Networks Of Coupled Oscillators Research Articles

Distributed Network of Coupled Oscillators With Multiple Limit Cycles

Distributed Network of Coupled Oscillators With Multiple Limit Cycles

Ultralow Power Electronic Analog of a Biological Fitzhugh-Nagumo Neuron.

Here, we introduce an electronic circuit that mimics the functionality of a biological spiking neuron following the Fitzhugh-Nagumo (FN) model. The circuit consists of a tunnel diode that exhibits negative differential resistance (NDR) and an active inductive element implemented by a single MOSFET. The FN neuron converts a DC voltage excitation into voltage spikes analogous to biological action potentials. We predict an energy cost of 2 aJ/cycle through detailed simulation and modeling for these FN neurons. Such an FN neuron is CMOS compatible and enables ultralow power oscillatory and spiking neural network hardware. We demonstrate that FN neurons can be used for oscillator-based computing in a coupled oscillator network to form an oscillator Ising machine (OIM) that can solve computationally hard NP-complete max-cut problems while showing robustness toward process variations.

Determining bifurcations to explosive synchronization for networks of coupled oscillators with higher-order interactions.

We determine bifurcations from gradual to explosive synchronization in coupled oscillator networks with higher-order coupling using self-consistency analysis. We obtain analytic bifurcation values for generic symmetric natural frequency distributions. We show that nonsynchronized, drifting, oscillators are non-negligible and play a crucial role in bifurcation. As such, the entire natural frequency distribution must be accounted for, rather than just the shape at the center. We verify our results for Lorentzian- and Gaussian-distributed natural frequencies.

Coherent olfactory bulb gamma oscillations arise from coupling independent columnar oscillators.

Spike timing-based representations of sensory information depend on embedded dynamical frameworks within neuronal networks that establish the rules of local computation and interareal communication. Here, we investigated the dynamical properties of olfactory bulb circuitry in mice of both sexes using microelectrode array recordings from slice and in vivo preparations. Neurochemical activation or optogenetic stimulation of sensory afferents evoked persistent gamma oscillations in the local field potential. These oscillations arose from slower, GABA(A) receptor-independent intracolumnar oscillators coupled by GABA(A)-ergic synapses into a faster, broadly coherent network oscillation. Consistent with the theoretical properties of coupled-oscillator networks, the spatial extent of zero-phase coherence was bounded in slices by the reduced density of lateral interactions. The intact in vivo network, however, exhibited long-range lateral interactions that suffice in simulation to enable zero-phase gamma coherence across the olfactory bulb. The timing of action potentials in a subset of principal neurons was phase-constrained with respect to evoked gamma oscillations. Coupled-oscillator dynamics in olfactory bulb thereby enable a common clock, robust to biological heterogeneities, that is capable of supporting gamma-band spike synchronization and phase coding across the ensemble of activated principal neurons.NEW & NOTEWORTHY Odor stimulation evokes rhythmic gamma oscillations in the field potential of the olfactory bulb, but the dynamical mechanisms governing these oscillations have remained unclear. Establishing these mechanisms is important as they determine the biophysical capacities of the bulbar circuit to, for example, maintain zero-phase coherence across a spatially extended network, or coordinate the timing of action potentials in principal neurons. These properties in turn constrain and suggest hypotheses of sensory coding.

Event-Triggered Pinning Impulsive Control for Cluster Synchronization of Coupled Genetic Oscillator Networks With Proportional Delay

Event-Triggered Pinning Impulsive Control for Cluster Synchronization of Coupled Genetic Oscillator Networks With Proportional Delay

Associative memory by virtual oscillator network based on single spin-torque oscillator

A coupled oscillator network may be able to perform an energy-efficient associative memory operation. However, its realization has been difficult because inhomogeneities unavoidably arise among the oscillators during fabrication and lead to an unreliable operation. This issue could be resolved if the oscillator network were able to be formed from a single oscillator. Here, we performed numerical simulations and theoretical analyses on an associative memory operation that uses a virtual oscillator network based on a spin-torque oscillator. The virtual network combines the concept of coupled oscillators with that of feedforward neural networks. Numerical experiments demonstrate successful associations of 60-pixel patterns with various memorized patterns. Moreover, the origin of the associative memory is shown to be forced synchronization driven by feedforward input, where phase differences among oscillators are fixed and correspond to the colors of the pixels in the pattern.

Numerical Method for Finding Synchronous Solutions of the Coupled Oscillator Networks

Numerical Method for Finding Synchronous Solutions of the Coupled Oscillator Networks

Superconducting-Oscillatory Neural Network With Pixel Error Detection for Image Recognition

Brain-inspired oscillatory neural networks (ONNs) utilize coupled oscillators to emulate biological neuronal dynamics. ONNs are naturally suitable for image/pattern recognition. Many hardware implementations of ONNs have been explored recently by using analog or digital CMOS systems, which can be limited by Moore's law. In this work, we used one of the advanced superconducting technologies for coupled oscillator networks to further improve the energy efficiency and processing speed. Inductively coupled ring oscillators using rapid single flux quantum (RSFQ) technology were designed and simulated. These Josephson junction (JJ) based oscillators can operate as fast as tens of GHz but only consume a energy of several aJ per operation. Excluding cryo-cooling factors, the estimated power consumption of our RSFQ oscillator is hundreds of nW at a frequency of 12.5 GHz, which is multiple times smaller than a typical CMOS ring oscillator. Furthermore, a system for pattern recognition with error detection was designed based on these oscillators. Synchronization dynamics in a pair of coupled oscillators were used to identify whether a test pixel matches a reference pixel. A network comprised of 16 pairs of oscillators were wired together, along with synchronization detectors and a fluxon integrator to demonstrate pattern recognition function. This work was performed using standard JJ models and demonstrated through WRspice and JoSIM software simulations. The circuits and systems proposed in this work aim to provide insights into the use of superconducting technology for implementing ONNs and may serve as basic building blocks for the design of more complex oscillatory computational networks in image processing and beyond. Single oscillator modeling and systematic network function exploration will be performed in future work.

Review of recent progress on solid‐state millimeter‐wave and terahertz signal sources

SummaryThe research and development on solid‐state source generators have been making progress on chip, aiming to generate low noise, high output power, and high DC‐to‐RF energy efficiency in the millimeter‐wave (mmW) and terahertz (THz) domains. In particular, their full integration in silicon technologies, such as advanced complementary‐metal‐oxide‐semiconductor processes, has motivated the design of low cost, high performance, and miniature integrated circuits (ICs). This manuscript focuses on providing a general overview of recent progress on solid‐state signal source operating beyond 60 GHz till the THz region, from the perspective of operating principle, design metrics, state‐of‐the‐art performance, testing results, and their applications in emerging mmW/THz systems. Finally, with the use of fully integrated source generators, several design arts are highlighted to showcase the recent advances of ICs toward millimeter‐wave and THz radiation, imaging, coupled oscillator network (CON), and communication. Potential future research directions enabled by mmW/THz signal sources will be envisioned.

Stasis in heterogeneous networks of coupled oscillators: discontinuous transition with hysteresis

We consider a heterogeneous ensemble of dynamical systems in that individually are either attracted to fixed points (and are termed inactive) or to limit cycles (in which case they are termed active). These distinct states are separated by bifurcations that are controlled by a single parameter. Upon coupling them globally, we find a discontinuous transition to global inactivity (or stasis) when the proportion of inactive components in the ensemble exceeds a threshold: there is a first–order phase transition from a globally oscillatory state to global oscillation death. There is hysteresis associated with these phase transitions. Numerical results for a representative system are supported by analysis using a system-reduction technique and different dynamical regimes can be rationalised through the corresponding bifurcation diagrams of the reduced set of equations.

Continuum Limits of Coupled Oscillator Networks Depending on Multiple Sparse Graphs

The continuum limit provides a useful tool for analyzing coupled oscillator networks. Recently, Medvedev (Commun Math Sci 17(4):883–898, 2019) gave a mathematical foundation for such an approach when the networks are defined on single graphs which may be dense or sparse, directed or undirected, and deterministic or random. In this paper, we consider coupled oscillator networks depending on multiple graphs, and extend his results to show that the continuum limit is also valid in this situation. Specifically, we prove that the initial value problem (IVP) of the corresponding continuum limit has a unique solution under general conditions and that the solution becomes the limit of those to the IVP of the networks in some adequate meaning. Moreover, we show that if solutions to the networks are stable or asymptotically stable when the node number is sufficiently large, then so are the corresponding solutions to the continuum limit, and that if solutions to the continuum limit are asymptotically stable, then so are the corresponding solutions to the networks in some weak meaning as the node number tends to infinity. These results can also be applied to coupled oscillator networks with multiple frequencies by regarding the frequencies as a weight matrix of another graph. We illustrate the theory for three variants of the Kuramoto model along with numerical simulations.

Designing Robust Networks of Coupled Phase Oscillators With Applications to the High Voltage Electric Grid

We propose a mathematical framework for designing robust networks of coupled phase-oscillators by leveraging a vulnerability measure proposed by Tyloo et al. that quantifies the impact of a small perturbation at an individual phase-oscillator's natural frequency to the system's global synchronized frequencies. Given a complex network topology with specific governing dynamics, the proposed framework finds an optimal allocation of edge weights that minimizes such vulnerability measure(s) at the node(s) for which we expect perturbations to occur by solving a tractable semi-definite programming problem. We specify the mathematical model to high voltage electric grids where each node corresponds to a voltage phase angle associated with a bus and edges correspond to transmission lines. Edge weights are determined by the susceptance values along the transmission lines. In this application, frequency synchronization is increasingly challenged by the integration of renewable energy, yet is imperative to the grid's health and functionality. Our framework helps to alleviate this challenge by optimizing the placement of renewable generation and the susceptance values along the transmission lines.

Large‐Scale Cardiac Muscle Cell‐Based Coupled Oscillator Network for Vertex Coloring Problem

Modern computers require an exponential increase in resources when solving computationally hard problems, motivating the need for an alternative computing platform to solve such problems in an energy‐efficient manner. Vertex coloring, a nondeterministic polynomial time (NP‐hard) combinatorial optimization problem, is one such problem. Herein, an experimental demonstration of using cardiac cell‐based bio‐oscillator network coupling dynamics to solve a vertex coloring problem in various scales of graphs using a simple cell patterning method to construct scalable and controlled cardiac cell networks is presented. Although there are limitations to using these cardiac cells as oscillators, such as their low frequency compared to complementary metal–oxide–semiconductor (CMOS) oscillators, that result in longer processing times, the accuracy in large graph instances, the significantly less amount of energy consumption, and the ease of fabrication and potential to extend this system to massively parallel 3D structures make the bio‐oscillators a promising new platform for collective computing applications.

Dual Control of Coupled Oscillator Networks

Robust coordination and organization in large ensembles of nonlinear oscillatory units play a vital role in a wide range of natural and engineered system. The control of self-organizing network-coupled systems has recently seen significant attention, but largely in the context of modifying or augmenting existing structures. This leaves a gap in our understanding of reactive control, where and how to design direct interventions, and what we may learn about structure and dynamics from such control strategies. Here we study reactive control of coupled oscillator networks and demonstrate dual control strategies, i.e., two different mechanisms for control, that may each be implemented on their own and interchangeably to achieve synchronization. These diverse strategies exploit different network properties, with the first directly targeting oscillators that are challenging to entrain, and the second focusing on oscillators with a strong influence on others. Thus, in addition to presenting alternative strategies for network control, the distinct control sets illuminate the oscillators' dynamical and structural roles within the system. The applicability of dual control is demonstrated using both synthetic and real networks.

Fixed-Time Synchronization of Coupled Oscillator Networks with a Pacemaker

This paper investigates the fixed-time synchronization problem of a Kuramoto-oscillator network in the presence of a pacemaker. Based on the framework of the cyber-physical system (CPS), fixed-time synchronization criteria of such network are presented respectively for identical and non-identical oscillators. In virtue of Lyapunov stability analyses, sufficient conditions are deduced for achieving phase agreement and frequency synchronization for arbitrary initial phases and/or frequencies under distributed control strategies. Theoretical analysis shows that synchronization can be achieved in a fixed time, which is unrelated to initial phases/frequencies. Furthermore, the upper bounds of synchronization time are also obtained. Finally, the numerical simulations also verify the effectiveness of the derived results.

Synchronization and stability in automotive transportation networks

AbstractThe aim of this study was to analyze and evaluate synchronization and stability issues for the planning, operation, and control of processes in automotive transportation networks. We modeled transportation networks by using a coupled oscillator network based on a modified Kuramoto model. Each transport process was mapped as a phase oscillator indicating the transport time, period, and delay. The method could be applied to complex networks where it has not been possible to find analytical solutions. The novel generic oscillator approach was then applied to two transport topologies, namely, milk‐run and just‐in‐sequence transport, based on real‐world problems from a German car manufacturer. We conducted a detailed study of the parameter regions where different synchronization regimes occurred and investigated how the topology influenced the stability and dynamic behavior of transport networks. In particular, we focused on how transport period offsets and transport delays affected synchronous states. We showed that by the introduction of a transport synchronization matrix, the synchronization states in a transport network could be represented in a compact and comprehensive manner. Moreover, thresholds for round‐trip stability could be calculated by analyzing the phase decoupling of a milk‐run. These results were used for the vehicle route planning of milk‐runs with synchronization constraints. Furthermore, the influence of the time delay of a track and trace system on the transport synchronization was analyzed. Finally, for the subsequent investigation of a just‐in‐sequence transport network, we showed how an adaptive control mechanism could re‐synchronize an out‐of‐tune delivery process.

Graphop mean-field limits and synchronization for the stochastic Kuramoto model

Models of coupled oscillator networks play an important role in describing collective synchronization dynamics in biological and technological systems. The Kuramoto model describes oscillator's phase evolution and explains the transition from incoherent to coherent oscillations under simplifying assumptions, including all-to-all coupling with uniform strength. Real world networks, however, often display heterogeneous connectivity and coupling weights that influence the critical threshold for this transition. We formulate a general mean-field theory (Vlasov-Focker Planck equation) for stochastic Kuramoto-type phase oscillator models, valid for coupling graphs/networks with heterogeneous connectivity and coupling strengths, using graphop theory in the mean-field limit. Considering symmetric odd-valued coupling functions, we mathematically prove an exact formula for the critical threshold for the incoherence-coherence transition. We numerically test the predicted threshold using large finite-size representations of the network model. For a large class of graph models, we find that the numerical tests agree very well with the predicted threshold obtained from mean-field theory. However, the prediction is more difficult in practice for graph structures that are sufficiently sparse. Our findings open future research avenues toward a deeper understanding of mean-field theories for heterogeneous systems.

Stability of modulated signals in the damped mechanical network of discontinuous coupled system oscillators with irrational nonlinearities

Stability of modulated signals in the damped mechanical network of discontinuous coupled system oscillators with irrational nonlinearities

Grass-roots optimization of coupled oscillator networks.

Despite the prevalence of biological and physical systems for which synchronization is critical, existing theory for optimizing synchrony depends on global information and does not sufficiently explore local mechanisms that enhance synchronization. Thus, there is a lack of understanding for the self-organized, collective processes that aim to optimize or repair synchronous systems, e.g., the dynamics of paracrine signaling within cardiac cells. Here we present "grass-roots" optimization of synchronization, which is a multiscale mechanism in which local optimizations of smaller subsystems cooperate to collectively optimize an entire system. Considering models of cardiac tissue and a power grid, we show that grass-roots-optimized systems are comparable to globally optimized systems, but they also have the added benefit of being robust to targeted attacks or subsystem islanding. Our findings motivate and support further investigation into the physics of local mechanisms that can support self-optimization for complex systems.

Linear Frequency Modulation of NbO2-Based Nanoscale Oscillator With Li-Based Electrochemical Random Access Memory for Compact Coupled Oscillatory Neural Network.

Oscillatory neural network (ONN)-based classification of clustered data relies on frequency synchronization to injected signals representing input data, showing a more efficient structure than a conventional deep neural network. A frequency tunable oscillator is a core component of the network, requiring energy-efficient, and area-scalable characteristics for large-scale hardware implementation. From a hardware viewpoint, insulator-metal transition (IMT) device-based oscillators are attractive owing to their simple structure and low power consumption. Furthermore, by introducing non-volatile analog memory, non-volatile frequency programmability can be obtained. However, the required device characteristics of the oscillator for high performance of coupled oscillator have not been identified. In this article, we investigated the effect of device parameters of IMT oscillator with non-volatile analog memory on coupled oscillators network for classification of clustered data. We confirmed that linear conductance response with identical pulses is crucial to accurate training. In addition, considering dispersed clustered inputs, a wide synchronization window achieved by controlling the hold voltage of the IMT shows resilient classification. As an oscillator that satisfies the requirements, we evaluated the NbO2-based IMT oscillator with non-volatile Li-based electrochemical random access memory (Li-ECRAM). Finally, we demonstrated a coupled oscillator network for classifying spoken vowels, achieving an accuracy of 85%, higher than that of a ring oscillator-based system. Our results show that an NbO2-based oscillator with Li-ECRAM has the potential for an area-scalable and energy-efficient network with high performance.