Asynchronous Dynamics Research Articles

Long‐term changes in littoral fish community structure and resilience of total catch to re‐oligotrophication in a large, peri‐alpine European lake

Abstract The littoral zone of lakes is used as spawning, shelter, or feeding habitat for many fish species and hence is of key importance for overall lake functioning. Despite this, hardly any studies exist examining the long‐term dynamics and response of the littoral fish community, composed mostly of juvenile fish, to environmental change. Here, we study the response of total catch per unit effort (CPUE) and individual species CPUE of such a community to 17 years of oligotrophication and examine whether the species responses can be characterised as synchronous or asynchronous. We analyse a data set of beach seine catches carried out during morning and twilight, late spring and late summer at three sites in large and deep Lake Constance from 1997 to 2014. Generalised additive mixed models were used to explore changes in CPUE of the overall community and of the most frequently occurring species, and Kendall's W test was applied to examine whether the dynamics of fish species were synchronous or asynchronous. Species‐specific and total CPUE strongly differed between morning and twilight and between spring and summer indicating an important role of behavioural and life cycle adaptations of species for CPUE. In addition, also the CPUE of some species seeking shelter behind larger stones was lower at sites without these. Total CPUE did not decline suggesting the overall abundance of littoral fish was resilient to declining nutrients. In contrast, fish community composition changed strongly during the study period due to increases in some species (dace, loach, perch) and decreases in others (bream, burbot, chub, ruffe), indicating response diversity of fish to oligotrophication. The type of community dynamics was scale‐dependent, whereby significantly synchronous dynamics according to Kendall's W were observed when taking seasonal variability into account. In contrast, significantly asynchronous species dynamics were observed when only the low‐frequency variability of species dynamics was considered separately for spring and summer time series. Resilience of littoral fish total CPUE to oligotrophication might have important consequences for ecosystem dynamics and ecosystem services beyond the littoral zone. As small fish often impose strong predation pressure on zooplankton, their resilience might sustain a high top‐down control on zooplankton resulting in a further reduction of zooplankton biomass. This could contribute to delayed food web responses and reduced growth of fish with oligotrophication.

Simulating Small Neural Circuits with a Discrete Computational Model

Simulations of neural activity are commonly based on differential equations. We address the question what can be achieved with a simplified discrete model. The proposed model resembles artificial neural networks enriched with additional biologically inspired features. A neuron has several states, and the state transitions follow endogenous patterns which roughly correspond to firing behavior observed in biological neurons: oscillatory, tonic, plateauing, etc. Neural interactions consist of two components: synaptic connections and extrasynaptic emission of neurotransmitters. The dynamics is asynchronous and event-based; the events correspond to the changes in neurons activity. This model is innovative in introducing discrete framework for modeling neurotransmitter interactions which play the important role in neuromodulation. We simulate rhythmic activity of small neural ensembles like central pattern generators (CPG). The modeled examples include: the biphasic rhythm generated by the half-center mechanism with the post-inhibitory rebound (like the leech heartbeat CPG), the triphasic rhythm (like in pond snail feeding CPG) and the pattern switch in the system of several neurons (like the switch between ingestion and egestion in Aplysia feeding CPG). The asynchronous dynamics allows to obtain multi-phasic rhythms with phase durations close to their biological prototypes. The perspectives of discrete modeling in biological research are discussed in the conclusion.

Genetic versus demographic stock structure of rainbow smelt in a large fragmented lake

Identification of fish stocks plays an important role in fisheries management, but stock identification often depends on the techniques used and the management goals as much as on actual population structure. Historically, stocks were identified by place of capture, population demography and morphology, but genetic stock identification has become a standard approach. Here, we evaluate the stock structure of rainbow smelt (Osmerus mordax) in three basins of Lake Champlain separated by causeways using genotype data from six microsatellite loci and 26 years of demographic data. No genetic differences among rainbow smelt from the different basins were evident, which suggests that gene flow occurs among basins. However, length, age, and catch-per-unit-effort of rainbow smelt suggests asynchronous population dynamics in the different basins, and thus each basin may hold populations that are at least partially isolated from one another. Consequently, we conclude that while rainbow smelt in Lake Champlain consist of at least three demographic stocks they may form only a single genetic stock. Our results concur with other studies that suggest care should be taken when only a single method of stock identification is used.

Additive negative effects of decadal warming and nitrogen addition on grassland community stability

Abstract Much recent research has explored how global warming and increased nitrogen (N) deposition, two important components of global environmental changes, influence the structure and functioning of natural ecosystems. However, how ecosystem dynamics respond to the combination of long‐term warming and N enrichment remains largely unexplored. We investigated the impact of warming and N addition on the temporal stability of plant communities in a decade‐long field experiment, conducted in a desert steppe in northern China, using a split‐plot design with warming as the main‐plot factor and N addition as the split‐plot factor. Long‐term warming and N addition had additive, negative effects on plant community stability. A warming‐induced decrease in species richness was not a significant driver of decreased community stability, which was instead driven by the decreased stability of dominant species under warming. On the other hand, a N‐induced decrease in community stability was ascribed to both decreased stability of dominant and common species and decreased asynchronous population dynamics under N addition. Synthesis. Our results suggest that ongoing anthropogenic environmental changes may have appreciable consequences for the stability of natural grassland functions and services while also highlighting the different mechanisms associated with the similar effects of climate warming and increased N deposition on grassland community stability.

Synchronous and asynchronous dynamics of the concerted three-body dissociations of temporary negative ion CH2F2.

Molecular concerted three-body dissociation is a fast process, but still can be classified into synchronous and asynchronous pathways. It is challenging in experiments to evaluate different contributions of the aforementioned mechanisms. Here, we report an experimental identification of the synchronous and asynchronous concerted three-body dissociations of temporary negative ion CH2F2 - at an electron-molecule resonant state formed by electron attachment. The synchronous-asynchronous branching ratios indicate that the asynchronous process is predominant although the synchronous contribution is slightly enhanced with the increase in the electron attachment energy. This study provides two intuitive pictures of the concerted three-body dissociations, in particular for the nonequivalent-bond cleavages of a polyatomic molecule.

Synchronization of Coupled Boolean Networks With Different Update Scheme

In this paper, the synchronization of coupled Boolean networks with asynchronous update scheme is investigated. First, the asynchronous Boolean dynamics is transformed into a linear expression with the semi-tensor product technique. Then, the synchronization cycle is proposed to establish the synchronization conditions of asynchronous Boolean networks. Furthermore, an algorithm is given, which can be used to quickly check whether there exists the synchronization cycle in the coefficient matrix of the Boolean function. Finally, an example is provided to verify the efficiency of the theoretical analysis.

Asynchronous Consensus Dynamics for Group of High-Order Agents Under Switching Topologies and Time-Varying Delays

This paper investigates the consensus problem of high-order multi-agent systems (MASs) under multiple communication constraints, including asynchronous communication, arbitrary switching topologies and time-varying delays. In the scenario of asynchronous communication, each agent only receives its neighbors’ states information at the time instants when it needs to adjust the state. For each agent, an distributed control protocol involving its own all-order state information and its neighbors’ first-order delayed state information is designed. With the help of state vector transformation technology, the high-order consensus problem under multiple communication constraints can be equivalently transformed into a convergence problem of products of nonnegative matrices. The related knowledge of nonnegative matrix theory is used to solve this convergence problem, and further establish a sufficient algebraic condition of achieving high-order consensus. Finally, the achievement of high-order consensus is demonstrated through a numerical example.

Distinct enhancer signatures in the mouse gastrula delineate progressive cell fate continuum during embryo development.

Primary germ layers have the potential to form all tissues in the mature organism, and their formation during gastrulation requires precise epigenetic modulation of both proximal and distal regulatory elements. Previous studies indicated that spatial and temporal patterns of gene expression in the gastrula predispose individual regions to distinct cell fates. However, the underlying epigenetic mechanisms remain largely unexplored. Here, we profile the spatiotemporal landscape of the epigenome and transcriptome of the mouse gastrula. We reveal the asynchronous dynamics of proximal chromatin states during germ layer formation as well as unique gastrula-specific epigenomic features of regulatory elements, which have strong usage turnover dynamics and clear germ layer-specific signatures. Importantly, we also find that enhancers around organogenetic genes, which are weakly expressed at the gastrulation stage, are frequently pre-marked by histone H3 lysine 27 acetylation (H3K27ac) in the gastrula. By using the transgenic mice and genome editing system, we demonstrate that a pre-marked enhancer, which is located in the intron of a brain-specific gene 2510009E07Rik, exhibits specific enhancer activity in the ectoderm and future brain tissue, and also executes important function during mouse neural differentiation. Taken together, our study provides the comprehensive epigenetic information for embryonic patterning during mouse gastrulation, demonstrates the importance of gastrula pre-marked enhancers in regulating the correct development of the mouse embryo, and thus broadens the current understanding of mammalian embryonic development and related diseases.

Model reconstruction from temporal data for coupled oscillator networks.

In a complex system, the interactions between individual agents often lead to emergent collective behavior such as spontaneous synchronization, swarming, and pattern formation. Beyond the intrinsic properties of the agents, the topology of the network of interactions can have a dramatic influence over the dynamics. In many studies, researchers start with a specific model for both the intrinsic dynamics of each agent and the interaction network and attempt to learn about the dynamics of the model. Here, we consider the inverse problem: given data from a system, can one learn about the model and the underlying network? We investigate arbitrary networks of coupled phase oscillators that can exhibit both synchronous and asynchronous dynamics. We demonstrate that, given sufficient observational data on the transient evolution of each oscillator, machine learning can reconstruct the interaction network and identify the intrinsic dynamics.

Predicting resilience of ecosystem functioning from co-varying species' responses to environmental change.

Understanding how environmental change affects ecosystem function delivery is of primary importance for fundamental and applied ecology. Current approaches focus on single environmental driver effects on communities, mediated by individual response traits. Data limitations present constraints in scaling up this approach to predict the impacts of multivariate environmental change on ecosystem functioning.We present a more holistic approach to determine ecosystem function resilience, using long‐term monitoring data to analyze the aggregate impact of multiple historic environmental drivers on species' population dynamics. By assessing covariation in population dynamics between pairs of species, we identify which species respond most synchronously to environmental change and allocate species into “response guilds.” We then use “production functions” combining trait data to estimate the relative roles of species to ecosystem functions. We quantify the correlation between response guilds and production functions, assessing the resilience of ecosystem functioning to environmental change, with asynchronous dynamics of species in the same functional guild expected to lead to more stable ecosystem functioning.Testing this method using data for butterflies collected over four decades in the United Kingdom, we find three ecosystem functions (resource provisioning, wildflower pollination, and aesthetic cultural value) appear relatively robust, with functionally important species dispersed across response guilds, suggesting more stable ecosystem functioning. Additionally, by relating genetic distances to response guilds we assess the heritability of responses to environmental change. Our results suggest it may be feasible to infer population responses of butterflies to environmental change based on phylogeny—a useful insight for conservation management of rare species with limited population monitoring data.Our approach holds promise for overcoming the impasse in predicting the responses of ecosystem functions to environmental change. Quantifying co‐varying species' responses to multivariate environmental change should enable us to significantly advance our predictions of ecosystem function resilience and enable proactive ecosystem management.

Numerical study of condensation in a Fermi-like model of counterflowing particles via Gini coefficient

The collective motion of self-driven particles shows interesting novel phenomena such as swarming and the emergence of patterns. We have recently proposed a model for counterflowing particles that captures this idea and exhibits clogging transitions. This model is based on a generalization of the Fermi–Dirac statistics wherein the maximal occupation of a cell is used. Here we present a detailed study comparing synchronous and asynchronous stochastic dynamics within this model. We show that an asynchronous updating scheme supports the mobile-clogging transition and eliminates some mobility anomalies that are present in synchronous Monte Carlo simulations. Moreover, we show that this transition is dependent upon its initial conditions. Although the Gini coefficient was originally used to model wealth inequalities, we show that it is also efficient for studying the mobile-clogging transition. Finally, we compare our stochastic simulation with direct numerical integration of partial differential equations used to describe this model.

Reconstructing positive and negative couplings in Ising spin networks by sorted local transfer entropy.

We employ the sorted local transfer entropy (SLTE) to reconstruct the coupling strengths of Ising spin networks with positive and negative couplings (J_{ij}), using only the time-series data of the spins. The SLTE method is model-free in the sense that no knowledge of the underlying dynamics of the spin system is required and is applicable to a broad class of systems. Contrary to the inference of coupling from pairwise transfer entropy, our method can reliably distinguish spin pair interactions with positive and negative couplings. The method is tested for the inverse Ising problem for different J_{ij} distributions and various spin dynamics, including synchronous and asynchronous update Glauber dynamics and Kawasaki exchange dynamics. It is found that the pairwise SLTE is proportional to the pairwise coupling strength to a good extent for all cases studied. In addition, the reconstruction works well for both the equilibrium and nonequilibrium cases of the time-series data. Comparison to other inverse Ising problem approaches using mean-field equations is also discussed.

The role of network structure and time delay in a metapopulation Wilson--Cowan model

We study the dynamics of a network Wilson--Cowan model (a system of connected Wilson--Cowan oscillators) for interacting excitatory and inhibitory neuron populations with time delays. Each node in this model corresponds to a population of neurons, including excitatory and inhibitory subpopulations, and hence it can be viewed as a metapopulation model. It is known that information transfer within each cortical area is not instantaneous, and therefore we consider a system of delay differential equations with two different kinds of discrete delays. We account for the time delay in information propagation between individual excitatory and inhibitory subpopulations at each node via intra-node time delays, and we account for time delay in information propagation between neuron populations at different nodes with inter-node time delays. The biologically relevant resting state solutions are oscillatory (stable limit cycles). After determining the influence of the coupling parameters between nodes, the intra-node delays, and the inter-node delays on the dynamics of the two coupled Wilson--Cowan oscillators, we then explore a variety of larger networks of 16 and 100 nodes, in order to determine how the network topology will influence time delayed Wilson--Cowan dynamics. We find that network structure can regularize or deregularize the dynamics, with networks of higher mean degree permitting stable limit cycles and networks with smaller mean degree yielding less regular dynamics (which may range from chaotic solutions, to solutions for which limit cycles collapse into steady states, which are biologically undesirable compared with the preferred stable limit cycles). Furthermore, heterogeneity in the degree distribution of the network (resulting from networks with nodes of varying degree) can result in asynchronous dynamics, even if at each node the local dynamics are that of a limit cycle, in contrast to the synchronization of dynamics between nodes seen when the degree of all nodes is equal. This suggests that homogeneous and well-connected networks permit robust limit cycles under time-delayed Wilson--Cowan dynamics, whereas heterogeneous or poorly connected networks may fail to provide such desirable dynamics, a phenomena akin to structural loss of neuron connections in neurodegenerative diseases.

Incentive-Based Control of Asynchronous Best-Response Dynamics on Binary Decision Networks

Various populations of interacting decision-making agents can be modeled by asynchronous best-response dynamics or, equivalently, linear threshold dynamics. Building upon recent convergence results in the absence of control, we now consider how such a network can be efficiently driven to a desired equilibrium state by offering payoff incentives or rewards for using a particular strategy, either uniformly or targeted to individuals. We begin by showing that strategy changes are monotone following an increase in payoffs in coordination games, and that the resulting equilibrium is unique. Based on these results, for the case when a uniform incentive is offered to all agents, we show how to compute the optimal incentive using a binary search algorithm. When different incentives can be offered to each agent, we propose a new algorithm to select which agents should be targeted based on maximizing a ratio between the cascading effect of a strategy switch by each agent and the incentive required to cause the agent to switch. Simulations show that this algorithm computes near-optimal targeted incentives for a wide range of networks and payoff distributions in coordination games and can also be effective for anticoordination games.

Neural activity of heterogeneous inhibitory spiking networks with delay.

We study a network of spiking neurons with heterogeneous excitabilities connected via inhibitory delayed pulses. For globally coupled systems the increase of the inhibitory coupling reduces the number of firing neurons by following a winner-takes-all mechanism. For sufficiently large transmission delay we observe the emergence of collective oscillations in the system beyond a critical coupling value. Heterogeneity promotes neural inactivation and asynchronous dynamics and its effect can be counteracted by considering longer time delays. In sparse networks, inhibition has the counterintuitive effect of promoting neural reactivation of silent neurons for sufficiently large coupling. In this regime, current fluctuations are on one side responsible for neural firing of subthreshold neurons and on the other side for their desynchronization. Therefore, collective oscillations are present only in a limited range of coupling values, which remains finite in the thermodynamic limit. Out of this range the dynamics is asynchronous and for very large inhibition neurons display a bursting behavior alternating periods of silence with periods where they fire freely in absence of any inhibition.

Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity.

Because natural ecosystems are complex, it is difficult to predict how their variability scales across space and levels of organization. The species-insurance hypothesis predicts that asynchronous dynamics among species should reduce variability when biomass is aggregated either from local species populations to local multispecies communities, or from metapopulations to metacommunities. Similarly, the spatial-insurance hypothesis predicts that asynchronous spatial dynamics among either local populations or local communities should stabilize metapopulation biomass and metacommunity biomass, respectively. In combination, both species and spatial insurance reduce variation in metacommunity biomass over time, yet these insurances are rarely considered together in natural systems. We partitioned the extent that species insurance and spatial insurance reduced the annual variation in macroalgal biomass in a southern California kelp forest. We quantified variability and synchrony at two levels of organization (population and community) and two spatial scales (local plots and region) and quantified the strength of species and spatial insurance by comparing observed variability and synchrony in aggregate biomass to null models of independent species or spatial dynamics based on cyclic-shift permutation. Spatial insurance was weak, presumably because large-scale oceanographic processes in the study region led to high spatial synchrony at both population- and community-level biomass. Species insurance was stronger due to asynchronous dynamics among the metapopulations of a few common species. In particular, a regional decline in the dominant understory kelp species Pterygophora californica was compensated for by the rise of three subdominant species. These compensatory dynamics were associated with positive values of the Pacific Decadal Oscillation, indicating that differential species tolerances to warmer temperature and nutrient-poor conditions may underlie species insurance in this system. Our results illustrate how species insurance can stabilize aggregate community properties in natural ecosystems where environmental conditions vary over broad spatial scales.

The Spectrum of Asynchronous Dynamics in Spiking Networks as a Model for the Diversity of Non-rhythmic Waking States in the Neocortex

SummaryThe awake cortex exhibits diverse non-rhythmic network states. However, how these states emerge and how each state impacts network function is unclear. Here, we demonstrate that model networks of spiking neurons with moderate recurrent interactions display a spectrum of non-rhythmic asynchronous dynamics based on the level of afferent excitation, from afferent input-dominated (AD) regimes, characterized by unbalanced synaptic currents and sparse firing, to recurrent input-dominated (RD) regimes, characterized by balanced synaptic currents and dense firing. The model predicted regime-specific relationships between different neural biophysical properties, which were all experimentally validated in the somatosensory cortex (S1) of awake mice. Moreover, AD regimes more precisely encoded spatiotemporal patterns of presynaptic activity, while RD regimes better encoded the strength of afferent inputs. These results provide a theoretical foundation for how recurrent neocortical circuits generate non-rhythmic waking states and how these different states modulate the processing of incoming information.

Reliable local dynamics in the brain across sessions are revealed by whole-brain modeling of resting state activity.

Resting state fMRI is a tool for studying the functional organization of the human brain. Ongoing brain activity at "rest" is highly dynamic, but procedures such as correlation or independent component analysis treat functional connectivity (FC) as if, theoretically, it is stationary and therefore the fluctuations observed in FC are thought as noise. Consequently, FC is not usually used as a single-subject level marker and it is limited to group studies. Here we develop an imaging-based technique capable of reliably portraying information of local dynamics at a single-subject level by using a whole-brain model of ongoing dynamics that estimates a local parameter, which reflects if each brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting-state sessions of one single subject and single resting-state sessions from a group of 50 participants we demonstrate that brain dynamics can be quantified consistently with respect to group dynamics using a scanning time of 20 min. We show that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger heterogeneity in its values compared to other lobules. Nevertheless, frontal regions and hubs showed higher consistency within the same subject while the inter-session variability found in primary visual and motor areas was only as high as the one found across subjects. The framework presented here can be used to study functional brain dynamics at group and, more importantly, at individual level, opening new avenues for possible clinical applications.

On the conversion of multivalued to Boolean dynamics

Results and tools on discrete interaction networks are often concerned with Boolean variables, whereas considering more than two levels is sometimes useful. Multivalued networks can be converted to partial Boolean maps, in a way that preserves the asynchronous dynamics. We investigate the problem of extending these maps to non-admissible states, i.e. states that do not have a multivalued counterpart. We observe that attractors are preserved if a stepwise version of the original function is considered for conversion. Different extensions of the Boolean conversion affect the structure of the interaction graphs in different ways. A particular technique for extending the partial Boolean conversion is identified, that ensures that feedback cycles are preserved. This property, combined with the conservation of the asymptotic behaviour, can prove useful for the application of results and analyses defined in the Boolean setting to multivalued networks, and vice versa. As a first application, by considering the conversion of a known example for the discrete multivalued case, we create a Boolean map showing that the existence of a cyclic attractor and the absence of fixed points are compatible with the absence of local negative cycles. We then state a multivalued version of a result connecting mirror states and local feedback cycles.

Memory in the margins: The connecting and colliding of vernacular war memories

This article examines how war memory circulates, connects and collides on digital media platforms driven by digital publics that form around popular culture. Through a case study of vernacular memory discourses emerging around a game inspired by the Yugoslav war, the article investigates how the commenting practices of YouTube users provide insights into the feelings of belonging of conflict-affected subjects that go beyond ethnicity and exceed geographical boundaries. The comments of 331 videos were analysed, using an open source tool and sequential mixed-method content analysis. Media-based collectivities emerging on YouTube are influenced by the reactive and asynchronous dynamics of comments that stimulate the emergence of micro-narratives. Within this plurality of voices, connective moments focus on shared memories of trauma and displacement beyond ethnicity. However, clashing collective memories cause disputes that reify identification along ethnic lines. The article concludes that memory discourses emerging in the margins of YouTube represent the affective reactions of serendipitous encounters between users of audio-visual content.