Synchronization enhancement subjected to adaptive blinking coupling.

Synchronization holds a significant role, notably within chaotic systems, in various contexts where the coordinated behavior of systems plays a pivotal and indispensable role. Hence, many studies have been dedicated to investigating the underlying mechanism of synchronization of chaotic systems. Networks with time-varying coupling, particularly those with blinking coupling, have been proven essential. The reason is that such coupling schemes introduce dynamic variations that enhance adaptability and robustness, making them applicable in various real-world scenarios. This paper introduces a novel adaptive blinking coupling, wherein the coupling adapts dynamically based on the most influential variable exhibiting the most significant average disparity. To ensure an equitable selection of the most effective coupling at each time instance, the average difference of each variable is normalized to the synchronous solution's range. Due to this adaptive coupling selection, synchronization enhancement is expected to be observed. This hypothesis is assessed within networks of identical systems, encompassing Lorenz, Rössler, Chen, Hindmarsh-Rose, forced Duffing, and forced van der Pol systems. The results demonstrated a substantial improvement in synchronization when employing adaptive blinking coupling, particularly when applying the normalization process.

The influence of synaptic pathways on the synchronization patterns of regularly structured mChialvo map network.

Synchronization of interconnecting units is one of the hottest topics many researchers are interested in. In addition, this emerging phenomenon is responsible for many biological processes, and thus, the synchronization of interacting neurons is an important field of study in neuroscience. Employing the memristive Chialvo (mChialvo) neuron map, this paper investigates the effect of electrical, inner-linking, chemical, and hybrid coupling functions on the synchronization state of a neuronal network with regular structure. Master stability function (MSF) analysis is performed to obtain the necessary conditions for synchronizing the built networks. Afterward, the MSF-based results are confirmed by calculating the synchronization error. Besides, the dynamics of the synchronous neurons are discussed based on the bifurcation analysis. Our results suggest that, compared to the electrical and inner-linking functions, chemical synapses facilitate mChialvo neurons' synchronization since the neurons can achieve synchrony with a negligible chemical coupling strength. Further studies reveal that based on the active synapses, coupled mChialvo neurons can reach cluster synchronization, chimera state, sine-like synchronization, phase synchronization, and cluster phase synchronization.

Energy computation and multiplier-less implementation of the two-dimensional FitzHugh-Nagumo (FHN) neural circuit.

In this work, with the aim of reducing the cost of the implementation of the traditional 2D FHN neuron circuit, a pair of diodes connected in an anti-parallel direction is used to replace the usual cubic nonlinearity (implemented with two multipliers). Based on the stability of the model, the generation of self-excited firing patterns is justified. Making use of the famous Helmholtz theorem, a Hamilton function is provided for the estimation of the energy released during each electrical activity of the model. From the investigation of the 1D evolution of the maxima of the membrane potential of the model, it was recorded that the considered model is able to experience a period of doubling bifurcation followed by a crisis that enables the increasing of the volume of the attractor. This contribution ends with the realization of a neural circuit without analog multipliers for the validation of the obtained results.

Impact of external excitations on blinking enhanced synchronization in bistable vibrational energy harvesters.

Vibrational energy harvesters are capable of converting low-frequency broad-band mechanical energy into electrical power and can be used in implantable medical devices and wireless sensors. With the use of such energy harvesters, it is feasible to generate continuous power that is more reliable and cost-effective. According to previous findings, the energy harvester can offer rich complex dynamics, one of which is obtaining the synchronization behavior, which is intriguing to achieve desirable power from energy harvesters. Therefore, we consider bistable energy harvesters with periodic and quasiperiodic excitations to investigate synchronization. Specifically, we introduce blinking into the coupling function to check whether it improves the synchronization. Interestingly, we discover that raising the normalized proportion of blinking can initiate synchronization behaviors even with lower optimal coupling strength than the absence of blinking in the coupling (i.e., continuous coupling). The existence of synchronization behaviors is confirmed by finding the largest Lyapunov exponents. In addition, the results show that the optimal coupling strength needed to achieve synchronization for quasiperiodic excitations is smaller than that for periodic excitations.

Investigation of an improved FitzHugh-Rinzel neuron and its multiplier-less circuit implementation.

Circuit implementation of the mathematical model of neurons represents an alternative approach for the validation of their dynamical behaviors for their potential applications in neuromorphic engineering. In this work, an improved FitzHugh-Rinzel neuron, in which the traditional cubic nonlinearity is swapped with a sine hyperbolic function, is introduced. This model has the advantage that it is multiplier-less since the nonlinear component is just implemented with two diodes in anti-parallel. The stability of the proposed model revealed that it has both stable and unstable nodes around its fixed points. Based on the Helmholtz theorem, a Hamilton function that enables the estimation of the energy released during the various modes of electrical activity is derived. Furthermore, numerical computation of the dynamic behavior of the model revealed that it was able to experience coherent and incoherent states involving both bursting and spiking. In addition, the simultaneous appearance of two different types of electric activity for the same neuron parameters is also recorded by just varying the initial states of the proposed model. Finally, the obtained results are validated using the designed electronic neural circuit, which has been analyzed in the Pspice simulation environment.

An optimization-based algorithm for obtaining an optimal synchronizable network after link addition or reduction.

Achieving a network structure with optimal synchronization is essential in many applications. This paper proposes an optimization algorithm for constructing a network with optimal synchronization. The introduced algorithm is based on the eigenvalues of the connectivity matrix. The performance of the proposed algorithm is compared with random link addition and a method based on the eigenvector centrality. It is shown that the proposed algorithm has a better synchronization ability than the other methods and also the scale-free and small-world networks with the same number of nodes and links. The proposed algorithm can also be applied for link reduction while less disturbing its synchronization. The effectiveness of the algorithm is compared with four other link reduction methods. The results represent that the proposed algorithm is the most appropriate method for preserving synchronization.

Multistable ghost attractors in a switching laser system.

This paper studies the effects of a switching parameter on the dynamics of a multistable laser model. The laser model represents multistability in distinct ranges of parameters. We assume that the system's parameter switches periodically between different values. Since the system is multistable, the presence of a ghost attractor is also dependent on the initial condition. It is shown that when the composing subsystems are chaotic, a periodic ghost attractor can emerge and vice versa, depending on the initial conditions. In contrast to the previous studies in which the attractor of the fast blinking systems approximates the average attractor, here, the blinking attractor differs from the average in some cases. It is shown that when the switching parameter values are distant from their average, the blinking and the average attractors are different, and as they approach, the blinking attractor approaches the average attractor too.

Theoretical study and circuit implementation of three chain-coupled self-driven Duffing oscillators.

In this paper, we describe the scenario from the birth of oscillations to multi-spiral chaos in a novel system composed of three chain-coupled self-driven Duffing oscillators. Eight of the equilibrium points develop (multiple) Hopf bifurcation when varying a parameter (e.g., coupling coefficient). Considering the computer integration of the state equations, the combined exploitation of Lyapunov exponent plots, bifurcation diagrams, basins of attraction, and phase portraits, unusual and attractive features were highlighted including the coexistence of eight bifurcation branches, Hopf bifurcations, a multitude of coexisting types of oscillations and a six-spiral chaotic attractor, just to cite a few. Using basic electronic components, the electronic circuit of the three chain-coupled Duffing oscillator system is performed. Orcad-PSpice simulated dynamics of the proposed chain-coupled analog circuit confirm the theoretically disclosed features. Moreover, the practical feasibility of the coupled system is demonstrated by considering microcontroller-based hardware realization.

Mixed-mode oscillations and extreme events in fractional-order Bonhoeffer-van der Pol oscillator.

In the present study, we investigate the dynamic behavior of the fractional-order Bonhoeffer-van der Pol (BVP) oscillator. Previous studies on the integer-order BVP have shown that it exhibits mixed-mode oscillations (MMOs) with respect to the frequency of external forcing. We explore the effect of fractional-order on these MMOs and observe interesting phenomena. For fractional-order q1, we find that as we vary the frequency of external forcing, the system exhibits increasingly small amplitude oscillations. Eventually, as q1 decreases, the MMOs disappear entirely, indicating that lower fractional orders eliminate the presence of MMOs in the BVP oscillator. On the other hand, for the fractional-order q2, we observe more complex MMOs compared to q1. However, we find that the elimination of MMOs occurs with less variation from the integer order 1. Intriguingly, as we change q2, the fractional-order BVP oscillator undergoes a phenomenon known as a crisis, where the attractor expands and extreme events occur. Overall, our study highlights the rich dynamics of the fractional-order BVP oscillator and its ability to display various modes of oscillations and crises as the order is changed.

Optimal time-varying coupling function can enhance synchronization in complex networks.

In this paper, we propose a time-varying coupling function that results in enhanced synchronization in complex networks of oscillators. The stability of synchronization can be analyzed by applying the master stability approach, which considers the largest Lyapunov exponent of the linearized variational equations as a function of the network eigenvalues as the master stability function. Here, it is assumed that the oscillators have diffusive single-variable coupling. All possible single-variable couplings are studied for each time interval, and the one with the smallest local Lyapunov exponent is selected. The obtained coupling function leads to a decrease in the critical coupling parameter, resulting in enhanced synchronization. Moreover, synchronization is achieved faster, and its robustness is increased. For illustration, the optimum coupling function is found for three networks of chaotic Rössler, Chen, and Chua systems, revealing enhanced synchronization.

A simple one-dimensional map-based model of spiking neurons with wide ranges of firing rates and complexities.

This paper introduces a simple 1-dimensional map-based model of spiking neurons. During the past decades, dynamical models of neurons have been used to investigate the biology of human nervous systems. The models simulate experimental records of neurons' voltages using difference or differential equations. Difference neuronal models have some advantages besides the differential ones. They are usually simpler, and considering the cost of needed computations, they are more efficient. In this paper, a simple 1-dimensional map-based model of spiking neurons is introduced. Sample entropy is applied to analyze the complexity of the model's dynamics. The model can generate a wide range of time series with different firing rates and different levels of complexities. Besides, using some tools like bifurcation diagrams and cobwebs, the introduced model is analyzed.

Strange nonchaotic dynamics in a discrete FitzHugh-Nagumo neuron model with sigmoidal recovery variable.

We report the appearance of strange nonchaotic attractors in a discrete FitzHugh-Nagumo neuron model with discontinuous resetting. The well-known strange nonchaotic attractors appear in quasiperiodically forced continuous-time dynamical systems as well as in a discrete map with a small intensity of noise. Interestingly, we show that a discrete FitzHugh-Nagumo neuron model with a sigmoidal recovery variable and discontinuous resetting generates strange nonchaotic attractors without external force. These strange nonchaotic attractors occur as intermittency behavior (locally unstable behavior in laminar flow) in the periodic dynamics. We use various characterization techniques to validate the existence of strange nonchaotic attractors in the considered system.

Synchronization in Hindmarsh-Rose neurons subject to higher-order interactions.

Higher-order interactions might play a significant role in the collective dynamics of the brain. With this motivation, we here consider a simplicial complex of neurons, in particular, studying the effects of pairwise and three-body interactions on the emergence of synchronization. We assume pairwise interactions to be mediated through electrical synapses, while for second-order interactions, we separately study diffusive coupling and nonlinear chemical coupling. For all the considered cases, we derive the necessary conditions for synchronization by means of linear stability analysis, and we compute the synchronization errors numerically. Our research shows that the second-order interactions, even if of weak strength, can lead to synchronization under significantly lower first-order coupling strengths. Moreover, the overall synchronization cost is reduced due to the introduction of three-body interactions if compared to pairwise interactions.

Dynamical effects of hypergraph links in a network of fractional-order complex systems.

In recent times, the fractional-order dynamical networks have gained lots of interest across various scientific communities because it admits some important properties like infinite memory, genetic characteristics, and more degrees of freedom than an integer-order system. Because of these potential applications, the study of the collective behaviors of fractional-order complex networks has been investigated in the literature. In this work, we investigate the influence of higher-order interactions in fractional-order complex systems. We consider both two-body and three-body diffusive interactions. To elucidate the role of higher-order interaction, we show how the network of oscillators is synchronized for different values of fractional-order. The stability of synchronization is studied with a master stability function analysis. Our results show that higher-order interactions among complex networks help the earlier synchronization of networks with a lesser value of first-order coupling strengths in fractional-order complex simplices. Besides that, the fractional-order also shows a notable impact on synchronization of complex simplices. For the lower value of fractional-order, the systems get synchronized earlier, with lesser coupling strengths in both two-body and three-body interactions. To show the generality in the outcome, two neuron models, namely, Hindmarsh-Rose and Morris-Leccar, and a nonlinear Rössler oscillator are considered for our analysis.

Synchronization of a higher-order network of Rulkov maps.

In neuronal network analysis on, for example, synchronization, it has been observed that the influence of interactions between pairwise nodes is essential. This paper further reveals that there exist higher-order interactions among multi-node simplicial complexes. Using a neuronal network of Rulkov maps, the impact of such higher-order interactions on network synchronization is simulated and analyzed. The results show that multi-node interactions can considerably enhance the Rulkov network synchronization, better than pairwise interactions, for involving more and more neurons in the network.

Investigation of vaccination game approach in spreading covid-19 epidemic model with considering the birth and death rates.

In this study, an epidemic model for spreading COVID-19 is presented. This model considers the birth and death rates in the dynamics of spreading COVID-19. The birth and death rates are assumed to be the same, so the population remains constant. The dynamics of the model are explained in two phases. The first is the epidemic phase, which spreads during a season based on the proposed SIR/V model and reaches a stable state at the end of the season. The other one is the "vaccination campaign", which takes place between two seasons based on the rules of the vaccination game. In this stage, each individual in the population decides whether to be vaccinated or not. Investigating the dynamics of the studied model during a single epidemic season without consideration of the vaccination game shows waves in the model as experimental knowledge. In addition, the impact of the parameters is studied via the rules of the vaccination game using three update strategies. The result shows that the pandemic speeding can be changed by varying parameters such as efficiency and cost of vaccination, defense against contagious, and birth and death rates. The final epidemic size decreases when the vaccination coverage increases and the average social payoff is modified.

Multimedia Cryptosystem for IoT Applications Based on a Novel Chaotic System around a Predefined Manifold.

Multimedia data play an important role in our daily lives. The evolution of internet technologies means that multimedia data can easily participate amongst various users for specific purposes, in which multimedia data confidentiality and integrity have serious security issues. Chaos models play an important role in designing robust multimedia data cryptosystems. In this paper, a novel chaotic oscillator is presented. The oscillator has a particular property in which the chaotic dynamics are around pre-located manifolds. Various dynamics of the oscillator are studied. After analyzing the complex dynamics of the oscillator, it is applied to designing a new image cryptosystem, in which the results of the presented cryptosystem are tested from various viewpoints such as randomness, time encryption, correlation, plain image sensitivity, key-space, key sensitivity, histogram, entropy, resistance to classical types of attacks, and data loss analyses. The goal of the paper is proposing an applicable encryption method based on a novel chaotic oscillator with an attractor around a pre-located manifold. All the investigations confirm the reliability of using the presented cryptosystem for various IoT applications from image capture to use it.

Dominant Attractor in Coupled Non-Identical Chaotic Systems.

The dynamical interplay of coupled non-identical chaotic oscillators gives rise to diverse scenarios. The incoherent dynamics of these oscillators lead to the structural impairment of attractors in phase space. This paper investigates the couplings of Lorenz-Rössler, Lorenz-HR, and Rössler-HR to identify the dominant attractor. By dominant attractor, we mean the attractor that is less changed by coupling. For comparison and similarity detection, a cost function based on the return map of the coupled systems is used. The possible effects of frequency and amplitude differences between the systems on the results are also examined. Finally, the inherent chaotic characteristic of systems is compared by computing the largest Lyapunov exponent. The results suggest that in each coupling case, the attractor with the greater largest Lyapunov exponent is dominant.

Comparing the chondrogenic potential of rabbit mesenchymal stem cells derived from the infrapatellar fat pad, periosteum & bone marrow.

Background & objectives: Rabbit model is commonly used to demonstrate the proof of concept in cartilage tissue engineering. However, limited studies have attempted to find an ideal source of rabbit mesenchymal stem cells (MSCs) for cartilage repair. This study aimed to compare the in vitro chondrogenic potential of rabbit MSCs isolated from three sources namely infrapatellar fat pad (IFP), periosteum (P) and bone marrow (BM). Methods: Rabbit MSCs from three sources were isolated and characterized using flow cytometry and multi-lineage differentiation assay. Cell proliferation was assessed using trypan blue dye exclusion test; in vitro chondrogenic potential was evaluated by histology and gene expression and the outcomes were compared amongst the three MSC sources. Results: MSCs from three sources shared similar morphology and expressed >99 per cent positive for CD44 and CD81 and <3 per cent positive for negative markers CD34, CD90 and human leukocyte antigen - DR isotype (HLA-DR). The BM-MSCs and IFP-MSCs showed significantly higher cell proliferation (P<0.001) than the P-MSCs from passage 4. Histologically, BM-MSCs formed a thicker cartilage pellet (P<0.01) with abundant matrix deposition than IFP and P-MSCs during chondrogenic differentiation. The collagen type 2 staining was significantly (P<0.05) higher in BM-MSCs than the other two sources. These outcomes were further confirmed by gene expression, where the BM-MSCs demonstrated significantly higher expression (P<0.01) of cartilage-specific markers (COL2A1, SOX9 and ACAN) with less hypertrophy. Interpretation & conclusions: This study demonstrated that BM-MSCs had superior chondrogenic potential and generated better cartilage than IFP and P-MSCs in rabbits. Thus, BM-MSCs remain a promising candidate for rabbit articular cartilage regeneration.

Size matters: Effects of the size of heterogeneity on the wave re-entry and spiral wave formation in an excitable media.

Network performance of neurons plays a vital role in determining the behavior of many physiological systems. In this paper, we discuss the wave propagation phenomenon in a network of neurons considering obstacles in the network. Numerous studies have shown the disastrous effects caused by the heterogeneity induced by the obstacles, but these studies have been mainly discussing the orientation effects. Hence, we are interested in investigating the effects of both the size and orientation of the obstacles in the wave re-entry and spiral wave formation in the network. For this analysis, we have considered two types of neuron models and a pancreatic beta cell model. In the first neuron model, we use the well-known differential equation-based neuron models, and in the second type, we used the hybrid neuron models with the resetting phenomenon. We have shown that the size of the obstacle decides the spiral wave formation in the network and horizontally placed obstacles will have a lesser impact on the wave re-entry than the vertically placed obstacles.