Continuous rate-based neural networks have been widely applied to modeling the dynamics of cortical circuits. However, cortical neurons in the brain exhibit irregular spiking activity with complex correlation structures that cannot be captured by mean firing rate alone. To close this gap, we consider a framework for modeling irregular spiking activity, called the moment neural network, which naturally generalizes rate models to second-order moments and can accurately capture the firing statistics of spiking neural networks. We propose an efficient numerical method that allows for rapid evaluation of moment mappings for neuronal activations without solving the underlying Fokker-Planck equation. This allows simulation of coupled interactions of mean firing rate and firing variability of large-scale neural circuits while retaining the advantage of analytical tractability of continuous rate models. We demonstrate how the moment neural network can explain a range of phenomena including diverse Fano factor in networks with quenched disorder and the emergence of irregular oscillatory dynamics in excitation-inhibition networks with delay.

We introduce a technique, traction rheoscopy, to carry out mechanical testing of colloidal solids. A confocal microscope is used to directly measure stress and strain during externally applied deformation. The stress is measured, with single-mPa resolution, by determining the strain in a compliant polymer gel in mechanical contact with the colloidal solid. Simultaneously, the confocal microscope is used to measure structural change in the colloidal solid with single particle resolution during the deformation. To demonstrate the utility and sensitivity of this technique, we deform a hard-sphere colloidal glass in simple shear, and from the macroscopic shear strain and measured stress determine the stress-strain curve. Using the stress-strain curve and measured shear modulus, we decompose the macroscopic shear strain into an elastic and a plastic component. We also determine a local strain tensor for each particle using the changes in its nearest-neighbor distances. These local strains are spatially heterogeneous throughout the sample, but, when averaged, match the macroscopic strain. A microscopic yield criterion is used to split the local strains into subyield and yielded partitions; averages over these partitions complement the macroscopic elastic-plastic decomposition obtained from the stress-strain curve. By combining mechanical testing with single-particle structural measurements, traction rheoscopy is a unique tool for the study of deformation mechanisms in a diverse range of soft materials.

Quantum theory famously entails the existence of incompatible measurements, pairs of system observables which cannot be simultaneously measured to arbitrary precision. Incompatibility is widely regarded to be a uniquely quantum phenomenon, linked to failure to commute of quantum operators. Even in the face of deep parallels between quantum commutators and classical Poisson brackets, no connection has been established between the Poisson algebra and any intrinsic limitations to classical measurement. Here I examine measurement in classical Hamiltonian physics as a process involving the joint evolution of an object-system and a finite-temperature measuring apparatus. Instead of the ideal measurement capable of extracting information without disturbing the system, I find a Heisenberg-like precision-disturbance relation: Measuring an observable leaves all Poisson-commuting observables undisturbed but inevitably disturbs all non-Poisson-commuting observables. In this classical uncertainty relation the role of h-bar is played by an apparatus-specific quantity, q-bar. While this is not a universal constant, the analysis suggests that q-bar takes a finite positive value for any apparatus that can be built. (Specifically: q-bar vanishes in the model only in the unreachable limit of zero absolute temperature.) I show that a classical version of Ozawa's model of quantum measurement [Ozawa, Phys. Rev. Lett. 60, 385 (1988)], originally proposed as a means to violate Heisenberg's relation, does not violate the classical relation. If this result were to generalize to all models of measurement, then incompatibility would prove to be a feature not only of quantum, but of classical physics too. Put differently: The approach presented here points the way to studying the (Bayesian) epistemology of classical physics, which was until now assumed to be trivial. It now seems possible that it is nontrivial and bears a resemblance to the quantum formalism. The present findings may be of interest to researchers working on foundations of quantum mechanics, particularly for $\psi$-epistemic interpretations. More practically, there may be applications in the fields of precision measurement, nanoengineering, and molecular machines.

In one-dimensional low-density Jaynes-Cummings Hubbard (JCH) models [Phys. Rev. E 106, 064107 (2022)], we proved that the eigenstate thermalization hypothesis (ETH) is valid when the tunneling strength and coupling strength are of the same order. Surprisingly, at the weak tunneling limit, we observed that the entanglement entropy and scaling law of kinetic energy operators also exhibit obvious quantum chaotic properties, this is an unexpected result. To substantiate these findings, we further discuss their nonequilibrium dynamics in this paper. Our analysis reveals that when the model is a weak tunneling limit after the quench and the initial state is an equilibrium state of chaos, the system reaches an equilibrium state. This observation supports the conclusion that the low-density JCH model at the weak tunneling limit is nonintegrable, corroborating our previous results [Phys. Rev. E 106, 064107 (2022)]. Additionally, by discussing the validity of the fluctuation-dissipation theorem (FDT) and the evolution behavior of entanglement entropy and fidelity, we numerically demonstrate the differences between the one-dimensional low-density JCH model and general nonintegrable systems. Specifically, in the low-density JCH model, when the Hamiltonian after the quench is integrable, the validity of FDT depends on the thermal behavior of the initial Hamiltonian, and a metastable state is observed during the evolution of entanglement entropy. Our research presents an an intriguing and unique nonintegrable model, enriching the current understanding of nonintegrable systems.

To show the impact of environmental noise on imitation dynamics, the stochastic stability and stochastic evolutionary stability of a discrete-time imitation dynamics with random payoffs are studied in this paper. Based on the stochastic local stability of fixation states and constant interior equilibria in a two-phenotype model, we extend the concept of stochastic evolutionary stability to the stochastic imitation dynamics, which is defined as a strategy such that, if all the members of the population adopt it, then the probability for any mutant strategy to invade the population successfully under the influence of natural selection is arbitrarily low. Our main results show clearly that the stochastic evolutionary stability of the system depends only on the properties of the mean matrix of the random payoff matrix and is independent of the randomness of the random payoff matrix. Moreover, as two examples, we show also that under the framework of stochastic imitation dynamics, the noise intensity affects the evolution of cooperative behavior in a stochastic prisoner's dilemma game and the system's nonlinear dynamic behavior.

Net velocity has been demonstrated for objects frictionally coupled to a flat plate that oscillates periodically in-plane with two frequencies, provided plate displacement is nonantiperiodic: the ratio of frequencies $\gamma$ cannot be the ratio of two odd integers. We give a mathematical derivation of the experimentally determined dependence of mean velocity on the relative amplitudes of the two frequency modes, and the phase lag between the modes, when $\gamma =2$, and when the magnitude of plate acceleration is much larger than the magnitude of acceleration by static friction. The approach uses an analysis of the symmetry properties of the roots of trigonometric polynomials, without explicit determination of those roots. The behavior when $\gamma =1/2$, and specific phase lags that inhibit net velocity for general $\gamma$, are also determined.

The susceptible-infected-recovered (SIR) model and its variants form the foundation of our understanding of the spread of diseases. Here, each agent can be in one of three states (susceptible, infected, or recovered), and transitions between these states follow a stochastic process. The probability of an agent becoming infected depends on the number of its infected neighbors, hence all agents are correlated. The simplest mean-field theory of the same stochastic process, however, assumes that the agents are statistically independent. This leads to a self-feedback effect in the approximation: when an agent infects its neighbors, this infection may subsequently travel back to the original agent at a later time, leading to a self-infection of the agent which is not present in the underlying stochastic process. We here compute the first-order correction to the mean-field assumption from a systematic expansion, called dynamical TAP theory. This correction, which takes fluctuations up to second order in the interaction strength into account, cancels the self-feedback effect, leading to smaller infection rates. The correction significantly improves predictions compared to mean-field theory. In particular, it captures how sparsity dampens the spread of the disease: this indicates that reducing the number of contacts is more effective than predicted by mean-field models. We further apply the expansion to variants of the SIR model, such as the SIRS model, in which the immunity of an individual to the disease wanes over time. We find that up to the second order, the correction terms in the SIR and SIRS model are equivalent, meaning that fluctuations partially cancel the self-feedback effect even when self-feedback is in principle allowed.

We extend a recent model of temporal random hyperbolic graphs by allowing connections and disconnections to persist across network snapshots with different probabilities ${\omega }_1$ and ${\omega }_2$. This extension, while conceptually simple, poses analytical challenges involving the Appell $F_1$ series. Despite these challenges, we are able to analyze key properties of the model, which include the distributions of contact and intercontact durations, as well as the expected time-aggregated degree. The incorporation of ${\omega }_1$ and ${\omega }_2$ enables more flexible tuning of the average contact and intercontact durations, and of the average time-aggregated degree, providing a finer control for exploring the effect of temporal network dynamics on dynamical processes. Overall, our results provide new insights into the analysis of temporal networks and contribute to a more general representation of real-world scenarios.

In quasi-one-dimensional circularly symmetric systems of active particles, experiments and simulations reveal an indirect interplay between particles and environmental drag effects, proving crucial in the realm of generalized parametrically controlled stigmergy. Our investigation goes deeper into understanding how stigmergy manifests itself, closely examining unconventional, more physically grounded interpretations in contrast to established concepts. Deeper insights into the complex dynamics of stigmergically interacting particle systems are gained by systematically studying the transition regions between short- and long-term stigmergic effects. Mechanical and computational modeling techniques complement each other to provide a comprehensive understanding of various clustering patterns, oscillatory modes, and system dynamics, where hysteresis may occur depending on the conditions.

A model for the single mode, two-dimensional Rayleigh-Taylor instability in ideal, incompressible, immiscible, and inviscid fluids is developed as an extension of a previous linear model based on the Newton's second law [A. R. Piriz et al., Am. J. Phys. 74, 1095 (2006)]. It describes the transition from linear to nonlinear regimes and takes into account the mass of fluids participating in the motion during the instability evolution, including the laterally displaced mass. This latter feature naturally leads to the bubble and spike velocity saturation without requiring the usual drag term necessary in the well-known buoyancy-drag model (BDM). In addition, it also provides an explanation to the latter phase of bubble reacceleration without appealing to the vorticity generation due to the Kelvin-Helmholtz instability. The model is in perfect agreement with the BDM buoyancy-drag model, but, apart from extending its range of application, it solves many of its issues of concern and provides a more consistent physical picture.

The Newton-Raphson method is a fundamental root-finding technique with numerous applications in physics. In this study, we propose a parameterized variant of the Newton-Raphson method, inspired by principles from physics. Through analytical and empirical validation, we demonstrate that this approach offers increased robustness and faster convergence during root-finding iterations. Furthermore, we establish connections to the Adomian series method and provide a natural interpretation within a series framework. Remarkably, the introduced parameter, akin to a temperature variable, enables an annealing approach. This advancement sets the stage for a fresh exploration of numerical iterative root-finding methodologies.

We consider a one-dimensional search process under stochastic resetting conditions. A target is located at $b\ge 0$ and a searcher, starting from the origin, performs a discrete-time random walk with independent jumps drawn from a heavy-tailed distribution. Before each jump, there is a given probability $r$ of restarting the walk from the initial position. The efficiency of a “myopic search”—in which the search stops upon crossing the target for the first time—is usually characterized in terms of the first-passage time $\tau$. On the other hand, great relevance is encapsulated by the leapover length $l=x_{\tau }-b$, which measures how far from the target the search ends. For symmetric heavy-tailed jump distributions, in the absence of resetting the average leapover is always infinite. Here we show instead that resetting induces a finite average leapover ${\ell }_b\left(r\right)$ if the mean jump length is finite. We compute exactly ${\ell }_b\left(r\right)$ and determine the condition under which resetting allows for nontrivial optimization, i.e., for the existence of $r^{*}$ such that ${\ell }_b\left(r^{*}\right)$ is minimal and smaller than the average leapover of the single jump.

This research investigates the impact of dynamic, time-varying interactions on cooperative behavior in social dilemmas. Traditional research has focused on deterministic rules governing pairwise interactions, yet the impact of interaction frequency and synchronization in groups on cooperation remains underexplored. Addressing this gap, our work introduces two temporal interaction mechanisms to model the stochastic or periodic participation of individuals in public goods games, acknowledging real-life variances due to exogenous temporal factors and geographical time differences. We consider that the interaction state significantly influences both game payoff calculations and the strategy updating process, offering new insights into the emergence and sustainability of cooperation. Our results indicate that maximum game participation frequency is suboptimal under a stochastic interaction mechanism. Instead, an intermediate activation probability maximizes cooperation, suggesting a vital balance between interaction frequency and inactivity security. Furthermore, local synchronization of interactions within specific areas is shown to be beneficial, as time differences hinder the spread of cross-structures but promote the formation of dense cooperative clusters with smoother boundaries. We also note that stronger clustering in networks, larger group sizes, and lower noise increase cooperation. This research contributes to understanding the role of node-based temporality and probabilistic interactions in social dilemmas, offering insights into fostering cooperation.

Biological cells have the ability to switch internal states depending on the density of other cells in their local environment, referred to as “quorum sensing.” The latter can be utilized to control collective structuring, such as in biofilm formation. In this work, we study a simple quorum sensing model of ideal (noninteracting) colloids with a switchable internal degree of freedom in the presence of external potentials. The colloids have two possible discrete states, in which they are affected differently by the external field, and switch with rates dependent on the local density in their environment. We study this model with reactive Brownian dynamics simulations, as well as with an appropriate reaction-diffusion theory. We find remarkable structuring in the system controlled by the density-mediated interactions between the ideal colloids. We report results of different functional forms for the rate dependence and quantify the influence of their parameters, in particular, discuss the role of the spatiotemporal sensing range, i.e., how the resulting structures depend on how the environmental information is “measured” by the colloids. Especially in the case of a rate function with sigmoidal dependence on local density, i.e., requiring a threshold density for switching, we observe significant correlation effects in the density profiles which are tuneable by the sensing ranges but also sensitive to noise and fluctuations. Hence, our model gives some basic insights into the nonequilibrium structuring mediated by simple quorum sensing protocols.

We investigate the motion of charged particles in a turbulent electrostatic potential using guiding-center theory. By increasing the Larmor radius, the dynamics exhibit close-to-ballistic transport properties. The transition from diffusive to ballistic transport is analyzed using nonlinear dynamics. It is found that twistless invariant tori in the guiding-center dynamics are responsible for this transition, drastically affecting transport properties of charged particles.

In this paper, we numerically investigate the vortex nucleation in a Bose-Einstein condensate (BEC) trapped in a double-well potential and subjected to a density-dependent gauge potential. A rotating Bose-Einstein condensate, when confined in a double-well potential, not only gives rise to visible vortices but also produces hidden vortices. We have empirically developed Feynman's rule for the number of vortices versus angular momentum in Bose-Einstein condensates in the presence of density-dependent gauge potentials. The variation of the average angular momentum with the number of vortices is also sensitive to the nature of the nonlinear rotation due to the density-dependent gauge potentials. The empirical result agrees well with the numerical simulations and the connection is verified by means of curve-fitting analysis. The modified Feynman rule is further confirmed for the BECs confined in harmonic and toroidal traps. In addition, we show the nucleation of vortices in double-well and toroidally confined Bose-Einstein condensates solely through nonlinear rotations (without any trap rotation) arising through the density-dependent gauge potential.

Isochronous islands in phase space emerge in twist Hamiltonian systems as a response to multiple resonant perturbations. According to the Poincaré-Birkhoff theorem, the number of islands depends on the system characteristics and the perturbation. We analyze, for the two-parameter standard map, also called two-harmonic standard map, how the island chains are modified as the perturbation amplitude increases. We identified three routes for the transition from one chain, associated with one harmonic, to the chain associated with the other harmonic, based on a combination of pitchfork and saddle-node bifurcations. These routes can present intermediate island chains configurations. Otherwise, the destruction of the islands always occurs through the pitchfork bifurcation.

The echo state property (ESP) represents a fundamental concept in the reservoir computing (RC) framework that ensures output-only training of reservoir networks by being agnostic to the initial states and far past inputs. However, the traditional definition of ESP does not describe possible nonstationary systems in which statistical properties evolve. To address this issue, we present two categories of ESP: nonstationary ESP, designed for potentially nonstationary systems, and subspace and subset ESP, designed for systems whose subsystems have ESP. Following the definitions, we numerically demonstrate the correspondence between nonstationary ESP in the quantum reservoir computer (QRC) framework with typical Hamiltonian dynamics and input encoding methods using nonlinear autoregressive moving-average tasks. We also confirm the correspondence by computing linear or nonlinear memory capacities that quantify input-dependent components within reservoir states. Our study presents an understanding of the practical design of QRC and other possibly nonstationary RC systems in which nonstationary systems and subsystems are exploited.

In the real world, individuals are often involved in collaboration on multiple issues, and these issues may interact with each other. Given the complexity of the interaction, we establish a multi-issue repeated game model, in which individuals participate in multiple social dilemma games simultaneously and repeatedly, and strategies in different issue games are correlated and reactive. We explore the cooperation dynamics of strategies in the population from a multiobjective perspective, in which an individual's preference for each issue is described by a weight vector, and heterogeneous preferences of individuals in the population are also considered. Through simulations on two-issue games, we find that compared to the uncorrelated case, the correlated strategy can significantly promote cooperation in both games regardless of which issue players prefer. Under the condition of homogeneous preference, an increase in the payoff weight of a given issue decreases the level of cooperation in that issue, and the optimal condition to sustain cooperation to the maximum extent is when the payoff weights of all issues are equal. Moreover, under the condition of heterogeneous preference, there exists an optimal proportion of players with different preferences under which the cooperation rate can reach its highest level in the population. This work highlights individual trade-offs on different issues when engaging in multiple games simultaneously and further enriches the research of evolutionary games from a multiobjective and correlated strategy perspective.