Synchronization of fractional‐order chaotic systems with disturbances via novel fractional‐integer integral sliding mode control and application to neuron models

In this paper, a novel fractional‐integer integral type sliding mode technique for control and generalized function projective synchronization of different fractional‐order chaotic systems with different dimensions in the presence of disturbances is presented. When the upper bounds of the disturbances are known, a sliding mode control rule is proposed to insure the existence of the sliding motion in finite time. Furthermore, an adaptive sliding mode control is designed when the upper bounds of the disturbances are unknown. The stability analysis of sliding mode surface is given using the Lyapunov stability theory. Finally, the results performed for synchronization of three‐dimensional fractional‐order chaotic Hindmarsh‐Rose (HR) neuron model and two‐dimensional fractional‐order chaotic FitzHugh‐Nagumo (FHN) neuron model.     

Unidirectional synchronization for Hindmarsh–Rose neurons via robust adaptive sliding mode control

This paper presents an adaptive neural network (NN) based sliding mode control for unidirectional synchronization of Hindmarsh–Rose (HR) neurons in a master–slave configuration. We first give the dynamics of single HR neuron which may exhibit spike-burst chaotic behaviors. Then we formulate the problem of unidirectional synchronization control of two HR neurons and propose a NN based sliding mode controller. The controller consists of two simple radial basis function (RBF) NNs which are used to approximate the desired sliding mode controller and the uncertain nonlinear part of the error dynamical system, respectively. The control scheme is robust to the uncertainties such as approximate errors, ionic channel noise and external disturbances. The simulation results demonstrate the validity of the proposed control method.     

Locally active Hindmarsh–Rose neurons

In this paper the locally active and the edge of chaos regions of the Hindmarsh–Rose (HR) model for neuron dynamics are studied. From these regions parameters are chosen to set emergent phenomena both in 2D and 3D grids of HR neurons.     

Effect of an autapse on the firing pattern transition in a bursting neuron

On the basis of the Hindmarsh–Rose (HR) neuron model, the dynamics of electrical activity and the transition of firing patterns induced by three types of autapses have been investigated in detail. The dynamic effect of an autapse is detected by imposing a feedback term with a specific time-delay and autaptic intensity. We found that the delayed autaptic feedback connection switches the electrical activities of the HR neuron among quiescent, periodic and chaotic firing patterns. In the case of an electrical autapse, the transition from a periodic to a chaotic state occurs depending on the specific autaptic intensity and the time-delay. The excitatory chemical autapse plays a positive role in generating and enhancing the chaotic state. A time delay could decrease and suppress the chaotic state in the case of inhibitory chemical self-connections with a proper autaptic intensity. The bifurcation diagram vs. time-delay and autaptic intensity has been extensively studied, and the time series of membrane potentials and the distribution of information entropy have also been calculated to confirm the bifurcation analysis.     

Deterministic integer multiple firing depending on initial state in Wang model

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables.     

Deterministic integer multiple firing depending on initial state in Wang model

We investigate numerically dynamical behaviour of the Wang model, which describes the rhythmic activities of thalamic relay neurons. The model neuron exhibits Type I excitability from a global view, but Type II excitability from a local view. There exists a narrow range of bistability, in which a subthreshold oscillation and a suprathreshold firing behaviour coexist. A special firing pattern, integer multiple firing can be found in the certain part of the bistable range. The characteristic feature of such firing pattern is that the histogram of interspike intervals has a multipeaked structure, and the peaks are located at about integer multiples of a basic interspike interval. Since the Wang model is noise-free, the integer multiple firing is a deterministic firing pattern. The existence of bistability leads to the deterministic integer multiple firing depending on the initial state of the model neuron, i.e., the initial values of the state variables.     

Simulated test of electric activity of neurons by using Josephson junction based on synchronization scheme

The chaotic circuit of resistive–capacitive–inductive-shunted Josephson junction is used to simulate behavior of Hindmarsh–Rose neuronal discharges. Based on tracking control theory, the controller contains two gain coefficients was constructed to control the chaotic system of Josephson junction to synchronize the chaotic Hindmarsh–Rose system, and the single controller was approached analytically. The results confirmed that the controller with appropriate gain coefficients was effective to reach complete synchronization (the amplitudes and rhythms of two systems are identical), phase synchronization (rhythms of two systems are identical) of Josephson junction and Hindmarsh–Rose neurons, respectively. The power consumption is estimated in a feasible way. As a result, the electric activities of Hindmarsh–Rose neurons could be simulated by using Josephson junction model completely.     

Collective behavior of interacting locally synchronized oscillations in neuronal networks

Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh–Rose type with electrical and/or chemical couplings. We construct large-scale models using networks of such units which capture the essential features of the dynamics of cells and their connectivity patterns. The profile of the spike synchronization is then investigated considering different model parameters such as strength and ratio of excitatory/inhibitory connections. We also show that transmission time-delay might enhance the spike synchrony. The influence of spike-timing-dependence-plasticity is also studies on the spike synchronization.     

Impulsive synchronization of time delay bursting neuron systems with unidirectional coupling

In the article, impulsive synchronization of chaotic bursting in Hindmarsh–Rose neuron systems with time delay via partial state signal is investigated. Based on impulsive control theory of dynamical systems, the sufficient conditions on feedback strength and impulsive interval are established to guarantee the synchronization. Numerical simulations show the effectiveness of the proposed scheme. The obtained results may be helpful to understand dynamical mechanism of signal transduction in real neuronal activity. © 2014 Wiley Periodicals, Inc. Complexity 21: 38–46, 2015     

Global dynamics of diffusive Hindmarsh–Rose equations with memristors

Global dynamics of the diffusive Hindmarsh–Rose equations with memristors as a new proposed model for neuron dynamics are investigated in this paper. We prove the existence and regularity of a global attractor for the solution semiflow through uniform analytic estimates showing the higher-order dissipative property and the asymptotically compact characteristics of the solutions by the approach of Kolmogorov–Riesz theorem. The quantitative bounds of the regions containing this global attractor respectively in the state space and in the regular space are explicitly expressed by the model parameters.     

Control of chaotic solutions of the Hindmarsh–Rose equations

Irregular bursting and spiking solutions of the Hindmarsh–Rose model for the electrical activity of neuron cell bodies have been converted by a chaos control algorithm to periodic spike trains. A proportional feedback method is used to control both chaotic spike trains and chaotic bursting by applying controlling perturbations to membrane parameters.     

Synchronization of boundary coupled Hindmarsh–Rose neuron network

In this work, we present a new mathematical model of a boundary coupled neuron network described by the partly diffusive Hindmarsh–Rose equations. We prove the global absorbing property of the solution semiflow and then the main result on the asymptotic synchronization of this neuron network at a uniform exponential rate provided that the boundary coupling strength and the stimulating signal exceed a quantified threshold in terms of the parameters.     

Bifurcations and multistability in the extended Hindmarsh–Rose neuronal oscillator

We report on the bifurcation analysis of an extended Hindmarsh–Rose (eHR) neuronal oscillator. We prove that Hopf bifurcation occurs in this system, when an appropriate chosen bifurcation parameter varies and reaches its critical value. Applying the normal form theory, we derive a formula to determine the direction of the Hopf bifurcation and the stability of bifurcating periodic flows. To observe this latter bifurcation and to illustrate its theoretical analysis, numerical simulations are performed. Hence, we present an explanation of the discontinuous behavior of the amplitude of the repetitive response as a function of system’s parameters based on the presence of the subcritical unstable oscillations. Furthermore, the bifurcation structures of the system are studied, with special care on the effects of parameters associated with the slow current and the slower dynamical process. We find that the system presents diversity of bifurcations such as period-doubling, symmetry breaking, crises and reverse period-doubling, when the afore mentioned parameters are varied in tiny steps. The complexity of the bifurcation structures seems useful to understand how neurons encode information or how they respond to external stimuli. Furthermore, we find that the extended Hindmarsh–Rose model also presents the multistability of oscillatory and silent regimes for precise sets of its parameters. This phenomenon plays a practical role in short-term memory and appears to give an evolutionary advantage for neurons since they constitute part of multifunctional microcircuits such as central pattern generators.     

Codimension-two bifurcation analysis on firing activities in Chay neuron model

Using codimension-two bifurcation analysis in the Chay neuron model, the relationship between the electric activities and the parameters of neurons is revealed. The whole parameter space is divided into two parts, that is, the firing and silence regions of neurons. It is found that the transition sets between firing and silence regions are composed of the Hopf bifurcation curves of equilibrium states and the saddle-node bifurcation curves of limit cycles, with some codimension-two bifurcation points. The transitions from silence to firing in neurons are due to the Hopf bifurcation or the fold limit cycle bifurcation, but the codimension-two singularities lead to complexity in dynamical behaviour of neuronal firing.     

Codimension-two bifurcation analysis on firing activities in Chay neuron model

Using codimension-two bifurcation analysis in the Chay neuron model, the relationship between the electric activities and the parameters of neurons is revealed. The whole parameter space is divided into two parts, that is, the firing and silence regions of neurons. It is found that the transition sets between firing and silence regions are composed of the Hopf bifurcation curves of equilibrium states and the saddle-node bifurcation curves of limit cycles, with some codimension-two bifurcation points. The transitions from silence to firing in neurons are due to the Hopf bifurcation or the fold limit cycle bifurcation, but the codimension-two singularities lead to complexity in dynamical behaviour of neuronal firing.     

A scheme of de-synchronization in globally coupled neural networks and its possible implications for vagus nerve stimulation

A scheme of de-synchronization via pulse stimulation is numerically investigated in the Hindmarsh Rose globally coupled neural networks. The simulations show that synchronization evolves into de-synchronization in the globally coupled HR neural network when a part (about 10%) of neurons are stimulated with a pulse current signal. The network de-synchronization appears to be sensitive to the stimulation parameters. For the case of the same stimulation intensity, those weakly coupled networks reach de-synchronization more easily than strongly coupled networks. There exists a homologous asymptotic behavior in the region of higher frequency, and exist the optimal stimulation interval and period of continuous stimulation time when other stimulation parameters remain invariable.     

Spiking patterns of a minimal neuron to ELF sinusoidal electric field

Neuron as the main information carrier in neural systems is able to generate diverse spiking trains in response to different stimuli. Neuronal spiking patterns are related to the information processing in neural system. This paper investigates the dynamical behaviors of a two-dimensional minimal neuron model exposed to externally-applied extremely low frequency (ELF) sinusoidal electric field (EF). By numerical stimulation, it is found that neuron can exhibit different spiking patterns such as p:q mode-locking (i.e. a periodic oscillation defined as p action potentials generated by q cycle stimulations) and chaotic behaviors, depending on the values of stimulus frequencies. Transitions between different spiking patterns during exposure to the external EF are explored by interspike intervals (ISIs) and average firing rate. It is found that frequencies of the external EF can act as a bifurcation parameter, whose small change can cause the transition in neuronal behaviors. It is shown that a rich bifurcation structure including period-adding without chaos and mode-locking alternated with chaos suggests frequency discrimination of the neuronal firing patterns. Our results can provide a useful insight into the organization of similar bifurcation structures in excitable systems such as neurons under periodic forcing. Through detail analysis of the spiking patterns, we have explained how neuron’s information (spiking patterns) encodes the stimulus information (frequency), and vice versa.     

Master–slave synchronization criteria for chaotic hindmarsh–rose neurons using linear feedback control

This article is concerned with master–slave synchronization for two chaotic Hindmarsh–Rose neurons. The main contribution of this article is that three synchronization criteria are derived by using linear feedback control without the estimation of bounds of state variables of controlled slave neurons. Three simulation examples are used to illustrate the effectiveness of our results. © 2015 Wiley Periodicals, Inc. Complexity 21: 319–327, 2016     

Generalized outer synchronization between complex dynamical networks with time delay and noise perturbation

In this paper, the generalized outer synchronization between two different delay-coupled complex dynamical networks with noise perturbation is investigated. With a nonlinear control scheme, the sufficient condition for almost sure generalized outer synchronization is developed based on the LaSalle-type invariance principle for stochastic differential equations. Numerical examples are examined to illustrate the effectiveness of the analytical results. The theoretic result is also applied to investigate the outer synchronization between two delay-coupled Hindmarsh–Rose neuronal networks with noise perturbation.     

On physical interpretations of fractional integration and differentiation

Is there a relation between fractional calculus and fractal geometry? Can a fractional order system be represented by a causal dynamical model? These are the questions recently debated in the scientific community. The author intends to answer these questions. In the first part of the paper, some recently suggested models are reviewed and no convincing evidence is found for any dynamic model of a fractional order system having been built with the help of fractals. Linear filters with lumped constant parameters have a very limited use as approximations of fractional order systems. The model suggested in the paper is a state-space representation with parameters as functions of the independent variable. Regularization of fractional differentiation is considered and asymptotic error estimates, as well as simulation results, are presented.