Two-dimensional Hindmarsh Rose Research Articles

Memristors-coupled neuron models with multiple firing patterns and homogeneous and heterogeneous multistability

Abstract Memristors are extensively used to estimate the external electromagnetic stimulation and synapses for neurons. In this paper, two distinct scenarios, i.e., an ideal memristor serves as external electromagnetic stimulation and a locally active memristor serves as a synapse, are formulated to investigate the impact of a memristor on a two-dimensional Hindmarsh–Rose neuron model. Numerical simulations show that the neuronal models in different scenarios have multiple burst firing patterns. The introduction of the memristor makes the neuronal model exhibit complex dynamical behaviors. Finally, the simulation circuit and DSP hardware implementation results validate the physical mechanism, as well as the reliability of the biological neuron model.

Coupled HR–HNN Neuron with a Locally Active Memristor

Local activity could be the source for complexity. In this study, a multistable locally active memristor is proposed, whose nonvolatile memory, as well as locally active characteristics, is validated by the power-off plot and DC [Formula: see text]–[Formula: see text] plot. Based on the two-dimensional Hindmarsh–Rose neuron and a one-dimensional Hopfield neuron, a simple neural network is constructed by connecting the two neurons with the locally active memristor. Coexisting multiple firing patterns under different initial conditions are investigated according to the controlled coupling factor. The results suggest that the system exhibits coexisting periodic and chaotic bursting with different firing patterns. Complex firing only occurs in the locally active area of the defined memristor, meanwhile the system shows a periodic oscillation in the passive area. Beyond this, the coupled neurons exhibit the specific phenomenon of attractor growing in the locally active region of the memristor. The circuit simulations by Power Simulation (PSIM) are included confirming the numerical simulations and theoretic analysis.

Coexisting firing patterns and phase synchronization in locally active memristor coupled neurons with HR and FN models

Local activity is regarded as the origin of complexity. In this study, a locally active memristor with coexisting two stable pinched hysteresis loops and two local activity regions is proposed. Its nonvolatile memory, as well as locally active characteristics, is validated by the power-off plot and DC V–I plot. Based on two-dimensional Hindmarsh–Rose and two-dimensional Fitzhugh–Nagumo neurons, a simple neural network is constructed by connecting the two neurons with the locally active memristor. Coexisting multiple firing patterns under different initial conditions are investigated by considering the coupling strength as a unique controlled parameter. The results suggest that the system exhibits coexisting periodic and chaotic bursting firing patterns as well as coexisting two periodic firing patterns with different topologies. Furthermore, state switching without parameters is also explored. In particular, phase synchronization of the memristor synapse-coupled neurons is discussed, which implies that two nonidentical neurons gradually become phase synchronized with the increase in the coupling strength. In order to confirm the effectiveness of numerical simulations, circuit simulations are included.

Elimination of spiral waves in a two-dimensional Hindmarsh–Rose neural network under long-range interaction effect and frequency excitation

The elimination of spiral waves is numerically studied in a network of Hindmarsh–Rose neurons in presence of long-range diffusive interactions and external frequency excitations. Some features of membrane potential, including time series, spatiotemporal and spatial diagrams are discussed. It is found that at low frequency excitations, weak and strong long-range couplings sustain target wave propagation and spiral wave formation. However, ultra-long-range connections fully suppress such patterns, especially in the case of a Kac–Baker-like long-range coupling. In addition, increase in the excitation frequency supports the transition from stable robust spiral waves to spiral turbulence at weak or strong long-range connections. The interplay between hyper-high-frequency excitation and Kac–Baker-like long-range interaction completely eliminates spiral waves and only regenerates target wave propagation. Our results suggest that both frequency excitation regimes and long-range interaction may efficiently regulate the electrical activity of thalamus neurons and effectively prevent some brain disorders such as epileptic seizures.

Bipolar Pulse-Induced Coexisting Firing Patterns in Two-Dimensional Hindmarsh–Rose Neuron Model

In this paper, a bipolar pulse (BP) current is taken to mimic a periodic stimulus effect on the membrane potential in the axon of a neuron. By introducing the BP current to substitute the externally applied constant current, a BP-forced two-dimensional Hindmarsh–Rose (HR) neuron model is proposed. Based on the proposed neuron model, the BP-switched equilibrium point and its stability evolution with the periodic variation in time are explored. Furthermore, coexisting asymmetric attractors (or coexisting firing patterns) with bistability are revealed by phase plane orbits, time sequences, and attraction basins, as well as the BP-induced coexisting asymmetric attractors’ behaviors are then elaborated through bifurcation analysis. The research results exhibit that, with the increase of the time, the stabilities of the neuron model are continually switched between an unstable node-focus and a stable point, resulting in the coexisting behaviors of numerous asymmetric attractors under the specified initials. Consequently, the newly introduced BP current stimulus, instead of the original constant current stimulus, allows the two-dimensional HR neuron model to possess complex dynamical behaviors for the membrane potential. Additionally, a hardware breadboard is fabricated and circuit experiments are carried out to validate the numerical simulations.

A digital implementation of 2D Hindmarsh–Rose neuron

Different architectures and techniques have developed in the neuromorphic field to mimic and investigate the activity of biological neural networks. This paper presents a set of piece-wise linear approximations of a two-dimensional Hindmarsh–Rose neuron model for digital circuit implementation to achieve higher speeds and lower hardware costs in large-scale implementation of the biological neural networks. The performance of the model was evaluated with a time domain signal error. Synthesis and hardware implementation on a field-programmable gate array, as a proof of concept, indicates that the proposed model reproduces several neuronal behaviors similar to the original model with higher performance and considerably lower implementation costs.

Frequency mode excitations in two-dimensional Hindmarsh–Rose neural networks

In this work, we explicitly show the existence of two frequency regimes in a two-dimensional Hindmarsh–Rose neural network. Each of the regimes, through the semi-discrete approximation, is shown to be described by a two-dimensional complex Ginzburg–Landau equation. The modulational instability phenomenon for the two regimes is studied, with consideration given to the coupling intensities among neighboring neurons. Analytical solutions are also investigated, along with their propagation in the two frequency regimes. These waves, depending on the coupling strength, are identified as breathers, impulses and trains of soliton-like structures. Although the waves in two regimes appear in some common regions of parameters, some phase differences are noticed and the global dynamics of the system is highly influenced by the values of the coupling terms. For some values of such parameters, the high-frequency regime displays modulated trains of waves, while the low-frequency dynamics keeps the original asymmetric character of action potentials. We argue that in a wide range of pathological situations, strong interactions among neurons can be responsible for some pathological states, including schizophrenia and epilepsy.

Order and chaos in the stochastic Hindmarsh–Rose model of the neuron bursting

We study the stochastic dynamics of the two-dimensional Hindmarsh–Rose model. In the deterministic case, this system demonstrates mono- and bi-stable dynamic regimes. In the parametric zone of the coexisting stable equilibrium and limit cycle, the phenomenon of noise-induced transitions between the attractors is studied. In another parametric region, where the deterministic system has the only stable equilibrium, the stochastic generation of high-amplitude oscillations is also observed. We show that under the random disturbances, the system demonstrates noise-induced bursting: an alternation of small fluctuations near the equilibrium and high-amplitude oscillations. For the quantitative analysis of noise-induced bursting, an approach combining stochastic sensitivity function technique and confidence domains method is suggested. Constructive abilities of this method for the estimation of critical values for noise intensity corresponding to the qualitative changes in stochastic dynamics are demonstrated and confirmed by the good agreement with the direct numerical simulation. An interplay between noise-induced bursting and mutual transformations “order–chaos” is discussed.

Changes of firing rate induced by changes of phase response curve in bifurcation transitions.

We study dynamical mechanisms responsible for changes of the firing rate during four different bifurcation transitions in the two-dimensional Hindmarsh-Rose (2DHR) neuron model: the saddle node on an invariant circle (SNIC) bifurcation to the supercritical Andronov-Hopf (AH) one, the SNIC bifurcation to the saddle-separatrix loop (SSL) one, the AH bifurcation to the subcritical AH (SAH) one, and the SSL bifurcation to the AH one. For this purpose, we study slopes of the firing rate curve with respect to not only an external input current but also temperature that can be interpreted as a timescale in the 2DHR neuron model. These slopes are mathematically formulated with phase response curves (PRCs), expanding the firing rate with perturbations of the temperature and external input current on the one-dimensional space of the phase [Formula: see text] in the 2DHR oscillator. By analyzing the two different slopes of the firing rate curve with respect to the temperature and external input current, we find that during changes of the firing rate in all of the bifurcation transitions, the calculated slope with respect to the temperature also changes. This is largely dependent on changes in the PRC size that is also related to the slope with respect to the external input current. Furthermore, we find phase transition-like switches of the firing rate with a possible increase of the temperature during the SSL-to-AH bifurcation transition.

Changes in Repetitive Firing Rate Related to Phase Response Curves for Andronov—Hopf Bifurcations

We study specific changes in repetitive firing in the two-dimensional Hindmarsh—Rose (2dHR) oscillatory system that undergoes a bifurcation transition from the supercritical Andronov—Hopf (AH) type to the subcritical Andronov—Hopf (SAH) type. We identify dynamical mechanisms which are responsible for changes of the repetitive firing rate during the AH to SAH bifurcation transitions. These include frequency-shift functions in response to small perturbations of a timescale parameter, its multiplicative parameter, and an external input current in the 2dHR oscillatory system. The frequency-shift functions are explicitly represented as functions relating to the phase response curves (PRCs). Then, we demonstrate that when the timescale is normal and relatively fast, the repetitive firing rate slightly increases and decreases respectively during the AH to SAH bifurcation transition with a change of the intrinsic parameter, whereas it decreases during the SAH to AH bifurcation transition with an increase in the timescale. By analyzing the three different frequency-shift functions, we show that such changes of the repetitive firing rate depend largely on changes of the PRC size. The PRC size for the SAH bifurcation shrinks to the PRC size for the AH bifurcation.

Transition frequencies in the neuronal oscillator

I present dynamical mechanisms responsible for transition frequencies, which are hidden in the homoclinic (HC) to supercritical Andronov-Hopf (AH) bifurcation switching in the two-dimensional Hindmarsh-Rose (2DHR) oscillator. For this purpose, I derive frequency gradients with respect to the input current and timescale in the 2DHR oscillator. The frequency gradients are formulated with phase response curves (PRCs) by expanding the firing frequency with small perturbations of the input current and timescale on the one-dimensional phase space of the 2DHR oscillator. I thus find two different types of striking breaks in the frequency plot with respect to the timescale, the sigmoid functional frequency and the discontinuous frequency. I also show effects of the unique PRC change on these transition frequencies.

APPLICATION OF A TWO-DIMENSIONAL HINDMARSH–ROSE TYPE MODEL FOR BIFURCATION ANALYSIS

In this study, we examine the bifurcation scenarios of a two-dimensional Hindmarsh–Rose type model [Tsuji et al., 2007] with four parameters and simulate some resemblances of neurophysiological features for this model using spike-and-reset conditions. We present possible classifications based on the results of the following assessments: (1) the number and stability of the equilibria are analyzed in detail using a table to demonstrate the matter in which the stability of the equilibrium changes and to determine which two equilibria collapse through the saddle-node bifurcation; (2) the sufficient conditions for an Andronov–Hopf bifurcation and a saddle-node bifurcation are mathematically confirmed; and (3) we elaborately evaluate the sufficient conditions for the Bogdanov–Takens (BT) and Bautin bifurcations. Several numerical simulations for these conditions are also presented. In particular, two types of bistable behaviors are numerically demonstrated: the BT and Bautin bifurcations. Notably, all of the bifurcation curves in the domain of the remaining parameters are similar when the time scale is large. Additionally, to show the potential for a limit cycle, the existence of a trapping region is demonstrated. These results present a variety of diverse behaviors for this model. The results of this study will be helpful in assessing suitable parameters for fitting the resemblances of experimental observations.

Bifurcation Analysis in a Neuronal Model without/with Delay

In this study, we provide a global picture of the bifurcation scenario of a two-dimensional Hindmarsh-Rose (HR) type model. We present all of the possible classifications based on the following results: first, the number and stability of the equilibrium are analyzed in detail with a table built to show not only how to change the stability of the equilibrium but also which two equilibria collapse through the saddle-node bifurcation; secondly, sufficient conditions for an Andronov-Hopf bifurcation and a saddle-node bifurcation are mathematically confirmed; and finally, we provide sufficient conditions for a Bogdanov-Takens (BT) bifurcation and a Bautin bifurcation. Finally, we present characteristic equation for the HR type model with delay. These results provide us a diversity of behaviors for the model. The results in the paper should be helpful when choosing suitable parameters for fitting experimental observations.

Codimension-two bifurcation analysis in two-dimensional Hindmarsh–Rose model

In this paper, we analyze the codimension-2 bifurcations of equilibria of a two-dimensional Hindmarsh–Rose model. By using the bifurcation methods and techniques, we give a rigorous mathematical analysis of Bautin bifurcation. The main result is that no more than two limit cycles can be bifurcated from the equilibrium via Hopf bifurcation; sufficient conditions for the existence of one or two limit cycles are obtained. This paper also shows that the model undergoes a Bogdanov–Takens bifurcation which includes a saddle-node bifurcation, an Andronov–Hopf bifurcation, and a homoclinic bifurcation. In some case, the globally asymptotical stability is discussed.

BIFURCATIONS IN TWO-DIMENSIONAL HINDMARSH–ROSE TYPE MODEL

We analyze a two-dimensional Hindmarsh–Rose type model exhibiting properties of both Class 1 and Class 2 neurons. Although the system is two-dimensional and contains only four parameters, the obtained bifurcation diagrams show that the bifurcation structure satisfies conditions for emergence of both features with constant stimuli.