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The effect of a parametric change in the excitation threshold on processes of generation of the spike activity in an ensemble of FitzHugh–Nagumo neurons has been analyzed. It has been shown that the periodic change in the excitation threshold results in the existence of chaotic regimes of generation of the spike activity with various statistical properties. It has been found that chaos in the model of an individual neuron appears through Pomeau–Manneville intermittence. It has been shown that the information characteristics of the neuron can be controlled by varying the modulation depth and the frequency of parametric action. It has been found that chaotic spike sequences forming fractal-like spatiotemporal patterns appear when neurons are joined in an ensemble.

We considered a network consisting of two populations of phase oscillators, the interaction of which is determined by different rules for the coupling adaptation. The introduction of various adaptation rules leads to the suppression of splay states and the emergence of each population complex non-stationary behavior called transient circulant clusters. In such states, each population contains a pair of anti-phase clusters whose size and composition slowly change over time as a result of successive transitions of oscillators between clusters. We show that an increase in the mismatch of the adaptation rules makes it possible to stop the process of rearrangement of clusters in one or both populations of the network. Transitions to such modes are always preceded by the appearance of solitary states in one of the populations.

Reduction of collective dynamics of large heterogeneous populations to low-dimensional mean-field models is an important task of modern theoretical neuroscience. Such models can be derived from microscopic equations, for example with the help of Ott-Antonsen theory. An often used assumption of the Lorentzian distribution of the unit parameters makes the reduction especially efficient. However, the Lorentzian distribution is often implausible as having undefined moments, and the collective behavior of populations with other distributions needs to be studied. In the present Letter we propose a method which allows efficient reduction for an arbitrary distribution and show how it performs for the Gaussian distribution. We show that a reduced system for several macroscopic complex variables provides an accurate description of a population of thousands of neurons. Using this reduction technique we demonstrate that the population dynamics depends significantly on the form of its parameter distribution. In particular, the dynamics of populations with Lorentzian and Gaussian distributions with the same center and width differ drastically.

We study the interplay of global attractive coupling and individual noise in a system of identical active rotators in the excitable regime. Performing a numerical bifurcation analysis of the nonlocal nonlinear Fokker–Planck equation for the thermodynamic limit, we identify a complex bifurcation scenario with regions of different dynamical regimes, including collective oscillations and coexistence of states with different levels of activity. In systems of finite size, this leads to additional dynamical features, such as collective excitability of different types and noise-induced switching and bursting. Moreover, we show how characteristic quantities such as macroscopic and microscopic variability of interspike intervals can depend in a non-monotonous way on the noise level.

The impact of frequency-degree and amplitude-degree correlation is studied for heterogeneous networks of coupled Stuart–Landau oscillators. It is shown that increasing coupling strength gives rise to hierarchical processes of oscillation quenching. In case of frequency-degree correlated networks, higher-frequency oscillators gradually become almost quenched while low-frequency ones still remain oscillating. In case of amplitude-degree correlated networks, there appear three distinct domains, two contain low-amplitude oscillations with positive and negative means, and the third includes high-amplitude oscillations around the origin.

We continue to apply the notion of mean complexities to study dynamical networks. We show that the mean complexities can help to single out the nodes with similar features (and dynamical behavior) and to reveal some properties of the topology of the networks. We found that the nodes with the same degree (number of connections) have equal values of the mean complexities in the regime of full binding. At the same time, the mean complexities of nodes with different degree follow a descending order with respect to the degree.

We report on the phenomenon of intersection of a chaotic attractor and a chaotic repeller in a system of two adaptively coupled phase oscillators. This is a feature of the presence of the so-called mixed dynamics, which is a new type of chaos characterized by the fundamental inseparability of conservative and dissipative behavior. The considered system is the first example of a time-irreversible system in which this type of dynamics is observed. We show that a crucial factor in this effect is the detuning of the natural frequencies of phase oscillators.

Adaptive dynamical networks appear in various real-word systems. One of the simplest phenomenological models for investigating basic properties of adaptive networks is the system of coupled phase oscillators with adaptive couplings. In this paper, we investigate the dynamics of this system. We extend recent results on the appearance of hierarchical frequency-multi-clusters by investigating the effect of the time-scale separation. We show that the slow adaptation in comparison with the fast phase dynamics is necessary for the emergence of the multi-clusters and their stability. Additionally, we study the role of double antipodal clusters, which appear to be unstable for all considered parameter values. We show that such states can be observed for a relatively long time, i.e., they are metastable. A geometrical explanation for such an effect is based on the emergence of a heteroclinic orbit., 16 pages, 8 figures

We study the dynamical regimes in the system of two identical interacting phase oscillators with plastic couplings. The joint evolution of the states of the elements and the interelement couplings is a feature of the system studied. It is shown that the introduction of plastic couplings leads to a multistable behavior of the system and emergence of the asynchronous regimes which are not observed for the considered parameter values in the case of static couplings. The parameter plane is divided into regions with different dynamic regimes of the system. In particular, the regions in which the system demonstrates bistable synchronous behavior and the region in which the coexistence of many various asynchronous regimes is observed are singled out.

We study a new type of attractor, the so-called reversible core, which is a mathematical image of mixed dynamics, in a strongly dissipative time-irreversible system of two adaptively coupled phase oscillators. The existence of mixed dynamics in this system was proved in our previous article [A. A. Emelianova and V. I. Nekorkin, Chaos 29, 111102 (2019)]. In this paper, we attempt to identify the dynamic mechanisms underlying the existence of mixed dynamics. We give the region of the existence of mixed dynamics on the parameter plane and demonstrate in what way, when a type of attractor changes, its main characteristics, such as its fractal dimension and the sum of Lyapunov exponents, transform. We demonstrate that when mixed dynamics appear in the system, the average frequencies of the oscillations in forward and reverse time begin to almost coincide, and its spectra gradually approach each other with an increase in the parameter responsible for the presence of mixed dynamics.

In a network of pulse-coupled oscillators with adaptive coupling, we discover a dynamical regime which we call an "itinerant chimera." Similarly as in classical chimera states, the network splits into two domains, the coherent and the incoherent. The drastic difference is that the composition of the domains is volatile, i.e., the oscillators demonstrate spontaneous switching between the domains. This process can be seen as traveling of the oscillators from one domain to another or as traveling of the chimera core across the network. We explore the basic features of the itinerant chimeras, such as the mean and the variance of the core size, and the oscillators lifetime within the core. We also study the scaling behavior of the system and show that the observed regime is not a finite-size effect but a key feature of the collective dynamics which persists even in large networks.

A system of two oscillators with delayed pulse coupling is studied analytically and numerically. The so-called jittering regimes with non-equal inter-spike intervals are observed. The analytical conditions for the emergence of in-phase and anti-phase jittering are derived. The obtained results suggest universality of the multi-jitter instability for systems with delayed pulse coupling.

In oscillatory systems with pulse coupling regular spiking regimes may destabilize via a peculiar scenario called “multi-jitter instability”. At the bifurcation point numerous so-called “jittering” regimes with distinct inter-spike intervals emerge simultaneously. Such regimes were first discovered in a single oscillator with delayed pulse feedback and later were found in networks of coupled oscillators. The present chapter reviews recent results on multi-jitter instability and discussed its features.

Phase response curve (PRC) is an extremely useful tool for studying the response of oscillatory systems, e.g. neurons, to sparse or weak stimulation. Here we develop a framework for studying the response to a series of pulses which are frequent or/and strong so that the standard PRC fails. We show that in this case, the phase shift caused by each pulse depends on the history of several previous pulses. We call the corresponding function which measures this shift the phase response function (PRF). As a result of the introduction of the PRF, a variety of oscillatory systems with pulse interaction, such as neural systems, can be reduced to phase systems. The main assumption of the classical PRC model, i.e. that the effect of the stimulus vanishes before the next one arrives, is no longer a restriction in our approach. However, as a result of the phase reduction, the system acquires memory, which is not just a technical nuisance but an intrinsic property relevant to strong stimulation. We illustrate the PRF approach by its application to various systems, such as Morris-Lecar, Hodgkin-Huxley neuron models, and others. We show that the PRF allows predicting the dynamics of forced and coupled oscillators even when the PRC fails.

We analyze the emergent regimes and the stimulus-response relationship of a population of noisy map neurons by means of a mean-field model, derived within the framework of cumulant approach complemented by the Gaussian closure hypothesis. It is demonstrated that the mean-field model can qualitatively account for stability and bifurcations of the exact system, capturing all the generic forms of collective behavior, including macroscopic excitability, subthreshold oscillations, periodic or chaotic spiking and chaotic bursting dynamics. Apart from qualitative analogies, we find a substantial quantitative agreement between the exact and the approximate system, as reflected in matching of the parameter domains admitting the different dynamical regimes, as well as the characteristic properties of the associated time series. The effective model is further shown to reproduce with sufficient accuracy the phase response curves of the exact system and the assembly's response to external stimulation of finite amplitude and duration., Comment: 12 pages, 11 figures

In this work, a new type of relaxation systems is considered. Their prominent feature is that they comprise two distinct epochs, one is slow regular motion and another is fast chaotic motion. Unlike traditionally studied slow-fast systems that have smooth manifolds of slow motions in the phase space and fast trajectories between them, in this new type one observes, apart the same geometric objects, areas of transient chaos. Alternating periods of slow regular motions and fast chaotic ones as well as transitions between them result in a specific chaotic attractor with chaos on a fast time scale. We formulate basic properties of such attractors in the framework of discrete-time systems and consider several examples. Finally, we provide an important application of such systems, the neuronal electrical activity in the form of chaotic spike-burst oscillations.

Dynamics of the ensemble of two model neurons interacting through electrical synapse is investigated. Both neurons are described by two-dimensional discontinuous map. It is shown that in four-dimensional phase space a chaotic attractor of relaxation type exists corresponding to spike-bursting chaotic oscillations. A new effect of recurrent transitory chaotic oscillations underlies a dynamical mechanism of chaotic bursts formation. It is shown that, under coupling, the transient from chaotic bursts generation into rest state occurs with a time delay. A new characteristic estimating the degree of spike-bursting synchronization is introduced. Dependence of the synchronism degree on the coupling strength is shown for some coupling interval where only activity synchronization occurs. A probabilistic study provides a dynamical explanation of these phenomena.

In this paper, we study the dynamics of a system of two model neurons interacting via the electrical synapse. Each neuron is described by a two-dimensional discontinuous map. A chaotic relaxational-type attractor, which corresponds to the spiking-bursting chaotic oscillations of neurons is shown to exist in a four-dimensional phase space. It is found that the dynamical mechanism of formation of chaotic bursts is based on a new phenomenon of generation of transient chaotic oscillations. It is demonstrated that transition from the chaotic-burst generation to the state of relative rest occurs with a certain time delay. A new characteristic which estimates the degree of synchronization of the spiking-bursting oscillations is introduced. The dependence of the synchronization degree on the strength of coupling of the ensemble elements is studied.

Inverse stochastic resonance is a phenomenon where an oscillating system influenced by noise exhibits a minimal oscillation frequency at an intermediate noise level. We demonstrate a novel generic scenario for such an effect in a multi-timescale system, considering an example of emergent oscillations in two adaptively coupled active rotators with excitable local dynamics. The impact of plasticity turns out to be twofold. First, at the level of multiscale dynamics, one finds a range of intermediate adaptivity rates that give rise to multistability between the limit cycle attractors and the stable equilibria, a condition necessary for the onset of the effect. Second, applying the fast-slow analysis, we show that the plasticity also plays a facilitatory role on a more subtle level, guiding the fast flow dynamics to parameter domains where the stable equilibria become focuses rather than nodes, which effectively enhances the influence of noise. The described scenario persists for different plasticity rules, underlying its robustness in the light of potential applications to neuroscience and other types of cell dynamics.

The phenomenon of self-referential phase reset is investigated in ensembles of interacting FitzHugh–Nagumo neurons with different topologies of couplings. It is shown that the reset phase of neurons oscillation is independent of the initial phase and is determined by parameters of the stimulus. This process does not require direct influence on all elements of the ensemble and takes place when the stimulus is applied to one of the interacting neurons only. The influence of inter-neuron couplings and stimulus parameters on ensemble dynamics and phase reset phenomenon is studied.

Space–time dynamics of the system modeling collective behaviour of electrically coupled nonlinear units is investigated. The dynamics of a local cell is described by the FitzHugh–Nagumo system with complex threshold excitation. It is shown that such a system supports formation of two distinct kinds of stable two-dimensional spatially localized moving structures without any external stabilizing actions. These are regular and polymorphic structures. The regular structures preserve their shape and velocity under propagation while the shape and velocity as well as other integral characteristics of polymorphic structures show rather complex temporal behaviour. Both kinds of structures represent novel sorts of spatially temporal patterns which have not been observed before in typical two-component reaction–diffusion type systems. It is demonstrated that there exist two types of regular structures: single and bound states and three types of polymorphic structures: periodic, quasiperiodic and even chaotic ones. The partition of the parameter plane into regions corresponding to the existence of these different types of structures is carried out. High multistability of regular structures is indicated. The interaction of regular structures is investigated. The correspondence between the structures and trajectories in multidimensional phase space associated with the system is given. Bifurcation mechanisms leading to the loss of stability of regular structures as well as to a transition from one type of polymorphic structure to another are indicated. The mechanisms of formation of regular and polymorphic structures are discussed.

This tutorial reviews a new important class of mathematical phenomenological models of neural activity generated by iterative dynamical systems: the so-called map-based systems. We focus on 1-D and 2-D maps for the replication of many features of the neural activity of a single neuron. It was shown that such systems can reproduce the basic activity modes such as spiking, bursting, chaotic spiking-bursting, subthreshold oscillations, tonic and phasic spiking, normal excitability, etc. of the real biological neurons. We emphasize on the representation of chaotic spiking-bursting oscillations by chaotic attractors of 2-D models. We also explain the dynamical mechanism of formation of such attractors and transition from one mode to another. We briefly present some synchronization mehanisms of chaotic spiking-bursting activity for two coupled neurons described by 1-D maps.

We propose a new model of synaptic transmission on the basis of a FitzHugh–Nagumo system with nonlinear recovery. It is shown that the Morrice–Lecar neuron ensemble formed by such couplings shows various structurally stable regimes of transient dynamics in the form of sequential transitions between various metastable oscillatory states of the system.

We consider a case study of perturbing a system with a boundary crisis of a chaotic attractor by periodic forcing. In the static case, the system exhibits persistent chaos below the critical value of the control parameter but transient chaos above the critical value. We discuss what happens to the system and particularly to the transient chaotic dynamics if the control parameter periodically oscillates. We find a non-exponential decaying behavior of the survival probability function, study the impact of the forcing frequency and amplitude on the escape rate, analyze the phase-space image of the observed dynamics, and investigate the influence of initial conditions.

Interaction via pulses is common in many natural systems, especially neuronal. In this article we study one of the simplest possible systems with pulse interaction: a phase oscillator with delayed pulsatile feedback. When the oscillator reaches a specific state, it emits a pulse, which returns after propagating through a delay line. The impact of an incoming pulse is described by the oscillator's phase reset curve (PRC). In such a system we discover an unexpected phenomenon: for a sufficiently steep slope of the PRC, a periodic regular spiking solution bifurcates with several multipliers crossing the unit circle at the same parameter value. The number of such critical multipliers increases linearly with the delay and thus may be arbitrary large. This bifurcation is accompanied by the emergence of numerous "jittering" regimes with non-equal interspike intervals (ISIs). Each of these regimes corresponds to a periodic solution of the system with a period roughly proportional to the delay. The number of different "jittering" solutions emerging at the bifurcation point increases exponentially with the delay. We describe the combinatorial mechanism that underlies the emergence of such a variety of solutions. In particular, we show how a periodic solution exhibiting several distinct ISIs can imply the existence of multiple other solutions obtained by rearranging of these ISIs. We show that the theoretical results for phase oscillators accurately predict the behavior of an experimentally implemented electronic oscillator with pulsatile feedback.

Oscillatory systems with time-delayed pulsatile feedback appear in various applied and theoretical research areas, and received a growing interest in the last years. For such systems, we report a remarkable scenario of destabilization of a periodic regular spiking regime. In the bifurcation point numerous regimes with non-equal interspike intervals emerge simultaneously. We show that this bifurcation is triggered by the steepness of the oscillator's phase resetting curve and that the number of the emerging, so-called "jittering" regimes grows exponentially with the delay value. Although this appears as highly degenerate from a dynamical systems viewpoint, the "multi-jitter" bifurcation occurs robustly in a large class of systems. We observe it not only in a paradigmatic phase-reduced model, but also in a simulated Hodgkin-Huxley neuron model and in an experiment with an electronic circuit.

The problem of reproduction of spatial images by lattices of oscillating neural units is discussed. We consider that each neuron can be at rest or can oscillate with fixed frequency and that the neurons are coupled electrically by a resistor. Then one layer of neurons, one lattice, is coupled to another similar layer. It is shown that for strong enough interlattice interaction relative to the intralattice diffusion, the shape of the pattern on one lattice is determined uniquely by the image of the other. The reproduction of a stimulus shape is possible even when the number of interlattice couplings is much smaller than the number of neurons in either lattice. Moreover, the spatial features of the images do not depend on the features of the eigenexcitations of the neural lattices, which are discrete, active nonequilibrium media.

The interaction of two coupled lattice dynamical systems of bistable elements is investigated. In particular, we give the critical value of the coupling strength and related variables for the mutual synchronization of regular and chaotic states.

The spatio-temporal dynamics of discrete lattices of coupled bistable elements is considered. It is shown that both regular and chaotic spatial field distributions can be realized depending on parameter values and initial conditions. For illustration, we provide results for two lattice systems: the FitzHugh-Nagumo model and a network of coupled bistable oscillators. For the latter we also prove the existence of phase clusters, with phase locking of elements in each cluster.

Mathematical modeling of phenomena in living systems by using discrete-time systems has a long history. In particular, in the 1940s N. Wiener and A. Rosenblueth developed a cellular automaton system for modeling the propagation of excitation pulses in the cardiac tissue. Cellular automata are regular lattices of elements (cells), each having a finite number of specific states. These states are updated synchronously at discrete time moments, according to some fixed rule. Recently, a new class of discrete-time systems has aroused considerable interest for studying cooperative processes in large-scale neural networks: systems of the coupled nonlinear maps. The state of a map varies at discrete time moments as a cellular automaton, but unlike the latter, takes continuous values. Map-based models hold certain advantages over continuous-time models, i.e. differential equation systems. For example, for reproducing oscillatory properties in continuous-time systems one needs at least two dimensions, and at least three dimensions for chaotic behavior. In discrete time both types of dynamics can be described even in a one-dimensional map. Such a benefit is especially important when modeling complex activity regimes of individual neurons as well as large-scale neural circuits composed of various structural units interacting with each other. For example, to simulate in continuous time the regime of chaotic spike-bursting oscillations, one of the key neural behaviors, one needs to have at least a three-dimensional system of nonlinear differential equations. On the other hand, there are discrete-time two-dimensional systems [1, 2] adequately reproducing this oscillatory activity as well as many other dynamical regimes. For example, the model of Chialvo [3] allows to simulate, among other things, the so-called normal and supernormal excitability. The model of Rulkov [4, 5] has several modifications, one of which is adjusted to simulate different spiking and bursting oscillatory regimes, the other can generate the so-called sub-threshold oscillations, i.e., small-amplitude oscillations below a threshold of excitability. Here we propose the authors’ model [6–9] of neural activity in the form of a two-dimensional map and describe some activity modes which it can reproduce. We show that the model is fairly universal and generates many regimes of neuronal electrical activity. Next, we present the results of modeling the dynamics of a complex neural structure, the olivo-cerebellar system (OCS) of vertebrates, using our basic discrete-time model of neural activity.

International audience; Space-time dynamics of the network system modeling collective behavior of electrically coupled nonlinear cells is investigated. The dynamics of a local cell is described by the dimensionless Morris-Lecar system. It is shown that such a system yields a special class of traveling localized collective activity so called "anti-phase wave patterns". The mechanisms of formation of the patterns are discussed and the region of their existence is obtained by using the weakly coupled oscillators theory.

The spatiotemporal behavior of a chain of diffusively coupled bistable oscillators is investigated. It is stated that there is spatial disorder and its evolutionary character is demonstrated.

Dynamics of a chain of coupled Chua's circuits is investigated. Existence of spatial disorder is revealed. Dynamics of wave fronts in the limiting case of a continuous medium is studied.

In modern science, “synergetics” owes its development to the extraordinary research output provided by H. Haken coupled with the many workshops he has organized in the past three decades. In short it deals with the emergence of dynamical and/or evolutionary features in complex systems where the total is not the mere addition of parts. Hence an emergent property belongs to a higher level of description than that of the underlying elements. Emergent properties have genuine laws of their own as is customary in biology, ecology, sociology, etc., and indeed engineering, chemistry and physics. Already thermodynamics is a science of emergence (of cooperative properties) in underlying atoms, and statistical mechanics is the bridge between the micro and macro (or even meso) levels. Equilibrium critical phenomena and phase transitions provide paradigmatic examples of transitions and cooperativity in nonevolving systems, and they define an area of physics rather well understood both at the emergent level and in the way emergent, macroscopic and phenomenological properties originate from the, microscopic, lower level. It is one of the areas where from first principles we know the recipe to account for emergent, synergetic, properties.The reductionism from the emergent to the lower level is far from trivial and clearly indicates that, although the basics are to be found at the microlevel, understanding the emergence of cooperative properties demands a great deal of imagination and, on occasion, computer power.

Starting with the discovery of deterministic chaos in a 3D dynamical system made by Lorenz in 1963, a great deal of effort was made to build electronic circuits exhibiting chaotic oscillations. At present a large number of such items have been proposed. In particular, special interest has been devoted to the so-called Chua’s circuit (oscillator). Although earlier related proposals existed, this circuit was introduced – in the proper context – by L.O. Chua at the opening lecture given in the Workshop on Nonlinear Theory and its Applications (NOLTA’92), held at Waseda University, Tokyo, in January 1992. Chua’s circuit possesses a large variety of possible dynamical behaviors. By changing the values of control parameters, one can obtain regular behavior or chaotic oscillations. [4.2, 4.6, 4.7]

In this chapter we consider lattice models with discrete time, i.e. dynamical systems with a discrete time, discrete space and continuous state and hence coupled map lattices (CML). CML describe the collective dynamics of a finite or infinite number of interacting finite-dimensional local dynamical systems (elements, units or cells) placed on the sites of a spatial lattice (the “physical” space). The dynamics of a single element is described by a point map. Thus, CML are characterized not only by their internal dynamics, i.e. that of the local elements, but also by the dynamics in the “physical” space. CML have been used to model a variety of phenomena including pattern formation, nonlinear waves, topological spatial chaos, distribution of defects in the spatial structures of nonlinear fields, reentry initiation in coupled parallel fibers, etc. [7.1–3, 7.6, 7.7, 7.9–16, 7.18, 7.20–22, 7.24, 7.25–31].

We analyze front wave (kink and antikink) propagation and pattern formation in a system composed of two coupled discrete Nagumo chains using analytical and numerical methods. In the case of homogeneous interaction among the chains, we show the possibility of the effective control on wave propagation. In addition, physical experiments on electrical chains confirm all theoretical behaviors.

We show that firing activity (spiking) can be regularized by noise in a FitzHugh-Nagumo (FHN) neuron model when operating slightly beyond the supercritical Hopf bifurcation (in the "canard" region). We also provide the conditions for imperfect phase Locking between interspike intervals and low amplitude quasiharmonic oscillations. For the imperfect phase locking no need exists of an external signal as it follows from the FHN intrinsic dynamics.

We consider a chain with many locally coupled bistable nonisochronous oscillators. We show that on the "background" of a disordered amplitude distribution either phase or frequency clusters form in the chain. Cluster location varies according to the amplitude distribution. In the case of frequency clusters their interaction leads to phase resetting in an isolated oscillator located between two neighboring frequency clusters.

Dynamics of a chain of coupled Chua's circuits is investigated. Existence of spatial disorder is revealed. Dynamics of wave fronts in the limiting case of a continuous medium is studied.