Time-delayed oscillator networks underlie diverse biological and physical systems, yet standard first-order phase reductions fail to capture their high-dimensional collective dynamics. In this work, we develop a universal second-order predictive reduction for time-delayed Kuramoto-Daido networks that maps delayed one-dimensional phase dynamics to a delay-free network of two-dimensional rotators. Delay induces effective inertia and triadic interactions, yielding accurate predictions of nontrivial attractors and their collective-state statistics, including splay, cyclops, and chimera states. The reduction reveals a division of roles: Inertia organizes higher-dimensional dynamics, whereas triadic terms are crucial for lower-dimensional patterns such as chimeras. Applicable to arbitrary topology, higher harmonics, and intrinsic-frequency heterogeneity, it provides a compact, parameter-explicit reduced model. The same framework also extends to time-delayed amplitude-phase oscillator networks, including swarmalators, yielding analogous reduced equations with emergent inertia and triadic higher-order couplings. This unified and readily deployable description enables systematic prediction and analysis of delay-controlled collective dynamics across oscillator networks.

The generation of high-power controlled pulse trains in semiconductor lasers is of significant theoretical and practical interest, with broad applications across many fields. Previous work on diode lasers has primarily focused on single-pulse emission from individual diodes, inherently limiting output power. Here, we demonstrate that large direct-current-driven external-cavity laser arrays, subject only to optical feedback and engineered frequency heterogeneity, can exhibit robust, coherent multi-pulsing dynamics. We uncover a multi-pulse generation mechanism driven by two-cluster formation and heterogeneity-induced interburst oscillations, resulting in desirable features such as multi-GHz operation, high peak power, and nearly perfect phase synchronization. Our results pave the way for designing and manufacturing miniature photonic chip arrays capable of scalable multi-pulse generation. Beyond photonics, the underlying multi-pulse mechanism may be broadly relevant to physical and biological networks, where oscillator heterogeneity can give rise to coherent spiking and bursting dynamics.

Background: In epilepsy, clinically useful electrophysiological markers often depend on seizure capture or transient epileptiform events, whereas the broadband aperiodic ``background'' of intracranial recordings is commonly treated as noise. Yet, scale-free spectral structure may reflect an imbalance between excitation and inhibition and cortical instability. Methods: We analyzed interictal intracranial electroencephalography from 55 patients with drug-resistant focal epilepsy using spectral parameterization to quantify aperiodic slope and broadband offset across epileptogenic and non-epileptogenic cortex and across recording modalities. Results: Here, we show that epileptogenic cortex exhibits steeper aperiodic slopes and higher broadband offsets than non-epileptogenic tissue, with these effects remaining robust across stereoelectroencephalography and electrocorticography. These abnormalities are detectable during interictal periods, without seizure capture, indicating that the ongoing background organization of neural activity contains biologically and clinically meaningful information. The findings support a persistent alteration in scale-free cortical dynamics consistent with excitation--inhibition imbalance and identify a compact interictal signature of epileptic network dysfunction. Conclusions: By showing that the aperiodic component of human intracranial recordings is signal rather than noise, this study identifies a seizure-independent and mechanistically grounded marker of epileptic network instability with potential translational value for invasive monitoring.

We present a novel approach for studying the global dynamics of a vibro-impact pair, that is, a ball moving in a harmonically forced capsule. Motivated by a specific context of vibro-impact energy harvesting, we develop the method with broader non-smooth systems in mind. The seeming complications of the impacts of the ball with the capsule are exploited as useful non-smooth features in selecting appropriate return maps. This choice yields a computationally efficient framework for constructing return maps on short-time realizations from the state space of possible initial conditions rather than via long-time simulations often used to generate more traditional maps. The different dynamics in sub-regions in the state space yield a small collection of reduced polynomial approximations. Combined into a piecewise composite map, these capture transient and attracting behaviors and reproduce bifurcation sequences of the full system. Further “separable” reductions of the composite map provide insight into both transient and global dynamics. This composite map is valuable for cobweb analysis, which opens the door to computer-assisted global analysis and is realized via conservative auxiliary maps based on the extreme bounds of the maps in each subregion. We study the global dynamics of energetically favorable states and illustrate the potential of this approach in broader classes of dynamics.

Disorder is often seen as detrimental to collective dynamics, yet recent work has shown that heterogeneity can enhance network synchronization. However, its constructive role in stabilizing nontrivial cooperative patterns remains largely unexplored. In this Letter, we show that frequency heterogeneity among oscillators can induce stable Cyclops and cluster states in Kuramoto networks with higher-mode coupling, even though these states are unstable in the identical oscillator case. Cyclops states, introduced in [Munyaev et al., Phys. Rev. Lett. 130, 107021 (2023)], feature two synchronized clusters and a solitary oscillator, requiring a delicate phase balance. Surprisingly, heterogeneity alone is sufficient to stabilize these patterns across a broad range of detuning values without needing to be compensated by other forms of disorder or external tuning. We introduce a mesoscopic collective coordinate approach that connects microscopic frequency structure, captured by the solitary oscillator's influence, with mean-field cluster-level stability. This constructive approach identifies favorable ranges of heterogeneity and suitable initial conditions for inducing robust multi-state dynamics, offering a foundation for their analysis in broader classes of heterogeneous biological and engineering networks.

Firearm injuries are a leading cause of death in the United States, surpassing fatalities from motor vehicle crashes. Despite this significant public health risk, Americans continue to purchase firearms in large quantities. Commonly cited drivers of firearm acquisition include fear of violent crime, fear of mass shootings, and panic-buying. Additionally, advocacy groups' activity on social media may capitalize on emotions like fear and influence firearm acquisition. The simultaneous effects of these variables have not been explored in a causal framework. In this study, we aim to elucidate the causal roles of media coverage of firearm laws and regulations, media coverage of mass shootings, media coverage of violent crimes, and the Twitter activity of anti- and proregulation advocacy groups in short-term firearm acquisition in the United States. We collect daily time series for these variables from 2012 to 2020 and employ the PCMCI+ framework to investigate the causal structures among them simultaneously. Our results indicate that the Twitter activity of antiregulation advocacy groups directly drives firearm acquisitions. We also find that media coverage of firearm laws and regulations and media coverage of violent crimes influence firearm acquisition. Although media coverage of mass shootings and online activity of proregulation organizations are potential drivers of firearm acquisition, in the short term, only the lobbying efforts of antiregulation organizations on social media and specific media coverage appear to influence individuals' decisions to purchase firearms.

Network synchronization of lasers is critical for achieving high-power outputs and enabling effective optical computing. However, the role of network topology in frequency synchronization of optical oscillators and lasers remains not well understood. Here, we report our significant progress toward solving this critical problem for networks of heterogeneous laser model oscillators with repulsive coupling. We discover a general approximate principle for predicting the onset of frequency synchronization from the spectral knowledge of a complex matrix representing a combination of the signless Laplacian induced by repulsive coupling and a matrix associated with intrinsic frequency detuning. We show that the gap between the two smallest eigenvalues of the complex matrix generally controls the coupling threshold for frequency synchronization. In stark contrast with attractive networks, we demonstrate that local rings and all-to-all networks prevent frequency synchronization, whereas full bipartite networks have optimal synchronization properties. Beyond laser models, we show that, with a few exceptions, the spectral principle can be applied to repulsive Kuramoto networks. Our results provide guidelines for optimal designs of scalable optical oscillator networks capable of achieving reliable frequency synchronization.

The dynamics of synaptic interactions within spiking neuron networks play a fundamental role in shaping emergent collective behavior. This paper studies a finite-size network of quadratic integrate-and-fire neurons interconnected via a general synaptic function that accounts for synaptic dynamics and time delays. Through asymptotic analysis, we transform this integrate-and-fire network into the Kuramoto-Sakaguchi model, whose parameters are explicitly expressed via synaptic function characteristics. This reduction yields analytical conditions on synaptic activation rates and time delays determining whether the synaptic coupling is attractive or repulsive. Our analysis reveals alternating stability regions for synchronous and partially synchronous firing, dependent on slow synaptic activation and time delay. We also demonstrate that the reduced microscopic model predicts the emergence of synchronization, weakly stable cyclops states, and non-stationary regimes remarkably well in the original integrate-and-fire network and its theta neuron counterpart. Our reduction approach promises to open the door to rigorous analysis of rhythmogenesis in networks with synaptic adaptation and plasticity.

BACKGROUND:Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Traditionally, stroke rehabilitation occurs in inpatient and outpatient rehabilitation facilities. However, recent literature increasingly explores moving the recovery process into the home and integrating technology-based interventions. This study advances this goal by promoting in-home, autonomous recovery for patients who experienced a stroke through robotics-assisted rehabilitation and classifying stroke residual severity using machine learning methods. OBJECTIVE:Our main objective is to use kinematics data collected during in-home, self-guided therapy sessions to develop supervised machine learning methods, to address a clinician's autonomous classification of stroke residual severity-labeled data toward improving in-home, robotics-assisted stroke rehabilitation. METHODS:In total, 33 patients who experienced a stroke participated in in-home therapy sessions using Motus Nova robotics rehabilitation technology to capture upper and lower body motion. During each therapy session, the Motus Hand and Motus Foot devices collected movement data, assistance data, and activity-specific data. We then synthesized, processed, and summarized these data. Next, the therapy session data were paired with clinician-informed, discrete stroke residual severity labels: "no range of motion (ROM)," "low ROM," and "high ROM." Afterward, an 80%:20% split was performed to divide the dataset into a training set and a holdout test set. We used 4 machine learning algorithms to classify stroke residual severity: light gradient boosting (LGB), extra trees classifier, deep feed-forward neural network, and classical logistic regression. We selected models based on 10-fold cross-validation and measured their performance on a holdout test dataset using F1-score to identify which model maximizes stroke residual severity classification accuracy. RESULTS:We demonstrated that the LGB method provides the most reliable autonomous detection of stroke severity. The trained model is a consensus model that consists of 139 decision trees with up to 115 leaves each. This LGB model boasts a 96.70% F1-score compared to logistic regression (55.82%), extra trees classifier (94.81%), and deep feed-forward neural network (70.11%). CONCLUSIONS:We showed how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class, with efforts to enhance in-home self-guided, individualized stroke rehabilitation. The model we trained relies only on session summary statistics, meaning it can potentially be integrated into similar settings for real-time classification, such as outpatient rehabilitation facilities.

Cyclops states are intriguing cluster patterns observed in oscillator networks, including neuronal ensembles. The concept of cyclops states formed by two distinct, coherent clusters and a solitary oscillator was introduced by Munyaev et al. [Phys. Rev. Lett. 130 , 107201 (2023)], where we explored the surprising prevalence of such states in repulsive Kuramoto networks of rotators with higher -mode harmonics in the coupling. This paper extends our analysis to understand the mechanisms responsible for destroying the cyclops' states and inducing dynamical patterns called breathing and switching cyclops states. We first analytically study the existence and stability of cyclops states in the Kuramoto-Sakaguchi networks of two-dimensional oscillators with inertia as a function of the second coupling harmonic. We then describe two bifurcation scenarios that give birth to breathing and switching cyclops states. We demonstrate that these states and their hybrids are prevalent across a wide coupling range and are robust against a relatively large intrinsic frequency detuning. Beyond the Kuramoto networks, breathing and switching cyclops states promise to strongly manifest in other physical and biological networks, including coupled theta neurons.

Networks of phase oscillators have become a widely established paradigmatic model for studying emergent collective behavior across several real-world systems, including neuronal networks, populations of chemical oscillators, and power grids. The Kuramoto model, involving one-dimensional or two-dimensional phase oscillators, demonstrates the potential for networks to showcase exceptional collective dynamics. This encompasses various outcomes such as full, partial, explosive, and asymmetry-induced synchronization, clusters, chimeras, solitary states, and generalized splay states. Notably, increasing all-to-all coupling in the classical Kuramoto model induces full synchronization as the most probable outcome and dominant rhythm. Kuramoto networks with repulsive coupling usually display splay, generalized, and cluster splay states, but the conditions under which a certain rhythm can arise and prevail are not entirely understood. Equally important for connecting Kuramoto networks to practical physical systems is understanding the function of higher-order coupling terms. These terms display a Fourier decomposition of a general 2π-periodic interaction function [1]. Previous studies have demonstrated that the inclusion of higher-order terms in the classical Kuramoto model of oscillators with all-to-all attractive coupling can lead to multiple synchronous states and switching between synchronization clusters. However, the impact of higher-order coupling modes on rhythm generation in repulsive networks remains unexplored. In this work, we present significant progress in addressing the critical issue related to repulsive Kuramoto–Sakaguchi networks of phase oscillators with phase-lagged first-order and higher-order coupling. We demonstrate that weakly repulsive networks of even and odd numbers of oscillators with first-order coupling are dominated by two-cluster and three-cluster splay states, respectively. The three-cluster splay states consist of two distinct coherent clusters and one solitary oscillator. These tripod states can be considered a fusion of a two-body chimera and a solitary state. We have dubbed these patterns of three oscillators as “Cyclops states” in reference to the Greek mythological giant with a single eye. The solitary oscillator and synchronous clusters respectively represent the Cyclops’ eye and shoulders. We present a remarkable discovery that the inclusion of higher-order coupling modes leads to worldwide stability of cyclops states across almost the entire range of the phase-lag parameter controlling repulsion [2]. Beyond the Kuramoto oscillators, we demonstrate the robust presence of this effect in networks of canonical theta-neurons with adaptive coupling. Furthermore, our results provide insight into identifying dominant rhythms within repulsive physical and biological networks.

We consider networks whose topology changes in time according to a stochastic rule. While the literature gives insight into the effects of fast stochastic connections, little is known about the effects of slower switching on the evolution of a network.We review recent analytical results on convergence properties of fast switching dynamical networks, including bounds on the probability of converging towards an attractor of a multistable network. We also discuss the advantages of slower switching over fast switching, and consider an example in which slow switching provides opportunities for network synchronization while fast switching does not. It is shown that there is an optimal window in which the switching frequency causes an unstable system to stabilize.

Network synchronization of lasers is critical for reaching high-power levels and for effective optical computing. Yet, the role of network topology for the frequency synchronization of lasers is not well understood. Here, we report our significant progress toward solving this critical problem for networks of heterogeneous laser model oscillators with repulsive coupling. We discover a general approximate principle for predicting the onset of frequency synchronization from the spectral knowledge of a complex matrix representing a combination of the signless Laplacian induced by repulsive coupling and a matrix associated with intrinsic frequency detuning. We show that the gap between the two smallest eigenvalues of the complex matrix generally controls the coupling threshold for frequency synchronization. In stark contrast with Laplacian networks, we demonstrate that local rings and all-to-all networks prevent frequency synchronization, whereas full bipartite networks have optimal synchronization properties. Beyond laser models, we show that, with a few exceptions, the spectral principle can be applied to repulsive Kuramoto networks. Our results may provide guidelines for optimal designs of scalable laser networks capable of achieving reliable synchronization.

Double-scroll attractors are one of the pillars of modern chaos theory. However, rigorous computer-free analysis of their existence and global structure is often elusive. Here, we address this fundamental problem by constructing an analytically tractable piecewise-smooth system with a double-scroll attractor. We derive a Poincaré return map to prove the existence of the double-scroll attractor and explicitly characterize its global dynamical properties. In particular, we reveal a hidden set of countably many saddle orbits associated with infinite-period Smale horseshoes. These complex hyperbolic sets emerge from an ordered iterative process that yields sequential intersections between different horseshoes and their preimages. This novel distinctive feature differs from the classical Smale horseshoes, directly intersecting with their own preimages. Our global analysis suggests that the structure of the classical Chua attractor and other figure-eight attractors might be more complex than previously thought.

Repulsive oscillator networks can exhibit multiple cooperative rhythms, including chimera and cluster splay states. Yet, understanding which rhythm prevails remains challenging. Here, we address this fundamental question in the context of Kuramoto-Sakaguchi networks of rotators with higher-order Fourier modes in the coupling. Through analysis and numerics, we show that three-cluster splay states with two distinct coherent clusters and a solitary oscillator are the prevalent rhythms in networks with an odd number of units. We denote such tripod patterns cyclops states with the solitary oscillator reminiscent of the Cyclops' eye. As their mythological counterparts, the cyclops states are giants that dominate the system's phase space in weakly repulsive networks with first-order coupling. Astonishingly, the addition of the second or third harmonics to the Kuramoto coupling function makes the cyclops states global attractors practically across the full range of coupling's repulsion. Beyond the Kuramoto oscillators, we show that this effect is robustly present in networks of canonical theta neurons with adaptive coupling. At a more general level, our results suggest clues for finding dominant rhythms in repulsive physical and biological networks.

Non-smooth dynamics induced by switches, impacts, sliding, and other abrupt changes are pervasive in physics, biology, and engineering. Yet, systems with non-smooth dynamics have historically received far less attention compared to their smooth counterparts. The classic "Bristol book" [di Bernardo et al., Piecewise-smooth Dynamical Systems. Theory and Applications (Springer-Verlag, 2008)] contains a 2008 state-of-the-art review of major results and challenges in the study of non-smooth dynamical systems. In this paper, we provide a detailed review of progress made since 2008. We cover hidden dynamics, generalizations of sliding motion, the effects of noise and randomness, multi-scale approaches, systems with time-dependent switching, and a variety of local and global bifurcations. Also, we survey new areas of application, including neuroscience, biology, ecology, climate sciences, and engineering, to which the theory has been applied.

Solitary states emerge in oscillator networks when one oscillator separates from the fully synchronized cluster and oscillates with a different frequency. Such chimera-type patterns with an incoherent state formed by a single oscillator were observed in various oscillator networks; however, there is still a lack of understanding of how such states can stably appear. Here, we study the stability of solitary states in Kuramoto networks of identical two-dimensional phase oscillators with inertia and a phase-lagged coupling. The presence of inertia can induce rotatory dynamics of the phase difference between the solitary oscillator and the coherent cluster. We derive asymptotic stability conditions for such a solitary state as a function of inertia, network size, and phase lag that may yield either attractive or repulsive coupling. Counterintuitively, our analysis demonstrates that (1) increasing the size of the coherent cluster can promote the stability of the solitary state in the attractive coupling case and (2) the solitary state can be stable in small-size networks with all repulsive coupling. We also discuss the implications of our stability analysis for the emergence of rotatory chimeras.

Real world networks contain multiple layers of links whose interactions can lead to extraordinary collective dynamics, including synchronization. The fundamental problem of assessing how network topology controls synchronization in multilayer networks remains open due to serious limitations of the existing stability methods. Towards removing this obstacle, we propose an approximation method which significantly enhances the predictive power of the master stability function for stable synchronization in multilayer networks. For a class of saddle-focus oscillators, including Rössler and piecewise linear systems, our method reduces the complex stability analysis to simply solving a set of linear algebraic equations. Using the method, we analytically predict surprising effects due to multilayer coupling. In particular, we prove that two coupling layers-one of which would alone hamper synchronization and the other would foster it-reverse their roles when used in a multilayer network. We also analytically demonstrate that increasing the size of a globally coupled layer, that in isolation would induce stable synchronization, makes the multilayer network unsynchronizable.

The emergence of the pedestrian-induced bridge instability is conventionally associated with crowd synchrony; however, this view has been challenged. In this paper, we use a bio-mechanical pedestrian model in the form of an active inverted pendulum to analyze the average contribution of a single pedestrian to possibly uncorrelated crowd dynamics and bridge oscillations. We obtained that depending on the ratio of the bridge vibration and walking frequencies, the pedestrian can amplify bridge vibration or, surprisingly, extract energy from the bridge and damp bridge oscillations. In particular, we show that different combinations of the bridge and pedestrian step frequencies corresponding to the same or close frequency ratios can trigger two drastically different bridge dynamics, with enhanced or suppressed oscillations far from the resonances.