in state-of-the-art supercomputers.

Brouillet, Matthew and Georgi Georgiev. Why and How do Complex Systems Self-Organize at All?. arXiv:2408.10278.. Assumption University, Worcester, MA physicists (search GG) provide a latest theoretic grounding of nature’s spontaneous animate development from a conducive universe to our societal retrospect. The paper’s subtitle is Average Action Efficiency as a Predictor, Measure, Driver, and Mechanism of Self-Organization which then informs some 40 pages of mathematical proofs.

Self-organization in complex systems is a process in which randomness is reduced and emergent structures appear due to energy gradients and dynamic principles. In regard, positive feedback loops connect this measure with all provide these complex systems with exponential growth, and power law relationships. In this study, we also proceed to model agent-based simulations, measure action efficiency and consider intentional applications. (Excerpt)

Self-organization is key to understanding the existence of, and the changes in all systems that lead to higher levels of complexity and perfection in development and evolution. It is a scientific as well as a philosophical question as our realization and understanding of this robust, resilient, competitive, vital process grows. Our goal is a better explanation that drives Cosmic Evolution from the Big Bang to the present, and into the future. Self-organization has a universality independent of the substrate of the system - physical, chemical, biological, or social - and explains all of its structures. (1)

Overview of the Theoretical Framework: We use the extension of Hamilton’s Principle of Stationary Action to a Principle of Dynamic Action, according to which action in self-organizing systems is changing in two ways: decreasing the average action for one event and increasing the total amount of action in the system during the process of self-organization, growth, evolution, and development. This view can lead to a deeper understanding of the fundamental principles of nature’s self-organization, evolution, and development in the universe, ourselves, and our society. (2)

Our findings contribute to a deeper understanding of the mechanisms underlying self-organization and offer a novel, quantitative approach to measuring organization in complex systems. This research opens up exciting possibilities for further exploration and practical applications, enhancing our ability to design and manage complex systems across various domains. By providing a quantitative measure of organization that can be applied universally, we enhance our ability to design and manage complex systems across various domains. Future research can build on our findings to explore the dynamics of self-organization in greater detail, develop new optimization strategies, and create more efficient and resilient systems. (45)

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