Synchronous Events Research Articles

Extrinsic control of the early postnatal CA1 hippocampal circuits

The adult CA1 region of the hippocampus produces coordinated neuronal dynamics with minimal reliance on its extrinsic inputs. By contrast, neonatal CA1 is tightly linked to externally generated sensorimotor activity, but the circuit mechanisms underlying early synchronous activity in CA1 remain unclear. Here, using a combination of invivo and exvivo circuit mapping, calcium imaging, and electrophysiological recordings in mouse pups, we show that early dynamics in the ventro-intermediate CA1 are under the mixed influence of entorhinal (EC) and thalamic (VMT) inputs. Both VMT and EC can drive internally generated synchronous events exvivo. However, movement-related population bursts detected invivo are exclusively driven by the EC. These differential effects on synchrony reflect the different intrahippocampal targets of these inputs. Hence, cortical and subcortical pathways act differently on the neonatal CA1, implying distinct contributions to the development of the hippocampal microcircuit and related cognitive maps.

Gemini: Enabling Multi-Tenant GPU Sharing Based on Kernel Burst Estimation

Recent years have seen rapid adoption of GPUs in various types of platforms because of the tremendous throughput powered by massive parallelism. However, as the computing power of GPU continues to grow at a rapid pace, it also becomes harder to utilize these additional resources effectively with the support of GPU sharing. In this work, we designed and implemented <i>Gemini</i> , a user-space runtime scheduling framework to enable fine-grained GPU allocation control with support for multi-tenancy and elastic allocation, which are critical for cloud and resource providers. Our key idea is to introduce the concept of <i>kernel burst</i> , which refers to a group of consecutive kernels launched together without being interrupted by synchronous events. Based on the characteristics of kernel burst, we proposed a low overhead <i>event-driven monitor</i> and a <i>dynamic time-sharing scheduler</i> to achieve our goals. Our experiment evaluations using five types of GPU applications show that Gemini enabled multi-tenant and elastic GPU allocation with less than 5% performance overhead. Furthermore, compared to static scheduling, Gemini achieved 20% <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 30% performance improvement without requiring prior knowledge of applications.

On tick automata for distributed timed DESs with synchronisations and minimal time constraints

This paper is about the representations of distributed timed discrete event systems (DESs) with synchronisation events and minimal time constraints. A subclass of Timed Petri nets with specific time semantics is first recalled as a reference model for the considered systems. Such a model is reformulated according to modular tick automata with minimal times that behave like logical automata. A synchronous composition of such automata is then defined and the result of this composition is a new tick automaton that has the same timed language as the original Petri net, which is not the case for the earlier model of tick automata with constant (exact) times.

Distributed Asynchronous Greedy Control of Large Networks of Thermostatically Controlled Loads for Electric Demand Response

This letter illustrates two multi-agent greedy demand-side response control schemes for networks of Thermostatically Controlled Loads. The objective is to provide simple but effective local control actions such that the overall power consumption tracks an aggregated desired profile. Compared with the existing literature the novelties are twofold. Since model-free, our schemes possess certain robustness features to the model deterioration and exogenous disturbances. Since greedy, they are of easy implementation also on cheap development boards which do not support optimization software, moreover because asynchronous do not require any network-wide synchronization event. Specifically, Algorithm 1 is very simple but it is applicable only on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -regular communication topologies. Such prerequisite is then removed in Algorithm 2 by including within its instruction list a dynamic consensus protocol to estimate the mean network power consumption. Performance analysis and numerical simulations confirm the effectiveness of the schemes.

CA2 orchestrates hippocampal network dynamics.

The hippocampus is composed of various subregions: CA1, CA2, CA3, and the dentate gyrus (DG). Despite the abundant hippocampal research literature, until recently, CA2 received little attention. The development of new genetic and physiological tools allowed recent studies characterizing the unique properties and functional roles of this hippocampal subregion. Despite its small size, the cellular content of CA2 is heterogeneous at the molecular and physiological levels. CA2 has been heavily implicated in social behaviors, including social memory. More generally, the mechanisms by which the hippocampus is involved in memory include the reactivation of neuronal ensembles following experience. This process is coordinated by synchronous network events known as sharp-wave ripples (SWRs). Recent evidence suggests that CA2 plays an important role in the generation of SWRs. The unique connectivity and physiological properties of CA2 pyramidal cells make this region a computational hub at the core of hippocampal information processing. Here, we review recent findings that support the role of CA2 in coordinating hippocampal network dynamics from a systems neuroscience perspective.

Definition of the estrogen negative feedback pathway controlling the GnRH pulse generator in female mice

The mechanisms underlying the homeostatic estrogen negative feedback pathway central to mammalian fertility have remained unresolved. Direct measurement of gonadotropin-releasing hormone (GnRH) pulse generator activity in freely behaving mice with GCaMP photometry demonstrated striking estradiol-dependent plasticity in the frequency, duration, amplitude, and profile of pulse generator synchronization events. Mice with Cre-dependent deletion of ESR1 from all kisspeptin neurons exhibited pulse generator activity identical to that of ovariectomized wild-type mice. An in vivo CRISPR-Cas9 approach was used to knockdown ESR1 expression selectively in arcuate nucleus (ARN) kisspeptin neurons. Mice with >80% deletion of ESR1 in ARN kisspeptin neurons exhibited the ovariectomized pattern of GnRH pulse generator activity and high frequency LH pulses but with very low amplitude due to reduced responsiveness of the pituitary. Together, these studies demonstrate that estrogen utilizes ESR1 in ARN kisspeptin neurons to achieve estrogen negative feedback of the GnRH pulse generator in mice.

Different response to priming in Ceiba aesculifolia seeds is associated to the initial transcriptome landscape and to differential regulation of ABA and lipid metabolism

The proper timing of germination is a crucial step for plant survival. In the tropical dry forest, water availability is one of the main factors affecting phenological transitions and synchronization of physiological events. The hydration-dehydration cycles that occur in this ecosystem enhance germination in the field, but the success of the treatment is affected by the quality of the seed batch. In Ceiba aesculifolia seeds, two priming-response phenotypes can arise from batch identity or time in storage, affecting the suitability of a given batch for plant recruitment from seed in restoration programs. We tracked the changes in gene expression during germination in six independent seed batches subjected to priming in order to understand the differences between priming phenotypes. The effects of priming over germination depended on the initial transcriptomic landscape of the seed batch, which in turn was affected during seed maturation or prolonged storage. The positive priming phenotype was associated to a fine-tuning of the transcriptome during germination, as well as a reduction of ABA content and the early mobilization of a fraction of the stored lipids during and after the treatment. In contrast, the seed batches with a negative priming phenotype presented a delayed imbibition-phase transition accompanied by a peak in ABA content during early imbibition, as well as an altered regulation of lipid metabolism that prevented the early mobilization of a small fraction of the storage lipids. Additionally, during the seed-seedling transition, the seeds with a negative response to priming also displayed alterations in seedling development that could compromise their survival in the field. Our data indicate that lipid mobilization, as an early indicative of the success of priming, could be an easy to implement test to assess seed batch quality aside from germination tests. Meanwhile, ABA seems to be involved in a water-status checkpoint that coordinates the decision of whether delay or progress into later stages of the germination program in C. aesculifolia . This checkpoint could be an important regulator of germination in non-dormant species from the seasonally dry tropical forest. • The transcriptomic landscape of the dry seed determines whether a seed will respond positively to the priming treatment. • Priming enhances germination but does not generate a new transcriptomic program. • Abscisic acid and its regulation, as well as an early mobilization of a small fraction of stored lipids participate in the positive response to the matric priming treatment. • Stress during development/maturation or storage can impair the response to priming.

The BrightEyes-TTM as an open-source time-tagging module for democratising single-photon microscopy

Fluorescence laser-scanning microscopy (LSM) is experiencing a revolution thanks to new single-photon (SP) array detectors, which give access to an entirely new set of single-photon information. Together with the blooming of new SP LSM techniques and the development of tailored SP array detectors, there is a growing need for (i) DAQ systems capable of handling the high-throughput and high-resolution photon information generated by these detectors, and (ii) incorporating these DAQ protocols in existing fluorescence LSMs. We developed an open-source, low-cost, multi-channel time-tagging module (TTM) based on a field-programmable gate array that can tag in parallel multiple single-photon events, with 30 ps precision, and multiple synchronisation events, with 4 ns precision. We use the TTM to demonstrate live-cell super-resolved fluorescence lifetime image scanning microscopy and fluorescence lifetime fluctuation spectroscopy. We expect that our BrightEyes-TTM will support the microscopy community in spreading SP-LSM in many life science laboratories.

First-year university students' self-regulated learning during the COVID-19 pandemic: a qualitative longitudinal study

When the COVID-19 pandemic began, many universities switched to fully online teaching. This unexpected switching to online teaching was challenging for both teachers and students, and restrictions that were put in place because of pandemic made this challenge even greater. However, new ways of teaching might also open new opportunities for students’ learning. The research question driving our study was as follows: how do students regulate their learning and specifically their choice of resources and peer learning in university mathematics classes that are fully taught online as offered during the COVID-19 pandemic? We report on a longitudinal, qualitative study in which students recorded a brief audio diary twice a week over one whole semester (14 weeks). We focused on three students who completed 70 interviews in total and finished the semester with varying degrees of success. The results show how the students structured their studying (e.g., the roles that deadlines or synchronous teaching events played). They illustrate the strengths and limitations of digital materials provided by the lecturer and the use of complementary media. Further, the pandemic uncovered the double-edged role of simple, often anonymous exchanges (e.g., via Discord servers), with few binding forces for either side, and the significance of stable learning partnerships for students’ success. Our research highlights aspects that should be focal points when comparing traditional instruction and online instruction during the pandemic from a self-regulatory perspective. Practical implications refer to how these aspects can be combined sensibly in fully online courses, but also in blended learning contexts.

Numerical Solution of Finite Kuramoto Model with Time-Dependent Coupling Strength: Addressing Synchronization Events of Nature

The synchronization of an ensemble of oscillators is a phenomenon present in systems of different fields, ranging from social to physical and biological systems. This phenomenon is often described mathematically by the Kuramoto model, which assumes oscillators of fixed natural frequencies connected by an equal and uniform coupling strength, with an analytical solution possible only for an infinite number of oscillators. However, most real-life synchronization systems consist of a finite number of oscillators and are often perturbed by external fields. This paper accommodates the perturbation using a time-dependent coupling strength K(t) in the form of a sinusoidal function and a step function using 32 oscillators that serve as a representative of finite oscillators. The temporal evolution of order parameter r(t) and phases θj(t), key indicators of synchronization, are compared between the uniform and time-dependent cases. The identical trends observed in the two cases give an important indication that the synchrony persists even under the influence of external factors, something very plausible in the context of real-life synchronization events. The occasional boosting of coupling strength is also enough to keep the assembly of oscillators in a synchronized state persistently.

Complex network reveals the spatiotemporal pattern of summer extreme precipitation in Eastern China

In this study, complex networks were constructed based on the synchronization of summer extreme precipitation events (SEPEs) in eastern China. Then, a detailed analysis of spatiotemporal patterns of SEPEs and the relationship between SEPEs in eastern China with the eastern Asian monsoon was presented. The results showed that (1) the event synchronization is low in the coastal region but high in the inland region; (2) the intensity of the monsoon varies at different phases of summer and the area and intensity of the monsoon's influence on the summer extreme rainfall events were different. In conclusion, this study provides valuable insights to reveal the influence of monsoon strength on SEPEs in different regions of China.

Interconnection between the Indian and the East Asian summer monsoon: Spatial synchronization patterns of extreme rainfall events

AbstractA deeper understanding of the intricate relationship between the two components of the Asian summer monsoon (ASM)—the Indian summer monsoon (ISM) and the East Asian summer monsoon (EASM)—is crucial to improve the subseasonal forecasting of extreme precipitation events. Using an innovative complex network‐based approach, we identify two dominant synchronization pathways between ISM and EASM—a southern mode between the Arabian Sea and southeastern China occurring in June, and a northern mode between the core ISM zone and northern China which peaks in July—and their associated large‐scale atmospheric circulation patterns. Furthermore, we discover that certain phases of the Madden–Julian oscillation and the lower frequency mode of the boreal summer intraseasonal oscillation (BSISO) seem to favour the overall synchronization of extreme rainfall events between ISM and EASM while the higher‐frequency mode of the BSISO is likely to support the shifting between the modes of ISM–EASM connection.

Deep learning-based feature extraction for prediction and interpretation of sharp-wave ripples in the rodent hippocampus.

Local field potential (LFP) deflections and oscillations define hippocampal sharp-wave ripples (SWRs), one of the most synchronous events of the brain. SWRs reflect firing and synaptic current sequences emerging from cognitively relevant neuronal ensembles. While spectral analysis have permitted advances, the surge of ultra-dense recordings now call for new automatic detection strategies. Here, we show how one-dimensional convolutional networks operating over high-density LFP hippocampal recordings allowed for automatic identification of SWR from the rodent hippocampus. When applied without retraining to new datasets and ultra-dense hippocampus-wide recordings, we discovered physiologically relevant processes associated to the emergence of SWR, prompting for novel classification criteria. To gain interpretability, we developed a method to interrogate the operation of the artificial network. We found it relied in feature-based specialization, which permit identification of spatially segregated oscillations and deflections, as well as synchronous population firing typical of replay. Thus, using deep learning-based approaches may change the current heuristic for a better mechanistic interpretation of these relevant neurophysiological events.

Identifying the spatiotemporal organization of high-traffic events in a mobile communication system using event synchronization and complex networks.

In mobile communication systems, congestion is related to high-traffic events (HTEs) that occur in the coverage areas of base stations. Understanding, recognizing, and predicting these HTEs and researching their occurrence rules provides theoretical and decision-making support for preventing system congestion. Communication sectors are regarded as nodes, and if HTEs occur synchronously among sectors, then the corresponding nodes are connected. The total number of synchronous HTEs determines the edge weights. The mobile-communication spatiotemporal data are mapped to a weighted network, with the occurrence locations of HTEs as the basic elements. Network analysis provides a structure for representing the interaction of HTEs. By analyzing the topological features of the event synchronization network, the associations among the occurrence times of HTEs can be mined. We find that the event synchronization network is a small-world network, the cumulative strength distribution is exponential, and the edge weight obeys a power law. Moreover, the node clustering coefficient is negatively correlated with the node degree. A congestion coefficient based on several topological parameters is proposed, and the system congestion is visualized. The congestion coefficient contains information about the synchronous occurrence of HTEs between a sector and its neighbors and information about the synchronous occurrence of HTEs among its neighbors. For the mobile communication system considered in this study, the congestion coefficient of a large number of sectors is small and the risk of system congestion is low.

Characterizing renewable energy compound events across Europe using a logistic regression‐based approach

AbstractThe transition towards decarbonized power systems requires accounting for the impacts of the climate variability and climate change on renewable energy sources. With the growing share of wind and solar power in the European power system and their strong weather dependence, balancing the energy demand and supply becomes a great challenge. We characterize energy compound events, defined as periods of simultaneous low renewable production of wind and solar power, and high electricity demand. Using a logistic regression approach, we examine the influence of meteorological and atmospheric drivers on energy compound events. Moreover, we assess the spatial coherence of energy compound events that pose a major challenge within an interconnected power grid, as they can affect multiple countries simultaneously. On average, European countries are exposed to winter energy compound events more than twice per year. The combination of extremely low temperatures and low wind speeds is associated with a higher probability of occurrence of energy compound events. Furthermore, we show that blocked weather regimes have a major influence on energy compound events. In particular, Greenland and European blocking lead to widespread energy compound events that affect multiple countries at the same time. Our results highlight the relevance of weather regimes resulting in synchronous spatial energy compound events, which might pose a greater risk within a potential fully interconnected European grid.

How Do Spike Collisions Affect Spike Sorting Performance?

Recently, a new generation of devices have been developed to record neural activity simultaneously from hundreds of electrodes with a very high spatial density, both for in vitro and in vivo applications. While these advances enable to record from many more cells, they also challenge the already complicated process of spike sorting (i.e., extracting isolated single-neuron activity from extracellular signals). In this work, we used synthetic ground-truth recordings with controlled levels of correlations among neurons to quantitatively benchmark the performance of state-of-the-art spike sorters focusing specifically on spike collisions. Our results show that while modern template-matching-based algorithms are more accurate than density-based approaches, all methods, to some extent, failed to detect synchronous spike events of neurons with similar extracellular signals. Interestingly, the performance of the sorters is not largely affected by the spiking activity in the recordings, with respect to average firing rates and spike-train correlation levels. Since the performances of all modern spike sorting algorithms can be affected as function of the activity of the recorded neurons, scientific claims on correlations and synchrony should be carefully assessed based on the analysis provided in this paper.

Learning populations with hubs govern the initiation and propagation of spontaneous bursts in neuronal networks after learning.

Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining high-resolution measurement with a 4,096-channel complementary metal-oxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these “learning populations” had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity.

How do we identify flash droughts? A case study in Central European Croplands

Abstract Many definitions and delineation methods exist for identifying flash droughts (FDs), which are events of rapid and unusual large depletion of root-zone soil moisture, in comparison to average moisture conditions, due to climatic compound conditions over a short period of several weeks. Six FD identification methods were compared to analyse their functioning using data from several experimental cropland sites across Central Europe. Co- and misidentification of the FD time series were assessed using confusion and synchronicity metrics on a local scale. Even though a large degree of synchronicity of individual FD events was observed, some divergence in drought periods was detected, which was related to four intrinsic differences in the underlying FD definitions: (1) type of critical variable; (2) velocity of drought intensification; (3) pre-set threshold values for final depletion and/or (4) minimum length of the duration of FDs. To balance the strengths and weaknesses of those methods that are not based on soil moisture, we suggest using an ensemble approach for event identification, which is validated in this study for the temperate central European region. In doing so, the current unclearly defined sub-types of FDs can be detected, regardless of the different combinations of compound drivers and differences in intensification dynamics. All methods were implemented in an R package and are available as a Shiny app for the public.

Amyloid pathology disrupts gliotransmitter release in astrocytes.

Accumulation of amyloid-beta (Aβ) is associated with synaptic dysfunction and destabilization of astrocytic calcium homeostasis. A growing body of evidence support astrocytes as active modulators of synaptic transmission via calcium-mediated gliotransmission. However, the details of mechanisms linking Aβ signaling, astrocytic calcium dynamics, and gliotransmission are not known. We developed a biophysical model that describes calcium signaling and the ensuing gliotransmitter release from a single astrocytic process when stimulated by glutamate release from hippocampal neurons. The model accurately captures the temporal dynamics of microdomain calcium signaling and glutamate release via both kiss-and-run and full-fusion exocytosis. We investigate the roles of two crucial calcium regulating machineries affected by Aβ: plasma-membrane calcium pumps (PMCA) and metabotropic glutamate receptors (mGluRs). When we implemented these Aβ-affected molecular changes in our astrocyte model, it led to an increase in the rate and synchrony of calcium events. Our model also reproduces several previous findings of Aβ associated aberrant calcium activity, such as increased intracellular calcium level and increased spontaneous calcium activity, and synchronous calcium events. The study establishes a causal link between previous observations of hyperactive astrocytes in Alzheimer's disease (AD) and Aβ-induced modifications in mGluR and PMCA functions. Analogous to neurotransmitter release, gliotransmitter exocytosis closely tracks calcium changes in astrocyte processes, thereby guaranteeing tight control of synaptic signaling by astrocytes. However, the downstream effects of AD-related calcium changes in astrocytes on gliotransmitter release are not known. Our results show that enhanced rate of exocytosis resulting from modified calcium signaling in astrocytes leads to a rapid depletion of docked vesicles that disrupts the crucial temporal correspondence between a calcium event and vesicular release. We propose that the loss of temporal correspondence between calcium events and gliotransmission in astrocytes pathologically alters astrocytic modulation of synaptic transmission in the presence of Aβ accumulation.

Real-Time Classification of Anthropogenic Seismic Sources from Distributed Acoustic Sensing Data: Application for Pipeline Monitoring

Abstract Distributed Acoustic Sensing (DAS) is an innovative method to record acoustic waves using an optical fiber as a network of sensors. Current DAS devices can monitor up to 50 km of optical fiber and the use of optical repeaters can raise even more this length, while allowing a spatial discretization of the order of a meter. Handling such amount of data is a challenge in terms of data management and data analysis (such as event source identification), more specifically for monitoring applications such as infrastructures or natural hazards. In this work, we propose a processing chain for real-time classification of anthropogenic sources using a combination of Random Forest (RF) and Random Markov Field (RMF). To develop the method, we choose to focus on the application of pipeline monitoring. The algorithm is therefore trained to recognize six classes of seismic sources: pedestrian, impact, backhoe, compactor, leak, and noise. All the sources were triggered and recorded on our own test bench under controlled conditions. The average sensitivity of our processing chain reaches 83% with the use of only RF and achieves 87% in combination with RMF. Classification maps show that the RMF approach can increase the average sensitivity by removing isolated signals. In addition to this improvement in sensitivity, this new approach also permits to identify synchronous events taking place at nearby positions, which is difficult with classical methods.