Neuronal population bursts, in which large numbers of neurons fire nearly simultaneously, are increasingly recognized as playing important roles in sensory processing and memory formation. In many sensory systems, information is encoded not only by which neurons are active, but also by the temporal order in which distributed neural populations are recruited. How such temporally structured inputs are learned and represented at the population level remains unclear. Here, we study a recurrent spiking neural network model in which extended sequences of population bursts, termed superbursts, emerge as a collective dynamical property. Through Pavlovian conditioning with brief sequential activations of neuronal subpopulations, combined with neuromodulator-gated spike-timing-dependent plasticity, an initially random network self-organizes into an intricate hybrid architecture containing a strong feedforward backbone embedded within sparse recurrent connectivity. After conditioning, learned spatiotemporal inputs reliably evoke stimulus-specific superbursts, whereas temporally or spatially perturbed inputs produce weaker and qualitatively distinct responses. These superbursts provide an amplified and noise-tolerant population representation capable of supporting millisecond-scale temporal discrimination over a broad ($\sim 100\mathrm{ms}$) temporal range.

Information-driven active matter relies on continuous feedback loops subject to intrinsic processing latency. We investigate how this temporal nonlocality interacts with the spatial fluctuations of homogeneous isotropic turbulence for agents operating in the dilute, noncommunicating limit. This interplay induces a spontaneous symmetry breaking: as the control latency increases, the velocity statistics of the active agents continuously reshape from a unimodal Gaussian distribution to a bimodal attractor. This transition occurs in a regime where the underlying deterministic dynamics are physically bounded to remain stable. Theoretical analysis, supported by direct numerical simulations, demonstrates that this phenomenon is a noise-induced phase transition driven by the multiplicative turbulent strain rate, which renormalizes the effective stochastic potential to overcome viscous dissipation. These results indicate that rather than minimizing latency to avoid instability, agents operating near the phase boundary can utilize finite delays to extract energy from microscopic fluid noise and sustain macroscopic motility.

Several filamentous biopolymers exhibit unusual polymerization-depolymerization kinetics where continued elongation (shortening) is possible only in an “active” (“inactive”) state while occasional random transition (switching) between the active and inactive states takes place. Here, we present a minimal stochastic model capturing only the key features of random elongation-shortening-switching kinetics of a generic filament. The structure and/or function of the filament ends when its length hits a critical value during its shortening; the time to reach this critical length can be formulated as a first-passage time (FPT) and for its calculation the critical length is treated as an absorbing boundary. Moreover, the lengths of the filaments that inspire our model are constrained to a finite maximum value $\mathcal{N}$; therefore, in our model we impose a reflecting boundary beyond which the filament cannot grow. We determined the distribution of the FPT and derived its first two moments. We identify a critical point where the drift of the tip of the filament vanishes making its movement purely diffusive; at this critical point, in the limit $\mathcal{N}\to \infty$, the fluctuations in the FPT diverges and mean FPT becomes infinite, indicating “critical slowing down”. Our analytical results will serve as the testing ground for the accuracy of the approximate theoretical results of more realistic models for polymerization-depolymerization kinetics of specific biofilaments.

We derive analytical solutions for macroscopic regimes in populations of identical and nonidentical quadratic integrate-and-fire neurons (QIFs), where the irregularity of synaptic input is represented by a continuous Gaussian noise. This amounts to the diffusion approximation well established in mathematical neuroscience. The obtained results rely on the first-passage time problem and allow understanding of the statistical physics properties of parabolic bursting systems, from quantum physics over intermittency theory to mathematical neuroscience. Within the developed theoretical framework, we study the effect of fluctuations on the interplay between electrical and chemical synapses in networks of QIF neurons.

This study investigates the shape of a flexible, inextensible ball chain initially straight and horizontal, which is arrested at its center by a cylindrical obstacle while in free fall. Motivated by practical applications in high-voltage line installation, we combine experiments, discrete element method simulations, and a theoretical model. Our key experimental and numerical findings reveal that the extremities and adjacent horizontal portions of the chain continue in free fall, while the remainder wraps around the rod. The theoretical model successfully captures this behavior by representing the chain as two straight segments: a free-falling horizontal end and an inclined section connected to the obstacle. The model's core principle is that the junction point between these segments, or “kink,” is a transverse wave that propagates along the chain at a wave speed determined by the tension induced by the acceleration toward the center of the horizontal portion, which nevertheless remains in free fall. The model accurately predicts the positions of the tip and the kink, demonstrating that the chain's shape is independent of its mass per unit length and scales with the total length. Furthermore, the dynamics are governed by a single dimensionless parameter, the Froude number, which compares the initial impact velocity to the effect of gravity, with the analysis capturing the limiting cases of zero initial velocity and negligible gravity.

Fluctuation theorems are key to understanding both fundamental and applied aspects of nonequilibrium thermodynamics of small systems. We study the non-Markovian entropy production fluctuation theorem for the diffusion process of charged particles in a gas inside a harmonic potential and under the action of a time-dependent electric field, using a generalized Langevin equation. By considering the influence of the electric field on both the tagged Brownian particle and the bath particles, an “induced” electric force arises. Despite the additional force, we demonstrate that Kubo's second fluctuation-dissipation theorem (FDT) remains unchanged. The FDT allows us to obtain the Gaussian probability density for the position along a single stochastic trajectory, which is the key to demonstrating the validity of the detailed fluctuation theorem for the total entropy production. We study the specific result of an Ornstein-Uhlenbeck-type friction memory kernel and an oscillating electric field, and analyze the average work and entropy production in different parameter regimes.

Geometric representations provide a useful perspective on critical phenomena in the Ising model. In a recent study [Phys. Rev. E 112, 034118 (2025)], we found that the two-dimensional critical Ising model exhibits two consecutive percolation transitions for geometric spin clusters as the bond-occupation probability $p$ between parallel spins increases. Here, through extensive Monte Carlo simulations, we show that this phenomenon does not persist in three dimensions, where we observe only a single percolation transition on critical Ising configurations. Further theoretical analysis of the Ising model on the complete graph also yields the same scenario. In addition, we study percolation on a two-dimensional layer embedded in the three-dimensional critical Ising model. For this layer system, we estimate the red-bond exponent $y_p=0.426\left(6\right)$ and the fractal dimensions of the largest cluster, hull, and shortest path as $d_f=1.8926\left(20\right), d_{\mathrm{hull}}=1.663\left(4\right)$, and $d_{\min }=1.080\left(10\right)$, respectively. These values indicate a distinct universality class induced by coupling to out-of-plane critical correlations.

The Eigenstate Thermalization Hypothesis (ETH) is a standard tool to understand the thermalization properties of an isolated quantum system. Its generalization to higher-order correlations of matrix elements of local operators, dubbed the full ETH, relates the ETH free cumulants to the corresponding thermal free cumulants in the thermodynamic limit. In this work, we numerically test the full-ETH predictions using exact diagonalization of two integrable spin models: the Ising and the XXZ Heisenberg models. The differences from the behavior predicted by full ETH in chaotic systems are highlighted and contrasted along the way. We further study the out-of-time-ordered correlator (OTOC) through its approximate decomposition into contributions involving the second- and fourth-order ETH free cumulants. We find that, although the fourth-order contribution governs the late-time behavior of the OTOC in these integrable models, its dynamics differ significantly from that found in nonintegrable systems.

Stochastic field theories are often constructed phenomenologically, without a systematic assessment of thermodynamic consistency or local detailed balance. This may hinder a physical description of irreversibility at the field-theoretic level beyond the standard statistical formulation of stochastic thermodynamics. Here, we develop a variational formulation for thermodynamically consistent stochastic field theories by extending Hamilton's principle of classical field theory. Introducing the second law as an axiom yields a consistent local thermodynamic structure in which novel fluctuation-dissipation relations emerge naturally, ensuring local detailed balance. Remarkably, the resulting entropy production takes the same form as in standard stochastic thermodynamics, up to a reformulation in an extended phase space incorporating both configurational and thermal variables. This correspondence extends key results, including individual trajectory-level thermodynamics and fluctuation theorems. The construction is formulated within a unified geometric framework based on a generalized Lagrange-d'Alembert principle, providing a top-down connection between phenomenological modeling and thermodynamic consistency. Potential applications include thermodynamically consistent modeling of complex fluids, Lagrangian reduction by symmetry in continuum systems, and structure-preserving numerical schemes for stochastic partial differential equations.

Transient growth is a counterintuitive dynamical process in which a system state or observable undergoes pronounced temporary amplification during its evolution, even in the presence of substantial dissipation. Here, we demonstrate time-domain transient growth in non-Hermitian cascaded LC circuits with unidirectional coupling. Under short-pulse excitation, the aggregate voltage-envelope response exhibits pronounced transient growth. As the number of dissipative resonators increases, the transient peak becomes substantially larger. Comparisons between uniform-loss and nonuniform-loss configurations show that the growth persists when the exact exceptional-point (EP) condition is lifted, indicating that it is governed mainly by unidirectional cascaded coupling and is not restricted to an exact EP condition. The normalized time-domain gain $G_N\left(t\right)$ further quantifies the finite-time growth of the cascaded system. These results establish unidirectionally coupled non-Hermitian circuits as a circuit-realizable platform for studying and engineering time-domain transient growth, thereby connecting non-Hermitian physics with transient growth.

Higher-order groups, formed through interactions beyond pairwise connections, are essential for the resilience of many complex systems. Prevailing hypernetwork resilience models that address functional group failure rely on two limiting assumptions: either a single node failure triggers group-wide collapse, or the group remains functional with at least one surviving node. While useful as conceptual limits, these models oversimplify the progressive loss of structural integrity in real systems. Here, we propose a tolerance-based framework for hypernetwork resilience that describes a structural disintegration process driven by cumulative node failures, where a hyperedge becomes nonfunctional only when the fraction of its failed nodes equals or exceeds a tunable tolerance threshold $c$. This approach naturally unifies the two classical limiting cases into a single continuous framework. We develop a theoretical framework based on the message-passing approach and validate its accuracy through extensive Monte Carlo simulations on synthetic homogeneous and heterogeneous hypernetworks. Our results reveal that the tolerance threshold $c$ acts as a decisive control parameter: increasing $c$ significantly improves system resilience by lowering the percolation threshold, whereas low $c$ exacerbates fragility. Furthermore, application of our model to diverse empirical networks—from political committees to commercial activities—demonstrates a robust framework for quantifying complex system stability.

Traffic movement on highways and data packet transmission across the Internet serve as prototypical examples of collective dynamical behavior in complex systems. Although enhancing traffic flow in complex networks has been extensively investigated, most existing studies focus on single-queue models. In this paper, we introduce a simple yet effective two-queue scheme with gate control that regulates vehicle entry and integrates it with the routing protocol. When vehicles arrive at an on-ramp gate, they first join a waiting queue and are permitted to enter the main flowing queue once it has emptied. This two-queue strategy is especially advantageous when highway inflow varies periodically with large fluctuations, as is commonly observed in real traffic. Moreover, it modifies the system's failure characteristics, replacing sudden, discontinuous jamming transitions with gradual, continuous ones. The approach is based on a simple rule using only local queue information, making it easy to implement and widely applicable. Beyond the specific setup studied here, it can be applied to other transportation networks under different conditions or extended to other queuing systems, such as Internet data packet routing.

Many quantities that characterize network elements are defined in an explicit form and calculated directly from the network structure; examples include several centrality measures, like degree, closeness, or betweenness. However, there are also implicitly defined quantitative measures, which are usually calculated iteratively, in a self-consistent manner, like PageRank or countries' fitness/products' complexity relations. The iteration algorithms involve calculations over the entire network; therefore, their convergence properties depend on the structure of the network. Here, we focus on investigating self-consistently defined quantities in bipartite networks of two sets of nodes where the quantities in one set are determined by the quantities in the other set and vice versa. We derive an explicit convergence criterion for iterations of these quantities and describe two different approaches to improve the convergence properties. In the first one, we identify “problematic nodes” that can be removed or merged, while in the second one we employ a nonhomogeneous regularization scheme that guarantees convergence.

A thorough understanding of wall-bounded turbulence has long been a central research topic in fluid dynamics. Resolvent analysis has been proven effective at qualitatively capturing key features of turbulent coherent structures in both wall-bounded and free shear flows, particularly when nonlinear forcing is approximated as white noise. However, in turbulent systems where the response is typically not low rank, the nontrivial structure of nonlinear forcing can induce considerable discrepancies between resolvent modes and data-derived modal features. To enhance the predictive capability of resolvent models, a rigorous closure of the nonlinear forcing term is therefore essential. In this study, fully resolved direct numerical simulations are used to study coherent structures in turbulent channel flow under subsonic and supersonic conditions. To quantitatively improve the performance of linear resolvent analysis, the effects of nonlinear forcing are incorporated through both a compressible eddy-viscosity model and a data-driven approach. Both the compressible eddy-viscosity model and the optimized eddy-viscosity field obtained from the data-driven approach yield a significant improvement in modal alignments between resolvent and spectral proper orthogonal decomposition modes, achieving a consistency of over 90% for both near-wall and large-scale coherent structures. To further boost predictive capability, a scaling factor is subsequently introduced into the compressible eddy-viscosity model. Calibration of a single constant parameter consistently yields improved predictions relative to the original compressible eddy-viscosity model, holding great promise for enhancing the predictive accuracy of coherent structures in compressible turbulent flows.

Collisionless converging ion flows can sustain particle energization over an extended path, but the field channel responsible for the energy gain is not always identifiable. In laser-driven bladed microtube implosion, energetic carbon ions were previously observed alongside a self-generated gigagauss-class axial magnetic field [D. Pan and M. Murakami, Phys. Plasmas 32, 072107 (2025)], leaving the origin of their energization unresolved. Using two-dimensional particle-in-cell simulations, field-particle power decomposition and trajectory-resolved work accounting show that radial electrostatic work accumulated along the converging path supplies more than $98\%$ of the net in-plane electric work, whereas the azimuthal channel associated with the time-varying magnetic field remains subdominant. The peak local mean ion energy factorizes, to good approximation, into an incident-flow contribution controlled by laser intensity and a geometric enhancement that grows approximately linearly with the inner radius because ions accumulate electrostatic work over a longer convergence path. The path-averaged radial electric field $\langle E_r\rangle$ scales with the peak laser intensity $I_L$ as $\langle E_r\rangle \propto I_L^{0.47}$, favoring a geometry-pinned, comoving charge-separation structure over Debye-limited scaling. The peak local mean carbon-ion energy reaches 0.89 GeV. A smooth-cylinder control exhibits comparable peak energization, showing that blade modulation mainly affects poststagnation ion residence rather than the energy source.

The theory of thermal fluctuations in crystalline membranes is put under scrutiny. In particular, the two critical regimes of the renormalization group diagram, which are often left out of the discussion because of their instability in one direction, are examined in detail. After studying the proper Goldstone mode counting around each of them, the properties of the fluctuations dominating the large scale spectrum are analyzed. This shows that the fixed point P2 is a good candidate to describe the melting of a crystalline membrane. The properties of the melted membrane are then compared to the known properties of fluid membranes. As a byproduct of this analysis, we show that the generation of a fluid membrane by melting a bidimensional crystal allows to formulate its correlation functions without being plagued by the ghosts that inevitably show up in the usual Canham-Helfrich action relying on the Monge parametrization.

We present results of a fluctuation-based investigation of size effects on damage and fracture of quasibrittle materials and structures. Set within the semigrand canonical ($\mathrm{\Delta }\mu VT$) ensemble, we find that size effects of geometrically similar structures are a system property rather than a material property. We reach this conclusion by considering damage and fracture as thermodynamic equilibrium states accessible through Monte Carlo simulations, and develop stress-strain curves and damage isotherms based on ensemble averaging, considering both short-range and long-range bond interactions. We suggest that, to preserve the probability distribution of damage and fracture states for geometrically similar structures, a scaling of the thermodynamic variables of the $\mathrm{\Delta }\mu VT$- ensemble needs to be considered, including a scaling of the bath potential, $\mathrm{\Delta }\mu \propto {\lambda }^{-d}-1$, for the system's size dilation $\lambda$, considering the geometric dimension $d$ of the fracture surface. Derived from mere thermodynamic equilibrium conditions in the fluctuation-based damage approach, we show that such scaling allows the transition between strength theories ($d=0$) and linear elastic fracture mechanics (LEFM), for which the strength scales as ${\sigma }_N\propto {\lambda }^{-1/2}$, for all geometries and types of bond interactions. The rationalization of size effects within the realm of statistical mechanics opens venues toward a large range of engineering applications involving damage-prone materials and structures, from concrete bridges and high-rise buildings to hydraulic fracturing of gas shale and damage of hard and soft biological tissues.

This comment addresses the article “Instabilities of rotating inclined buoyancy layers” by Xiao et al. [Phys. Rev. E 108, 025102 (2023)], which presents linear stability results of Prandtl's slope flow subjected to surface cooling and the additional effect of rotation. Our main point of contention concerns the steady base flow used in their work, which the authors claim fulfills all physically plausible boundary conditions, including vanishing temperature difference and upslope velocity at infinite height. While the analytical base flow solution provided in their study satisfies the flow equations and boundary conditions of the standard Prandtl model in a mathematical sense, it suffers from a major physical inadequacy that greatly restricts its applicability to real flows, previously highlighted in literature. This restriction is due to the imposition of an additional (that is, not included in the original Prandtl model) upslope wind forcing term representing an upslope perturbation pressure gradient force. To obtain a steady state, this term must be artificially linked to the slope angle, surface buoyancy, and other governing parameters. Moreover, this term must be imposed externally and becomes infinite for vanishing slope angle, both conditions that are physically unjustifiable. As a second point, we contest the authors' classification of the traveling waves in their study as Tollmien-Schlichting modes, which are characterized by the destabilizing effect of viscosity. Instead, we assert that the nature of the traveling waves—also present in nonrotating Prandtl slope flows as shown in previous literature—are inviscid and should be attributed to an inertial-type Kelvin-Helmholtz instability, amplified by the infinite number of inflection points in the analytical base flow profile.