Global Network Dynamics Research Articles

Local paths to global coherence: cutting networks down to size.

How does connectivity impact network dynamics? We address this question by linking network characteristics on two scales. On the global scale, we consider the coherence of overall network dynamics. We show that such global coherence in activity can often be predicted from the local structure of the network. To characterize local network structure, we use "motif cumulants," a measure of the deviation of pathway counts from those expected in a minimal probabilistic network model. We extend previous results in three ways. First, we give acombinatorial formulation of motif cumulants that relates to the allied concept in probability theory. Second, we show that the link between global network dynamics and local network architecture is strongly affected by heterogeneity in network connectivity. However, we introduce a network-partitioning method that recovers a tight relationship between architecture and dynamics. Third, for a particular set of models, we generalize the underlying theory to treat dynamical coherence at arbitrary orders (i.e., triplet correlations and beyond). We show that at any order, only a highly restricted set of motifs impacts dynamical correlations.

The state, institutional frameworks and the dynamics of capital in global production networks

For some time now, calls have been made for a more adequate theorization of the state and institutional frameworks in work on global production networks (GPNs) and global value chains (GVCs). However, despite claims in GPN research that institutional contexts matter, there remains an absence of theoretical frameworks on the state in GPNs. Consequently, understanding of the relations between state action and the changing geographies of production networks remains in its infancy. This paper explores a strategic-relational understanding of the state and the rearticulation of scales of state formation to understand the dynamics of global production networks in macro-regional spaces.

Hemispheric Distribution of Subsurface Kinetic Helicity and Its Variation with Magnetic Activity

We study the hemispheric distribution of the kinetic helicity of subsurface flows in the near-surface layers of the solar convection zone and its variation with magnetic activity. We determine subsurface flows with a ring-diagram analysis applied to Global Oscillation Network Group (GONG) Dopplergrams and Dynamics Program data from the Michelson Doppler Imager (MDI) instrument onboard the Solar and Heliospheric Observatory (SOHO). We determine the average kinetic helicity density as a function of Carrington rotation and latitude. The average kinetic helicity density at all depths and the kinetic helicity, integrated over 2 – 7 Mm, follow the same hemispheric rule as the current/magnetic helicity proxies with predominantly positive values in the southern and negative ones in the northern hemisphere. This holds true for all levels of magnetic activity from quiet to active regions. However, this is a statistical result; only about 55 % of all locations follow the hemispheric rule. But these locations have larger helicity values than those that do not follow the rule. The average values of helicity density increase with depth for all levels of activity, which might reflect an increase of the characteristic size of convective motions with greater depth. The average helicity of subsets of high magnetic activity is about five times larger than that of subsets of low activity. The solar-cycle variation of helicity is thus mainly due to the presence or absence of active regions. During the rising phase of cycle 24, locations of high magnetic activity at low latitudes show a weaker hemispheric behavior compared to the rising phase of cycle 23.

Global dynamics of discrete neural networks allowing non-monotonic activation functions

We show that in discrete models of Hopfield type some properties of global stability and chaotic behaviour are coded in the dynamics of a related one-dimensional equation. Using this fact, we obtain some new results on stability and chaos for a system of delayed neural networks; some relevant properties of our results are that we do not require monotonicity properties in the activation function, we allow any architecture of the network, and the conclusions are independent of the size of the time delays.

Neural networks of colored sequence synesthesia.

Synesthesia is a condition in which normal stimuli can trigger anomalous associations. In this study, we exploit synesthesia to understand how the synesthetic experience can be explained by subtle changes in network properties. Of the many forms of synesthesia, we focus on colored sequence synesthesia, a form in which colors are associated with overlearned sequences, such as numbers and letters (graphemes). Previous studies have characterized synesthesia using resting-state connectivity or stimulus-driven analyses, but it remains unclear how network properties change as synesthetes move from one condition to another. To address this gap, we used functional MRI in humans to identify grapheme-specific brain regions, thereby constructing a functional "synesthetic" network. We then explored functional connectivity of color and grapheme regions during a synesthesia-inducing fMRI paradigm involving rest, auditory grapheme stimulation, and audiovisual grapheme stimulation. Using Markov networks to represent direct relationships between regions, we found that synesthetes had more connections during rest and auditory conditions. We then expanded the network space to include 90 anatomical regions, revealing that synesthetes tightly cluster in visual regions, whereas controls cluster in parietal and frontal regions. Together, these results suggest that synesthetes have increased connectivity between grapheme and color regions, and that synesthetes use visual regions to a greater extent than controls when presented with dynamic grapheme stimulation. These data suggest that synesthesia is better characterized by studying global network dynamics than by individual properties of a single brain region.

Mechanisms of sharp wave-ripple generation and autonomous replay in a hippocampal network model

Several distinct patterns of population activity can be recorded in the hippocampus in vivo. These neural activity patterns depend on the behavioral state of the animal, and include theta-modulated gamma oscillations as well as low-level irregular activity with periodically occurring large-amplitude sharp wave-ripple (SWR) events. During SWRs, neuronal populations in the hippocampus have been found to replay, on a faster time scale, activity recorded during theta-gamma activity in the exploring animal. Such replay may be important for the establishment, maintenance and consolidation of long-term memory. Our aim was to develop a mechanistic understanding of cellular and network mechanisms underlying the generation of SWRs in general, and spatio-temporal sequence replay during SWRs in particular, based on in vitro and in vivo experimental observations. A recently developed hippocampal slice preparation, in which SWRs arise spontaneously, has allowed the collection of a large and diverse set of data regarding the properties of SWRs, as well as the characterization of several cell types and synapses which are critical in their generation. Based on these data, combined with phase-plane analysis of the network dynamics, we developed a large-scale network model of the hippocampal CA3 region. We found that our model based on measured cellular and synaptic parameters could faithfully reproduce the experimentally observed SWR activity as long as we included an appropriate slow feedback mechanism which was responsible for the termination of SWR bursts. Our model allowed us to rule out several potential candidates (such as neuronal adaptation) for the slow feedback process, suggesting that either slowly activating interneuronal feedback or short-term synaptic plasticity of connections within CA3 might terminate SWRs. Statistical analysis and fitting of the inter-event interval distribution of SWRs in vitro suggested that, following an initial 'refractory period' after each SWR, the next SWR is initiated stochastically, requiring the simultaneous activation of a threshold number of pyramidal cells. When we implemented the changes in cellular and synaptic properties measured in the slice following the activation of cholinergic receptors, our model replicated the experimentally observed transition from SWR activity to gamma oscillations. Finally, applying a spike-timing-dependent plasticity rule to the recurrent excitatory weights during simulated exploration, the emerging weight structure led to the spontaneous replay of sequences of place cell representations during simulated SWRs. We also found that using structured rather than random weights substantially altered the global network dynamics, resulting in a sparse participation of pyramidal neurons in individual SWR events, and allowing our model to match more closely the corresponding experimental observations.

Noninvasive brain stimulation: from physiology to network dynamics and back

Noninvasive brain stimulation techniques have been widely used for studying the physiology of the CNS, identifying the functional role of specific brain structures and, more recently, exploring large-scale network dynamics. Here we review key findings that contribute to our understanding of the mechanisms underlying the physiological and behavioral effects of these techniques. We highlight recent innovations using noninvasive stimulation to investigate global brain network dynamics and organization. New combinations of these techniques, in conjunction with neuroimaging, will further advance the utility of their application.

Stabilizing gene regulatory networks through feedforward loops

The global dynamics of gene regulatory networks are known to show robustness to perturbations in the form of intrinsic and extrinsic noise, as well as mutations of individual genes. One molecular mechanism underlying this robustness has been identified as the action of so-called microRNAs that operate via feedforward loops. We present results of a computational study, using the modeling framework of stochastic Boolean networks, which explores the role that such network motifs play in stabilizing global dynamics. The paper introduces a new measure for the stability of stochastic networks. The results show that certain types of feedforward loops do indeed buffer the network against stochastic effects.

Foundations of Open Semantic Service Networks

This article provides the foundations for the new field of research coined Service Networks research. Understanding what factors explain the structure and dynamics of global service networks may lead to a more efficient and balanced society and economy. The concept of service network is formally represented as a business structure made up of services systems which are nodes connected by one or more specific types of relationships. They can be used to represent the global economy, i.e. a complex network composed of national economies, which are themselves networks of markets, and markets are also networks of providers, brokers, intermediaries, and consumers. The authors’ key challenge consists in developing a novel perspective on socio-economic dynamics by connecting service models representing service systems (e.g. consulting, governmental, and educational services). Theories to be developed will enable to understand, describe, explain, analyze, predict, and control the evolution of global service networks..

Global innovation networks, human capital, and development

In this Special Issue, we explore the dynamics of global innovation networks (GINs), and their implications for development. In the neo-Shumpeterian and ‘new trade theory’ context, knowledge is see...

Rethinking governance and value in commodity chains through global recycling networks

The dominant political‐economic approaches to global trade flows known as global value chains and global production networks offer powerful insights into the coordination and location of globally stretched supply chains, in particular from global South to North. By way of both conceptual and empirical challenge, this paper highlights flows of end‐of‐life goods from the global North towards the global South. This involves the disassembly and destruction of goods to recover secondary resources for further rounds of commodity production. Global recycling networks take things of rubbish value (often spent or end‐of‐life goods) and turn them back into resources in other places and production networks. They operate not through adding value, but by connecting different regimes of value. The paper does not set out a new conceptual framework, but asks what challenges the rekindling of value in used goods creates for global commodity chain analysis and what insights those approaches bring to looking at waste flows. The examples of used clothing and end‐of‐life merchant ships are mobilised to illustrate the dynamics of global recycling networks and to challenge prevailing commodity chain approaches in three key areas – supply logics and crosscutting networks, value and materiality, and inter‐firm governance. We argue that resource recovery engenders highly complex and brokered forms of governance that relate to practices of valuing heterogeneous materials and that contrast markedly with the modes of coordination dominated by big capital typical of global production networks for consumer goods.

A method to identify important dynamical states in boolean models of regulatory networks: application to regulation of stomata closure by ABA in A. thaliana

BackgroundWe introduce a method to analyze the states of regulatory Boolean models that identifies important network states and their biological influence on the global network dynamics. It consists in (1) finding the states of the network that are most frequently visited and (2) the identification of variable and frozen nodes of the network. The method, along with a simulation that includes random features, is applied to the study of stomata closure by abscisic acid (ABA) in A. thaliana proposed by Albert and coworkers.ResultsWe find that for the case of study, that the dynamics of wild and mutant networks have just two states that are highly visited in their space of states and about a third of all nodes of the wild network are variable while the rest remain frozen in True or False states. This high number of frozen elements explains the low cardinality of the space of states of the wild network. Similar results are observed in the mutant networks. The application of the method allowed us to explain how wild and mutants behave dynamically in the SS and determined an essential feature of the activation of the closure node (representing stomata closure), i.e. its synchronization with the AnionEm node (representing anion efflux at the plasma membrane). The dynamics of this synchronization explains the efficiency reached by the wild and each of the mutant networks.ConclusionsFor the biological problem analyzed, our method allows determining how wild and mutant networks differ ‘phenotypically’. It shows that the different efficiencies of stomata closure reached among the simulated wild and mutant networks follow from a dynamical behavior of two nodes that are always synchronized. Additionally, we predict that the involvement of the anion efflux at the plasma membrane is crucial for the plant response to ABA.AvailabilityThe algorithm used in the simulations is available upon request.

Guiding the self-organization of random Boolean networks

Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews eight different methods for guiding the self-organization of RBNs. In particular, the article is focused on guiding RBNs toward the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are reviewed. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical.

The molecular and mathematical basis of Waddington's epigenetic landscape: A framework for post‐Darwinian biology?

The Neo-Darwinian concept of natural selection is plausible when one assumes a straightforward causation of phenotype by genotype. However, such simple 1:1 mapping must now give place to the modern concepts of gene regulatory networks and gene expression noise. Both can, in the absence of genetic mutations, jointly generate a diversity of inheritable randomly occupied phenotypic states that could also serve as a substrate for natural selection. This form of epigenetic dynamics challenges Neo-Darwinism. It needs to incorporate the non-linear, stochastic dynamics of gene networks. A first step is to consider the mathematical correspondence between gene regulatory networks and Waddington's metaphoric 'epigenetic landscape', which actually represents the quasi-potential function of global network dynamics. It explains the coexistence of multiple stable phenotypes within one genotype. The landscape's topography with its attractors is shaped by evolution through mutational re-wiring of regulatory interactions - offering a link between genetic mutation and sudden, broad evolutionary changes.

Determinant factors in dynamics of global manufacturing virtual networks

Purpose: The purpose of this work is to demonstrate the existence of a number of structural mechanisms that networks actors use to exert tacit power in the GMVNs. Design/methodology/approach: The analysis of the engine manufacturing aeronautical industry based on a quantitative approach with a binomial logistic model with a logit function. Findings and Originality/value: Original Equipment Manufacturers (OEMs) exert a tacit power by occupying central positions in the network and having many structural holes. Suppliers, nevertheless, establish many strong and direct ties to increase trust with other network actors; while new actors establish, primarily, indirect ties to gain explicit knowledge. Research limitations/implications: Another similar works focused in other industrial segments like electronics, chemical o automotive would strengthen the findings of this work. Practical implications: The results of this work can facilitate, ex ante, the design of new GMVNs and increase drastically their efficiency. Originality/value: It classifies the internal operations of these organizations that, so far, were not analysed under this approach. Its conclusions can be used as a prescriptive model for other manufacturing networks.

Regional Development and the Competitive Dynamics of Global Production Networks: An East Asian Perspective

Yeung H. W.-C. Regional development and the competitive dynamics of global production networks: an East Asian perspective, Regional Studies. The debate on the nature and dynamics of regional development in both academic and policy circles has now moved on from the earlier focus on endogenous regional assets to analysing the complex relationship between globalization and regional change. This position paper attempts to engage with this debate through the experience of regional development in East Asia. The paper shows that regional development cannot be understood independently of the changing dynamics of global production networks. While the existing literature on East Asia tends to focus on the state as the key driver of economic development at the national and regional levels, it is argued that the developmental state is a necessary but not sufficient condition for regional development to take place. Instead, one needs to study the complex strategic coupling of those economic actors, particularly large ...

Control of single neuron activity by global network dynamics in auditory cortex

Event Abstract Back to Event Control of single neuron activity by global network dynamics in auditory cortex The electrical activity of neurons reflects an interplay of sensory input and the brain's own internal dynamics. In sensory cortices, neuronal firing correlates well with characteristics of sensory stimuli; on the other hand, each cortical neuron receives the vast majority of its inputs from other cortical neurons, suggesting that spiking is largely controlled by the activity of the local network. This viewpoint is supported by simultaneous recordings of multiple cells, which show synchronized patterns of spontaneous activity beyond those imposed by the structure of the sensory stimulus. In prior work, we found that the global dynamics of population activity in rat auditory cortex is well approximated by a low-dimensional self-exciting system model, and that the variations in population dynamics with cortical state can be captured by changes in the parameters of this model. Here we investigate the relationship of electrophysiologically identified single neurons to global population dynamics in this context. In the model, population activity is characterized by a pair of 'mean field' variables, v and w, that reflect the average population firing rate and integrated recent past activity, respectively. To predict the spike times of individual neurons from population activity, we use an approach analogous to the computation of hippocampal "place fields", by predicting the mean firing probability of each neuron as a function of position in the v-w plane, which we term the 'activity field.' We find that activity fields for different neurons are quite localized in distinct regions of the v-w plane. We conclude that, while the mean activity of a column is well approximated by a simple low-dimensional dynamical system, individual neurons show widely different relationships to this dynamics. For any neuron, activity fields computed from spontaneous and sensory-evoked activity were similar, further suggesting that the structure of sensory responses reflects the same network dynamics that shape cortical spontaneous activity. This work is supported by NIH Grants MH073245 and R01DC009947. CC was also supported by a Courant Instructorship. Conference: Computational and systems neuroscience 2009, Salt Lake City, UT, United States, 26 Feb - 3 Mar, 2009. Presentation Type: Poster Presentation Topic: Poster Presentations Citation: (2009). Control of single neuron activity by global network dynamics in auditory cortex. Front. Syst. Neurosci. Conference Abstract: Computational and systems neuroscience 2009. doi: 10.3389/conf.neuro.06.2009.03.152 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 02 Feb 2009; Published Online: 02 Feb 2009. Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Google Google Scholar PubMed Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Quantifying local structure effects in network dynamics

Mutual information between the time series of two dynamical elements measures how well their activities are coordinated. In a network of interacting elements, the average mutual information over all pairs of elements I is a global measure of the correlation between the elements' dynamics. Local topological features in the network have been shown to affect I . Here we define a generalized clustering coefficient C_{p} and show that this quantity captures the effects of local structures on the global dynamics of networks. Using random Boolean networks (RBNs) as models of networks of interacting elements, we show that the variation of I ( I averaged over an ensemble of RBNs with the number of nodes N and average connectivity k ) with N and k is caused by the variation of C_{p} . Also, the variability of I between RBNs with equal N and k is due to their distinct values of C_{p} . Consequently, we propose a rewiring method to generate ensembles of BNs, from ordinary RBNs, with fixed values of C_{p} up to order 5, while maintaining in- and out-degree distributions. Using this methodology, the dependency of C_{p} on N and k and the variability of I for RBNs with equal N and k are shown to disappear in RBNs with C_{p} set to zero. The I of ensembles of RBNs with fixed, nonzero C_{p} values, also becomes almost independent of N and k . In addition, it is shown that C_{p} exhibits a power-law dependence on N in ordinary RBNs, suggesting that the C_{p} affects even relatively large networks. The method of generating networks with fixed C_{p} values is useful to generate networks with small N whose dynamics have the same properties as those of large scale networks, or to generate ensembles of networks with the same C_{p} as some specific network, and thus comparable dynamics. These results show how a system's dynamics is constrained by its local structure, suggesting that the local topology of biological networks might be shaped by selection, for example, towards optimizing the coordination between its components.

Contract Labour: The ‘Achilles Heel’ of Corporate Codes in Commercial Value Chains

ABSTRACTCodes of labour practice implemented by corporate buyers in their global production networks are one dimension of corporate social responsibility (CSR). Research indicates the benefits of codes for workers are limited and they fail to reach the most vulnerable workers, particularly those employed by labour contractors who face the worst employment conditions. This contribution argues that the commercial dynamics of global production networks provides an opening for civil society organizations to pressure for codes, but simultaneously drives the use of a vulnerable and insecure workforce that is the ‘Achilles Heel’ of codes. Whilst codes have a role to play, inherent tensions underpinned by a commercial logic mean they should only ever be viewed as one strand in broader strategies that address the rights of the most vulnerable workers in global production.

Innovation offshoring and Asia's electronics industry – the new dynamics of global networks

This paper explores how innovation offshoring gives rise to Global Innovation Networks (GINs) in the electronics industry. The focus is on chip design. The paper documents the scope of offshoring and the progress achieved in the complexity of design stages and in capability upgrading in Asia. The paper explores the forces that are responsible for the organisational and geographical mobility of innovation within GINs emphasising their systemic nature. The paper argues that innovation offshoring is likely to accelerate Asia's transformation from the 'global factory' model to 'upgrading through innovation'. But massive challenges must be mastered before Asia's leading electronics exporting countries can exploit these opportunities. Such challenges result from the very demanding requirements that locations need to fulfil in order to qualify for R&D investments by Transnational Corporations (TNCs).