# brain: apropos of mandering minds, the 'default mode' network.

<< When your mind is wandering, your brain’s “default mode” network is active. Its discovery 20 years ago inspired a raft of research into networks of brain regions and how they interact with each other. >>

<< The default mode was one of the first brain networks characterized by science. It consists of a handful of brain regions, including a few at the front of the brain, like the dorsal and ventral medial prefrontal cortices, and others scattered throughout the organ, like the posterior cingulate cortex, the precuneus and the angular gyrus. These regions are associated with memory, experience replay, prediction, action consideration, reward/ punishment and information integration. >> ️

<< The default mode is clearly up to something complicated; it’s involved in many different processes that can’t be neatly described. >>

<< “It’s kind of silly to think that we’re ever going to be like, ‘This one brain region or one brain network does one thing,’” (..). “I don’t think that’s how it works.” (..) “Network interactions are more elucidating to study in some ways than just a network in isolation because they do work together and then come apart and then change what they’re doing over time”. >> Lucina Uddin.️

Lucina Uddin << is particularly interested in how the default mode network interacts with the ️salience network, which seems to help us identify the most relevant piece of information at any given time. Her work suggests that the salience network detects when something is important to pay attention to and then acts as an off switch for the default mode network. >>

Vinod Menon << has developed what he calls️ ️the triple network theory. It posits that abnormal interactions between the default mode network, the salience network and a third one called the frontoparietal network could contribute to mental health disorders. >>️

Nora Bradford. What Your Brain Is Doing When You’re Not Doing Anything.  quantamagazine.org. Feb 5, 2024. 

https://www.quantamagazine.org/what-your-brain-is-doing-when-youre-not-doing-anything-20240205/

FonT: the cat when it is in a contemplative state ... Who knows in what forms and with what results an artificial intelligence (AI) will be able to structure itself in networks of this type.

Also: brain, brain default mode network, in  https://pubmed.ncbi.nlm.nih.gov/?term=brain+default+mode+network

Also: brain, salience network, in https://pubmed.ncbi.nlm.nih.gov/?term=brain+salience+network

Also: brain, triple network theory, in https://pubmed.ncbi.nlm.nih.gov/?term=brain+triple+network+theory

Also: brain, ai (artificial intell), analogy,  in https://www.inkgmr.net/kwrds.html

Keywords: brain, mind, default mode network, salience network, triple network theory, AI

# behav: the benefit of ignorance for traffic through a random congestible network.

<< When traffic is routed through a network that is susceptible to congestion, the self-interested decisions made by individual users do not, in general, produce the optimal flow. This discrepancy is quantified by the so-called "price of anarchy." >>

AA << consider whether the traffic produced by self-interested users is made better or worse when users have uncertain knowledge about the cost functions of the links in the network, and define a parallel concept that (They) call the "price of ignorance."  >>

AA << introduce a simple model in which fast, congestible links and slow, incongestible links are mixed randomly in a large network and users plan their routes with finite uncertainty about which of the two cost functions describes each link. >>

<< One of (Their) key findings is that a small level of user ignorance universally improves traffic, regardless of the network composition. Further, there is an optimal level of ignorance which, in (the) model, causes the self-interested user behavior to coincide with the optimum. Many features of (AA) model can be understood analytically, including the optimal level of user ignorance and the existence of critical scaling near the percolation threshold for fast links, where the potential benefit of user ignorance is greatest. >>️

Alican Saray, Calvin Pozderac, et al. The benefit of ignorance for traffic through a random congestible network. arXiv: 2503.09684v1 [cond-mat.dis-nn]. Mar 12, 2025.

https://arxiv.org/abs/2503.09684

Also:  network, behav, random, uncertainty, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, networks, behavior,  randomness, uncertainty, price of anarchy, price of ignorance

# brain: time delay in 'reservoir brain' as a reservoir network, a hypothesis

<< Both the predictive power and the memory storage capability of an artificial neural network called a reservoir computer increase when time delays are added into how the network processes signals, according to a new model. >>️

<< They also suggest that incorporating time delays could offer advantages to living neural networks (such as those found in human and animal brains). Such a finding would be tantalizing, as time delays are known to decrease performance in living systems. For example, for a baseball player facing an oncoming ball, a longer time delay between perception and action (which is learned from experience) will decrease the likelihood they hit a home run. Are there instead cases in which time delays increase an organism’s ability to perform some task? Has evolution shaped our brains, which could perhaps be thought of as a collection of reservoir computers, so that the time delay between one neuron sending a signal and a second receiving it is exactly the right length for understanding the visual and audio that constantly impinge upon our eyes and ears? Does adding time delays impact the number of neurons the brain needs to operate correctly? Further work is needed to answer these questions, but such work could lead to a new understanding of how biological organism’s function.  >>️

Sarah Marzen. Time Delays Improve Performance of Certain Neural Networks. Physics 17, 111. July 22, 2024. 

https://physics.aps.org/articles/v17/111 

Also: pause, silence, jazz, network, brain, ai (artificial intell), in https://www.inkgmr.net/kwrds.html 

Keywords: gst, brain, network, neural network, reservoir network, reservoir computer, time delay, ai, artificial intelligence

# gst: apropos of nuanced dependencies in intricately coupled network, attractive-repulsive challenge in swarmalators with time-dependent speed.

AA << examine a network of entities whose internal and external dynamics are intricately coupled, modeled through the concept of ``swarmalators'' as introduced by O'Keeffe et al. (Nat. Commun., 8(1):1–13, 2017). (They) investigate how the entities' natural velocities impact the network's collective dynamics and path to synchronization. >>

<< Specifically, (AA) analyze two scenarios: one in which each entity has an individual natural velocity, and another where a group velocity is defined by the average of all velocities. >> 

Their << findings reveal two distinct forms of phase synchronization -- static and rotational -- each preceded by a complex state of attractive-repulsive interactions between entities. This interaction phase, which depends sensitively on initial conditions, allows for selective modulation within the network. By adjusting initial parameters, (AA) can isolate specific entities to experience attractive-repulsive interactions distinct from the group, prior to the onset of full synchronization. >>

<< This nuanced dependency on initial conditions offers valuable insights into the role of natural velocities in tuning synchronization behavior within coupled dynamic networks. >>️

Steve J. Kongni, Thierry Njougouo, et al. Attractive-repulsive challenge in swarmalators with time-dependent speed. arXiv: 2501.06048v1 [nlin.AO]. Jan 10, 2025. 

https://arxiv.org/abs/2501.06048

Also: swarm, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, swarm, swarmalators, network

# gst: neural networks know their knots.

<< The use of neural networks in physics is booming. Recently, the tool has helped researchers uncover everything from new magnetic materials (..) to ways to reduce noise in electron beams produced at synchrotrons (..) Seeking their own neural network success, (AA) wondered if the tool could classify knots, a computationally challenging problem. >>

They << applied two different neural networks to the problem—a recurrent neural network (RNN) and a feed-forward neural network (FFNN). >>

<< The RNN achieved 99% accuracy >>

 

Katherine Wright. Neural Networks Know Their Knots. Physics 13, s19. Feb 11, 2020.

https://physics.aps.org/articles/v13/s19.

img  https://physics.aps.org/assets/b3a39285-5bde-4137-8a54-5f9d9b9e2738/es19_1.png

Olafs Vandans, Kaiyuan Yang, et al. Identifying knot types of polymer conformations by machine learning. Phys. Rev. E 101, 022502. Feb 11, 2020.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.101.022502

Also: network, neuro, in: https://www.inkgmr.net/kwrds.html

Keywords: gst, network, neural network, knots

# gst: apropos of 'transitions', slow dynamics of complex connected networks can control the rate of demixing

<< A space- spanning network structure is a basic morphology in phase separation of soft and biomatter, alongside a droplet one. Despite its fundamental and industrial importance, the physical principle underlying such network- forming phase separation remains elusive. >>

AA << find that phase- separation dynamics is controlled by mechanical relaxation of the network- forming dense phase, whose limiting process is permeation flow of the solvent for colloidal suspensions and heat transport for pure fluids. This universal coarsening law would contribute to the fundamental physical understanding of network-forming phase separation. >>

Michio Tateno, Hajime Tanaka. Power-law coarsening in network-forming phase separation governed by mechanical relaxation. Nat Commun 12, 912. doi: 10.1038/  s41467-020-20734-8. Feb 10,  2021.

https://www.nature.com/articles/s41467-020-20734-8

️

Discovery of a new law of phase separation. University of Tokyo. Feb 10, 2021. 

https://phys.org/news/2021-02-discovery-law-phase.html

Also

keyword 'transition' in FonT

https://flashontrack.blogspot.com/search?q=transition

keyword 'transition' | 'transizion*' in Notes (quasi-stochastic poetry)

https://inkpi.blogspot.com/search?q=transition

https://inkpi.blogspot.com/search?q=transizione

https://inkpi.blogspot.com/search?q=transizioni

# game: apropos of Parrondo's paradox, winning with losses driven by reputation and reciprocity

AA << investigate two such social behaviors, reputation and reciprocity, and their role in explaining Darwin’s survival of the fittest, examining how these fundamental principles govern individual interactions and shape broader social dynamics. >>

<< Current theories hint at two main facets of social interaction, reputation and reciprocity, as potential drivers behind this cooperative evolution. Reputation revolves around building and sustaining trust, social worth, and overall community standing. Conversely, reciprocity governs the mutual exchange of actions or benefits, influencing our choices. >>

<< One intriguing concept explored in this domain is Parrondo’s paradox: combining or switching between two losing strategies might surprisingly achieve a winning outcome. The role of Parrondo’s paradox in complex systems has sparked key research into chaotic many-body, quantum, and algorithmic network applications, where combining elements yields opposing beneficial results. Similarly, social physicists aim to uncover hidden mechanisms that govern societal phenomena by integrating the paradox’s counterintuitive principles. >>️

<< The game-theoretic Parrondo’s paradox emerges through multiple iterations of these interactions (..) A naive observation might conclude that in either scheme the chance of individuals losing to the environment is higher than gaining from the environment. For the reputation scheme, one is rewarded with a singular capital from the environment but is punished with two. Similarly, the reciprocity scheme only allows for the redistribution of capital or loss of capital. In reality, diverse schemes can be adopted by different individuals. Thus, (AA) suggest two forms of switching: (1) stochastic switching, where the individual randomly selects one of two schemes to employ with equal probability, and (2) rule-based switching, where the individual only selects the reputation scheme if it passes the reputation threshold ρ; otherwise, it employs the reciprocity scheme. >>

AA << also performed simulations on other network topologies (..) Parrondo’s paradox is strongly observed in small-world networks, weakly in the Erdős-Rényi network, and absent in scale-free networks. >>

To conclude, some of these observations << underscore the profound capability of rule-based switching mechanisms inherent in Parrondo’s paradox to emulate and forecast key aspects of real-world social phenomena. Such insights are invaluable for developing sophisticated models and strategies in various fields, ranging from social sciences to policy making, where accurate predictions of social behavior and dynamics are crucial. >>

Joel Weijia Lai, Kang Hao Cheong. Winning with Losses: The Surprising Success of Negative Strategies in Social Interaction Behavior. Phys. Rev. Lett. 133, 167401. Oct 16, 2024. 

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.133.167401

Also: Parrondo, tit-for-tat, game, behav, network, in https://www.inkgmr.net/kwrds.html 

Keywords: Parrondo, tit-for-tat, game, behavior, behaviour, network

# gst: apropos of critical transitions, a new approach to extreme events.

FIG. 1. Dynamics of excitable complex networks [coupling topologies: random (RN); small-world (SW); scale-free (SF); all-to-all (complete; CP)]. 

<< Unexpected and often irreversible shifts in the state or the dynamics of a complex system often accumulate in extreme events with likely disastrous impact on the system and its environment. Detection, understanding, and possible prediction of such critical transitions are thus of paramount importance across a variety of scientific fields. >>

<< The rather modest improvement achieved so far may be due previous research mostly concentrating on either particular subsystems, considered to be of vital importance for the generating mechanism of a critical transition, or on the system as a whole. These approaches only rarely take into account the intricate, time-dependent interrelatedness of subsystems that can essentially determine emerging behaviors underlying critical transitions. >> 

AA << uncover subsystems, network vertices, and the interrelatedness of certain subsystems, network edges, as tipping elements in a networked dynamical system, forming a time-evolving tipping subnetwork. (They)  demonstrate the existence of tipping subnetworks in excitable complex networks and in human epileptic brains. These systems can repeatedly undergo critical transitions that result in extreme events. >>

AA << findings reveal that tipping subnetworks encapsulate key properties of mechanisms involved in critical transitions. >>

Timo Bröhl, Klaus Lehnertz. Emergence of a tipping subnetwork during a critical transition in networked systems: A new avenue to extreme events. Phys. Rev. Research 7, 023109. May 1, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.023109

Also: network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, networks, excitable complex networks, network edges, network vertices, subnetwork, tipping subnetworks, small-worlds, unexpected shifts, transitions, critical transition, extreme events, interrelatedness, time-dependent interrelatedness.

# gst: discontinuous transition to chaos in a canonical random neural network

AA << study a paradigmatic random recurrent neural network introduced by Sompolinsky, Crisanti, and Sommers (SCS). In the infinite size limit, this system exhibits a direct transition from a homogeneous rest state to chaotic behavior, with the Lyapunov exponent gradually increasing from zero. (AA)  generalize the SCS model considering odd saturating nonlinear transfer functions, beyond the usual choice 𝜙⁡(𝑥)=tanh⁡𝑥. A discontinuous transition to chaos occurs whenever the slope of 𝜙 at 0 is a local minimum [i.e., for 𝜙′′′⁢(0)>0]. Chaos appears out of the blue, by an attractor-repeller fold. Accordingly, the Lyapunov exponent stays away from zero at the birth of chaos. >>

In the figure 7 << the pink square is located at the doubly degenerate point (𝑔,𝜀)=(1,1/3). >>️️

Diego Pazó. Discontinuous transition to chaos in a canonical random neural network. Phys. Rev. E 110, 014201. July 1, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.014201

Also: chaos, random, network, transition, neuro, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chaos, random, network, transition, neuro

# gst: emergence of the traveling chimera, imperfect-traveling, traveling multi-clusters, and alternating traveling chimera in a two-dimensional network

<< Neuronal synchronization being a phenomenon linked to several brain pathologies such as epilepsy, Parkinson diseases, Alzheimer, autism and schizophrenia, does not always appear in a singular way in neuronal networks. Its presence (state of coherence) accompanied simultaneously by an asynchronous state (state of incoherence) has been demonstrated (..) in the networks of identical oscillators not locally coupled. This phenomenon was later termed the chimera state. (..) Chimera states are analogously to the cerebral behaviors of certain aquatic mammals and migratory birds, which during their movements half part of their brains asleep while the rest are awake.  >>️

AA << study the emergence of the traveling chimera state in a two-dimensional network of (..) burst neurons with the mutual presence of local and non-local couplings. (AA) show that in the unique presence of the non-local chemical coupling modeled by a nonlinear function, the traveling chimera phenomenon occurs with a displacement in both directions of the plane of the grid. The introduction of local electrical coupling shows that the mutual influence of the two types of coupling can, for certain values, generate traveling chimera, imperfect-traveling, traveling multi-clusters, and alternating traveling chimera, ie the presence in the network under study, of patterns of coherent elements interspersed by other incoherent elements in movement and alternately changing their position over time. The confirmation of the states of coherence is done by introducing the parameter of instantaneous local order parameter in two dimensions. >>️

Gael R. Simo, Patrick Louodop, et al. Traveling chimera patterns in two-dimensional neuronal network. arXiv:2106.08400v1. Jun 9, 2021.

https://arxiv.org/abs/2106.08400

# gst: apropos of transitions, towards a theory for the formation of chimera patterns in complex networks

This AA work << formalizes a systematic method by evoking pattern formation theory to explain the emergence of chimera states in complex networks. >>

They << show that the randomness of network topology, as reflected in the localization of the graph Laplacian eigenvectors, determines the emergence of chimera patterns, underscoring the critical role of network structure. In particular, this approach explains how amplitude and phase chimeras arise separately and explores whether phase chimeras can be chaotic or not. (AA) findings suggest that chimeras result from the interplay between local and global dynamics at different time scales. >>

Malbor Asllani, Alex Arenas. Towards a Theory for the Formation of Chimera Patterns in Complex Networks. arXiv: 2412.05504v1 [nlin.AO]. Dec 7, 2024.

https://arxiv.org/abs/2412.05504

Also: chimera, network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, chimera, network, transition

# gst: weird Nature; randomly arranged nanowire networks seem to behave, at the edge of chaos, like cortical neuronal cultures

<< an artificial network of nanowires can be tuned to respond in a brain-like way when electrically stimulated. >>️

<< If the signal stimulating the network was too low, then the pathways were too predictable and orderly and did not produce complex enough outputs to be useful. If the electrical signal overwhelmed the network, the output was completely chaotic and useless for problem solving. The optimal signal for producing a useful output was at the edge of this chaotic state. >>️

<< Some theories in neuroscience suggest the human mind could operate at this edge of chaos, or what is called the critical state, (..) Some neuroscientists think it is in this state where we achieve maximal brain performance. (..) What's so exciting about this result is that it suggests that these types of nanowire networks can be tuned into regimes with diverse, brain-like collective dynamics, which can be leveraged to optimize information processing. >> Zdenka Kuncic.️

<< In the nanowire network the junctions between the wires allow the system to incorporate memory and operations into a single system. This is unlike standard computers, which separate memory (RAM) and operations (CPUs). >>

<< These junctions act like computer transistors but with the additional property of remembering that signals have traveled that pathway before. As such, they are called 'memristors', >> Joel Hochstetter.

️

'Edge of chaos' opens pathway to artificial intelligence discoveries. University of Sydney. Jun 29, 2021.

https://phys.org/news/2021-06-edge-chaos-pathway-artificial-intelligence.html

Joel Hochstetter, Ruomin Zhu, et al. Avalanches and edge-of-chaos learning in neuromorphic nanowire networks. Nat Commun 12, 4008. doi: 10.1038/ s41467-021-24260-z. Jun 29, 2021.

https://www.nature.com/articles/s41467-021-24260-z

# gst: apropos of noise-assisted phenomena, self-organized transport in noisy dynamic networks.

AA << present a numerical study of multicommodity transport in a noisy, nonlinear network. The nonlinearity determines the dynamics of the edge capacities, which can be amplified or suppressed depending on the local current flowing across an edge. (AA) consider network self-organization for three different nonlinear functions: For all three (They) identify parameter regimes where noise leads to self-organization into more robust topologies, that are not found by the sole noiseless dynamics. Moreover, the interplay between noise and specific functional behavior of the nonlinearity gives rise to different features, such as (i) continuous or discontinuous responses to the demand strength and (ii) either single or multistable solutions. (AA) study shows the crucial role of the activation function on noise-assisted phenomena. >>️

Frederic Folz, Kurt Mehlhorn, Giovanna Morigi. Self-organized transport in noisy dynamic networks. Phys. Rev. E 110, 044310. Oct 21, 2024. 

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.044310

Also: network, noise, behavior, self-assembly, instability, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, noise, behavior, self-assembly, stability 

# gst: how noise affects memory in linear recurrent networks

<< The effects of noise on memory in a linear recurrent network are theoretically investigated. Memory is characterized by its ability to store previous inputs in its instantaneous state of network, which receives a correlated or uncorrelated noise. >>

<< Two major properties are revealed: First, the memory reduced by noise is uniquely determined by the noise's power spectral density (PSD). Second, the memory will not decrease regardless of noise intensity if the PSD is in a certain class of distribution (including power law). >>

JingChuan Guan, Tomoyuki Kubota, et al. How noise affects memory in linear recurrent networks. Phys. Rev. Research 7, 023049. Apr 14, 2025.

https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.7.023049

Also: noise, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, noise, network, memory

# gst: the strangeness of networks, the hypothesis of a connection between the kinetics of networks and anomalous transport theory.

<< Many real-world networks change over time. Think, for example, of social interactions, gene activation in a cell, or strategy making in financial markets, where connections and disconnections occur all the time. >>

AA team << has gained groundbreaking insights into this problem by recasting the discrete dynamics of a network as a continuous time series (..). In doing so, the researchers have discovered that if the breaking and forming of links are represented as a particle moving in a suitable geometric space, then its motion is subdiffusive—that is, slower than it would be if it diffused normally. What’s more, the particles’ motions are well described by fractional Brownian motion, a generalization of Einstein’s classic model. This feat establishes a profound connection between the kinetics of time-varying or “temporal” networks and anomalous transport theory, opening fresh prospects for developing predictive equations of motion for networks. >>️

Ivan Bonamassa. Strange Kinetics Shape Network Growth. Physics 17, 96. Jun 17, 2024.

https://physics.aps.org/articles/v17/96

Evangelos S. Papaefthymiou, Costas Iordanou, Fragkiskos Papadopoulos. Fundamental Dynamics of Popularity-Similarity Trajectories in Real Networks. Phys. Rev. Lett. 132, 257401. Jun 17, 2024. 

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.257401

Also: network, transition, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, network, transition

# gst: self-repelling species could self-organize.

<< Catalytically active particles form clusters when they respond not only to their own chemical targets but to those of other catalysts, too. >>️

AA  << show that the phenomenon of self-organization depends strongly on the network topology. >>️

They << modeled a three-species system (..) systems where each species responds chemotactically only to its own substrate cannot self-organize unless one species is self-attracting. >>️

<< Next, they developed a model that allowed species to respond to both their substrates and their products. Pair interactions between different species in this more complex model drove an instability that spread throughout the three-species system, causing the catalysts to clump together. Surprisingly, this self-organization process occurred even among particles that were individually self-repelling. >>️

Rachel Berkowitz. Self-Repelling Species Still Self-Organize. Physics 16, s128. Sept 19, 2023. 

https://physics.aps.org/articles/v16/s128

Vincent Ouazan-Reboul, Ramin Golestanian, Jaime Agudo-Canalejo. Network effects lead to self-organization in metabolic cycles of self-repelling catalysts. Phys. Rev. Lett. 131, 128301. Sep 19, 2023. 

http://dx.doi.org/10.1103/PhysRevLett.131.128301

Also: self-assembly, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, self-assembly, network,  topology.

# gst: apropos of diffusive anomalies, anomalous diffusion of active Brownian particles in responsive elastic gels.

Here, AA << examine via extensive computer simulations the dynamics of SPPs (self-propelled particles) in deformable gellike structures responsive to thermal fluctuations. (AA) treat tracer particles comparable to and larger than the mesh size of the gel. (They) observe distinct trapping events of active tracers at relatively short times, leading to subdiffusion; it is followed by an escape from meshwork-induced traps due to the flexibility of the network, resulting in superdiffusion. >>

AA << thus find crossovers between different transport regimes. (They) also find pronounced nonergodicity in the dynamics of SPPs and non-Gaussianity at intermediate times. The distributions of trapping times of the tracers escaping from “cages” in (..)  quasiperiodic gel often reveal the existence of two distinct timescales in the dynamics. At high activity of the tracers these timescales become comparable. >>

<< Furthermore, (AA) find that the mean waiting time exhibits a power-law dependence on the activity of SPPs (in terms of their Péclet number). (Their) results additionally showcase both exponential and nonexponential trapping events at high activities. Extensions of this setup are possible, with the factors such as anisotropy of the particles, different topologies of the gel network, and various interactions between the particles (also of a nonlocal nature) to be considered. >>

Koushik Goswami, Andrey G. Cherstvy, et al. Anomalous diffusion of active Brownian particles in responsive elastic gels: Nonergodicity, non-Gaussianity, and distributions of trapping times. Phys. Rev. E 110, 044609. Oct 29, 2024.

https://journals.aps.org/pre/abstract/10.1103/PhysRevE.110.044609

Also: particle, random, escape, network, in https://www.inkgmr.net/kwrds.html 

Keywords: gst, particle, random, random walks, escape, network

# behav: a viral marketing generated by low levels of advertising

<< What they discovered refutes Gladwell's (Malcolm Gladwell) concept that network position is always paramount. They found that in instances where there is even a small amount of advertising—even when it is just a quarter of a percent as strong as word-of-mouth—there's virtually no difference between the influence of the person at the center of a network and those further out on the string. >> 

 << It's not that word-of-mouth doesn't matter—it's that nobody is particularly important for the word-of-mouth process, (..) What we saw is that when advertising doesn't exist, when advertising is exactly zero, it looks like whoever is Mr. Popular, whoever has the most central connections, really matters. And in that scenario, if you start with that person at the center of the network, like the leader of an organization or company, rather than the intern, then whatever you're selling gets an uptick. >> Gabriel Rossman. 

Jessica Wolf. Forget what you think you know about viral marketing, study suggests. University of California, Los Angeles. Feb 25, 2021.

https://phys.org/news/2021-02-viral.html  

Gabriel Rossman,  Jacob C. Fisher. Network hubs cease to be influential in the presence of low levels of advertising. PNAS. 118 (7) e2013391118. doi: 10.1073/ pnas.2013391118. Feb 16, 2021. 

https://www.pnas.org/content/118/7/e2013391118

FonT

this could also occur in the generation of fake news ...

keyword "fake" in FonT

https://flashontrack.blogspot.com/search?q=fake

# brain: to trace transitions from consciousness to unconscious subliminal perception

AA << study the transition in the functional networks that characterize the human brains’ conscious-state to an unconscious subliminal state of perception >>

<< the most inner core (i.e., the most connected kernel) of the conscious-state functional network corresponds to areas which remain functionally active when the brain transitions from the conscious-state to the subliminal-state. That is, the inner core of the conscious network coincides with the subliminal-state. >>

<< This finding imposes constraints to theoretical models of consciousness, in that the location of the core of the functional brain network is in the unconscious part of the brain rather than in the conscious state as previously thought. >>

Francesca Arese Lucini, Gino Del Ferraro, et al. How the Brain Transitions from Conscious to Subliminal Perception. Neuroscience. Volume 411, Jul 15, 2019, Pages 280-290.

https://www.sciencedirect.com/science/article/abs/pii/S0306452219302052

<< The k-core of the conscious state is reduced to three active regions of the brain, the fusiform gyrus (left and right) and the precentral gyrus. These regions are the only active in the subliminal state. >>

Physicists use mathematics to trace neuro transitions. City College of New York. Jul 18, 2019.

https://m.medicalxpress.com/news/2019-07-physicists-mathematics-neuro-transitions.html  

# gst: soft and stiff modes in colloidal particle networks

<< Floppy microscale spring networks are widely studied in theory and simulations, but no well-controlled experimental system currently exists. >> 

AA << show that square lattices consisting of colloid-supported lipid bilayers functionalized with DNA linkers act as microscale floppy spring networks. (AA) extract their normal modes by inverting the particle displacement correlation matrix, showing the emergence of a spectrum of soft modes with low effective stiffness in addition to stiff modes that derive from linker interactions. >>

<< Evaluation of the softest mode, a uniform shear mode, reveals that shear stiffness decreases with lattice size. >>

 AA << results reveal the importance of entropic steric effects. >>

️

Julio Melio, Silke E. Henkes, Daniela J. Kraft. Soft and Stiff Normal Modes in Floppy Colloidal Square Lattices. Phys. Rev. Lett. 132, 078202. Feb 14, 2024. 

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.132.078202

Also: particle, nano, colloids, network, in https://www.inkgmr.net/kwrds.html

Keywords: gst, particle, nano, colloids, network, colloidal network