Asynchronous Dynamics Research Articles

Role of delay in brain dynamics

Significant variations of delays among connecting neurons cause an inevitable disadvantage of asynchronous brain dynamics compared to synchronous deep learning. However, this study demonstrates that this disadvantage can be converted into a computational advantage using a network with a single output and M multiple delays between successive layers, thereby generating a polynomial time-series outputs with M. The proposed role of delay in brain dynamics (RoDiB) model, is capable of learning increasing number of classified labels using a fixed architecture, and overcomes the inflexibility of the brain to update the learning architecture using additional neurons and connections. Moreover, the achievable accuracies of the RoDiB system are comparable with those of its counterpart tunable single delay architectures with M outputs. Further, the accuracies are significantly enhanced when the number of output labels exceeds its fully connected input size. The results are mainly obtained using simulations of VGG-6 on CIFAR datasets and also include multiple label inputs. However, currently only a small fraction of the abundant number of RoDiB outputs is utilized, thereby suggesting its potential for advanced computational power yet to be discovered.

River flow intermittence influence biodiversity-stability relationships across spatial scales: Implications for an uncertain future.

Climate change is increasing the proportion of river networks experiencing flow intermittence, which in turn reduces local diversity (i.e., α-diversity) but enhances variation in species composition among sites (i.e., β-diversity), with potential consequences on ecosystem stability. Indeed, the multiscale theory of stability proposes that regional stability can be attained not only by local processes but also by spatial asynchrony among sites. However, it is still unknown whether and how scale-dependent changes in biodiversity associated with river flow intermittence influence stability across spatial scales. To elucidate this, we here focus on multiple metacommunities of French rivers experiencing contrasting levels of flow intermittence. We clearly show that the relative contribution of spatial asynchrony to regional stability was higher for metacommunities of intermittent than perennial rivers. Surprisingly, spatial asynchrony was mainly linked to asynchronous population dynamics among sites, but not to β-diversity. This finding was robust for both truly aquatic macroinvertebrates and for taxa that disperse aerially during their adult stages, implying the need to conserve multiple sites across the landscape to attain regional stability in intermittent rivers. By contrast, metacommunities of truly aquatic macroinvertebrates inhabiting perennial rivers were mainly stabilized by local processes. Our study provides novel evidence that metacommunities of perennial and intermittent rivers are stabilized by contrasting processes operating at different spatial scales. We demonstrate that flow intermittence enhances spatial asynchrony among sites, thus resulting in a regional stabilizing effect on intermittent river networks. Considering that climate change is increasing the proportion of intermittent rivers worldwide, our results suggest that managers need to focus on the spatial dynamics of metacommunities more than on local-scale processes to monitor, restore, and conserve freshwater biodiversity.

STOCHASTICITY, SCALE, AND REFRACTION: DETERMINANTS OF POPULATION SYNCHRONY IN PARASITES OF CENTRARCHIDS IN J. STROM THURMOND LAKE, SOUTH CAROLINA.

Among-deme asynchrony has the potential to influence community richness and diversity by increasing the likelihood of regional persistence for a species. Parasites of Lepomis spp. collected from 4 localities at J. Strom Thurmond Lake, South Carolina over a 1-yr period were used to evaluate patterns of parasite population synchrony. Localities were separated by approximately 5 km to increase the likelihood that the parasites sampled represented different demes. Tylodelphys scheuringi and Crinicleidus longus, exhibited negative covariation between synchrony and among-locality distances. The degree of synchrony exhibited by Neoechinorhynchus cylindratis, Crepidostomum cornutum, and Clavunculus bifurcatus was associated with the degree of similarity in habitat structure between localities. Patterns of synchrony for Posthodiplostomum minimum and Spinitectus sp. were not associated with any of the habitat variables examined. The influence of habitat structure on parasite population synchrony, possibly through the refraction of large-scale environmental drivers, has the potential to produce asynchronous dynamics that are independent of the distance between demes, thereby promoting regional persistence by increasing the likelihood of rescue effects.

A Mean Field to Capture Asynchronous Irregular Dynamics of Conductance-Based Networks of Adaptive Quadratic Integrate-and-Fire Neuron Models.

Mean-field models are a class of models used in computational neuroscience to study the behavior of large populations of neurons. These models are based on the idea of representing the activity of a large number of neurons as the average behavior of mean-field variables. This abstraction allows the study of large-scale neural dynamics in a computationally efficient and mathematically tractable manner. One of these methods, based on a semianalytical approach, has previously been applied to different types of singleneuron models, but never to models based on a quadratic form. In this work, we adapted this method to quadratic integrate-and-fire neuron models with adaptation and conductance-based synaptic interactions. We validated the mean-field model by comparing it to the spiking network model. This mean-field model should be useful to model large-scale activity based on quadratic neurons interacting with conductance-based synapses.

Coexistence of asynchronous and clustered dynamics in noisy inhibitory neural networks

A regime of coexistence of asynchronous and clustered dynamics is analysed for globally coupled homogeneous and heterogeneous inhibitory networks of quadratic integrate-and-fire (QIF) neurons subject to Gaussian noise. The analysis is based on accurate extensive simulations and complemented by a mean-field description in terms of low-dimensional next generation neural mass models for heterogeneously distributed synaptic couplings. The asynchronous regime is observable at low noise and becomes unstable via a sub-critical Hopf bifurcation at sufficiently large noise. This gives rise to a coexistence region between the asynchronous and the clustered regime. The clustered phase is characterised by population bursts in the γ-range (30–120 Hz), where neurons are split in two equally populated clusters firing in alternation. This clustering behaviour is quite peculiar: despite the global activity being essentially periodic, single neurons display switching between the two clusters due to heterogeneity and/or noise.

Stability in plant-pollinator communities across organizational levels: present, gaps, and future.

Abstract. The study of ecological stability continues to fill the pages of scientific journals almost seven decades after the first ecologists initiated this line of research. The many advances in this field have focused on understanding the stability of populations, communities or functions within single guilds or trophic levels, with less research conducted across multiple trophic levels and considering the different interactions that relate species to each other. Here, we review the recent literature on the multiple dimensions of ecological stability specifically within plant-pollinator communities. We then focus on one of stability´s dimensions, temporal invariability, and adapt an existing partitioning framework that bridges invariability and synchrony measures across spatial scales and organizational levels to accommodate interactions between plants and their pollinators. Finally, we use this framework to analyse temporal invariability in plant reproductive success, partitioning it on invariability and synchrony components across plant and pollinator populations and communities, as well as their interactions, using a well-resolved dataset that encompasses data for two years. Our review of the literature points to several significant gaps in our current knowledge, with simulation studies clearly overrepresented in the literature as opposed to experimental or empirical approaches. Our quantitative approach to partitioning invariability shows similar patterns of decreasing temporal invariability across increasing organizational levels driven by asynchronous dynamics amongst populations and communities, which overall stabilize ecosystem functioning (plant reproductive success). This study represents a first step towards a better comprehension of temporal invariability in ecosystem functions defined by interactions between species and provides a blueprint for the type of spatially replicated multi-year data that needs to be collected in the future to further our understanding of ecological stability within multi-trophic communities.

Asynchronous opinion dynamics in social networks

Opinion spreading in a society decides the fate of elections, the success of products, and the impact of political or social movements. A prominent model to study opinion formation processes is due to Hegselmann and Krause. It has the distinguishing feature that stable states do not necessarily show consensus, i.e., the population of agents might not agree on the same opinion. We focus on the social variant of the Hegselmann–Krause model. There are n agents, which are connected by a social network. Their opinions evolve in an iterative, asynchronous process, in which agents are activated one after another at random. When activated, an agent adopts the average of the opinions of its neighbors having a similar opinion (where similarity of opinions is defined using a parameter ε\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varepsilon $$\\end{document}). Thus, the set of influencing neighbors of an agent may change over time. We show that such opinion dynamics are guaranteed to converge for any social network. We provide an upper bound of O(n|E|2(ε/δ)2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {O}}(n|E|^2 (\\varepsilon /\\delta )^2)$$\\end{document} on the expected number of opinion updates until convergence to a stable state, where |E|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$|E|$$\\end{document} is the number of edges of the social network, and δ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\delta $$\\end{document} is a parameter of the stability concept. For the complete social network we show a bound of O(n3(n2+(ε/δ)2))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {O}}(n^3(n^2 + (\\varepsilon /\\delta )^2))$$\\end{document} that represents a major improvement over the previously best upper bound of O(n9(ε/δ)2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {O}}(n^9 (\\varepsilon /\\delta )^2)$$\\end{document}.

Investigating the Potential Strategic Implications of COVID-19 for Biological Weapons Pursuit: A New Expert Simulation.

To investigate the impact of the COVID-19 pandemic on the strategic decisionmaking of leaders with respect to biological weapons, this study employed a prospective simulation technique called Asynchronous Strategic Dynamics Red Teaming. Using an immersive, multimedia simulation conducted remotely and asynchronously, the effort engaged 240 carefully selected and curated expert participants in either biological weapons or the countries of interest (as well as 60 naïve participants). Across our sample of 30 countries, simulated interest in pursuing some type of biological weapons program (defensive or offensive) remained low to moderate. While such interest increased after the simulated onset of the COVID-19 pandemic, it was limited overall, with only a handful of states showing salient increases in offensive biological weapon interest. When directly referencing why their countries might have changed their post-COVID-19 interest in biological weapons, the most commonly cited reasons were: (1) COVID-19 demonstrated the power of biological weapons to disrupt societies and cause large-scale economic harm, and (2) the pandemic revealed either the state's own or its rivals' vulnerability to diseases like COVID-19, as well as an inability to efficiently respond and contain such diseases. In sum, despite a global pandemic with massive consequences, the simulation revealed that most states are not likely to dramatically change their strategic posture regarding pursuit of offensive biological weapons.

The anatomy of government bond yields synchronization in the Eurozone

Abstract We investigate the synchronization of the Eurozone’s government bond yields at different maturities. For this purpose, we combine principal component analysis with random matrix theory. We find that synchronization depends on yield maturity. Short-term yields are not synchronized. Medium- and long-term yields, instead, were highly synchronized early after the introduction of the Euro. Synchronization then decreased significantly during the Great Recession and the European Debt Crisis, to partially recover after 2015. We interpret our empirical results using portfolio theory, and we point to divergence trades as a source of the self-sustained yield asynchronous dynamics. Our results envisage synchronization as a requirement for the smooth transmission of conventional monetary policy in the Eurozone.

Measurement-Induced Quantum Synchronization and Multiplexing.

Measurements are able to fundamentally affect quantum dynamics. We here show that a continuously measured quantum many-body system can undergo a spontaneous transition from asynchronous stochastic dynamics to noise-free stable synchronization at the level of single trajectories. We formulate general criteria for this quantum phenomenon to occur and demonstrate that the number of synchronized realizations can be controlled from none to all. We additionally find that ergodicity is typically broken, since time and ensemble averages may exhibit radically different synchronization behavior. We further introduce a quantum type of multiplexing that involves individual trajectories with distinct synchronization frequencies. Measurement-induced synchronization appears as a genuine nonclassical form of synchrony that exploits quantum superpositions.

The Interplay of Common Noise and Finite Pulses on Biological Neurons

The response of neurons is highly sensitive to the stimulus. The stimulus can be associated with a direct injection in vitro experimentation (e.g., time dependent and independent inputs); or post-synaptic potentials resulting from the interaction of many neurons. A typical incoming stimulus resembles a noise which in principle can be described as a random variable. In computational neuroscience, the noise has been extensively studied for different setups. In this study, we investigate the effect of noisy inputs in a minimal network of two identical leaky integrate-and-fire (LIF) neurons interacting with finite pulses. In particular, we consider a Gaussian white noise as a standard function for stochastic modelling of neurons, while taking into account the pulse width as an elementary component for the signal transmission. By exploring the role of noise and finite pulses, the two neurons show a synchronous spiking behaviour characterized by fluctuations in the interspike intervals. Above some critical values the synchronous regime collapses onto asynchronous dynamics. The abrupt change in such dynamics is accompanied by a hysteresis, i.e., the coexistence of synchronous and asynchronous firing behaviour.

Asynchronous domain dynamics and equilibration in layered oxide battery cathode

To improve lithium-ion battery technology, it is essential to probe and comprehend the microscopic dynamic processes that occur in a real-world composite electrode under operating conditions. The primary and secondary particles are the structural building blocks of battery cathode electrodes. Their dynamic inconsistency has profound but not well-understood impacts. In this research, we combine operando coherent multi-crystal diffraction and optical microscopy to examine the chemical dynamics in local domains of layered oxide cathode. Our results not only pinpoint the asynchronicity of the lithium (de)intercalation at the sub-particle level, but also reveal sophisticated diffusion kinetics and reaction patterns, involving various localized processes, e.g., chemical onset, reaction front propagation, domains equilibration, particle deformation and motion. These observations shed new lights onto the activation and degradation mechanisms of state-of-the-art battery cathode materials.

Growing-season drought and nitrogen addition interactively impair grassland ecosystem stability by reducing species diversity, asynchrony, and stability

Aboveground net primary productivity controls the amount of energy available to sustain all living organisms, and its sustainable provision relies on the stability of grassland ecosystems. Human activities leading to global changes, such as increased nitrogen (N) deposition and the more frequent occurrence of extreme precipitation events, with N addition increasing the sensitivity of ecosystem production stability to changes in the precipitation regime. However, whether N addition, in combination with seasonal precipitation increases or severe drought, affects ecosystem stability remains unclear. In this study, we conducted a six-year environmental change monitoring experiment in a semiarid grassland in northern China to test the effects of N addition, seasonal drought, and precipitation increases on the temporal stability of ecosystem productivity. Our study revealed that an interaction between drought and N addition reduced species diversity, species asynchrony, species stability, and thus ecosystem stability. These environmental change drivers (except for precipitation increase) induced a positive relationship between species asynchrony and diversity, whereas N addition interactively with drought and precipitation increase led to a negative relationship between diversity and species stability. Only N addition interactively with drought induced a positive species diversity–ecosystem stability relationship because lower species stability was overcome by increased species asynchrony. Our study is great importance to illustrate that production temporal stability tends to be inhibited with drought, though interactively with nutrient N addition. These findings highlight the primary role of asynchronous dynamics among species in modulating the effects of environmental change on diversity-stability relationships.

High-Density Exploration of Activity States in a Multi-Area Brain Model

To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia.

Selective logging destabilizes the functioning and composition of forest ecosystems at multiple spatial scales

Selective logging is one of the most prevalent land uses of forests worldwide, affecting biodiversity and ecosystem functioning. However, the effect of selective logging on the dual nature of temporal stability, and the scale dependence of this effect, remain to be elucidated. By conducting several decade‐long experiments in temperate forest ecosystems, we tested the effects of selective logging on aggregate and compositional stability at multiple spatial scales. As expected, forest ecosystem stability at larger spatial scales was enhanced both by the stability of local scales (i.e. α stability) and asynchronous dynamics among local communities (i.e. spatial asynchrony). We found that the negative effects of selective logging on both facets of forest stability propagated from local to larger spatial scales due to reduced α stability and the biological insurance effects of α diversity. However, both spatial aggregate and compositional asynchrony were not affected by selective logging. Interestingly, despite the selective logging, α diversity still provided biological insurance effects for maintaining aggregate and compositional stability. Our results imply that selective logging may destabilize the aggregate ecosystem functioning and species composition of forest ecosystems at local and larger spatial scales. To our knowledge, this study provides the first evidence of the scale dependence of aggregate and compositional stability of forest ecosystems in response to selective logging. Our findings suggest that forest management should avoid excessive selective logging and strive to protect forest diversity to safeguard the sustainability of the functioning and composition of natural forest ecosystems at multiple spatial scales.Keywords: aggregate stability, compositional stability, diversity, ecosystem functioning, spatial asynchrony, stability

Nash Equilibrium in Iterated Multiplayer Games Under Asynchronous Best-Response Dynamics

As an important branch of evolutionary game theory, iterated games describe the situations that interacting agents play repeatedly based on previous outcomes by using the conditional strategies. A new class of zero-determinant (ZD) strategies, which can control a linear relation between the expected payoffs of a single agent and the co-players, has dramatically changed the viewpoint on iterated games. Here, we focus on the decision-making behaviors in iterated multiplayer gaming (IMG) systems with the underlying scenarios of two competing ZD strategies. The results show that, under the asynchronous best-response dynamics, IMG systems starting from any initial state will converge to Nash equilibrium (NE) in finite time. Particularly, the convergence occurs not only in finite time, but can be limited by the number of strategy switches which is no more than the total amount of agents in the population. Further studies on calculating the NE points reveal that, there is a threshold for the ZD slope, above which agents with higher baseline payoff dominate, while below which agents of lower baseline payoff prevail. The results of system convergence and NE states highlight the fixation of long-run decision-making behaviors in IMG. Finally, an example of the iterated public goods games is provided for the application of the proposed IMG model.

Detecting partial synchrony in a complex oscillatory network using pseudovortices.

Partial synchronization is an important dynamical process of coupled oscillators on various natural and artificial networks, which can remain undetected due to the system complexity. With an analogy between pairwise asynchrony of oscillators and topological defects, i.e., vortices, in the two-dimensional XY model, we propose a robust and data-driven method to identify the partial synchronization on complex networks. The proposed method is based on an integer matrix whose element is pseudovorticity that discretely quantifies asynchronous phase dynamics in every two oscillators, which results in graphical and entropic representations of partial synchrony. As a first trial, we apply our method to 200 FitzHugh-Nagumo neurons on a complex small-world network. Partially synchronized chimera states are revealed by discriminating synchronized states even with phase lags. Such phase lags also appear in partial synchronization in chimera states. Our topological, graphical, and entropic method is implemented solely with measurable phase dynamics data, which will lead to a straightforward application to general oscillatory networks including neural networks in the brain.

Stability of ecosystems under oscillatory driving with frequency modulation.

Consumer-resource cycles are widespread in ecosystems, and seasonal forcing is known to influence them profoundly. Typically, seasonal forcing perturbs an ecosystem with time-varying frequency; however, previous studies have explored the dynamics of such systems under oscillatory forcing with constant frequency. Studies of the effect of time-varying frequency on ecosystem stability are lacking. Here we investigate isolated and network models of a cyclic consumer-resource ecosystem with oscillatory driving subjected to frequency modulation. We show that frequency modulation can induce stability in the system in the form of stable synchronized solutions, depending on intrinsic model parameters and extrinsic modulation strength. The stability of synchronous solutions is determined by calculating the maximal Lyapunov exponent, which determines that the fraction of stable synchronous solution increases with an increase in the modulation strength. We also uncover intermittent synchronization when synchronous dynamics are intermingled with episodes of asynchronous dynamics. Using the phase-reduction method for the network model, we reduce the system into a phase equationthat clearly distinguishes synchronous, intermittently synchronous, and asynchronous solutions. While investigating the role of network topology, we find that variation in rewiring probability has a negligible effect on the stability of synchronous solutions. This study deepens our understanding of ecosystems under seasonal perturbations.

COVID-19 is linked to changes in the time-space dimension of human mobility.

Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.

Grassland stability decreases with increasing number of global change factors: A meta-analysis

Experiments manipulating a single global change factor (GCF) have provided increasing evidence that global environmental changes, such as eutrophication, precipitation change, and warming, generally affect the temporal stability of grassland productivity. Whether the combined impact of global changes on grassland stability increases as the number of global changes increases remains unknown. Using a meta-analysis of 673 observations from 143 sites worldwide, including 7 different GCFs, we examined the responses of grassland temporal stability of productivity to increasing numbers of GCFs. We quantified the links between community stability, biotic factors (i.e., species richness, species stability, and species asynchrony), and abiotic factors (i.e., aridity index, experimental duration, and experimental intensity). Although inconsistent responses of community stability were found with different GCF types and combinations, when integrating existing GCFs studies and ignoring the identity of GCFs, we found a general decrease in community stability as the number of GCFs increases, but the main drivers of community stability varied with the numbers of GCFs. Specifically, one GCF mainly reduced species stability through species richness and thus weakened community stability. Two GCFs weakened community stability via independently weakening species stability and species asynchrony. Three GCFs reduce community stability mainly via independently weakening species asynchrony. Moreover, for single factor, the impact of GCFs on community stability was weaker under dryer conditions, but stronger when two or three factors were manipulated. In addition, the negative effect of GCFs on community stability was weaker with increasing experimental duration. Our study reveals that reduced community stability with increasing numbers of GCFs is caused by a shift from reduced species stability to reduced species asynchrony, suggesting that persistent global changes will destabilize grassland productivity by reducing asynchronous dynamics among species in response to natural environmental fluctuations.