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Chen, Qianyang and Mikhail Prokopenko. Why collective behaviours self-organise to criticality: A primer on information-theoretic and thermodynamic utility measures. arXiv:2409.15668. Centre for Complex Systems, University of Sydney physicists (search MP) contribute a further qualification of nature’s apparent whole scale persistence to arrange itself across every infinity by way of this critical poise, best balance, optimum state of more or less order. See also Biological Arrow of Time by Mikhail Prokopenko, et al (arXiv:2409.12029) for another instantiation as a revolutionary ecosmic natural genesis universe just now becomes a profound reality.

Collective behaviours are frequently observed to self-organise to criticality. Existing models such as Self-organised Criticality (SOC) occur across disciplines but in our view do not fully explain. Here we propose an information-driven approach with predictive content, empowerment, and active inference, as well as thermodynamic efficiencies. By interpreting the Ising model as a perception-action loop, we compare how intrinsic utilities shape collective behaviour. Finally, we define a Principle of Super-efficiency whereby collective behaviours arrive at a critical regime as an optimal balance with respect to the entropy reduction relative to the thermodynamic costs. (Abstract)

Self-organisation is a process where a system spontaneously develops new structured patterns or functions, without control by an external force. From a physics perspective, the effect is viewed as entropy reduction or increase in order in an open system. In a biological sense, self-organisation is defined as a pattern-formation process that relies on interactions among many lower-level components. Three key aspects are Spontaneous order: the system evolves into a more coherent state; Emergent transition to a more collective behaviour and Local interactions and long-range correlations: system components operate on local information but exhibit long-range connectivity. (1)

The Ising model is a mathematical expression of ferromagnetism in statistical mechanics. It consists of discrete variables that represent atomic "spins" in one of two states arranged in a lattice allowing each spin to interact with its neighbors. The model allows the identification of phase transitions as a simplified model of reality.

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 Phenomenal, PhiloSophia, Propaedutic, WumanWise, Participatory Endeavor

Bialek, William. Ambitions for theory in the physics of life. arXiv:2401.15538. The Princeton polyphysicist opens his 2024 entries with a review of 2023 presentations that he made at the Les Houches Summer School in the Swiss Alps (Google for annual programs). Search WB herein and arXiv.com preprint for several collegial contributions. As I gather and record into wider war October, along with other integral postings (Prokopenko, et al), it truly does seem that a revolutionary (family) reunion of universe and humanverse is finally becoming an actual reality. Into 2025 going forward, this is an awesome, historic achievement as our collective pediasphere comes to learn and discover on her/his own.

Theoretical physicists have been fascinated by the phenomena of life for more than a century. As we can presently engage more realistic descriptions of living systems, however, things get complicated. After reviewing different reactions to this complexity, I explore the optimization of information flow as a potentially general theoretical principle. The primary example is a genetic network guiding development of the fly embryo, but each idea also is illustrated by examples from neural systems. In each case, optimization makes detailed, largely parameter-free predictions that connect quantitatively with experiment.

This Summer School celebrates a special moment in the long history of interactions between physics and biology. Just one generation back, physicists and biologists thought that searching for a theoretical physics of life was a waste of time. Physicists saw biology as too messy, and biologists saw the physicists’ simplicity as a poor match to the complex diversity of life. Much has changed. Enormous progress in experiment has created a reproducibility far beyond what once was imagined. Thus, aspects of early embryonic development can take their place alongside classical examples such as photon counting in vision and molecule counting in bacterial chemotaxis. (63)

Meshulam, Leenoy and William Bialek. Statistical mechanics for networks of real neurons.. arXiv:2409.00412. University of Washington, Seattle and Princeton University biophysicists post another late 2024 significant, comprehensive cross-integration of complex cerebral systems with newly perceived groundings in physical principles. As the quotes say, several features gain stronger notice such as an invariant similar scale, constant self-organization, and a long sought integral universality. As an eminent polyscholar, Bialek (LMs doctoral advisor at Princeton) notes the historic relevance of finally achieving a robust confluence. See also his collegial arXiv.com preprints this year such as Maximum entropy models for patterns of gene expression (arXiv:2408.08037), Ambitions for theory in the physics of life (WB, arXiv:2401.15538, herein) and Scale invariance in early embryonic development (arXiv:2312.17684).

Perceptions and actions, thoughts and memories result from coordinated activity in thousands of neurons in the brain. It is an old dream of the physics community to provide a statistical mechanics basis for these and other emergent phenomena of biological life. Our proposal here is that these aspirations are just now being fulfilled by an array of new abilities to measure the multiphase electrical activity throughout the brain. We review progress as it brings theory and experiment together by a focus on maximum entropy and renormalization groups. These confluent approaches can then discern quantitatively reproducible collective behaviors in layered networks of real neurons, and provide independent, parameter-free predictions. (Abstract)

In populations of bacteria, swarms of insects, schools of fish, and flocks of birds we see collective movements and decision making. In all these examples - akin to cerebral networks of neurons — what we recognize as the functional behavior of living systems is a macroscopic behavior that emerges from interactions of many components on a smaller scale. In the inanimate world, statistical mechanics provides a powerful and predictive framework within which to understand emergent phenomena. It has long been a goal that we could have a statistical mechanics of emergent phenomena in the living world as well. We encourage the reader to think of what we review here as progress toward this realization. (2)

In natural swarms one sees finite size and dynamical scaling behaviors that provide more direct evidence for criticality, independent of a particular instance. While each example must stand on its own, again we have wondered if tuning to this optimum spot might unify our understanding of disparate living systems. (47)

Not so long ago all we have said herein would have seemed like a remote glimmer. What has changed, dramatically, is that all these ideas — Ising models, correlation functions, scaling behaviors and the RG, and more — are connected to quantitative experiments on networks of multiplex neurons. We can now connect all the way from physical concepts to the details of specific brain regions. Our experimentalist friends will continue to move the frontier to make the brain accessible in this way. The outlook for theory is bright. (56)

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

Cusack, Rhodri, et al. Helpless infants are learning a foundation model. Trends in Cognitive Sciences. 28/8, 2024. We refer to this contribution by Trinity College Dublin, Google DeepMind, London, and Auburn University neuropsychologists including Christine Charvet for latest views of the first three months neonatal to infant phase but also for its notice of a comparative affinity with how Artificial Intelligence language methods seem to be processed and learn. This section now contains several similar views which then provide an empirical basis for an actual pediakind sapience.

Humans have a protracted postnatal period, attributed to human-specific maternal constraints which cause an early birth when the brain is highly immature. By aligning neurodevelopmental events across species, however, it has been found that humans are not born with underdeveloped brains compared with animal species with a shorter helpless period. Consistent with this, the advancing field of infant neuroimaging has found that brain connectivity and functional activation at birth share many similarities with the mature brain. As a parallel approach, we consider deep neural network machine learning which also benefits from a ‘helpless period’ of pre-training. As a result, we propose that human infants are forming a foundational set of vital representations in preparation for later cognitive abilities with high performance and rapid generalisation. (Abstract)

Czaplicka, Agnieszka, et al. Mutual benefits of social learning and algorithmic mediation for cumulative culture. arXiv:2410.00780. MPI Human Development and University of Pennsylvania computer scientists post an initial consideration of how AI machine learning codes in algorithmic equation form can facilitate the social collectivity that so distinguishes our Earthumanity.

The evolutionary success of humans is attributed to complex cultural artefacts that enable us to cope with environmental challenges. The evolution of complex culture is usually modeled as a collective process in which individuals invent new artefacts (innovation) and copy from others (social learning). However, in our present digital age, intelligent algorithms are often mediating information between humans. Building on cultural evolution models, we investigate network-based public learning and algorithmic mediation on cultural accumulation and find that this feature tends to be optimal when social education and algorithmic mediation are combined. (Excerpt)

DiPaolo, Laura, et al. Active inference goes to school: the importance of active learning in the age of large language models. Philosophical Transactions of the Royal Society B. August, 2024. In an article for a Minds in movement: embodied cognition in the age of artificial intelligence issue, this entry by University of Sussex cognitive scientists including Axel Constant and Andy Clark is noted for its meld of embodied thinking with free energies and also for a turn to educational approaches as an appropriate way to try to understand and manage these voluminous AI faculties. In specific regard, the widely used (Maria 1870-1952) Montessori method is extensively reviewed as especially suitable because of its intrinsic open creativity which engages and empowers children in group settings with hands-on activities. See also Differences in spatiotemporal brain network dynamics of Montessori and traditionally schooled students by Paola Zanchi, et al in npj Science of Learning (Vol. 9/Art. 45, 2024, herein).

Human learning often involves embodied interactions with the material world. But today this means an increasing amount of generative artificial intelligence content. Here we ask how to assimilate these resources into our educational practices. Our focus will be on approaches that foster exploration and interaction such as the carefully organized settings of Montessori methods. We surmise that generative AI should be a natural feature in these learning environs to facilitate sequences of prediction error and enabling trajectories of self-correction. (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)

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.

Arnscheidt, Constantin and Hassan Alkhayuon. Rate-induced biosphere collapse in the Daisyworld model. arXiv:2410.00043.. Earth system scientists at the Centre for the Study of Existential Risk, Cambridge University and Mathematical Sciences, University College Cork, Ireland add a temporal dimension to James Lovelock’s 1983 popular thought example about ways to consider Earth as a self-regulating bioplanet with regard to how fast its fertile state may change.

There is much interest in the phenomenon of rate-induced tipping, where a system changes abruptly when forcings change faster than some critical rate. Here, we analyse rate-induced tipping in the classic "Daisyworld" model (james Lovelock 1983) which considers a hypothetical planet inhabited only by two species of daisies with different reflectivities. It is notable because the daisies lead to an emergent "regulation" of the planet's temperature. The new discovery of rate-induced tipping in such a well-studied model provides further supporting evidence that this sudden shift to a new better or worse state may be common in a wide range of systems. (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)

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