Bursting as an emergent phenomenon in coupled chaotic maps

Asymmetric coupling of nonchaotic Rulkov neurons: Fractal attractors, quasimultistability, and final state sensitivity

Although neuron models have been well studied for their rich dynamics and biological properties, limited research has been done on the complex geometries that emerge from the basins of attraction and basin boundaries of multistable neuron systems. In this paper, we investigate the geometrical properties of the strange attractors, four-dimensional basins, and fractal basin boundaries of an asymmetrically electrically coupled system of two identical nonchaotic Rulkov neurons. We discover a quasimultistability in the system emerging from the existence of a chaotic spiking-bursting pseudo-attractor, and we classify and quantify the system's basins of attraction, which are found to have complex fractal geometries. Using the method of uncertainty exponents, we also find that the system exhibits extreme final state sensitivity, which results in a dynamical uncertainty that could have important applications in neurobiology.

A modified Ricker map and its bursting oscillations

In our search to understand complex oscillation in discrete dynamic systems, we modify the Ricker map, where one parameter is also a dynamic variable. Using the bistable behavior of the fixed point solution, we analyze two response functions that characterize the change of the dynamic parameter. The 2D map sustains different types of burst oscillations that depend on the response functions. In either case, the parameter values yield a slow dynamic variable required to observe bursting-type oscillations.

Intermittent evolution routes to the periodic or the chaotic orbits in Rulkov map

This paper concerns the intermittent evolution routes to the asymptotic regimes in the Rulkov map. That is, the windows with transient approximate periodic and transient chaotic behaviors occur alternatively before the system reaches the periodic or the chaotic orbits. Meanwhile, the evolution routes to chaotic orbits can be classified into different types according to the windows before reaching asymptotic chaotic states. In addition, the initial values can be regarded as a key factor affecting the asymptotic behaviors and the evolution routes. The effects of the initial values are given by parameter planes, bifurcation diagrams, and waveforms. In order to investigate whether the intermittent evolution routes can be learned by machine learning, some experiments are given to understanding the differences between the trajectories of the Rulkov map generated by the numerical simulations and predicted by the neural networks. These results show that there is about 60% accuracy rate of successfully predicting both the evolution routes and the asymptotic period-3 orbits using a three-layer feedforward neural network, while the bifurcation diagrams can be reconstructed using reservoir computing except a few parameter conditions.

Synchronization of Rulkov neuron networks coupled by excitatory and inhibitory chemical synapses

This paper takes into account a neuron network model in which the excitatory and the inhibitory Rulkov neurons interact each other through excitatory and inhibitory chemical coupling, respectively. Firstly, for two or more identical or non-identical Rulkov neurons, the existence conditions of the synchronization manifold of the fixed points are investigated, which have received less attention over the past decades. Secondly, the master stability equation of the arbitrarily connected neuron network under the existence conditions of the synchronization manifold is discussed. Thirdly, taking three identical Rulkov neurons as an example, some new results are presented: (1) topological structures that can make the synchronization manifold exist are given, (2) the stability of synchronization when different parameters change is discussed, and (3) the roles of the control parameters, the ratio, as well as the size of the coupling strength and sigmoid function are analyzed. Finally, for the chemical coupling between two non-identical neurons, the transversal system is given and the effect of two coupling strengths on synchronization is analyzed.

Chimera states in two-dimensional networks of locally coupled oscillators

Chimera state is defined as a mixed type of collective state in which synchronized and desynchronized subpopulations of a network of coupled oscillators coexist and the appearance of such anomalous behavior has strong connection to diverse neuronal developments. Most of the previous studies on chimera states are not extensively done in two-dimensional ensembles of coupled oscillators by taking neuronal systems with nonlinear coupling function into account while such ensembles of oscillators are more realistic from a neurobiological point of view. In this paper, we report the emergence and existence of chimera states by considering locally coupled two-dimensional networks of identical oscillators where each node is interacting through nonlinear coupling function. This is in contrast with the existence of chimera states in two-dimensional nonlocally coupled oscillators with rectangular kernel in the coupling function. We find that the presence of nonlinearity in the coupling function plays a key role to produce chimera states in two-dimensional locally coupled oscillators. We analytically verify explicitly in the case of a network of coupled Stuart-Landau oscillators in two dimensions that the obtained results using Ott-Antonsen approach and our analytical finding very well matches with the numerical results. Next, we consider another type of important nonlinear coupling function which exists in neuronal systems, namely chemical synaptic function, through which the nearest-neighbor (locally coupled) neurons interact with each other. It is shown that such synaptic interacting function promotes the emergence of chimera states in two-dimensional lattices of locally coupled neuronal oscillators. In numerical simulations, we consider two paradigmatic neuronal oscillators, namely Hindmarsh-Rose neuron model and Rulkov map for each node which exhibit bursting dynamics. By associating various spatiotemporal behaviors and snapshots at particular times, we study the chimera states in detail over a large range of coupling parameter. The existence of chimera states is confirmed by instantaneous angular frequency, order parameter and strength of incoherence.

Emergence of antiphase bursting in two populations of randomly spiking elements

Animal locomotion activity relies on the generation and control of coordinated periodic actions in a central pattern generator (CPG). A core element of many CPGs responsible for the rhythm generation is a pair of reciprocally coupled neuron populations. Recent interest in the development of highly reduced models of CPG networks is motivated by utilization of CPG models in applications for biomimetic robotics. This paper considers the use of a reduced model in the form of a discrete time system to study the emergence of antiphase bursting activity in two reciprocally coupled populations evoked by the postinhibitory rebound effect.

Coupling and noise induced spiking-bursting transition in a parabolic bursting model

The transition from tonic spiking to bursting is an important dynamic process that carry physiologically relevant information. In this work, coupling and noise induced spiking-bursting transition is investigated in a parabolic bursting model with specific discussion on their cooperation effects. Fast/slow analysis shows that weak coupling may help to induce the bursting by changing the geometric property of the fast subsystem so that the original unstable periodical solution are stabilized. It turned out that noise can play the similar stabilization role and induce bursting at appropriate moderate intensity. However, their cooperation may either strengthen or weaken the overall effect depending on the choice of noise level.

Hybrid discrete-time neural networks

Hybrid dynamical systems combine evolution equations with state transitions. When the evolution equations are discrete-time (also called map-based), the result is a hybrid discrete-time system. A class of biological neural network models that has recently received some attention falls within this category: map-based neuron models connected by means of fast threshold modulation (FTM). FTM is a connection scheme that aims to mimic the switching dynamics of a neuron subject to synaptic inputs. The dynamic equations of the neuron adopt different forms according to the state (either firing or not firing) and type (excitatory or inhibitory) of their presynaptic neighbours. Therefore, the mathematical model of one such network is a combination of discrete-time evolution equations with transitions between states, constituting a hybrid discrete-time (map-based) neural network. In this paper, we review previous work within the context of these models, exemplifying useful techniques to analyse them. Typical map-based neuron models are low-dimensional and amenable to phase-plane analysis. In bursting models, fast-slow decomposition can be used to reduce dimensionality further, so that the dynamics of a pair of connected neurons can be easily understood. We also discuss a model that includes electrical synapses in addition to chemical synapses with FTM. Furthermore, we describe how master stability functions can predict the stability of synchronized states in these networks. The main results are extended to larger map-based neural networks.

A mathematical model of β-cells in an islet of Langerhans sensing a glucose gradient

Pancreatic β-cells release insulin in response to increased glucose levels. Compared to isolated β-cells, β-cells embedded within the islets of Langerhans network exhibit a coordinated and greater insulin secretion response to glucose. This coordinated activity is considered to rely on gap-junctions. We investigated the β-cell electrophysiology and the calcium dynamics in islets in response to glucose gradients. While at constant glucose the network of β-cells fires in a correlated fashion, a glucose gradient induces a sharp division into an active and an inactive part. We hypothesized that this sharp transition is mediated by the specific properties of the gap-junctions. We used a mathematical model of the β-cell electrophysiology in islets to discuss possible origins of this sharp transition in electrical activity. In silico, gap-junctions were required for such a transition. However, the small width of transition was only found when a stochastic variability of the expression of key transmembrane proteins, such as the ATP-dependent potassium channel, was included. The agreement with experimental data was further improved by assuming a delay of gap-junction currents, which points to a role of spatial constraints in the β-cell. This result clearly demonstrates the power of mathematical modeling in disentangling causal relationships in complex systems.

Bursting regimes in map-based neuron models coupled through fast threshold modulation

A system consisting of two map-based neurons coupled through reciprocal excitatory or inhibitory chemical synapses is discussed. After a brief explanation of the basic mechanism behind generation and synchronization of bursts, parameter space is explored to determine less obvious but biologically meaningful regimes and effects. Among them, we show how excitatory synapses without any delays may induce antiphase synchronization; that a synapse may change its character from excitatory to inhibitory and vice versa by changing its conductance, without any change in reversal potential; and that small variations in the synaptic threshold may result in drastic changes in the synchronization of spikes within bursts. Finally we show how the synchronization effects found in the two-neuron system carry over to larger networks.

Chaotic oscillations in a map-based model of neural activity

We propose a discrete time dynamical system (a map) as a phenomenological model of excitable and spiking-bursting neurons. The model is a discontinuous two-dimensional map. We find conditions under which this map has an invariant region on the phase plane, containing a chaotic attractor. This attractor creates chaotic spiking-bursting oscillations of the model. We also show various regimes of other neural activities (subthreshold oscillations, phasic spiking, etc.) derived from the proposed model.

Sensitivity versus resonance in two-dimensional spiking-bursting neuron models

Through phase plane analysis of a class of two-dimensional spiking and bursting neuron models, covering some of the most popular map-based neuron models, we show that there exists a trade-off between the sensitivity of the neuron to steady external stimulation and its resonance properties, and how this trade-off may be tuned by the neutral or asymptotic character of the slow variable. Implications of the results for the suprathreshold behavior of the neurons, both by themselves and as part of networks, are presented in different regimes of interest, such as the excitable, regular spiking, and bursting regimes. These results establish a consistent link between single-neuron parameters and resulting network dynamics, and will hopefully be useful as a guide for modeling.

Synchronization and propagation of bursts in networks of coupled map neurons

The present paper studies regular and complex spatiotemporal behaviors in networks of coupled map-based bursting oscillators. In-phase and antiphase synchronization of bursts are studied, explaining their underlying mechanisms in order to determine how network parameters separate them. Conditions for emergent bursting in the coupled system are derived from our analysis. In the region of emergence, patterns of chaotic transitions between synchronization and propagation of bursts are found. We show that they consist of transient standing and rotating waves induced by symmetry-breaking bifurcations, and can be viewed as a manifestation of the phenomenon of chaotic itinerancy.

Glucose metabolism and oscillatory behavior of pancreatic islets

A variety of oscillations are observed in pancreatic islets. We establish a model incorporating two oscillatory systems of different time scales: One is the well-known bursting model in pancreatic cells and the other is the glucose-insulin feedback model which considers direct and indirect feedback of secreted insulin. These two are coupled to interact with each other in the combined model, and two basic assumptions are made on the basis of biological observations: The conductance gK(ATP) for the ATP-dependent potassium current is a decreasing function of the glucose concentration whereas the insulin secretion rate is given by a function of the intracellular calcium concentration. Obtained via extensive numerical simulations are complex oscillations including clusters of bursts, slow and fast calcium oscillations, and so on. We also consider how the intracellular glucose concentration depends upon the extracellular glucose concentration, and examine the inhibitory effects of insulin.

Fokker-Planck analysis of stochastic coherence in models of an excitable neuron with noise in both fast and slow dynamics

We provide a detailed and quantitative Fokker-Planck analysis of noise-induced periodicity (stochastic coherence, also known as coherence resonance) in both a discrete-time model and a continuous-time model of excitable neurons. In particular, we show that one-dimensional models can explain why the effects of noise added to the fast and slow dynamics of the models are dramatically different. We argue that such effects should occur in any excitable system with two or more distinct time scales and need to be taken into account in experiments investigating stochastic coherence.

How noise and coupling induce bursting action potentials in pancreatic {beta}-cells

Unlike isolated beta-cells, which usually produce continuous spikes or fast and irregular bursts, electrically coupled beta-cells are apt to exhibit robust bursting action potentials. We consider the noise induced by thermal fluctuations as well as that by channel-gating stochasticity and examine its effects on the action potential behavior of the beta-cell model. It is observed numerically that such noise in general helps single cells to produce a variety of electrical activities. In addition, we also probe coupling via gap junctions between neighboring cells, with heterogeneity induced by noise, to find that it enhances regular bursts.

Emergence of chaotic attractor and anti-synchronization for two coupled monostable neurons

The dynamics of two coupled piece-wise linear one-dimensional monostable maps is investigated. The single map is associated with Poincare section of the FitzHugh-Nagumo neuron model. It is found that a diffusive coupling leads to the appearance of chaotic attractor. The attractor exists in an invariant region of phase space bounded by the manifolds of the saddle fixed point and the saddle periodic point. The oscillations from the chaotic attractor have a spike-burst shape with anti-phase synchronized spiking.

Analysis of the noise-induced bursting-spiking transition in a pancreatic beta-cell model

A stochastic model of the electrophysiological behavior of the pancreatic beta cell is studied, as a paradigmatic example of a bursting biological cell embedded in a noisy environment. The analysis is focused on the distortion that a growing noise causes to the basic properties of the membrane potential signals, such as their periodic or chaotic nature, and their bursting or spiking behavior. We present effective computational tools to obtain as much information as possible from these signals, and we suggest that the methods could be applied to real time series. Finally, a universal dependence of the main characteristics of the membrane potential on the size of the considered cell cluster is presented.

Inducing coherence in networks of bistable maps by varying the interaction range

Ordinarily, two different topologies have been used to model spatiotemporal chaos and to study complexity in networks of maps: one where sites interact only with nearest neighbors (e.g., the standard diffusive coupling) and one where sites interact with all sites in the network (global coupling). Here we investigate intermediate regimes considering the interaction range as a free tunable parameter. The synchronization behavior normally seen in globally coupled maps is found to set in for interaction ranges considerably smaller than the system size. In addition, we analytically derive stability conditions for the onset of coherent states (full synchronization) from which the minimum interaction range needed to induce coherence in homogeneously coupled maps can be determined. Such conditions are also obtained for inhomogeneous situations when the coupling strength decreases linearly with the distance. The characteristic range for the onset of coherence is studied in detail as a function of model parameters.

Emergent properties of CNS neuronal networks as targets for pharmacology: application to anticonvulsant drug action

CNS drugs may act by modifying the emergent properties of complex CNS neuronal networks. Emergent properties are network characteristics that are not predictably based on properties of individual member neurons. Neuronal membership within networks is controlled by several mechanisms, including burst firing, gap junctions, endogenous and exogenous neuroactive substances, extracellular ions, temperature, interneuron activity, astrocytic integration and external stimuli. The effects of many CNS drugs in vivo may critically involve actions on specific brain loci, but this selectivity may be absent when the same neurons are isolated from the network in vitro where emergent properties are lost. Audiogenic seizures (AGS) qualify as an emergent CNS property, since in AGS the acoustic stimulus evokes a non-linear output (motor convulsion), but the identical stimulus evokes minimal behavioral changes normally. The hierarchical neuronal network, subserving AGS in rodents is initiated in inferior colliculus (IC) and progresses to deep layers of superior colliculus (DLSC), pontine reticular formation (PRF) and periaqueductal gray (PAG) in genetic and ethanol withdrawal-induced AGS. In blocking AGS, certain anticonvulsants reduce IC neuronal firing, while other agents act primarily on neurons in other AGS network sites. However, the NMDA receptor channel blocker, MK-801, does not depress neuronal firing in any network site despite potently blocking AGS. Recent findings indicate that MK-801 actually enhances firing in substantia nigra reticulata (SNR) neurons in vivo but not in vitro. Thus, the MK-801-induced firing increases in SNR neurons observed in vivo may involve an indirect effect via disinhibition, involving an action on the emergent properties of this seizure network.

Modeling of spiking-bursting neural behavior using two-dimensional map

A simple model that replicates the dynamics of spiking and spiking-bursting activity of real biological neurons is proposed. The model is a two-dimensional map that contains one fast and one slow variable. The mechanisms behind generation of spikes, bursts of spikes, and restructuring of the map behavior are explained using phase portrait analysis. The dynamics of two coupled maps that model the behavior of two electrically coupled neurons is discussed. Synchronization regimes for spiking and bursting activities of these maps are studied as a function of coupling strength. It is demonstrated that the results of this model are in agreement with the synchronization of chaotic spiking-bursting behavior experimentally found in real biological neurons.