Diffusion plays an important role in a wide variety of phenomena, from bacterial quorum sensing to the dynamics of traffic flow. While it generally tends to level out gradients and inhomogeneities, diffusion has nonetheless been shown to promote pattern formation in certain classes of systems. Formation of stable structures often serves as a key factor in promoting the emergence and persistence of cooperative behavior in otherwise competitive environments, however, an in-depth analysis on the impact of diffusion on such systems is lacking. We therefore investigate the effects of diffusion on cooperative behavior using a cellular automaton (CA) model of the noisy spatial iterated prisoner's dilemma (IPD), physical extension, and stochasticity being unavoidable characteristics of several natural phenomena. We further derive a mean-field (MF) model that captures the three-species predation dynamics from the CA model and highlight how pattern formation arises in this new model, then characterize how including diffusion by interchange similarly enables the emergence of large scale structures in the CA model as well. We investigate how these emerging patterns favors cooperative behavior for parameter space regions where IPD error rates classically forbid such dynamics. We thus demonstrate how the coupling of diffusion with nonlinear dynamics can, counterintuitively, promote large-scale structure formation and in return establish new grounds for cooperation to take hold in stochastic spatial systems.

A simple model is used to simulate seizures in a population of spiking excitatory neurons experiencing a uniform effect from inhibitory neurons. A key feature is introduced into the model, i.e., a mechanism that weakens the firing thresholds. This weakening mechanism adds memory to the dynamics. We find a seizure-prone state in a “mode-switching” phase. In this phase, the system can suddenly switch from a “healthy” state with small scale-free avalanches to a “seizure” state with almost periodic large avalanches (“seizures”). Simulations of the model predict statistics for the average time spent in the seizure state (the seizure “duration”) that agree with experiments and theoretical examples of similar behavior in neuronal systems. Our study points to. different connections between seizures and fracture and also offers an alternative view on the type of critical point controlling neuronal avalanches.

The local field potential (LFP) is as a measure of the combined activity of neurons within a region of brain tissue. While biophysical modeling schemes for LFP in cortical circuits are well established, there is a paramount lack of understanding regarding the LFP properties along the states assumed in cortical circuits over long periods. Here we use a symbolic information approach to determine the statistical complexity based on Jensen disequilibrium measure and Shannon entropy of LFP data recorded from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice. Using these information quantifiers, we find consistent relations between LFP recordings and measures of cortical states at the neuronal level. More specifically, we show that LFP's statistical complexity is sensitive to cortical state (characterized by spiking variability), as well as to cortical layer. In addition, we apply these quantifiers to characterize behavioral states of freely moving mice, where we find indirect relations between such states and spiking variability.

The heterogeneity of the viscoelasticity of a lamellar gel network based on cetyl-trimethylammonium chloride and cetostearyl alcohol was studied using particle-tracking microrheology. A recurrent neural network (RNN) architecture was used for estimating the Hurst exponent, $H$, on small sections of tracks of probe spheres moving with fractional Brownian motion. Thus, dynamic segmentation of tracks via neural networks was used in microrheology and it is significantly more accurate than using mean square displacements (MSDs). An ensemble of 414 particles produces a MSD that is subdiffusive in time, $t$, with a power law of the form $t^{0.74\pm 0.02}$, indicating power law viscoelasticity. RNN analysis of the probability distributions of $H$, combined with detailed analysis of the time-averaged MSDs of individual tracks, revealed diverse diffusion processes belied by the simple scaling of the ensemble MSD, such as caging phenomena, which give rise to the complex viscoelasticity of lamellar gels.

Locally activated random walks are defined as random processes, whose dynamical parameters are modified upon visits to given activation sites. Such dynamics naturally emerge in living systems as varied as immune and cancer cells interacting with spatial heterogeneities in tissues, or, at larger scales, animals encountering local resources. At the theoretical level, these random walks provide an explicit construction of strongly non-Markovian and aging dynamics. We propose a general analytical framework to determine various statistical properties characterizing the position and dynamical parameters of the random walker on $d$-dimensional lattices. Our analysis applies in particular to both passive (diffusive) and active (run and tumble) dynamics, and quantifies the aging dynamics and potential trapping of the random walker; it finally identifies clear signatures of activated dynamics for potential use in experimental data.

A theoretical model for investigating the radiative transfer of an x-ray free electron laser (XFEL) pulse is developed based on a one-dimensional radiative transfer equation. The population dynamics of energy levels is obtained by rate equation approximation coupling with the Fokker-Planck equation, in which the electron energy distribution function (EEDF) is self-consistently determined. As an illustrative example, XFEL pulse propagation through a solid-density aluminum (Al) is investigated. The characteristics of the temporal evolution of the x-ray pulse shape, level population, and EEDF are demonstrated. The EEDF usually has two parts in XFEL-Al interactions: the near equilibrium part in the lower energy regions and the nonequilibrium part in the higher energy region. The deep gap between the two parts is quickly filled in the solid-density Al plasma. The pulse shape is distorted and the duration shortens as the x-ray pulse propagates through the Al sample. The x-ray transmission spectra were compared with experimental and other theoretical results, and good agreement was found. There are slight discrepancies between the transmission obtained by solving the Fokker-Planck equation and Maxwellian assumptions because nonequilibrium electrons in the higher energy region account for only a small fraction of the total electrons.

We present electron transport calculations of shocked argon based on an average-atom modeling of the plasma and compare them with measurements, involving both incident and reflected shock waves. Since the corresponding experiments are subject to a 5 T magnetic field, the impact of the latter on the Rankine-Hugoniot equations is taken into account, starting from the magnetoresistive hydrodynamics, and the resistivity tensor is deduced from the Boltzmann equation. The resistivity tensor yields the electrical and Hall resistivities. Our average-atom code Paradisio provides the quantities required for the calculation of electrical resistivity within the Ziman-Evans formalism, as well as for the Hall resistivity. We obtain good agreement between calculated conductivities and experimental values, both for the incident and reflected shocks. Our values of the Hall constant are compared to experimental values derived from Hall voltage measurements, as well as to theoretical ones based on the quantum statistical linear-relaxation-time approach.

We develop a numerical method for simulating the dynamics of a droplet immersed in a generic time-dependent velocity gradient field. This approach is grounded on the hybrid coupling between the lattice Boltzmann (LB) method, employed for the flow simulation, and the immersed boundary (IB) method, utilized to couple the droplet with the surrounding fluid. We show how to enrich the numerical scheme with a mesh regularization technique, allowing droplets to sustain large deformations. The resulting methodology is adapted to simulate the dynamics of droplets in homogeneous and isotropic turbulence, with the characteristic size of the droplet being smaller than the characteristic Kolmogorov scale of the outer turbulent flow. We report statistical results for droplet deformation and orientation collected from an ensemble of turbulent trajectories, as well as comparisons with theoretical models in the limit of small deformation.

Many natural or human-made systems encompassing local reactions and diffusion processes exhibit spatially distributed patterns of some relevant dynamical variable. These interactions, through self-organization and critical phenomena, give rise to power-law distributions, where emergent patterns and structures become visible across vastly different scales. Recent observations reveal power-law distributions in the spatial organization of, e.g., tree clusters and forest patch sizes. Crucially, these patterns do not follow a spatially periodic order but rather a statistical one. Unlike the spatially periodic patterns elucidated by the Turing mechanism, the statistical order of these particular vegetation patterns suggests an incomplete understanding of the underlying mechanisms. Here, we present a self-segregation mechanism, driving the emergence of power-law scalings in pattern-forming systems. The model incorporates an Allee-logistic reaction term, responsible for the local growth, and a nonlinear diffusion process accounting for positive interactions and limited resources. According to a self-organized criticality (SOC) principle, after an initial decrease, the system mass reaches an analytically predictable threshold, beyond which it self-segregates into distinct clusters, due to local positive interactions that promote cooperation. Numerical investigations show that the distribution of cluster sizes obeys a power law with an exponential cutoff.

Dense bacterial suspensions display collective motion exhibiting coherent flow structures reminiscent of turbulent flows. However, in contrast to inertial turbulence, the microscopic dynamics underlying bacterial turbulence is only beginning to be understood. Here, we report experiments revealing correlations between microscopic dynamics and the emergence of collective motion in bacterial suspensions. Our results demonstrate the existence of three microscopic dynamical regimes: initial ballistic dynamics followed by an intermittent Lévy walk before the intriguing decay to random Gaussian fluctuations. Our experiments capture that the fluid correlation time earmarks the transition from Lévy to Gaussian fluctuations demonstrating the microscopic reason underlying the observation. By harnessing the flow activity via bacterial concentration, we reveal systematic control over the flow correlation timescales, which, in turn, allows controlling the duration of the Lévy walk.

The simple inclusion process (SIP) interpolates between two well-known lattice gas models: the independent random walkers and the Kipnis-Marchioro-Presutti model. Here we study large deviations of nonstationary mass transfer in the SIP at long times in one dimension. We suppose that $N\gg 1$ particles start from a single lattice site at the origin, and we are interested in the probability $\mathcal{P}(M,N,T)$ of observing $M$ of the particles, $0\le M\le N$, to the right of the origin at a specified time $T\gg 1$. At large times, the corresponding full probability distribution has a large-deviation behavior, $-ln\mathcal{P}(M,N,T)\simeq \sqrt{T}s(M/N,N/\sqrt{T})$. We determine the rate function $s$ exactly by uncovering and utilizing complete integrability, by the inverse scattering method, of the underlying equations of the macroscopic fluctuation theory. We also analyze different asymptotic limits of the rate function $s$.

The mean-square displacement per collision of a molecule immersed in a gas at equilibrium is given by its mean-square displacement between two consecutive collisions (mean-square free path) corrected by a prefactor in the form of a series. The $n\mathrm{th}$ term of the series is proportional to the mean value of the scalar product ${\mathbf{r}}_1·{\mathbf{r}}_n$, where ${\mathbf{r}}_i$ is the displacement of the molecule between the $(i-1)$-th and $i\mathrm{th}$ collisions. Simple arguments are used to obtain approximate expressions for each term. The key finding is that the ratio of consecutive terms in the series closely approximates the so-called mean persistence ratio. Exact expressions for the terms in the series are considered and their ratios for several consecutive terms are calculated for the case of hard spheres, showing an excellent agreement with the mean persistence ratio. These theoretical results are confirmed by solving the Boltzmann equation by means of the direct simulation Monte Carlo method. By summing the series, the mean-square displacement and the diffusion coefficient can be determined using only two quantities: the mean-square free path and the mean persistence ratio. A simple and an improved expression for the diffusion coefficient $D$ are considered and compared with the so-called first and second Sonine approximations to $D$ as well as with computer simulations of the Boltzmann equation. It is found that the improved diffusion coefficient shows very good agreement with simulation results over all intruder and molecule mass ranges. When the intruder mass is smaller than that of the gas molecules, the improved formula even outperforms the first Sonine approximation.

Fluctuations in conjugate thermodynamic variables are studied using the cross-correlation function. A new procedure is given enabling the derivation of fluctuation formulas for a system in equilibrium. Specifically, the cross-correlation function between heat and temperature is employed for thermal variables. Additionally, fluctuation-dissipation relations involving the frequency-dependent specific heat are established. Moreover, a general relation concerning the average entropy production is also given, which is the microscopic analog of the dissipation formula of the linear response theory. In the case of thermal variables, this formula finds application in various scenarios describing fluctuating thermal systems in equilibrium.

We study a discrete model of an heterogeneous elastic line with internal disorder, submitted to thermal fluctuations. The monomers are connected through random springs with independent and identically distributed elastic constants drawn from $p\left(k\right)\sim k^{\mu -1}$ for $k\to 0$. When $\mu >1$, the scaling of the standard Edwards-Wilkinson model is recovered. When $\mu <1$, the elastic line exhibits an anomalous scaling of the type observed in many growth models and experiments. Here we derive and use the exact probability distribution of the line shape at equilibrium, as well as the spectral properties of the matrix containing the random couplings, to fully characterize the sample to sample fluctuations. Our results lead to scaling predictions that partially disagree with previous works, but that are corroborated by numerical simulations. We also provide an interpretation of the anomalous scaling in terms of the abrupt jumps in the line's shape that dominate the average value of the observable.

Despite the success of fractional Brownian motion (fBm) in modeling systems that exhibit anomalous diffusion due to temporal correlations, recent experimental and theoretical studies highlight the necessity for a more comprehensive approach of a generalization that incorporates heterogeneities in either the tracers or the environment. This work presents a modification of Lévy's representation of fBm for the case in which the generalized diffusion coefficient is a stochastic process. We derive analytical expressions for the autocovariance function and both ensemble- and time-averaged mean squared displacements. Further, we validate the efficacy of the developed framework in two-state systems, comparing analytical asymptotic expressions with numerical simulations.

With the original Bak-Tang-Wisenefeld (BTW) sandpile we uncover the $1/\phi$ noise in the mechanism maintaining self-organized criticality (SOC)—the question raised together with the concept of SOC. The BTW sandpile and the phenomenon of SOC in general are built on the slow time scale at which the system is loaded and the fast time scale at which the stress is transported outward from overloaded locations. Exploring the dynamics of stress in the slow time in the BTW sandpile, we posit that it follows cycles of gradual stress accumulation that end up with an abrupt stress release and the drop of the system to subcritical state. As the system size grows, the intracycle dynamics exhibits the $1/\phi$-like spectrum that extends boundlessly and corresponds to the stress release within the critical state.

Incorporating additive noise components to an ensemble of McCulloch-Pitts neurons can enhance the information representation of the input, asymptotically approaching the average firing probability for large enough ensembles. We further multiply the input by the average firing probability to control the higher probability of self-gating, thereby forming a unified noise-boosted activation model with learnable noise-related hyperparameters. This gating strategy plays a crucial role in improving the performance of neural networks, as evidenced by the optimization of the autoencoder loss at nonzero optimal-noise-scaling hyperparameters, a phenomenon termed self-gating stochastic resonance. Experiments with designed autoencoders using noise-boosted activation functions demonstrate the potential applications of the self-gating stochastic resonance effect in the field of unsupervised learning.

We 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. We generalize the SCS model considering odd saturating nonlinear transfer functions, beyond the usual choice $\varphi \left(x\right)=tanhx$. A discontinuous transition to chaos occurs whenever the slope of $\varphi$ at 0 is a local minimum [i.e., for ${\varphi }^{\prime \prime \prime }\left(0\right)>0]$. 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 present work, a general model of the electrokinetics and dielectric response of a concentrated salt-free colloid is developed which includes consideration of the finite size of the counterions released by the particles to the solution, a nonhomogeneous permittivity of the solution, the existence of Born and dielectrophoretic forces acting on the counterions, and especially the fact that the solution viscosity and diffusion counterion coefficient are allowed to be functions of the local counterion concentration. These effects have recently been discussed by J. J. López-García et al. [Phys. Rev. Fluids 4, 103702 (2019)] in the case of dilute colloids in general electrolyte solutions. The objective of this work is to explore the new effects and their influence on the electrokinetic response of concentrated salt-free systems. Present results confirm previous findings regarding the important increases of the dc electrophoretic mobility and dc electrical conductivity, as well as huge increments of the dynamic electrophoretic mobilities at high frequencies when finite-ion-size effects were taken into account. In addition, consideration of the viscosity of the solution and of the counterion diffusion coefficient as functions of the local counterion concentration leads to a decrease of the magnitude of the previous electrokinetic results. The theory incorporates a more convenient hard-sphere hydrodynamic model to account for the nonhomogeneous viscosity of the solution than others proposed in previous works in the literature. A comparison is elaborated on between electrokinetic and dielectric responses with different levels of complexity of the theoretical model, starting from the case of pointlike counterions and following with the inclusion in sequence of additional aspects such as finite counterion size, nonhomogeneous electrical permittivity with associated Born and dielectrophoretic effects, and, finally, position-dependent viscosity and diffusion counterion coefficient, and clearly shows the influence of individual effects on the general electrokinetic response and especially the relevant role the nonhomogeneous viscosity on the dc and ac electrokientic behavior of salt-free colloids.

The Szilard engine stands as a compelling illustration of the intricate interplay between information and thermodynamics. While at thermodynamic equilibrium, the apparent breach of the second law of thermodynamics was reconciled by Landauer and Bennett's insights into memory writing and erasure, recent extensions of these concepts into regimes featuring active fluctuations have unveiled the prospect of exceeding Landauer's bound, capitalizing on information to divert free energy from dissipation towards useful work. To explore this question further, we investigate an autonomous dynamic information engine, addressing the thermodynamic consistency of work extraction and measurement costs by extending the phase space to incorporate an auxiliary system, which plays the role of an explicit measurement device. The nonreciprocal coupling between active particle and measurement device introduces a feedback control loop, and the cost of measurement is quantified through a suitably defined auxiliary entropy production. The study considers different measurement scenarios, highlighting the role of measurement precision in determining engine efficiency.