The dynamics of purely memristive circuits has been shown to depend on a projection operator which expresses the Kirchhoff constraints, is naturally non-local in nature, and does represent the interaction between memristors. In the present paper we show that for the case of planar circuits, for which a meaningful Hamming distance can be defined, the elements of such projector can be bounded by exponentially decreasing functions of the distance. We provide a geometrical interpretation of the projector elements in terms of determinants of Dirichlet Laplacian of the dual circuit. For the case of linearized dynamics of the circuit for which a solution is known, this can be shown to provide a light cone bound for the interaction between memristors. This result establishes a finite speed of propagation of signals across the network, despite the non-local nature of the system.

It is well known that the addition of noise to a multistable dynamical system can induce random transitions from one stable state to another. For low noise, the times between transitions have an exponential tail and Kramers' formula gives an expression for the mean escape time in the asymptotic limit. If a number of multistable systems are coupled into a network structure, a transition at one site may change the transition properties at other sites. We study the case of escape from a “quiescent” attractor to an “active” attractor in which transitions back can be ignored. There are qualitatively different regimes of transition, depending on coupling strength. For small coupling strengths, the transition rates are simply modified but the transitions remain stochastic. For large coupling strengths, transitions happen approximately in synchrony—we call this a “fast domino” regime. There is also an intermediate coupling regime where some transitions happen inexorably but with a delay that may be arbitrarily long—we call this a “slow domino” regime. We characterize these regimes in the low noise limit in terms of bifurcations of the potential landscape of a coupled system. We demonstrate the effect of the coupling on the distribution of timings and (in general) the sequences of escapes of the system.

Bacterial cells can often self-organize into multicellular structures with complex spatiotemporal morphology. In this work, we study the spatiotemporal dynamics of a growing microbial colony in the presence of cell death. We present an individual-based model of nonmotile bacterial cells which grow and proliferate by consuming diffusing nutrients on a semisolid two-dimensional surface. The colony spreads by growth forces and sliding motility of cells and undergoes cell death followed by subsequent disintegration of the dead cells in the medium. We model cell death by considering two possible situations: In one of the cases, cell death occurs in response to the limitation of local nutrients, while the other case corresponds to an active death process, known as apoptotic or programmed cell death. We demonstrate how the colony morphology is influenced by the presence of cell death. Our results show that cell death facilitates transitions from roughly circular to highly branched structures at the periphery of an expanding colony. Interestingly, our results also reveal that for the colonies which are growing in higher initial nutrient concentrations, cell death occurs much earlier compared to the colonies which are growing in lower initial nutrient concentrations. This work provides new insights into the branched patterning of growing bacterial colonies as a consequence of complex interplay among the biochemical and mechanical effects.

Single chain in mean-field Monte Carlo simulations were employed to study the self-assembly of block copolymers (BCP) in thin films that use trapezoidal guidelines to direct the orientation and alignment of lamellar patterns. The present study explored the influence of sidewall interactions and geometry of the trapezoidal guidelines on the self-assembly of perpendicularly oriented lamellar morphologies. When both the sidewall and the top surface exhibit preferential interactions to the same block of the BCP, trapezoidal guidelines with intermediate taper angles were found to result in less defective perpendicularly orientated morphologies. Similarly, when the sidewall and top surface are preferential to distinct blocks of the BCP, intermediate tapering angles were found to be optimal in promoting defect free structures. Such results are rationalized based on the energetics arising in the formation of perpendicularly oriented lamella on patterned substrates.

A polymer-stabilized liquid crystal based on $4^{\prime }$-(octyloxy)biphenyl-4-carboxylate 2-fluoro-4-((octyl-2-yloxy)carbonyl)phenyl (D16) and 1,6 hexanediol diacrylate as a monomer was prepared by in situ photopolymerization. The selected antiferroelectric liquid crystal contains a fast-switching smectic $C_{\alpha }^{*}$ phase (${\mathrm{SmC}}_{\alpha }^{*}$), and the influence of the polymer network on the thermodynamic stability of this phase and its switching behavior under applying time-dependent electric field were studied. Using dielectric spectroscopy and polarizing microscopy, the liquid crystal materials were characterized, and subsequently with the use of the reversal current method (RCM) the current response, especially from the ${\mathrm{SmC}}_{\alpha }^{*}$ phase was carefully analyzed. The current response is complex and also depends on the neighboring liquid crystal phases. In the liquid crystal-polymer system, as well as in the liquid crystal-monomer mixture, a significant shift of the temperature range of the ${\mathrm{SmC}}_{\alpha }^{*}$ phase toward lower temperatures was observed; however, the thermodynamic instability related to the transformation to the crystalline phase was also noted and characterized. Because of the fuzzy phase transitions detected in the liquid crystal-polymer system by dielectric spectroscopy and also because of the lack of the characteristic dielectric signature of ${\mathrm{SmC}}_{\alpha }^{*}$ after polymerization, we proposed the use of the RCM, as a complementary one, to identify the ${\mathrm{SmC}}_{\alpha }^{*}$ phase even in such complex materials.

We investigate percolation, a probabilistic model for continuous phase transition, on square and weighted planar stochastic lattices. In its thermal counterpart, entropy is minimally low where order parameter (OP) is maximally high and vice versa. In addition, specific heat, OP, and susceptibility exhibit power law when approaching the critical point and the corresponding critical exponents $\alpha ,\beta ,\gamma$ respectably obey the Rushbrooke inequality (RI) $\alpha +2\beta +\gamma \ge 2$. Their analogs in percolation, however, remain elusive. We define entropy and specific heat and redefine susceptibility for percolation and show that they behave exactly in the same way as their thermal counterpart. We also show that RI holds for both the lattices albeit they belong to different universality classes.

The double-tetrahedral chain in a longitudinal magnetic field, whose nodal lattice sites occupied by the localized Ising spins regularly alternate with triangular plaquettes with the dynamics described by the Hubbard model, is rigorously investigated. It is demonstrated that the uniform change of electron concentration controlled by the chemical potential in a combination with the competition between model parameters and the external magnetic field leads to the formation of one chiral and seven nonchiral phases at the absolute zero temperature. Rational plateaux at one-third and one-half of the saturation magnetization can also be identified in the low-temperature magnetization curves. On the other hand, the gradual electron doping results in 11 different ground-state regions that distinguish from each other by the evolution of the electron distribution during this process. Several doping-dependent magnetization plateaux are observed in the magnetization process as a result of the continuous change of electron content in the model.

We investigate the dynamics of a Kuramoto-type system of globally coupled phase oscillators with equidistant natural frequencies and a coupling strength below the synchronization threshold. It turns out that in such cases one can observe a stable regime of sharp pulses in the mean field amplitude with a pulsation frequency given by spacing of the natural frequencies. This resembles a process known as mode locking in lasers and relies on the emergence of a phase relation induced by the nonlinear coupling. We discuss the role of the first and second harmonics in the phase-interaction function for the stability of the pulsations and present various bifurcating dynamical regimes such as periodically and chaotically modulated mode locking, transitions to phase turbulence, and intermittency. Moreover, we study the role of the system size and show that in certain cases one can observe type II supertransients, where the system reaches the globally stable mode-locking solution only after an exponentially long transient of phase turbulence.

Line attractors in neuronal networks have been suggested to be the basis of many brain functions, such as working memory, oculomotor control, head movement, locomotion, and sensory processing. In this paper, we make the connection between line attractors and pulse gating in feed-forward neuronal networks. In this context, because of their neutral stability along a one-dimensional manifold, line attractors are associated with a time-translational invariance that allows graded information to be propagated from one neuronal population to the next. To understand how pulse-gating manifests itself in a high-dimensional, nonlinear, feedforward integrate-and-fire network, we use a Fokker-Planck approach to analyze system dynamics. We make a connection between pulse-gated propagation in the Fokker-Planck and population-averaged mean-field (firing rate) models, and then identify an approximate line attractor in state space as the essential structure underlying graded information propagation. An analysis of the line attractor shows that it consists of three fixed points: a central saddle with an unstable manifold along the line and stable manifolds orthogonal to the line, which is surrounded on either side by stable fixed points. Along the manifold defined by the fixed points, slow dynamics give rise to a ghost. We show that this line attractor arises at a cusp catastrophe, where a fold bifurcation develops as a function of synaptic noise; and that the ghost dynamics near the fold of the cusp underly the robustness of the line attractor. Understanding the dynamical aspects of this cusp catastrophe allows us to show how line attractors can persist in biologically realistic neuronal networks and how the interplay of pulse gating, synaptic coupling, and neuronal stochasticity can be used to enable attracting one-dimensional manifolds and, thus, dynamically control the processing of graded information.

We study spreading of a nonmotile bacteria colony on a hard agar plate by using agent-based and continuum models. We show that the spreading dynamics depends on the initial nutrient concentration, the motility, and the inherent demographic noise. Population fluctuations are inherent in an agent-based model, whereas for the continuum model we model them by using a stochastic Langevin equation. We show that the intrinsic population fluctuations coupled with nonlinear diffusivity lead to a transition from a diffusion limited aggregation type of morphology to an Eden-like morphology on decreasing the initial nutrient concentration.

We investigate a time-dependent extension of the Tonks-Langmuir model for a one-dimensional plasma discharge with collisionless kinetic ions and Boltzmann electrons. Ions are created uniformly throughout the volume and flow from the center of the discharge to the boundary wall due to a self-consistent, zero-order electric field. Solving this model using a particle-in-cell simulation, we observe coherent low-frequency, long-wavelength unstable ion waves which move toward the boundary with a speed below both the ion acoustic speed and the average ion velocity. The maximum amplitude of the wave potential fluctuations peaks at $\approx 0.09T_e$ near the wall, where $T_e$ is the electron temperature in electron volts. Using linear kinetic theory, we identify this instability as slow ion-acoustic wave modes which are destabilized by the zero-order electric field.

Periodically driven systems are a common topic in modern physics. In optical lattices specifically, driving is at the origin of many interesting phenomena. However, energy is not conserved in driven systems, and under periodic driving, heating of a system is a real concern. In an effort to better understand this phenomenon, the heating of single-band systems has been studied, with a focus on disorder- and interaction-induced effects, such as many-body localization. Nevertheless, driven systems occur in a much wider context than this, leaving room for further research. Here, we fill this gap by studying a noninteracting model, characterized by discrete, periodically spaced energy levels that are unbounded from above. We couple these energy levels resonantly through a periodic drive, and discuss the heating dynamics of this system as a function of the driving protocol. In this way, we show that a combination of stimulated emission and absorption causes the presence of resonant stable states. This will serve to elucidate the conditions under which resonant driving causes heating in quantum systems.

Microscopic heat engines are microscale systems that convert energy flows between heat reservoirs into work or systematic motion. We have experimentally realized a minimal microscopic heat engine. It consists of a colloidal Brownian particle optically trapped in an elliptical potential well and simultaneously coupled to two heat baths at different temperatures acting along perpendicular directions. For a generic arrangement of the principal directions of the baths and the potential, the symmetry of the system is broken, such that the heat flow drives a systematic gyrating motion of the particle around the potential minimum. Using the experimentally measured trajectories, we quantify the gyrating motion of the particle, the resulting torque that it exerts on the potential, and the associated heat flow between the heat baths. We find excellent agreement between the experimental results and the theoretical predictions.

Motivated by the recent growing interest about the thermodynamic cost of shortcuts to adiabaticity, we consider the cost of driving a classical system by the so-called counterdiabatic driving (CD). To do so, we proceed in three steps: first we review a general definition recently put forward in the literature for the thermodynamic cost of driving a Hamiltonian system; then we provide a new complementary definition of cost, which is of particular relevance for cases where the average excess work vanishes; finally, we apply our general framework to the case of CD. Interestingly, we find that in such a case our results are the exact classical counterparts of those reported by Funo et al. [Phys. Rev. Lett. 118, 100602 (2017)]. In particular we show that a universal trade-off between speed and cost for CD also exists in the classical case. To illustrate our points we consider the example of a time-dependent harmonic oscillator subject to different strategies of adiabatic control.

Hyperbolic lattices interpolate between finite-dimensional lattices and Bethe lattices, and they are interesting in their own right, with ordinary percolation exhibiting not one but two phase transitions. We study four constraint percolation models—$k$-core percolation (for $k=1,2,3$) and force-balance percolation—on several tessellations of the hyperbolic plane. By comparing these four different models, our numerical data suggest that all of the $k$-core models, even for $k=3$, exhibit behavior similar to ordinary percolation, while the force-balance percolation transition is discontinuous. We also provide proof, for some hyperbolic lattices, of the existence of a critical probability that is less than unity for the force-balance model, so that we can place our interpretation of the numerical data for this model on a more rigorous footing. Finally, we discuss improved numerical methods for determining the two critical probabilities on the hyperbolic lattice for the $k$-core percolation models.

We use the $H$ theorem to establish the entropy and the entropic additivity law for a system composed of subsystems, with the dynamics governed by the Klein-Kramers equations, by considering relations among the dynamics of these subsystems and their entropies. We start considering the subsystems governed by linear Klein-Kramers equations and verify that the Boltzmann-Gibbs entropy is appropriated to this dynamics, leading us to the standard entropic additivity, $S_{BG}^{(1\cup 2)}=S_{BG}^1+S_{BG}^2$, consistent with the fact that the distributions of the subsystem are independent. We then extend the dynamics of these subsystems to independent nonlinear Klein-Kramers equations. For this case, the results show that the $H$ theorem is verified for a generalized entropy, which does not preserve the standard entropic additivity for independent distributions. In this scenario, consistent results are obtained when a suitable coupling among the nonlinear Klein-Kramers equations is considered, in which each subsystem modifies the other until an equilibrium state is reached. This dynamics, for the subsystems, results in the Tsallis entropy for the system and, consequently, verifies the relation $S_q^{(1\cup 2)}=S_q^1+S_q^2+(1-q)S_q^1{S}_q^2/k$, which is a nonadditive entropic relation.

Some noisy nonlinear systems could be exploited to operate reliable logic operation in an optimal window of noise intensity, which is termed as logical stochastic resonance (LSR). We investigated the LSR phenomenon in bistable systems when internal noise and external noise are correlated. The LSR effect is evaluated by the success probability of the obtained desired output with various combinations of logic inputs. It is shown that the or-nor, and-nand, and Latch operations still can operate reliably with the correlated internal noise and external noise. A positive correlation strength tends to enhance or-nor logic and suppress and-nand logic. The negative correlation strength tends to suppress or-nor logic and enhance and-nand logic. The results provide possible corroboration for implementing reliable LSR when internal noise and external noise are correlated.

We observe extremely large amplitude intermittent spikings in a dynamical variable of a periodically forced Liénard-type oscillator and characterize them as extreme events, which are rare, but recurrent and larger in amplitude than a threshold. The extreme events occur via two processes, an interior crisis and intermittency. The probability of occurrence of the events shows a long-tail distribution in both the cases. We provide evidence of the extreme events in an experiment using an electronic analog circuit of the Liénard oscillator that shows good agreement with our numerical results.

We develop a statistical framework for studying recurrent networks with broad distributions of the number of synaptic links per neuron. We treat each group of neurons with equal input degree as one population and derive a system of equations determining the population-averaged firing rates. The derivation rests on an assumption of a large number of neurons and, additionally, an assumption of a large number of synapses per neuron. For the case of binary neurons, analytical solutions can be constructed, which correspond to steps in the activity versus degree space. We apply this theory to networks with degree-correlated topology and show that complex, multi-stable regimes can result for increasing correlations. Our work is motivated by the recent finding of subnetworks of highly active neurons and the fact that these neurons tend to be connected to each other with higher probability.

A fundamental issue in the dynamics of complex systems is the resilience of the network in response to targeted attacks. This paper explores the local dynamics of the network attack process by investigating the order of removal of the nodes that have maximal degree, and shows that this dynamic network response can be predicted from the graph's initial connectivity. We demonstrate numerically that the maximal degree M(τ) of the network at time step τ decays exponentially with τ via a topology-dependent exponent. Moreover, the order in which sites are removed can be approximated by considering the network's “hierarchy” function $h$, which measures for each node $V_i$ how many of its initial nearest neighbors have lower degree versus those that have a higher one. Finally, we show that the exponents we identified for the attack dynamics are related to the exponential behavior of spreading activation dynamics. The results suggest that the function $h$, which has both local and global properties, is a novel nodal measurement for network dynamics and structure.