Networks Of Hindmarsh-Rose Neurons Research Articles

In-phase and anti-phase bursting dynamics and synchronisation scenario in neural network by varying coupling phase.

We have analysed the synchronisation scenario and the rich spatiotemporal patterns in the network of Hindmarsh-Rose neurons under the influence of self, mixed and cross coupling of state variables which are realised by varying coupling phase. We have introduced a coupling matrix in the model to vary coupling phase. The excitatory and inhibitory couplings in the membrane potential induce in-phase and anti-phase bursting dynamics, respectively, in the two coupled system. When the off-diagonal elements of the matrix are zero, the system shows self coupling of the three variables, which helps to attain synchrony. The off-diagonal elements give cross interactions between the variables, which reduces synchrony. The stability of the synchrony attained is analysed using Lyapunov function approach. In our study, we found that self coupling in three variables is sufficient to induce chimera states in non-local coupling. The strength of incoherence and discontinuity measure validates the existence of chimera and multichimera states. The inhibitor self coupling in local interaction induces interesting patterns like Mixed Oscillatory State and clusters. The results may help in understanding the spatiotemporal communications of the brain, within the limitations of the size of the network analysed in this study.

Adaptive control of synchronization for the heterogeneous Hindmarsh-Rose network

Abstract The paper is devoted to the adaptive control problem of synchronization in the network of Hindmarsh-Rose neurons. The case of identical neurons was predominantly considered in the previous studies, which are concerned with the synchronization of the biological neuron models. However, the natural networks of biological neurons are heterogeneous, since living neurons can have different shapes and characteristics. In order to take into account the effect of the natural network heterogeneity, the neuronal models are considered with different parameters in our paper. It was shown that the adaptive synchronization of the heterogeneous Hindmarsh-Rose network is possible, and it is achieved by using the developed controller. The obtained results were mathematically proved and confirmed by the simulations.

Hybrid modeling and prediction of dynamical systems.

Scientific analysis often relies on the ability to make accurate predictions of a system’s dynamics. Mechanistic models, parameterized by a number of unknown parameters, are often used for this purpose. Accurate estimation of the model state and parameters prior to prediction is necessary, but may be complicated by issues such as noisy data and uncertainty in parameters and initial conditions. At the other end of the spectrum exist nonparametric methods, which rely solely on data to build their predictions. While these nonparametric methods do not require a model of the system, their performance is strongly influenced by the amount and noisiness of the data. In this article, we consider a hybrid approach to modeling and prediction which merges recent advancements in nonparametric analysis with standard parametric methods. The general idea is to replace a subset of a mechanistic model’s equations with their corresponding nonparametric representations, resulting in a hybrid modeling and prediction scheme. Overall, we find that this hybrid approach allows for more robust parameter estimation and improved short-term prediction in situations where there is a large uncertainty in model parameters. We demonstrate these advantages in the classical Lorenz-63 chaotic system and in networks of Hindmarsh-Rose neurons before application to experimentally collected structured population data.

Hopf Bifurcations in Directed Acyclic Networks of Linearly Coupled Hindmarsh-Rose Systems.

This paper addresses the existence of Hopf bifurcations in a directed acyclic network of neurons, each of them being modeled by a Hindmarsh-Rose (HR) neuronal model. The bifurcation parameter is the small parameter corresponding to the ratio of time scales between the fast and the slow dynamics. We first prove that, under certain hypotheses, the single uncoupled neuron can undergo a Hopf bifurcation. Hopf bifurcation occurrences in a directed acyclic network of HR neurons are then discussed. Numerical simulations are carried out to observe these bifurcations and to illustrate the theoretical results.

Do Brain Networks Evolve by Maximizing Their Information Flow Capacity?

We propose a working hypothesis supported by numerical simulations that brain networks evolve based on the principle of the maximization of their internal information flow capacity. We find that synchronous behavior and capacity of information flow of the evolved networks reproduce well the same behaviors observed in the brain dynamical networks of Caenorhabditis elegans and humans, networks of Hindmarsh-Rose neurons with graphs given by these brain networks. We make a strong case to verify our hypothesis by showing that the neural networks with the closest graph distance to the brain networks of Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks evolved with coupling strengths that maximize information flow capacity. Surprisingly, we find that global neural synchronization levels decrease during brain evolution, reflecting on an underlying global no Hebbian-like evolution process, which is driven by no Hebbian-like learning behaviors for some of the clusters during evolution, and Hebbian-like learning rules for clusters where neurons increase their synchronization.

NETWORK COMPLEXITY AND SYNCHRONOUS BEHAVIOR — AN EXPERIMENTAL APPROACH

We discuss synchronization in networks of Hindmarsh-Rose neurons that are interconnected via gap junctions, also known as electrical synapses. We present theoretical results for interactions without time-delay. These results are supported by experiments with a setup consisting of sixteen electronic equivalents of the Hindmarsh-Rose neuron. We show experimental results of networks where time-delay on the interaction is taken into account. We discuss in particular the influence of the network topology on the synchronization.

Multistability in networks of Hindmarsh-Rose neurons

We investigate the dynamical states of a two-dimensional network of Hindmarsh-Rose spiking neurons, in the vicinity of the current threshold where the single neuron becomes active. Each neuron is electrically coupled with neurons in its close neighborhood. The existence of multistable synchronization states is established and discussed. We also show that, provided adequate initial conditions, the collective behavior is able to keep the network in activity, even for current values far below the activity threshold of the single neuron. A phase diagram of the different network states is presented for a large interval of the coupling-current parameter space.