Synchronization In Neuronal Networks Research Articles

Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling

Alternating chimera states and synchronization in multilayer neuronal networks with ephaptic intralayer coupling

Dynamic modulation of external excitation enhance synchronization in complex neuronal network

Understanding and controlling neural network synchronization is crucial for neuroscience in revealing brain functions and addressing neurological disorders. This study explores the innovative use of dynamic learning of synchronization (DLS) technology to enhance synchronization within neuronal networks. Using the Hodgkin-Huxley model across various network topologies, including Erdős-Rényi random graphs, small-world, and scale-free networks, it dynamically adjusts external electrical excitation to study its effects on network synchrony. To further demonstrate the universality of DLS technology, this study also validates the main results using larger-scale networks and the Izhikevich and FitzHugh-Nagumo models. The research quantifies the enhancement of synchrony through DLS, using root-mean-square error (RMSE) and synchronization factors as metrics. Findings show that DLS effectively boosts network synchrony by dynamically adjusting external excitation in response to node differences, significantly in both small-world and scale-free networks, irrespective of synaptic connections. Furthermore, DLS demonstrates potential for targeted synchronization enhancement in specific region of network. This paper highlights DLS technology's effectiveness in modulating external excitation to improve complex neural network synchrony, providing new insights into neural synchronization and information transmission.

Complete synchronization of three-layer Rulkov neuron network coupled by electrical and chemical synapses.

This paper analyzes the complete synchronization of a three-layer Rulkov neuron network model connected by electrical synapses in the same layers and chemical synapses between adjacent layers. The outer coupling matrix of the network is not Laplacian as in linear coupling networks. We develop the master stability function method, in which the invariant manifold of the master stability equations (MSEs) does not correspond to the zero eigenvalues of the connection matrix. After giving the existence conditions of the synchronization manifold about the nonlinear chemical coupling, we investigate the dynamics of the synchronization manifold, which will be identical to that of a synchronous network by fixing the same parameters and initial values. The waveforms show that the transient chaotic windows and the transient approximate periodic windows with increased or decreased periods occur alternatively before asymptotic behaviors. Furthermore, the Lyapunov exponents of the MSEs indicate that the network with a periodic synchronization manifold can achieve complete synchronization, while the network with a chaotic synchronization manifold can not. Finally, we simulate the effects of small perturbations on the asymptotic regimes and the evolution routes for the synchronous periodic and the non-synchronous chaotic network.

Astroglial calcium signaling and homeostasis in tuberous sclerosis complex

Tuberous Sclerosis Complex (TSC) is a multisystem genetic disorder characterized by the development of benign tumors in various organs, including the brain, and is often accompanied by epilepsy, neurodevelopmental comorbidities including intellectual disability and autism. A key hallmark of TSC is the hyperactivation of the mechanistic target of rapamycin (mTOR) signaling pathway, which induces alterations in cortical development and metabolic processes in astrocytes, among other cellular functions. These changes could modulate seizure susceptibility, contributing to the progression of epilepsy and its associated comorbidities. Epilepsy is characterized by dysregulation of calcium (Ca2+) channels and intracellular Ca2+ dynamics. These factors contribute to hyperexcitability, disrupted synaptogenesis, and altered synchronization of neuronal networks, all of which contribute to seizure activity. This study investigates the intricate interplay between altered Ca2+ dynamics, mTOR pathway dysregulation, and cellular metabolism in astrocytes. The transcriptional profile of TSC patients revealed significant alterations in pathways associated with cellular respiration, ER and mitochondria, and Ca2+ regulation. TSC astrocytes exhibited lack of responsiveness to various stimuli, compromised oxygen consumption rate and reserve respiratory capacity underscoring their reduced capacity to react to environmental changes or cellular stress. Furthermore, our study revealed significant reduction of store operated calcium entry (SOCE) along with strong decrease of basal mitochondrial Ca2+ concentration and Ca2+ influx in TSC astrocytes. In addition, we observed alteration in mitochondrial membrane potential, characterized by increased depolarization in TSC astrocytes. Lastly, we provide initial evidence of structural abnormalities in mitochondria within TSC patient-derived astrocytes, suggesting a potential link between disrupted Ca2+ signaling and mitochondrial dysfunction. Our findings underscore the complexity of the relationship between Ca2+ signaling, mitochondria dynamics, apoptosis, and mTOR hyperactivation. Further exploration is required to shed light on the pathophysiology of TSC and on TSC associated neuropsychiatric disorders offering further potential avenues for therapeutic development.

Synchronization of scale-free neuronal network with small-world property induced by spike-timing-dependent plasticity under time delay

Spike-timing-dependent plasticity (STDP) is one of the important rules for the change of synaptic weights between neurons in biological nervous systems. In this paper, we study the effect of STDP on the synchronization phenomenon induced by time delay in the neuronal network which is the scale-free network with small-world property, and nodes of the network are constructed by Izhikevich neuron and connected by chemical synapses. For appropriate time delay values, there exists an optimal range of STDP maximum weight value in which the synchronization of the network is better, and in addition the synchronization is decreased with the increasing of STDP maximum weight value. The network with high synchronization has a centralized distribution of synaptic weights within it, while conversely, an unsynchronized network has a more discrete distribution of synaptic weights. When the STDP maximum weight value is too small, the collective firing pattern of network is not affected by synaptic current, and the synchronization of the network is also not affected. Interestingly, comparing with the small-world network and the scale-free network, it is found that a network has a smaller range of optimal STDP maximum weight values when the network is of larger average clustering coefficient, shorter average shortest path length, and higher small-world property. Our results can illuminate the potential significance of STDP for information processing and transmission in the nervous system.

Electrical activity and synchronization of HR-tabu neuron network coupled by Chua Corsage Memristor

Electrical activity and synchronization of HR-tabu neuron network coupled by Chua Corsage Memristor

Stochastic Morris–Lecar model with time delay under magnetic field excitation

In this paper, a four-dimensional Morris–Lecar (M–L) model with a memristor is improved by introducing synaptic dynamics, and an M–L model with time delay and Gaussian white noise is established. The stability of the model at the equilibrium point was demonstrated theoretically, and the correctness of the theory was verified by numerical simulations. In the numerical simulation, the model is found to have multiple discharge modes of spike discharge, cluster discharge, chaotic discharge, and coexistence of multiple discharge modes by changing important parameters such as time delay, magnetic field feedback strength, and noise intensity. This lays the foundation for further exploration of the M–L neuron coupling model with time delay and noise as well as the study of synchronization of neuronal networks.

The Impact of Higher-Order Interactions on the Synchronization of Hindmarsh–Rose Neuron Maps under Different Coupling Functions

In network analysis, links depict the connections between each pair of network nodes. However, such pairwise connections fail to consider the interactions among more agents, which may be indirectly connected. Such non-pairwise or higher-order connections can be signified by involving simplicial complexes. The higher-order connections become even more noteworthy when it comes to neuronal network synchronization, an emerging phenomenon responsible for the many biological processes in real-world phenomena. However, involving higher-order interactions may considerably increase the computational costs. To confound this issue, map-based models are more suitable since they are faster, simpler, more flexible, and computationally more optimal. Therefore, this paper addresses the impact of pairwise and non-pairwise neuronal interactions on the synchronization state of 10 coupled memristive Hindmarsh–Rose neuron maps. To this aim, electrical, inner linking, and chemical synaptic functions are considered as two- and three-body interactions in three homogeneous and two heterogeneous cases. The results show that through chemical pairwise and non-pairwise synapses, the neurons achieve synchrony with the weakest coupling strengths.

GABAB receptor outward currents are transiently disclosed by the convulsant 4-aminopyridine in vitro

The K+ channel blocker 4-aminopyridine (4AP) has been extensively used to investigate the mechanisms underlying neuronal network synchronization in both in vitro and in vivo animal models of focal epilepsy. 4AP-induced effects are paralleled by an increase in both excitatory and inhibitory neurotransmitter release, but the mechanisms of action of 4AP on neuronal networks remain unclear. By employing simultaneous whole-cell patch clamp and field potential recordings from hippocampal CA3/4 pyramidal layer of acute brain slices obtained from mice (n = 30), we found that the appearance of epileptiform discharges induced by 4AP (100 μM) is consistently preceded by the transient recurrence of presumptive GABAB outward currents, which are not mirrored by any field activity. These GABAB outward currents still occurred during application of ionotropic glutamatergic antagonists (n = 12 cells) but were blocked by the GABAB receptor antagonist CGP55845 (n = 7). Our findings show that the transient occurrence of distinct GABAB outward currents precedes the appearance of 4AP-induced neuronal network synchronization leading to epileptiform activity in the rodent hippocampus in vitro.

INTERLAYER AND INTRALAYER SYNCHRONIZATION IN MULTIPLEX FRACTIONAL-ORDER NEURONAL NETWORKS

Fractional-order models describing neuronal dynamics often exhibit better compatibility with diverse neuronal firing patterns that can be observed experimentally. Due to the overarching significance of synchronization in neuronal dynamics, we here study synchronization in multiplex neuronal networks that are composed of fractional-order Hindmarsh–Rose neurons. We compute the average synchronization error numerically for different derivative orders in dependence on the strength of the links within and between network layers. We find that, in general, fractional-order models synchronize better than integer-order models. In particular, we show that the required interlayer and intralayer coupling strengths for interlayer or intralayer synchronization can be weaker if we reduce the derivative order of the model describing the neuronal dynamics. Furthermore, the dependence of the interlayer or intralayer synchronization on the intralayer or interlayer coupling strength vanishes with decreasing derivative order. To support these results analytically, we use the master stability function approach for the considered multiplex fractional-order neuronal networks, by means of which we obtain sufficient conditions for the interlayer and intralayer synchronizations that are in agreement with numerical results.

Effect of temperature on synchronization of scale-free neuronal network

Effect of temperature on synchronization of scale-free neuronal network

Bursting hierarchy in an adaptive exponential integrate-and-fire network synchronization.

Neuronal network synchronization has received wide interest. In the present manuscript, we study the influence of initial membrane potentials together with network topology on bursting synchronization, in particular the sequential order of stabilized bursting among neurons. We find a hierarchical phenomenon on their bursting order. With a focus on situations where network coupling advances spiking times of neurons, we grade neurons into different layers. Together with the neuronal network structure, we construct directed graphs to indicate bursting propagation between different layers. More explicitly, neurons in upper layers burst earlier than those in lower layers. More interestingly, we find that among the same layer, bursting order of neurons is mainly associated with the number of neurons they connected to the upper layer; more stimuli lead to earlier bursting. Receiving effectively the same stimuli from the upper layer, we observe neurons with fewer connections would burst earlier.

Patterns of synchronization in 2D networks of inhibitory neurons.

Neural firing in many inhibitory networks displays synchronous assembly or clustered firing, in which subsets of neurons fire synchronously, and these subsets may vary with different inputs to, or states of, the network. Most prior analytical and computational modeling of such networks has focused on 1D networks or 2D networks with symmetry (often circular symmetry). Here, we consider a 2D discrete network model on a general torus, where neurons are coupled to two or more nearest neighbors in three directions (horizontal, vertical, and diagonal), and allow different coupling strengths in all directions. Using phase model analysis, we establish conditions for the stability of different patterns of clustered firing behavior in the network. We then apply our results to study how variation of network connectivity and the presence of heterogeneous coupling strengths influence which patterns are stable. We confirm and supplement our results with numerical simulations of biophysical inhibitory neural network models. Our work shows that 2D networks may exhibit clustered firing behavior that cannot be predicted as a simple generalization of a 1D network, and that heterogeneity of coupling can be an important factor in determining which patterns are stable.

Modelling the modulation of cortical Up-Down state switching by astrocytes.

Up-Down synchronization in neuronal networks refers to spontaneous switches between periods of high collective firing activity (Up state) and periods of silence (Down state). Recent experimental reports have shown that astrocytes can control the emergence of such Up-Down regimes in neural networks, although the molecular or cellular mechanisms that are involved are still uncertain. Here we propose neural network models made of three populations of cells: excitatory neurons, inhibitory neurons and astrocytes, interconnected by synaptic and gliotransmission events, to explore how astrocytes can control this phenomenon. The presence of astrocytes in the models is indeed observed to promote the emergence of Up-Down regimes with realistic characteristics. Our models show that the difference of signalling timescales between astrocytes and neurons (seconds versus milliseconds) can induce a regime where the frequency of gliotransmission events released by the astrocytes does not synchronize with the Up and Down phases of the neurons, but remains essentially stable. However, these gliotransmission events are found to change the localization of the bifurcations in the parameter space so that with the addition of astrocytes, the network enters a bistability region of the dynamics that corresponds to Up-Down synchronization. Taken together, our work provides a theoretical framework to test scenarios and hypotheses on the modulation of Up-Down dynamics by gliotransmission from astrocytes.

Astrocyte regulation of neural circuit activity and network states.

Astrocytes are known to influence neuronal activity through different mechanisms, including the homeostatic control of extracellular levels of ions and neurotransmitters and the exchange of signaling molecules that regulate synaptic formation, structure, and function. While a great effort done in the past has defined many molecular mechanisms and cellular processes involved in astrocyte-neuron interactions at the cellular level, the consequences of these interactions at the network level in vivo have only relatively recently been identified. This review describes and discusses recent findings on the regulatory effects of astrocytes on the activity of neuronal networks in vivo. Accumulating but still limited, evidence indicates that astrocytes regulate neuronal network rhythmic activity and synchronization as well as brain states. These studies demonstrate a critical contribution of astrocytes to brain activity and are paving the way for a more thorough understanding of the cellular bases of brain function.

Fabrication of Highly Sensitive Pt-black Electrochemical Sensors for GABA Detection.

GABA (Gamma-aminobutyric acid) is the main inhibitory neurotransmitter in the central nervous system of mammals. It is known to be related with various neurological disorders. GABA plays a crucial role in normal neuronal activity, information processing and plasticity, and neuronal network synchronization. To date, microdialysis has been widely used to monitor the level of GABA but the temporal and spatial resolution is limited. Besides, electrochemical sensors for neurotransmitter measurement, having high temporal and spatial resolution, overcome this problem. Here, using a cost-effective method of electrodeposition of platinum black (Pt-black), a highly sensitive, GABA specific, amperometric electrochemical sensor is fabricated. Nanostructured Pt-black increases the active surface area of the electrode contributing to higher sensitivity. Along with that, a self-referencing site and an exclusion layer are integrated to increase the selectivity and the signal-to-noise ratio (SNR) of the biosensor. This provides a prototype for a highly sensitive GABA sensor that could later be used to study various neurological disorders related to GABA concentrations.Clinical Relevance- This electrochemical sensor allows real-time monitoring of major inhibitory neurotransmitter (GABA) with high sensitivity which can be used for studying various neurological disorders.

Network Model With Reduced Metabolic Rate Predicts Spatial Synchrony of Neuronal Activity.

In a cerebral hypometabolic state, cortical neurons exhibit slow synchronous oscillatory activity with sparse firing. How such a synchronization spatially organizes as the cerebral metabolic rate decreases have not been systemically investigated. We developed a network model of leaky integrate-and-fire neurons with an additional dependency on ATP dynamics. Neurons were scattered in a 2D space, and their population activity patterns at varying ATP levels were simulated. The model predicted a decrease in firing activity as the ATP production rate was lowered. Under hypometabolic conditions, an oscillatory firing pattern, that is, an ON-OFF cycle arose through a failure of sustainable firing due to reduced excitatory positive feedback and rebound firing after the slow recovery of ATP concentration. The firing rate oscillation of distant neurons developed at first asynchronously that changed into burst suppression and global synchronization as ATP production further decreased. These changes resembled the experimental data obtained from anesthetized rats, as an example of a metabolically suppressed brain. Together, this study substantiates a novel biophysical mechanism of neuronal network synchronization under limited energy supply conditions.

Neuronal Dynamics of Pain in Parkinson's Disease.

Pain is an important non-motor symptom of Parkinson’s disease (PD). It negatively impacts the quality of life. However, the pathophysiological mechanisms underlying pain in PD remain to be elucidated. This study sought to use electroencephalographic (EEG) coherence analysis to compare neuronal synchronization in neuronal networks between patients with PD, with and without pain. Twenty-four patients with sporadic PD were evaluated for the presence of pain. Time-frequency and coherence analyses were performed on their EEG data. Whole-brain and regional coherence were calculated and compared between pain-positive and pain-negative patients. There was no significant difference in the whole-brain coherence between the pain-positive and pain-negative groups. However, temporal–temporal coherence differed significantly between the two groups (p = 0.031). Our findings indicate that aberrant synchronization of inter-temporal regions is involved in PD-related pain. This will further our understanding of the mechanisms underlying pain in PD.

Reverse optogenetics of G protein signaling by zebrafish non-visual opsin Opn7b for synchronization of neuronal networks

Opn7b is a non-visual G protein-coupled receptor expressed in zebrafish. Here we find that Opn7b expressed in HEK cells constitutively activates the Gi/o pathway and illumination with blue/green light inactivates G protein-coupled inwardly rectifying potassium channels. This suggests that light acts as an inverse agonist for Opn7b and can be used as an optogenetic tool to inhibit neuronal networks in the dark and interrupt constitutive inhibition in the light. Consistent with this prediction, illumination of recombinant expressed Opn7b in cortical pyramidal cells results in increased neuronal activity. In awake mice, light stimulation of Opn7b expressed in pyramidal cells of somatosensory cortex reliably induces generalized epileptiform activity within a short (<10 s) delay after onset of stimulation. Our study demonstrates a reversed mechanism for G protein-coupled receptor control and Opn7b as a tool for controlling neural circuit properties.

Miro1-dependent mitochondrial dynamics in parvalbumin interneurons

The spatiotemporal distribution of mitochondria is crucial for precise ATP provision and calcium buffering required to support neuronal signaling. Fast-spiking GABAergic interneurons expressing parvalbumin (PV+) have a high mitochondrial content reflecting their large energy utilization. The importance for correct trafficking and precise mitochondrial positioning remains poorly elucidated in inhibitory neurons. Miro1 is a Ca²+-sensing adaptor protein that links mitochondria to the trafficking apparatus, for their microtubule-dependent transport along axons and dendrites, in order to meet the metabolic and Ca2+-buffering requirements of the cell. Here, we explore the role of Miro1 in PV+ interneurons and how changes in mitochondrial trafficking could alter network activity in the mouse brain. By employing live and fixed imaging, we found that the impairments in Miro1-directed trafficking in PV+ interneurons altered their mitochondrial distribution and axonal arborization, while PV+ interneuron-mediated inhibition remained intact. These changes were accompanied by an increase in the ex vivo hippocampal γ-oscillation (30-80 Hz) frequency and promoted anxiolysis. Our findings show that precise regulation of mitochondrial dynamics in PV+ interneurons is crucial for proper neuronal signaling and network synchronization.