Synchronization Of Networks Research Articles

Robust Collaborative Visual-Inertial SLAM for Mobile Augmented Reality.

Achieving precise real-time localization and ensuring robustness are critical challenges in multi-user mobile AR applications. Leveraging collaborative information to augment tracking accuracy on lightweight devices and fortify overall system robustness emerges as a crucial necessity. In this paper, we propose a robust centralized collaborative rnulti-agent VI-SLAM system for mobile AR interaction and server-side efficient consistent mapping. The system deploys a lightweight VIO frontend on mobile devices for real-time tracking, and a backend running on a remote server to update multiple submaps. When overlapping areas between submaps across agents are detected, the system performs submap fusion to establish a globally consistent map. Additionally, we propose a map registration and fusion strategy based on covisibility areas for online registration and fusion in multi-agent scenarios. To improve the tracking accuracy of the frontend on agent, we introduce a strategy for updating the global map to the local map at a moderate frequency between the camera-rate pose estimation of the frontend VIO and the low-frequency global map optimization, using a tightly coupled strategy to achieve consistency of the multi-agent frontend poses estimation in the global map. The effectiveness of the proposed method is further confirmed by executing backend mapping on the server and deploying VIO frontends on multiple mobile devices for AR demostration. Additionally, we discuss the scalability of the proposed system by analyzing network traffic, synchronization frequency, and other factors at both the agent and server ends.

CACNA1A haploinsufficiency leads to reduced synaptic function and increased intrinsic excitability.

Haploinsufficiency of the CACNA1A gene, encoding the pore-forming α1 subunit of P/Q-type voltage-gated calcium channels, is associated with a clinically variable phenotype ranging from cerebellar ataxia, to neurodevelopmental syndromes with epilepsy and intellectual disability. To understand the pathological mechanisms of CACNA1A loss-of-function variants, we characterized a human neuronal model for CACNA1A haploinsufficiency, by differentiating isogenic induced pluripotent stem cell lines into glutamatergic neurons, and investigated the effect of CACNA1A haploinsufficiency on mature neuronal networks through a combination of electrophysiology, gene expression analysis, and in silico modeling. We observed an altered network synchronization in CACNA1A+/- networks alongside synaptic deficits, notably marked by an augmented contribution of GluA2-lacking AMPA receptors. Intriguingly, these synaptic perturbations coexisted with increased non-synaptically driven activity, as characterized by inhibition of NMDA and AMPA receptors on micro-electrode arrays. Single-cell electrophysiology and gene expression analysis corroborated this increased intrinsic excitability through reduced potassium channel function and expression. Moreover, we observed partial mitigation of the CACNA1A+/- network phenotype by 4-aminopyridine, a therapeutic intervention for episodic ataxia type 2. Positive modulation of KCa2 channels could reverse the CACNA1A+/- network electrophysiological phenotype. In summary, our study pioneers the characterization of a human induced pluripotent stem cell-derived neuronal model for CACNA1A haploinsufficiency, and has unveiled novel mechanistic insights. Beyond showcasing synaptic deficits, this neuronal model exhibited increased intrinsic excitability mediated by diminished potassium channel function, underscoring its potential as a therapeutic discovery platform with predictive validity.

Parallelized Mechanical Stimulation of Neuronal Calcium Through Cell-Internal Nanomagnetic Forces Provokes Lasting Shifts in the Network Activity State.

Neurons differentiate mechanical stimuli force and rate to elicit unique functional responses, driving the need for further tools to generate various mechanical stimuli. Here, cell-internal nanomagnetic forces (iNMF) are introduced by manipulating internalized magnetic nanoparticles with an external magnetic field across cortical neuron networks in vitro. Under iNMF, cortical neurons exhibit calcium (Ca2+) influx, leading to modulation of activity observed through Ca2+ event rates. Inhibiting particle uptake or altering nanoparticle exposure time reduced the neuronal response to nanomagnetic forces, exposing the requirement of nanoparticle uptake to induce the Ca2+ response. In highly active cortical networks, iNMF robustly modulates synchronous network activity, which is lasting and repeatable. Using pharmacological blockers, it is shown that iNMF activates mechanosensitive ion channels to induce the Ca2+ influx. Then, in contrast to transient mechanically evoked neuronal activity, iNMFactivates Ca2+-activated potassium (KCa) channelsto stabilize the neuronal membrane potential and induce network activity shifts. The findings reveal the potential of magnetic nanoparticle-mediated mechanical stimulation to modulate neuronal circuit dynamics, providing insights into the biophysics of neuronal computation.

Almost global synchronization of Kuramoto oscillators with symmetry breaking terms

M.A. Lohe first proposed the high-dimensional Kuramoto model with symmetry-breaking terms (Quantum synchronization over quantum networks, Journal of Physics A: Mathematical and Theoretical, 43:465301, 2010), which is the real form of a quantum network synchronization model. For the homogeneous case, by analyzing the limit set and the spectrum of the linearized model, the almost global synchronization is derived from the topology of some kind of connecting graphs.

Sliding mode control for finite-time projective synchronisation in distinct Caputo fractional-order delayed neural networks with inconsistent orders

This manuscript delves into the realm of projective synchronisation in delayed neural networks governed by Caputo fractional-order dynamics, addressing the intricate challenges of attaining finite-time synchronisation in networks with distinct structures, functions, and orders. To start, the integration of a compensation controller within the control architecture is examined, aiming to formulate the projection error dynamics. By leveraging projection error system analysis alongside output observation and sliding mode control, a meticulously crafted derivative-based sliding mode surface along with a sliding mode controller are devised, ensuring both convergence and seamless sliding motion. Afterwards, the accessibility of the surface is assessed through relevant lemmas, Caputo derivative characteristics and the Lyapunov direct approach. The subsequent stability of sliding path is corroborated, and indispensable prerequisites for achieving finite-time projective synchronisation are outlined. Furthermore, the establishment of an upper bound for synchronisation time of distinct Caputo fractional-order delayed neural networks (CFDNNs) with inconsistent orders is carried out. Ultimately, simulations on distinct CFDNNs with consistent and inconsistent orders are conducted to exemplify our approach, employing the continuous frequency distribution model.

Contrasting topologies of synchronous and asynchronous functional brain networks

ABSTRACT We generated asynchronous functional networks (aFNs) using a novel method called optimal causation entropy and compared aFN topology with the correlation-based synchronous functional networks (sFNs), which are commonly used in network neuroscience studies. Functional magnetic resonance imaging (fMRI) time series from 212 participants of the National Consortium on Alcohol and Neurodevelopment in Adolescence study were used to generate aFNs and sFNs. As a demonstration of how aFNs and sFNs can be used in tandem, we used multivariate mixed effects models to determine whether age interacted with node efficiency to influence connection probabilities in the two networks. After adjusting for differences in network density, aFNs had higher global efficiency but lower local efficiency than the sFNs. In the aFNs, nodes with the highest outgoing global efficiency tended to be in the brainstem and orbitofrontal cortex. aFN nodes with the highest incoming global efficiency tended to be members of the default mode network in sFNs. Age interacted with node global efficiency in aFNs and node local efficiency in sFNs to influence connection probability. We conclude that the sFN and aFN both offer information about functional brain connectivity, which the other type of network does not.

Human Astrocytes Synchronize Neural Organoid Networks.

Biological neural networks exhibit synchronized activity within and across interconnected regions of the central nervous system. Understanding how these coordinated networks are established and maintained may reveal therapeutic targets for neurodegeneration and neuromodulation. Here, we tested the influence of astrocytes upon synchronous network activity using human pluripotent stem cell-derived bioengineered neural organoids. This study revealed that astrocytes significantly increase activity within individual organoids and across long distances among numerous rapidly merged organoids via influencing synapses and bioenergetics. Treatment of amyloid protein inhibited synchronous activity during neurodegeneration, yet this can be rescued by propagating activity from neighboring networks. Altogether, this study identifies critical contributions of human astrocytes to biological neural networks and delivers a rapid, reproducible, and scalable model to investigate long-range functional communication of the nervous system in healthy and disease states.

Multiple-Access Time and Frequency Transfer over Fiber and Free-Space Link Based on Optical Frequency Comb

We have demonstrated a multiple-access transfer of time and frequency signal over a fiber and free-space link based on an optical frequency comb (OFC). With this transfer technique, two time–frequency signals were disseminated separately from a master site to two slave sites over a 3.9 km fiber and 100 m free-space link for 10,000 s. The timing fluctuations and instabilities of the time and frequency transfer were measured, estimated, and discussed. The experimental results show that the total root-mean-square (RMS) timing fluctuation of the transfer from site A to B is about 119 ps, with a fractional frequency instability on the order of 3.3 × 10−11 at 1 s and 2.8 × 10−14 at 2000 s. The RMS timing fluctuation of the transfer from site A to C is about 59.5 ps, with a fractional frequency instability on the order of 3.0 × 10−11 at 1 s and 2.6 × 10−14 at 2000 s. These results indicate that the multiple-access transfer technique proposed in this paper can provide important support for the application of a large-scale time–frequency synchronization network.

Synchrony dynamics underlie irregular neocortical spiking.

Cortical neurons are characterized by their variable spiking patterns. We challenge prevalent theories for the origin of spiking variability. We examine the specific hypothesis that cortical synchrony drives spiking variability in vivo . Using dynamic clamp, we demonstrate that intrinsic neuronal properties do not contribute substantially to spiking variability, but rather spiking variability emerges from weakly synchronous network drive. With large-scale electrophysiology we quantify the degree of synchrony and its time scale in cortical networks in vivo . We demonstrate that physiological levels of synchrony are sufficient to generate irregular responses found in vivo . Further, this synchrony shifts over timescales ranging from 25 to 200 ms, depending on the presence of external sensory input. Such shifts occur when the network moves from spontaneous to driven modes, leading naturally to a decline in response variability as observed across cortical areas. Finally, while individual neurons exhibit reliable responses to physiological drive, different neurons respond in a distinct fashion according to their intrinsic properties, contributing to stable synchrony across the neural network.

Current Status of Logistics Infrastructure Associated with the Development of Vietnam’s Sea Tourism

With a coastline of 3260 kilometers, 2.773 islands, and 28 coastal provinces, Vietnam has a great potential for sea tourism development. One of the factors affecting this development is logistics infrastructure. This research paper aims to analyze all of the logistics infrastructure factors including highways, railways, inland waterways, seaway, and airways; and the connection between logistics transport infrastructure and marine tourism development. The result shows that coastal tourism areas have made strong investments in logistics infrastructure and future planning. However, the lack of synchronization in transport networks connecting tourist destinations, the adaptation of the seaport systems and waterway transports, along with the overall infrastructure serving the needs of accommodations and tourism services have not met the increasing demand. Based on these bases, this paperwork proposes solutions to developing the logistics infrastructure associated with the development of Vietnam’s sea tourism.

Mittag-Leffler Stability and Synchronization of Multi-delayed Fractional Neural Networks via Halanay Inequality

Mittag-Leffler Stability and Synchronization of Multi-delayed Fractional Neural Networks via Halanay Inequality

Direct approach on projective synchronization of inertial quaternion-valued neural networks with proportional delays

Direct approach on projective synchronization of inertial quaternion-valued neural networks with proportional delays

Polynomial synchronization of quaternion-valued fuzzy cellular neural networks with proportional delays

Polynomial synchronization of quaternion-valued fuzzy cellular neural networks with proportional delays

Cluster approximate synchronization for probabilistic asynchronous finite field networks

Cluster approximate synchronization for probabilistic asynchronous finite field networks

7170 Plasticity of the Hypothalamic Tuberoinfundibular Dopaminergic Neuronal Network in Lactating Rats

Abstract Disclosure: P. Papaioannou: None. T. Georgescu: None. D.R. Grattan: None. S. Bunn: None. S. Yip: None. The tuberoinfundibular dopaminergic (TIDA) neurons play a crucial role in regulating prolactin secretion. Their synchronized network activity with slow but highly rhythmic firing pattern is important for dopamine release in rats[1]. While this unique TIDA network activity is vital for prolactin negative feedback in non-lactating conditions, its behaviour during lactation, when prolactin demand is high, remains unknown. Our hypothesis posited that the TIDA neuronal network in lactating rats becomes desynchronized, disrupting the negative feedback loop. To test this, we utilized ex-vivo Ca2+ imaging to simultaneously monitor population-wide TIDA neuron activity in non-lactating (NL; n=13) and lactating (L; n=10) rats injected with a cre-inducible adeno-associated virus (AAV) containing the Ca2+ indicator, GCaMP6s, into their arcuate nucleus. Analysis of neuronal network synchronicity revealed significant desynchronization in the TIDA network during lactation, as indicated by a markedly lower mean correlation coefficient matrix (CM) compared to non-lactating conditions (NL: 0.87±0.02, n= 26 sections vs L: 0.22±0.03, n=29 sections; p<0.001, Student’s t-test). Furthermore, the oscillatory activity patterns of these desynchronized TIDA neurons displayed a significantly lower cell rhythmicity index (RI) in lactating animals compared to non-lactating ones (NL: 0.17±0.01, n=186 cells vs L: 0.70±0.02, n=77 cells; p<0.001, Student’s t-test). Interestingly, within these low rhythmic neurons, some exhibited higher while others showed lower firing frequencies compared to non-lactating TIDA neurons. After 7 days post-weaning (n=4 animals), the TIDA network activity returned to non-lactating behaviour (CM = 0.85 ± 0.04, n=6 sections and RI = 0.17±0.01; n=47 cells). In summary, our findings demonstrate a reversible reconfiguration of the TIDA neuronal networks during lactation, characterized by low rhythmic oscillations and individual unique firing patterns, leading to diminished intercellular synchrony that may impede dopamine release, ultimately facilitating prolactin release to support lactation. Once weaned, the TIDA neuronal network reset to NL state to allow a new reproductive cycle. [1] Lyons, D. J., Horjales-Araujo, E. & Broberger, C. Synchronized network oscillations in rat tuberoinfundibular dopamine neurons: switch to tonic discharge by thyrotropin-releasing hormone. Neuron 65, 217-229, doi:10.1016/j.neuron.2009.12.024 (2010). Presentation: 6/2/2024

Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators

Neuromodulatory effects on synchrony and network reorganization in networks of coupled Kuramoto oscillators

Achieving synchronization and chimera state of modular neural networks by using dynamic learning to adjust electromagnetic induction

Achieving synchronization and chimera state of modular neural networks by using dynamic learning to adjust electromagnetic induction

Synchronization of nonlinear neural networks with hybrid couplings and uncertain time-varying perturbations: A novel distributed-delay impulsive comparison principle

This paper investigates the synchronization of nonlinear drive-response neural networks subject to uncertain time-varying perturbations, non-delayed coupling, and distributed delay coupling. To address the influence of distributed and discrete delays on the system, we establish a novel impulsive comparison principle, extending the Halanay inequality. By leveraging Lyapunov stability theory, we derive sufficient conditions for the exponential synchronization of the neural networks using a delayed impulsive controller with historical status information. This approach relaxes the conventional constraint that impulsive delays must be smaller than impulsive intervals, thereby generalizing existing synchronization results for distributed delay networks. Numerical simulations for chaotic neural networks validate the theoretical results and demonstrate the sensitivity of the control gain matrix.

Bounded and Saturation Control-Based Fixed-Time Synchronization of Discontinuous Fuzzy Competitive Networks With State-Dependent Switching

Bounded and Saturation Control-Based Fixed-Time Synchronization of Discontinuous Fuzzy Competitive Networks With State-Dependent Switching

Impact of multiplexing noise on multilayer networks of bistable maps

We explore numerically the complex dynamics of multilayer networks (consisting of three and one hundred layers) of cubic maps in the presence of noise-modulated interlayer coupling (multiplexing noise). The coupling strength is defined by independent discrete-time sources of color Gaussian noise. Uncoupled layers can demonstrate different complex structures, such as double-well chimeras, coherent and spatially incoherent regimes. Regions of partial synchronization of these structures are identified in the presence of multiplexing noise. We elucidate how synchronization of a three-layer network depends on the initially observed structures in the layers and construct synchronization regions in the plane of multiplexing noise parameters “noise spectrum width – noise intensity”. It is shown that in a hundred-layer network, clusters of synchronized layers can be formed at certain optimal values of multiplexing noise parameters. The performed numerical studies confidently indicate that the spatial dynamics of multilayer networks can be controlled by varying multiplexing noise parameters.