Rapid shape memory TEMPO-oxidized cellulose nanofibers/polyacrylamide/gelatin hydrogels with enhanced mechanical strength.

PubMed

Li, Nan; Chen, Wei; Chen, Guangxue; Tian, Junfei

2017-09-01

TEMPO-oxidized cellulose nanofibers/polyacrylamide/gelatin shape memory hydrogels were successfully fabricated through a facile in-situ free-radical polymerization method, and double network was formed by chemically cross-linked polyacrylamide (PAM) network and physically cross-linked gelatin network. TEMPO-oxidized cellulose nanofibers (TOCNs) were introduced to improve the mechanical properties of the hydrogel. The structure, shape memory behaviors and mechanical properties of the resulting composite gels with varied gel compositions were investigated. The results obtained from those different studies revealed that TOCNs, gelatin, and PAM could mix with each other homogeneously. Due to the thermoreversible nature of the gelatin network, the composite hydrogels exhibited attractive thermo-induced shape memory properties. In addition, good mechanical properties (strength >200kPa, strain >650%) were achieved. Such composite hydrogels with good shape memory behavior and enhanced mechanical strength would be an attractive candidate for a wide variety of applications. Copyright © 2017 Elsevier Ltd. All rights reserved.

Network reconfiguration and neuronal plasticity in rhythm-generating networks.

PubMed

Koch, Henner; Garcia, Alfredo J; Ramirez, Jan-Marino

2011-12-01

Neuronal networks are highly plastic and reconfigure in a state-dependent manner. The plasticity at the network level emerges through multiple intrinsic and synaptic membrane properties that imbue neurons and their interactions with numerous nonlinear properties. These properties are continuously regulated by neuromodulators and homeostatic mechanisms that are critical to maintain not only network stability and also adapt networks in a short- and long-term manner to changes in behavioral, developmental, metabolic, and environmental conditions. This review provides concrete examples from neuronal networks in invertebrates and vertebrates, and illustrates that the concepts and rules that govern neuronal networks and behaviors are universal.

Interneuronal Mechanism for Tinbergen’s Hierarchical Model of Behavioral Choice

PubMed Central

Pirger, Zsolt; Crossley, Michael; László, Zita; Naskar, Souvik; Kemenes, György; O’Shea, Michael; Benjamin, Paul R.; Kemenes, Ildikó

2014-01-01

Summary Recent studies of behavioral choice support the notion that the decision to carry out one behavior rather than another depends on the reconfiguration of shared interneuronal networks [1]. We investigated another decision-making strategy, derived from the classical ethological literature [2, 3], which proposes that behavioral choice depends on competition between autonomous networks. According to this model, behavioral choice depends on inhibitory interactions between incompatible hierarchically organized behaviors. We provide evidence for this by investigating the interneuronal mechanisms mediating behavioral choice between two autonomous circuits that underlie whole-body withdrawal [4, 5] and feeding [6] in the pond snail Lymnaea. Whole-body withdrawal is a defensive reflex that is initiated by tactile contact with predators. As predicted by the hierarchical model, tactile stimuli that evoke whole-body withdrawal responses also inhibit ongoing feeding in the presence of feeding stimuli. By recording neurons from the feeding and withdrawal networks, we found no direct synaptic connections between the interneuronal and motoneuronal elements that generate the two behaviors. Instead, we discovered that behavioral choice depends on the interaction between two unique types of interneurons with asymmetrical synaptic connectivity that allows withdrawal to override feeding. One type of interneuron, the Pleuro-Buccal (PlB), is an extrinsic modulatory neuron of the feeding network that completely inhibits feeding when excited by touch-induced monosynaptic input from the second type of interneuron, Pedal-Dorsal12 (PeD12). PeD12 plays a critical role in behavioral choice by providing a synaptic pathway joining the two behavioral networks that underlies the competitive dominance of whole-body withdrawal over feeding. PMID:25155505

[Measurement and performance analysis of functional neural network].

PubMed

Li, Shan; Liu, Xinyu; Chen, Yan; Wan, Hong

2018-04-01

The measurement of network is one of the important researches in resolving neuronal population information processing mechanism using complex network theory. For the quantitative measurement problem of functional neural network, the relation between the measure indexes, i.e. the clustering coefficient, the global efficiency, the characteristic path length and the transitivity, and the network topology was analyzed. Then, the spike-based functional neural network was established and the simulation results showed that the measured network could represent the original neural connections among neurons. On the basis of the former work, the coding of functional neural network in nidopallium caudolaterale (NCL) about pigeon's motion behaviors was studied. We found that the NCL functional neural network effectively encoded the motion behaviors of the pigeon, and there were significant differences in four indexes among the left-turning, the forward and the right-turning. Overall, the establishment method of spike-based functional neural network is available and it is an effective tool to parse the brain information processing mechanism.

Study of network resource allocation based on market and game theoretic mechanism

NASA Astrophysics Data System (ADS)

Liu, Yingmei; Wang, Hongwei; Wang, Gang

2004-04-01

We work on the network resource allocation issue concerning network management system function based on market-oriented mechanism. The scheme is to model the telecommunication network resources as trading goods in which the various network components could be owned by different competitive, real-world entities. This is a multidisciplinary framework concentrating on the similarity between resource allocation in network environment and the market mechanism in economic theory. By taking an economic (market-based and game theoretic) approach in routing of communication network, we study the dynamic behavior under game-theoretic framework in allocating network resources. Based on the prior work of Gibney and Jennings, we apply concepts of utility and fitness to the market mechanism with an intention to close the gap between experiment environment and real world situation.

Mechanical critical phenomena and the elastic response of fiber networks

NASA Astrophysics Data System (ADS)

Mackintosh, Fred

The mechanics of cells and tissues are largely governed by scaffolds of filamentous proteins that make up the cytoskeleton, as well as extracellular matrices. Evidence is emerging that such networks can exhibit rich mechanical phase behavior. A classic example of a mechanical phase transition was identified by Maxwell for macroscopic engineering structures: networks of struts or springs exhibit a continuous, second-order phase transition at the isostatic point, where the number of constraints imposed by connectivity just equals the number of mechanical degrees of freedom. We present recent theoretical predictions and experimental evidence for mechanical phase transitions in in both synthetic and biopolymer networks. We show, in particular, excellent quantitative agreement between the mechanics of collagen matrices and the predictions of a strain-controlled phase transition in sub-isostatic networks.

From behavior to neural dynamics: An integrated theory of attention

PubMed Central

Buschman, Timothy J.; Kastner, Sabine

2015-01-01

The brain has a limited capacity and therefore needs mechanisms to selectively enhance the information most relevant to one’s current behavior. We refer to these mechanisms as ‘attention’. Attention acts by increasing the strength of selected neural representations and preferentially routing them through the brain’s large-scale network. This is a critical component of cognition and therefore has been a central topic in cognitive neuroscience. Here we review a diverse literature that has studied attention at the level of behavior, networks, circuits and neurons. We then integrate these disparate results into a unified theory of attention. PMID:26447577

Social Insects: A Model System for Network Dynamics

NASA Astrophysics Data System (ADS)

Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

Dysregulation of Prefrontal Cortex-Mediated Slow-Evolving Limbic Dynamics Drives Stress-Induced Emotional Pathology.

PubMed

Hultman, Rainbo; Mague, Stephen D; Li, Qiang; Katz, Brittany M; Michel, Nadine; Lin, Lizhen; Wang, Joyce; David, Lisa K; Blount, Cameron; Chandy, Rithi; Carlson, David; Ulrich, Kyle; Carin, Lawrence; Dunson, David; Kumar, Sunil; Deisseroth, Karl; Moore, Scott D; Dzirasa, Kafui

2016-07-20

Circuits distributed across cortico-limbic brain regions compose the networks that mediate emotional behavior. The prefrontal cortex (PFC) regulates ultraslow (<1 Hz) dynamics across these networks, and PFC dysfunction is implicated in stress-related illnesses including major depressive disorder (MDD). To uncover the mechanism whereby stress-induced changes in PFC circuitry alter emotional networks to yield pathology, we used a multi-disciplinary approach including in vivo recordings in mice and chronic social defeat stress. Our network model, inferred using machine learning, linked stress-induced behavioral pathology to the capacity of PFC to synchronize amygdala and VTA activity. Direct stimulation of PFC-amygdala circuitry with DREADDs normalized PFC-dependent limbic synchrony in stress-susceptible animals and restored normal behavior. In addition to providing insights into MDD mechanisms, our findings demonstrate an interdisciplinary approach that can be used to identify the large-scale network changes that underlie complex emotional pathologies and the specific network nodes that can be used to develop targeted interventions. Copyright © 2016 Elsevier Inc. All rights reserved.

Advanced Polymer Network Structures

DTIC Science & Technology

2016-02-01

double networks in a single step was identified from coarse-grained molecular dynamics simulations of polymer solvents bearing rigid side chains dissolved...in a polymer network. Coarse-grained molecular dynamics simulations also explored the mechanical behavior of traditional double networks and...DRI), polymer networks, polymer gels, molecular dynamics simulations , double networks 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF

Impact of Social Punishment on Cooperative Behavior in Complex Networks

NASA Astrophysics Data System (ADS)

Wang, Zhen; Xia, Cheng-Yi; Meloni, Sandro; Zhou, Chang-Song; Moreno, Yamir

2013-10-01

Social punishment is a mechanism by which cooperative individuals spend part of their resources to penalize defectors. In this paper, we study the evolution of cooperation in 2-person evolutionary games on networks when a mechanism for social punishment is introduced. Specifically, we introduce a new kind of role, punisher, which is aimed at reducing the earnings of defectors by applying to them a social fee. Results from numerical simulations show that different equilibria allowing the three strategies to coexist are possible as well as that social punishment further enhance the robustness of cooperation. Our results are confirmed for different network topologies and two evolutionary games. In addition, we analyze the microscopic mechanisms that give rise to the observed macroscopic behaviors in both homogeneous and heterogeneous networks. Our conclusions might provide additional insights for understanding the roots of cooperation in social systems.

Direct and Indirect Influence of Altruistic Behavior in a Social Network.

PubMed

Liu, Pei-Pei; Safin, Vasiliy; Yang, Barry; Luhmann, Christian C

2015-01-01

Prior research has suggested that recipients of generosity behave more generously themselves (a direct social influence). In contrast, there is conflicting evidence about the existence of indirect influence (i.e., whether interacting with a recipient of generosity causes one to behave more generously), casting doubt on the possibility that altruistic behavior can cascade through social networks. The current study investigated how far selfish and generous behavior can be transmitted through social networks and the cognitive mechanisms that underlie such transmission. Participants played a sequence of public goods games comprising a chain network. This network is advantageous because it permits only a single, unambiguous path of influence. Furthermore, we experimentally manipulated the behavior of the first link in the chain to be either generous or selfish. Results revealed the presence of direct social influence, but no evidence for indirect influence. Results also showed that selfish behavior exerted a substantially greater influence than generous behavior. Finally, expectations about future partners' behavior strongly mediated the observed social influence, suggesting an adaptive basis for such influence.

Cooperative Networks: Altruism, Group Solidarity, Reciprocity, and Sanctioning in Ugandan Producer Organizations.

PubMed

Baldassarri, Delia

2015-09-01

Repeated interaction and social networks are commonly considered viable solutions to collective action problems. This article identifies and systematically measures four general mechanisms--that is, generalized altruism, group solidarity, reciprocity, and the threat of sanctioning--and tests which of them brings about cooperation in the context of Ugandan producer organizations. Using an innovative methodological framework that combines "lab-in-the-field" experiments with survey interviews and complete social networks data, the article goes beyond the assessment of a relationship between social networks and collective outcomes to study the mechanisms that favor cooperative behavior. The article first establishes a positive relationship between position in the network structure and propensity to cooperate in the producer organization and then uses farmers' behavior in dictator and public goods games to test different mechanisms that may account for such a relationship. Results show that cooperation is induced by patterns of reciprocity that emerge through repeated interaction rather than other-regarding preferences like altruism or group solidarity.

The Role of Network Architecture in Collagen Mechanics.

PubMed

Jansen, Karin A; Licup, Albert J; Sharma, Abhinav; Rens, Robbie; MacKintosh, Fred C; Koenderink, Gijsje H

2018-06-05

Collagen forms fibrous networks that reinforce tissues and provide an extracellular matrix for cells. These networks exhibit remarkable strain-stiffening properties that tailor the mechanical functions of tissues and regulate cell behavior. Recent models explain this nonlinear behavior as an intrinsic feature of disordered networks of stiff fibers. Here, we experimentally validate this theoretical framework by measuring the elastic properties of collagen networks over a wide range of self-assembly conditions. We show that the model allows us to quantitatively relate both the linear and nonlinear elastic behavior of collagen networks to their underlying architecture. Specifically, we identify the local coordination number (or connectivity) 〈z〉 as a key architectural parameter that governs the elastic response of collagen. The network elastic response reveals that 〈z〉 decreases from 3.5 to 3 as the polymerization temperature is raised from 26 to 37°C while being weakly dependent on concentration. We furthermore infer a Young's modulus of 1.1 MPa for the collagen fibrils from the linear modulus. Scanning electron microscopy confirms that 〈z〉 is between three and four but is unable to detect the subtle changes in 〈z〉 with polymerization conditions that rheology is sensitive to. Finally, we show that, consistent with the model, the initial stress-stiffening response of collagen networks is controlled by the negative normal stress that builds up under shear. Our work provides a predictive framework to facilitate future studies of the regulatory effect of extracellular matrix molecules on collagen mechanics. Moreover, our findings can aid mechanobiological studies of wound healing, fibrosis, and cancer metastasis, which require collagen matrices with tunable mechanical properties. Copyright © 2018 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Finding meaning in social media: content-based social network analysis of QuitNet to identify new opportunities for health promotion.

PubMed

Myneni, Sahiti; Cobb, Nathan K; Cohen, Trevor

2013-01-01

Unhealthy behaviors increase individual health risks and are a socioeconomic burden. Harnessing social influence is perceived as fundamental for interventions to influence health-related behaviors. However, the mechanisms through which social influence occurs are poorly understood. Online social networks provide the opportunity to understand these mechanisms as they digitally archive communication between members. In this paper, we present a methodology for content-based social network analysis, combining qualitative coding, automated text analysis, and formal network analysis such that network structure is determined by the content of messages exchanged between members. We apply this approach to characterize the communication between members of QuitNet, an online social network for smoking cessation. Results indicate that the method identifies meaningful theme-based social sub-networks. Modeling social network data using this method can provide us with theme-specific insights such as the identities of opinion leaders and sub-community clusters. Implications for design of targeted social interventions are discussed.

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

PubMed Central

Ye, Ye; Cheong, Kang Hao; Cen, Yu-wan; Xie, Neng-gang

2016-01-01

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed. PMID:27845430

Effects of behavioral patterns and network topology structures on Parrondo’s paradox

NASA Astrophysics Data System (ADS)

Ye, Ye; Cheong, Kang Hao; Cen, Yu-Wan; Xie, Neng-Gang

2016-11-01

A multi-agent Parrondo’s model based on complex networks is used in the current study. For Parrondo’s game A, the individual interaction can be categorized into five types of behavioral patterns: the Matthew effect, harmony, cooperation, poor-competition-rich-cooperation and a random mode. The parameter space of Parrondo’s paradox pertaining to each behavioral pattern, and the gradual change of the parameter space from a two-dimensional lattice to a random network and from a random network to a scale-free network was analyzed. The simulation results suggest that the size of the region of the parameter space that elicits Parrondo’s paradox is positively correlated with the heterogeneity of the degree distribution of the network. For two distinct sets of probability parameters, the microcosmic reasons underlying the occurrence of the paradox under the scale-free network are elaborated. Common interaction mechanisms of the asymmetric structure of game B, behavioral patterns and network topology are also revealed.

A Mathematical Model to study the Dynamics of Epithelial Cellular Networks

PubMed Central

Abate, Alessandro; Vincent, Stéphane; Dobbe, Roel; Silletti, Alberto; Master, Neal; Axelrod, Jeffrey D.; Tomlin, Claire J.

2013-01-01

Epithelia are sheets of connected cells that are essential across the animal kingdom. Experimental observations suggest that the dynamical behavior of many single-layered epithelial tissues has strong analogies with that of specific mechanical systems, namely large networks consisting of point masses connected through spring-damper elements and undergoing the influence of active and dissipating forces. Based on this analogy, this work develops a modeling framework to enable the study of the mechanical properties and of the dynamic behavior of large epithelial cellular networks. The model is built first by creating a network topology that is extracted from the actual cellular geometry as obtained from experiments, then by associating a mechanical structure and dynamics to the network via spring-damper elements. This scalable approach enables running simulations of large network dynamics: the derived modeling framework in particular is predisposed to be tailored to study general dynamics (for example, morphogenesis) of various classes of single-layered epithelial cellular networks. In this contribution we test the model on a case study of the dorsal epithelium of the Drosophila melanogaster embryo during early dorsal closure (and, less conspicuously, germband retraction). PMID:23221083

Neurobiological correlates of social functioning in autism.

PubMed

Neuhaus, Emily; Beauchaine, Theodore P; Bernier, Raphael

2010-08-01

Although autism is defined by deficits in three areas of functioning (social, communicative, and behavioral), impairments in social interest and restricted behavioral repertoires are central to the disorder. As a result, a detailed understanding of the neurobiological systems subserving social behavior may have implications for prevention, early identification, and intervention for affected families. In this paper, we review a number of potential neurobiological mechanisms--across several levels of analysis--that subserve normative social functioning. These include neural networks, neurotransmitters, and hormone systems. After describing the typical functioning of each system, we review available empirical findings specific to autism. Among the most promising potential mechanisms of social behavioral deficits in autism are those involving neural networks including the amygdala, the mesocorticolimbic dopamine system, and the oxytocin system. Particularly compelling are explanatory models that integrate mechanisms across biological systems, such as those linking dopamine and oxytocin with brain regions critical to reward processing. Copyright 2010 Elsevier Ltd. All rights reserved.

Brain and Social Networks: Fundamental Building Blocks of Human Experience.

PubMed

Falk, Emily B; Bassett, Danielle S

2017-09-01

How do brains shape social networks, and how do social ties shape the brain? Social networks are complex webs by which ideas spread among people. Brains comprise webs by which information is processed and transmitted among neural units. While brain activity and structure offer biological mechanisms for human behaviors, social networks offer external inducers or modulators of those behaviors. Together, these two axes represent fundamental contributors to human experience. Integrating foundational knowledge from social and developmental psychology and sociology on how individuals function within dyads, groups, and societies with recent advances in network neuroscience can offer new insights into both domains. Here, we use the example of how ideas and behaviors spread to illustrate the potential of multilayer network models. Copyright © 2017 Elsevier Ltd. All rights reserved.

Hydrophobic Drug Encapsulation Mechanisms of an Injectable Self-Assembling Peptide Hydrogel

NASA Astrophysics Data System (ADS)

Sun, Jessie E. P.; Schneider, Joel P.; Pochan, Darrin J.

2012-02-01

We examined a beta-hairpin peptide network that is a shear thinning injectable solid with immediate rehealing behavior. These rheological properties result from the entangled and branched fibrillar nanostructure of the hydrogel networks. The fibrils are formed by the intramolecular folding and subsequent intermolecular assembly of the self-assembling peptides. Taking advantage of the nanofibrillar peptide structures, the hydrogel can be used to encapsulate curcumin, a hydrophobic, natural anticancer agent and indian spice. The hydrogel shields curcumin from the environment while depositing it exactly where it is intended through syringe injection, taking advantage of the hydrogel shear thinning and rehealing behavior. How the network envelopes and interacts with the curcumin is examined using fluoresence and electron microscopy methods to better understand the exact mechanisms and behaviors of the gel itself and the gel-curcumin construct.

Effect of crosslink torsional stiffness on elastic behavior of semiflexible polymer networks

NASA Astrophysics Data System (ADS)

Hatami-Marbini, H.

2018-02-01

Networks of semiflexible filaments are building blocks of different biological and structural materials such as cytoskeleton and extracellular matrix. The mechanical response of these systems when subjected to an applied strain at zero temperature is often investigated numerically using networks composed of filaments, which are either rigidly welded or pinned together at their crosslinks. In the latter, filaments during deformation are free to rotate about their crosslinks while the relative angles between filaments remain constant in the former. The behavior of crosslinks in actual semiflexible networks is different than these idealized models and there exists only partial constraint on torques at crosslinks. The present work develops a numerical model in which two intersecting filaments are connected to each other by torsional springs with arbitrary stiffness. We show that fiber networks composed of rigid and freely rotating crosslinks are the limiting case of the present model. Furthermore, we characterize the effects of stiffness of crosslinks on effective Young's modulus of semiflexible networks as a function of filament flexibility and crosslink density. The effective Young's modulus is determined as a function of the mechanical properties of crosslinks and is found to vanish for networks composed of very weak torsional springs. Independent of the stiffness of crosslinks, it is found that the effective Young's modulus is a function of fiber flexibility and crosslink density. In low density networks, filaments primarily bend and the effective Young's modulus is much lower than the affine estimate. With increasing filament bending stiffness and/or crosslink density, the mechanical behavior of the networks becomes more affine and the stretching of filaments depicts itself as the dominant mode of deformation. The torsional stiffness of the crosslinks significantly affects the effective Young's modulus of the semiflexible random fiber networks.

Transmural variation in elastin fiber orientation distribution in the arterial wall.

PubMed

Yu, Xunjie; Wang, Yunjie; Zhang, Yanhang

2018-01-01

The complex three-dimensional elastin network is a major load-bearing extracellular matrix (ECM) component of an artery. Despite the reported anisotropic behavior of arterial elastin network, it is usually treated as an isotropic material in constitutive models. Our recent multiphoton microscopy study reported a relatively uniform elastin fiber orientation distribution in porcine thoracic aorta when imaging from the intima side (Chow et al., 2014). However it is questionable whether the fiber orientation distribution obtained from a small depth is representative of the elastin network structure in the arterial wall, especially when developing structure-based constitutive models. To date, the structural basis for the anisotropic mechanical behavior of elastin is still not fully understood. In this study, we examined the transmural variation in elastin fiber orientation distribution in porcine thoracic aorta and its association with elastin anisotropy. Using multi-photon microscopy, we observed that the elastin fibers orientation changes from a relatively uniform distribution in regions close to the luminal surface to a more circumferential distribution in regions that dominate the media, then to a longitudinal distribution in regions close to the outer media. Planar biaxial tensile test was performed to characterize the anisotropic behavior of elastin network. A new structure-based constitutive model of elastin network was developed to incorporate the transmural variation in fiber orientation distribution. The new model well captures the anisotropic mechanical behavior of elastin network under both equi- and nonequi-biaxial loading and showed improvements in both fitting and predicting capabilities when compared to a model that only considers the fiber orientation distribution from the intima side. We submit that the transmural variation in fiber orientation distribution is important in characterizing the anisotropic mechanical behavior of elastin network and should be considered in constitutive modeling of an artery. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computational Models and Emergent Properties of Respiratory Neural Networks

PubMed Central

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Mechanical instability and percolation of deformable particles through porous networks

NASA Astrophysics Data System (ADS)

Benet, Eduard; Lostec, Guillaume; Pellegrino, John; Vernerey, Franck

2018-04-01

The transport of micron-sized particles such as bacteria, cells, or synthetic lipid vesicles through porous spaces is a process relevant to drug delivery, separation systems, or sensors, to cite a few examples. Often, the motion of these particles depends on their ability to squeeze through small constrictions, making their capacity to deform an important factor for their permeation. However, it is still unclear how the mechanical behavior of these particles affects collective transport through porous networks. To address this issue, we present a method to reconcile the pore-scale mechanics of the particles with the Darcy scale to understand the motion of a deformable particle through a porous network. We first show that particle transport is governed by a mechanical instability occurring at the pore scale, which leads to a binary permeation response on each pore. Then, using the principles of directed bond percolation, we are able to link this microscopic behavior to the probability of permeating through a random porous network. We show that this instability, together with network uniformity, are key to understanding the nonlinear permeation of particles at a given pressure gradient. The results are then summarized by a phase diagram that predicts three distinct permeation regimes based on particle properties and the randomness of the pore network.

Symmetry Breaking in Space-Time Hierarchies Shapes Brain Dynamics and Behavior.

PubMed

Pillai, Ajay S; Jirsa, Viktor K

2017-06-07

In order to maintain brain function, neural activity needs to be tightly coordinated within the brain network. How this coordination is achieved and related to behavior is largely unknown. It has been previously argued that the study of the link between brain and behavior is impossible without a guiding vision. Here we propose behavioral-level concepts and mechanisms embodied as structured flows on manifold (SFM) that provide a formal description of behavior as a low-dimensional process emerging from a network's dynamics dependent on the symmetry and invariance properties of the network connectivity. Specifically, we demonstrate that the symmetry breaking of network connectivity constitutes a timescale hierarchy resulting in the emergence of an attractive functional subspace. We show that behavior emerges when appropriate conditions imposed upon the couplings are satisfied, justifying the conductance-based nature of synaptic couplings. Our concepts propose design principles for networks predicting how behavior and task rules are represented in real neural circuits and open new avenues for the analyses of neural data. Copyright © 2017 Elsevier Inc. All rights reserved.

Implicit and Explicit Learning Mechanisms Meet in Monkey Prefrontal Cortex.

PubMed

Chafee, Matthew V; Crowe, David A

2017-10-11

In this issue, Loonis et al. (2017) provide the first description of unique synchrony patterns differentiating implicit and explicit forms of learning in monkey prefrontal networks. Their results have broad implications for how prefrontal networks integrate the two learning mechanisms to control behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

Rapid Neocortical Dynamics: Cellular and Network Mechanisms

PubMed Central

Haider, Bilal; McCormick, David A.

2011-01-01

The highly interconnected local and large-scale networks of the neocortical sheet rapidly and dynamically modulate their functional connectivity according to behavioral demands. This basic operating principle of the neocortex is mediated by the continuously changing flow of excitatory and inhibitory synaptic barrages that not only control participation of neurons in networks but also define the networks themselves. The rapid control of neuronal responsiveness via synaptic bombardment is a fundamental property of cortical dynamics that may provide the basis of diverse behaviors, including sensory perception, motor integration, working memory, and attention. PMID:19409263

Sociometric network structure and its association with methamphetamine use norms among homeless youth

PubMed Central

Barman-Adhikari, Anamika; Begun, Stephanie; Rice, Eric; Yoshioka-Maxwell, Amanda; Perez-Portillo, Andrea

2016-01-01

Homeless youths' social networks are consistently linked with their substance use. Social networks influence behavior through several mechanisms, especially social norms. This study used sociometric analyses to understand whether social norms of drug use behaviors are clustered in network structures and whether these perceived norms (descriptive and injunctive) influence youths' drug use behaviors. An event-based approach was used to delineate boundaries of the two sociometric networks of homeless youth, one in Los Angeles, CA (n = 160) and the other in Santa Monica, CA (n = 130). Network characteristics included centrality (i.e., popularity) and cohesiveness (location in dense subnetworks). The primary outcome was recent methamphetamine use. Results revealed that both descriptive and injunctive norms influenced methamphetamine use. Network cohesion was found to be associated with perception of both descriptive and injunctive norms in both networks, however in opposite directions. Network interventions therefore might be effective if designed to capitalize on social influence that naturally occurs in cohesive parts of networks. PMID:27194667

Automatic Screening for Perturbations in Boolean Networks.

PubMed

Schwab, Julian D; Kestler, Hans A

2018-01-01

A common approach to address biological questions in systems biology is to simulate regulatory mechanisms using dynamic models. Among others, Boolean networks can be used to model the dynamics of regulatory processes in biology. Boolean network models allow simulating the qualitative behavior of the modeled processes. A central objective in the simulation of Boolean networks is the computation of their long-term behavior-so-called attractors. These attractors are of special interest as they can often be linked to biologically relevant behaviors. Changing internal and external conditions can influence the long-term behavior of the Boolean network model. Perturbation of a Boolean network by stripping a component of the system or simulating a surplus of another element can lead to different attractors. Apparently, the number of possible perturbations and combinations of perturbations increases exponentially with the size of the network. Manually screening a set of possible components for combinations that have a desired effect on the long-term behavior can be very time consuming if not impossible. We developed a method to automatically screen for perturbations that lead to a user-specified change in the network's functioning. This method is implemented in the visual simulation framework ViSiBool utilizing satisfiability (SAT) solvers for fast exhaustive attractor search.

Simple deterministic models and applications. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Yang, Hyun Mo

2015-12-01

Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.

Mechanical behavior in living cells consistent with the tensegrity model

NASA Technical Reports Server (NTRS)

Wang, N.; Naruse, K.; Stamenovic, D.; Fredberg, J. J.; Mijailovich, S. M.; Tolic-Norrelykke, I. M.; Polte, T.; Mannix, R.; Ingber, D. E.

2001-01-01

Alternative models of cell mechanics depict the living cell as a simple mechanical continuum, porous filament gel, tensed cortical membrane, or tensegrity network that maintains a stabilizing prestress through incorporation of discrete structural elements that bear compression. Real-time microscopic analysis of cells containing GFP-labeled microtubules and associated mitochondria revealed that living cells behave like discrete structures composed of an interconnected network of actin microfilaments and microtubules when mechanical stresses are applied to cell surface integrin receptors. Quantitation of cell tractional forces and cellular prestress by using traction force microscopy confirmed that microtubules bear compression and are responsible for a significant portion of the cytoskeletal prestress that determines cell shape stability under conditions in which myosin light chain phosphorylation and intracellular calcium remained unchanged. Quantitative measurements of both static and dynamic mechanical behaviors in cells also were consistent with specific a priori predictions of the tensegrity model. These findings suggest that tensegrity represents a unified model of cell mechanics that may help to explain how mechanical behaviors emerge through collective interactions among different cytoskeletal filaments and extracellular adhesions in living cells.

Firing patterns transition and desynchronization induced by time delay in neural networks

NASA Astrophysics Data System (ADS)

Huang, Shoufang; Zhang, Jiqian; Wang, Maosheng; Hu, Chin-Kun

2018-06-01

We used the Hindmarsh-Rose (HR) model (Hindmarsh and Rose, 1984) to study the effect of time delay on the transition of firing behaviors and desynchronization in neural networks. As time delay is increased, neural networks exhibit diversity of firing behaviors, including regular spiking or bursting and firing patterns transitions (FPTs). Meanwhile, the desynchronization of firing and unstable bursting with decreasing amplitude in neural system, are also increasingly enhanced with the increase of time delay. Furthermore, we also studied the effect of coupling strength and network randomness on these phenomena. Our results imply that time delays can induce transition and desynchronization of firing behaviors in neural networks. These findings provide new insight into the role of time delay in the firing activities of neural networks, and can help to better understand the firing phenomena in complex systems of neural networks. A possible mechanism in brain that can cause the increase of time delay is discussed.

[Motivation and Emotional States: Structural Systemic, Neurochemical, Molecular and Cellular Mechanisms].

PubMed

Bazyan, A S

2016-01-01

The structural, systemic, neurochemical, molecular and cellular mechanisms of organization and coding motivation and emotional states are describe. The GABA and glutamatergic synaptic systems of basal ganglia form a neural network and participate in the implementation of voluntary behavior. Neuropeptides, neurohormones and paracrine neuromodulators involved in the organization of motivation and emotional states, integrated with synaptic systems, controlled by neural networks and organizing goal-directed behavior. Structural centers for united and integrated of information in voluntary and goal-directed behavior are globus pallidus. Substantia nigra pars reticulata switches the information from corticobasal networks to thalamocortical networks, induces global dopaminergic (DA) signal and organize interaction of mesolimbic and nigostriatnoy DA systems controlled by prefrontal and motor cortex. Together with the motor cortex, substantia nigra displays information in the brainstem and spinal cord to implementation of behavior. Motivation states are formed in the interaction of neurohormonal and neuropeptide systems by monoaminergic systems of brain. Emotional states are formed by monoaminergic systems of the mid-brain, where the leading role belongs to the mesolimbic DA system. The emotional and motivation state of the encoded specific epigenetic molecular and chemical pattern of neuron.

Creep-induced anisotropy in covalent adaptable network polymers.

PubMed

Hanzon, Drew W; He, Xu; Yang, Hua; Shi, Qian; Yu, Kai

2017-10-11

Anisotropic polymers with aligned macromolecule chains exhibit directional strengthening of mechanical and physical properties. However, manipulating the orientation of polymer chains in a fully cured thermoset is almost impossible due to its permanently crosslinked nature. In this paper, we demonstrate that rearrangeable networks with bond exchange reactions (BERs) can be utilized to tailor the anisotropic mechanical properties of thermosetting polymers. When a constant force is maintained at BER activated temperatures, the malleable thermoset creeps in the direction of stress, and macromolecule chains align themselves in the same direction. The aligned polymer chains result in an anisotropic network with a stiffer mechanical behavior in the direction of creep, while with a more compliant behavior in the transverse direction. The degree of network anisotropy is proportional to the amount of creep strain. A multi-length scale constitutive model is developed to study the creep-induced anisotropy of thermosetting polymers. The model connects the micro-scale BER kinetics, orientation of polymer chains, and directional mechanical properties of network polymers. Without any fitting parameters, it is able to predict the evolution of creep strain at different temperatures and anisotropic stress-strain behaviors of CANs after creep. Predictions on the chain orientation are verified by molecular dynamics (MD) simulation. Based on parametric studies, it is shown that the influences of creep time and temperature on the network anisotropy can be generalized into a single parameter, and the evolution of directional modulus follows an Arrhenius type time-temperature superposition principle (TTSP). The presented work provides a facile approach to transform isotropic thermosets into anisotropic ones using simple heating, and their directional properties can be readily tailored by the processing conditions.

Social networks dynamics revealed by temporal analysis: An example in a non-human primate (Macaca sylvanus) in "La Forêt des Singes".

PubMed

Sosa, Sebastian; Zhang, Peng; Cabanes, Guénaël

2017-06-01

This study applied a temporal social network analysis model to describe three affiliative social networks (allogrooming, sleep in contact, and triadic interaction) in a non-human primate species, Macaca sylvanus. Three main social mechanisms were examined to determine interactional patterns among group members, namely preferential attachment (i.e., highly connected individuals are more likely to form new connections), triadic closure (new connections occur via previous close connections), and homophily (individuals interact preferably with others with similar attributes). Preferential attachment was only observed for triadic interaction network. Triadic closure was significant in allogrooming and triadic interaction networks. Finally, gender homophily was seasonal for allogrooming and sleep in contact networks, and observed in each period for triadic interaction network. These individual-based behaviors are based on individual reactions, and their analysis can shed light on the formation of the affiliative networks determining ultimate coalition networks, and how these networks may evolve over time. A focus on individual behaviors is necessary for a global interactional approach to understanding social behavior rules and strategies. When combined, these social processes could make animal social networks more resilient, thus enabling them to face drastic environmental changes. This is the first study to pinpoint some of the processes underlying the formation of a social structure in a non-human primate species, and identify common mechanisms with humans. The approach used in this study provides an ideal tool for further research seeking to answer long-standing questions about social network dynamics. © 2017 Wiley Periodicals, Inc.

Transfer of Training: Adding Insight through Social Network Analysis

ERIC Educational Resources Information Center

Van den Bossche, Piet; Segers, Mien

2013-01-01

This article reviews studies which apply a social network perspective to examine transfer of training. The theory behind social networks focuses on the interpersonal mechanisms and social structures that exist among interacting units such as people within an organization. A premise of this perspective is that individual's behaviors and outcomes…

Auxetic metamaterials from disordered networks

NASA Astrophysics Data System (ADS)

Reid, Daniel R.; Pashine, Nidhi; Wozniak, Justin M.; Jaeger, Heinrich M.; Liu, Andrea J.; Nagel, Sidney R.; de Pablo, Juan J.

2018-02-01

Recent theoretical work suggests that systematic pruning of disordered networks consisting of nodes connected by springs can lead to materials that exhibit a host of unusual mechanical properties. In particular, global properties such as Poisson’s ratio or local responses related to deformation can be precisely altered. Tunable mechanical responses would be useful in areas ranging from impact mitigation to robotics and, more generally, for creation of metamaterials with engineered properties. However, experimental attempts to create auxetic materials based on pruning-based theoretical ideas have not been successful. Here we introduce a more realistic model of the networks, which incorporates angle-bending forces and the appropriate experimental boundary conditions. A sequential pruning strategy of select bonds in this model is then devised and implemented that enables engineering of specific mechanical behaviors upon deformation, both in the linear and in the nonlinear regimes. In particular, it is shown that Poisson’s ratio can be tuned to arbitrary values. The model and concepts discussed here are validated by preparing physical realizations of the networks designed in this manner, which are produced by laser cutting 2D sheets and are found to behave as predicted. Furthermore, by relying on optimization algorithms, we exploit the networks’ susceptibility to tuning to design networks that possess a distribution of stiffer and more compliant bonds and whose auxetic behavior is even greater than that of homogeneous networks. Taken together, the findings reported here serve to establish that pruned networks represent a promising platform for the creation of unique mechanical metamaterials.

Neural Network Models of Simple Mechanical Systems Illustrating the Feasibility of Accelerated Life Testing

NASA Technical Reports Server (NTRS)

Fusaro, Robert L.; Jones, Steven P.; Jansen, Ralph

1996-01-01

A complete evaluation of the tribological characteristics of a given material/mechanical system is a time-consuming operation since the friction and wear process is extremely systems sensitive. As a result, experimental designs (i.e., Latin Square, Taguchi) have been implemented in an attempt to not only reduce the total number of experimental combinations needed to fully characterize a material/mechanical system, but also to acquire life data for a system without having to perform an actual life test. Unfortunately, these experimental designs still require a great deal of experimental testing and the output does not always produce meaningful information. In order to further reduce the amount of experimental testing required, this study employs a computer neural network model to investigate different material/mechanical systems. The work focuses on the modeling of the wear behavior, while showing the feasibility of using neural networks to predict life data. The model is capable of defining which input variables will influence the tribological behavior of the particular material/mechanical system being studied based on the specifications of the overall system.

Thermotaxis is a Robust Mechanism for Thermoregulation in C. elegans Nematodes

PubMed Central

Ramot, Daniel; MacInnis, Bronwyn L.; Lee, Hau-Chen; Goodman, Miriam B.

2013-01-01

Many biochemical networks are robust to variations in network or stimulus parameters. Although robustness is considered an important design principle of such networks, it is not known whether this principle also applies to higher-level biological processes such as animal behavior. In thermal gradients, C. elegans uses thermotaxis to bias its movement along the direction of the gradient. Here we develop a detailed, quantitative map of C. elegans thermotaxis and use these data to derive a computational model of thermotaxis in the soil, a natural environment of C. elegans. This computational analysis indicates that thermotaxis enables animals to avoid temperatures at which they cannot reproduce, to limit excursions from their adapted temperature, and to remain relatively close to the surface of the soil, where oxygen is abundant. Furthermore, our analysis reveals that this mechanism is robust to large variations in the parameters governing both worm locomotion and temperature fluctuations in the soil. We suggest that, similar to biochemical networks, animals evolve behavioral strategies that are robust, rather than strategies that rely on fine-tuning of specific behavioral parameters. PMID:19020047

A statistical mechanics approach to autopoietic immune networks

NASA Astrophysics Data System (ADS)

Barra, Adriano; Agliari, Elena

2010-07-01

In this work we aim to bridge theoretical immunology and disordered statistical mechanics. We introduce a model for the behavior of B-cells which naturally merges the clonal selection theory and the autopoietic network theory as a whole. From the analysis of its features we recover several basic phenomena such as low-dose tolerance, dynamical memory of antigens and self/non-self discrimination.

Sociometric network structure and its association with methamphetamine use norms among homeless youth.

PubMed

Barman-Adhikari, Anamika; Begun, Stephanie; Rice, Eric; Yoshioka-Maxwell, Amanda; Perez-Portillo, Andrea

2016-07-01

Homeless youths' social networks are consistently linked with their substance use. Social networks influence behavior through several mechanisms, especially social norms. This study used sociometric analyses to understand whether social norms of drug use behaviors are clustered in network structures and whether these perceived norms (descriptive and injunctive) influence youths' drug use behaviors. An event-based approach was used to delineate boundaries of the two sociometric networks of homeless youth, one in Los Angeles, CA (n = 160) and the other in Santa Monica, CA (n = 130). Network characteristics included centrality (i.e., popularity) and cohesiveness (location in dense subnetworks). The primary outcome was recent methamphetamine use. Results revealed that both descriptive and injunctive norms influenced methamphetamine use. Network cohesion was found to be associated with perception of both descriptive and injunctive norms in both networks, however in opposite directions. Network interventions therefore might be effective if designed to capitalize on social influence that naturally occurs in cohesive parts of networks. Copyright © 2016 Elsevier Inc. All rights reserved.

A natural experiment of social network formation and dynamics.

PubMed

Phan, Tuan Q; Airoldi, Edoardo M

2015-05-26

Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.

A natural experiment of social network formation and dynamics

PubMed Central

Phan, Tuan Q.; Airoldi, Edoardo M.

2015-01-01

Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities. PMID:25964337

Mechanical and structural model of fractal networks of fat crystals at low deformations.

PubMed

Narine, S S; Marangoni, A G

1999-12-01

Fat-crystal networks demonstrate viscoelastic behavior at very small deformations. A structural model of these networks is described and supported by polarized light and atomic-force microscopy. A mechanical model is described which allows the shear elastic modulus (G') of the system to be correlated with forces acting within the network. The fractal arrangement of the network at certain length scales is taken into consideration. It is assumed that the forces acting are due to van der Waals forces. The final expression for G' is related to the volume fraction of solid fat (Phi) via the mass fractal dimension (D) of the network, which agrees with the experimental verification of the scaling behavior of fat-crystal networks [S. S. Narine and A. G. Marangoni, Phys. Rev. E 59, 1908 (1999)]. G' was also found to be inversely proportional to the diameter of the primary particles (sigma approximately equal to 6 microm) within the network (microstructural elements) as well as to the diameter of the microstructures (xi approximately equal to 100 microm) and inversely proportional to the cube of the intermicrostructural element distance (d(0)). This formulation of the elastic modulus agrees well with experimental observations.

A Comprehensive Model for Real Gas Transport in Shale Formations with Complex Non-planar Fracture Networks

PubMed Central

Yang, Ruiyue; Huang, Zhongwei; Yu, Wei; Li, Gensheng; Ren, Wenxi; Zuo, Lihua; Tan, Xiaosi; Sepehrnoori, Kamy; Tian, Shouceng; Sheng, Mao

2016-01-01

A complex fracture network is generally generated during the hydraulic fracturing treatment in shale gas reservoirs. Numerous efforts have been made to model the flow behavior of such fracture networks. However, it is still challenging to predict the impacts of various gas transport mechanisms on well performance with arbitrary fracture geometry in a computationally efficient manner. We develop a robust and comprehensive model for real gas transport in shales with complex non-planar fracture network. Contributions of gas transport mechanisms and fracture complexity to well productivity and rate transient behavior are systematically analyzed. The major findings are: simple planar fracture can overestimate gas production than non-planar fracture due to less fracture interference. A “hump” that occurs in the transition period and formation linear flow with a slope less than 1/2 can infer the appearance of natural fractures. The sharpness of the “hump” can indicate the complexity and irregularity of the fracture networks. Gas flow mechanisms can extend the transition flow period. The gas desorption could make the “hump” more profound. The Knudsen diffusion and slippage effect play a dominant role in the later production time. Maximizing the fracture complexity through generating large connected networks is an effective way to increase shale gas production. PMID:27819349

A Comprehensive Model for Real Gas Transport in Shale Formations with Complex Non-planar Fracture Networks.

PubMed

Yang, Ruiyue; Huang, Zhongwei; Yu, Wei; Li, Gensheng; Ren, Wenxi; Zuo, Lihua; Tan, Xiaosi; Sepehrnoori, Kamy; Tian, Shouceng; Sheng, Mao

2016-11-07

A complex fracture network is generally generated during the hydraulic fracturing treatment in shale gas reservoirs. Numerous efforts have been made to model the flow behavior of such fracture networks. However, it is still challenging to predict the impacts of various gas transport mechanisms on well performance with arbitrary fracture geometry in a computationally efficient manner. We develop a robust and comprehensive model for real gas transport in shales with complex non-planar fracture network. Contributions of gas transport mechanisms and fracture complexity to well productivity and rate transient behavior are systematically analyzed. The major findings are: simple planar fracture can overestimate gas production than non-planar fracture due to less fracture interference. A "hump" that occurs in the transition period and formation linear flow with a slope less than 1/2 can infer the appearance of natural fractures. The sharpness of the "hump" can indicate the complexity and irregularity of the fracture networks. Gas flow mechanisms can extend the transition flow period. The gas desorption could make the "hump" more profound. The Knudsen diffusion and slippage effect play a dominant role in the later production time. Maximizing the fracture complexity through generating large connected networks is an effective way to increase shale gas production.

Encoding Hydrogel Mechanics via Network Cross-Linking Structure.

PubMed

Schweller, Ryan M; West, Jennifer L

2015-05-11

The effects of mechanical cues on cell behaviors in 3D remain difficult to characterize as the ability to tune hydrogel mechanics often requires changes in the polymer density, potentially altering the material's biochemical and physical characteristics. Additionally, with most PEG diacrylate (PEGDA) hydrogels, forming materials with compressive moduli less than ∼10 kPa has been virtually impossible. Here, we present a new method of controlling the mechanical properties of PEGDA hydrogels independent of polymer chain density through the incorporation of additional vinyl group moieties that interfere with the cross-linking of the network. This modification can tune hydrogel mechanics in a concentration dependent manner from <1 to 17 kPa, a more physiologically relevant range than previously possible with PEG-based hydrogels, without altering the hydrogel's degradation and permeability. Across this range of mechanical properties, endothelial cells (ECs) encapsulated within MMP-2/MMP-9 degradable hydrogels with RGDS adhesive peptides revealed increased cell spreading as hydrogel stiffness decreased in contrast to behavior typically observed for cells on 2D surfaces. EC-pericyte cocultures exhibited vessel-like networks within 3 days in highly compliant hydrogels as compared to a week in stiffer hydrogels. These vessel networks persisted for at least 4 weeks and deposited laminin and collagen IV perivascularly. These results indicate that EC morphogenesis can be regulated using mechanical cues in 3D. Furthermore, controlling hydrogel compliance independent of density allows for the attainment of highly compliant mechanical regimes in materials that can act as customizable cell microenvironments.

Causal Interrogation of Neuronal Networks and Behavior through Virally Transduced Ivermectin Receptors.

PubMed

Obenhaus, Horst A; Rozov, Andrei; Bertocchi, Ilaria; Tang, Wannan; Kirsch, Joachim; Betz, Heinrich; Sprengel, Rolf

2016-01-01

The causal interrogation of neuronal networks involved in specific behaviors requires the spatially and temporally controlled modulation of neuronal activity. For long-term manipulation of neuronal activity, chemogenetic tools provide a reasonable alternative to short-term optogenetic approaches. Here we show that virus mediated gene transfer of the ivermectin (IVM) activated glycine receptor mutant GlyRα1 (AG) can be used for the selective and reversible silencing of specific neuronal networks in mice. In the striatum, dorsal hippocampus, and olfactory bulb, GlyRα1 (AG) promoted IVM dependent effects in representative behavioral assays. Moreover, GlyRα1 (AG) mediated silencing had a strong and reversible impact on neuronal ensemble activity and c-Fos activation in the olfactory bulb. Together our results demonstrate that long-term, reversible and re-inducible neuronal silencing via GlyRα1 (AG) is a promising tool for the interrogation of network mechanisms underlying the control of behavior and memory formation.

A three-dimensional collagen-fiber network model of the extracellular matrix for the simulation of the mechanical behaviors and micro structures.

PubMed

Dong, Shoubin; Huang, Zetao; Tang, Liqun; Zhang, Xiaoyang; Zhang, Yongrou; Jiang, Yi

2017-07-01

The extracellular matrix (ECM) provides structural and biochemical support to cells and tissues, which is a critical factor for modulating cell dynamic behavior and intercellular communication. In order to further understand the mechanisms of the interactive relationship between cell and the ECM, we developed a three-dimensional (3D) collagen-fiber network model to simulate the micro structure and mechanical behaviors of the ECM and studied the stress-strain relationship as well as the deformation of the ECM under tension. In the model, the collagen-fiber network consists of abundant random distributed collagen fibers and some crosslinks, in which each fiber is modeled as an elastic beam and a crosslink is modeled as a linear spring with tensile limit, it means crosslinks will fail while the tensile forces exceed the limit of spring. With the given parameters of the beam and the spring, the simulated tensile stress-strain relation of the ECM highly matches the experimental results including damaged and failed behaviors. Moreover, by applying the maximal inscribed sphere method, we measured the size distribution of pores in the fiber network and learned the variation of the distribution with deformation. We also defined the alignment of the collagen-fibers to depict the orientation of fibers in the ECM quantitatively. By the study of changes of the alignment and the damaged crosslinks against the tensile strain, this paper reveals the comprehensive mechanisms of four stages of 'toe', 'linear', 'damage' and 'failure' in the tensile stress-strain relation of the ECM which can provide further insight in the study of cell-ECM interaction.

Dissociable intrinsic functional networks support noun-object and verb-action processing.

PubMed

Yang, Huichao; Lin, Qixiang; Han, Zaizhu; Li, Hongyu; Song, Luping; Chen, Lingjuan; He, Yong; Bi, Yanchao

2017-12-01

The processing mechanism of verbs-actions and nouns-objects is a central topic of language research, with robust evidence for behavioral dissociation. The neural basis for these two major word and/or conceptual classes, however, remains controversial. Two experiments were conducted to study this question from the network perspective. Experiment 1 found that nodes of the same class, obtained through task-evoked brain imaging meta-analyses, were more strongly connected with each other than nodes of different classes during resting-state, forming segregated network modules. Experiment 2 examined the behavioral relevance of these intrinsic networks using data from 88 brain-damaged patients, finding that across patients the relative strength of functional connectivity of the two networks significantly correlated with the noun-object vs. verb-action relative behavioral performances. In summary, we found that verbs-actions and nouns-objects are supported by separable intrinsic functional networks and that the integrity of such networks accounts for the relative noun-object- and verb-action-selective deficits. Copyright © 2017 Elsevier Inc. All rights reserved.

A security mechanism based on evolutionary game in fog computing.

PubMed

Sun, Yan; Lin, Fuhong; Zhang, Nan

2018-02-01

Fog computing is a distributed computing paradigm at the edge of the network and requires cooperation of users and sharing of resources. When users in fog computing open their resources, their devices are easily intercepted and attacked because they are accessed through wireless network and present an extensive geographical distribution. In this study, a credible third party was introduced to supervise the behavior of users and protect the security of user cooperation. A fog computing security mechanism based on human nervous system is proposed, and the strategy for a stable system evolution is calculated. The MATLAB simulation results show that the proposed mechanism can reduce the number of attack behaviors effectively and stimulate users to cooperate in application tasks positively.

Synchronization of networked chaotic oscillators under external periodic driving.

PubMed

Yang, Wenchao; Lin, Weijie; Wang, Xingang; Huang, Liang

2015-03-01

The dynamical responses of a complex system to external perturbations are of both fundamental interest and practical significance. Here, by the model of networked chaotic oscillators, we investigate how the synchronization behavior of a complex network is influenced by an externally added periodic driving. Interestingly, it is found that by a slight change of the properties of the external driving, e.g., the frequency or phase lag between its intrinsic oscillation and external driving, the network synchronizability could be significantly modified. We demonstrate this phenomenon by different network models and, based on the method of master stability function, give an analysis on the underlying mechanisms. Our studies highlight the importance of external perturbations on the collective behaviors of complex networks, and also provide an alternate approach for controlling network synchronization.

The human sexual response cycle: brain imaging evidence linking sex to other pleasures.

PubMed

Georgiadis, J R; Kringelbach, M L

2012-07-01

Sexual behavior is critical to species survival, yet comparatively little is known about the neural mechanisms in the human brain. Here we systematically review the existing human brain imaging literature on sexual behavior and show that the functional neuroanatomy of sexual behavior is comparable to that involved in processing other rewarding stimuli. Sexual behavior clearly follows the established principles and phases for wanting, liking and satiety involved in the pleasure cycle of other rewards. The studies have uncovered the brain networks involved in sexual wanting or motivation/anticipation, as well as sexual liking or arousal/consummation, while there is very little data on sexual satiety or post-orgasmic refractory period. Human sexual behavior also interacts with other pleasures, most notably social interaction and high arousal states. We discuss the changes in the underlying brain networks supporting sexual behavior in the context of the pleasure cycle, the changes to this cycle over the individual's life-time and the interactions between them. Overall, it is clear from the data that the functional neuroanatomy of sex is very similar to that of other pleasures and that it is unlikely that there is anything special about the brain mechanisms and networks underlying sex. Copyright © 2012 Elsevier Ltd. All rights reserved.

Microeconomics-based resource allocation in overlay networks by using non-strategic behavior modeling

NASA Astrophysics Data System (ADS)

Analoui, Morteza; Rezvani, Mohammad Hossein

2011-01-01

Behavior modeling has recently been investigated for designing self-organizing mechanisms in the context of communication networks in order to exploit the natural selfishness of the users with the goal of maximizing the overall utility. In strategic behavior modeling, the users of the network are assumed to be game players who seek to maximize their utility with taking into account the decisions that the other players might make. The essential difference between the aforementioned researches and this work is that it incorporates the non-strategic decisions in order to design the mechanism for the overlay network. In this solution concept, the decisions that a peer might make does not affect the actions of the other peers at all. The theory of consumer-firm developed in microeconomics is a model of the non-strategic behavior that we have adopted in our research. Based on it, we have presented distributed algorithms for peers' "joining" and "leaving" operations. We have modeled the overlay network as a competitive economy in which the content provided by an origin server can be viewed as commodity and the origin server and the peers who multicast the content to their downside are considered as the firms. On the other hand, due to the dual role of the peers in the overlay network, they can be considered as the consumers as well. On joining to the overlay economy, each peer is provided with an income and tries to get hold of the service regardless to the behavior of the other peers. We have designed the scalable algorithms in such a way that the existence of equilibrium price (known as Walrasian equilibrium price) is guaranteed.

Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study

PubMed Central

Choi, Jun-Ho; Lee, Jong-Seok

2016-01-01

Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods. PMID:26793137

Online Social Networks for Crowdsourced Multimedia-Involved Behavioral Testing: An Empirical Study.

PubMed

Choi, Jun-Ho; Lee, Jong-Seok

2015-01-01

Online social networks have emerged as effective crowdsourcing media to recruit participants in recent days. However, issues regarding how to effectively exploit them have not been adequately addressed yet. In this paper, we investigate the reliability and effectiveness of multimedia-involved behavioral testing via social network-based crowdsourcing, especially focused on Facebook as a medium to recruit participants. We conduct a crowdsourcing-based experiment for a music recommendation problem. It is shown that different advertisement methods yield different degrees of efficiency and there exist significant differences in behavioral patterns across different genders and different age groups. In addition, we perform a comparison of our experiment with other multimedia-involved crowdsourcing experiments built on Amazon Mechanical Turk (MTurk), which suggests that crowdsourcing-based experiments using social networks for recruitment can achieve comparable efficiency. Based on the analysis results, advantages and disadvantages of social network-based crowdsourcing and suggestions for successful experiments are also discussed. We conclude that social networks have the potential to support multimedia-involved behavioral tests to gather in-depth data even for long-term periods.

Ultrasensitive Wearable Soft Strain Sensors of Conductive, Self-healing, and Elastic Hydrogels with Synergistic "Soft and Hard" Hybrid Networks.

PubMed

Liu, Yan-Jun; Cao, Wen-Tao; Ma, Ming-Guo; Wan, Pengbo

2017-08-02

Robust, stretchable, and strain-sensitive hydrogels have recently attracted immense research interest because of their potential application in wearable strain sensors. The integration of the synergistic characteristics of decent mechanical properties, reliable self-healing capability, and high sensing sensitivity for fabricating conductive, elastic, self-healing, and strain-sensitive hydrogels is still a great challenge. Inspired by the mechanically excellent and self-healing biological soft tissues with hierarchical network structures, herein, functional network hydrogels are fabricated by the interconnection between a "soft" homogeneous polymer network and a "hard" dynamic ferric (Fe 3+ ) cross-linked cellulose nanocrystals (CNCs-Fe 3+ ) network. Under stress, the dynamic CNCs-Fe 3+ coordination bonds act as sacrificial bonds to efficiently dissipate energy, while the homogeneous polymer network leads to a smooth stress-transfer, which enables the hydrogels to achieve unusual mechanical properties, such as excellent mechanical strength, robust toughness, and stretchability, as well as good self-recovery property. The hydrogels demonstrate autonomously self-healing capability in only 5 min without the need of any stimuli or healing agents, ascribing to the reorganization of CNCs and Fe 3+ via ionic coordination. Furthermore, the resulted hydrogels display tunable electromechanical behavior with sensitive, stable, and repeatable variations in resistance upon mechanical deformations. Based on the tunable electromechanical behavior, the hydrogels can act as a wearable strain sensor to monitor finger joint motions, breathing, and even the slight blood pulse. This strategy of building synergistic "soft and hard" structures is successful to integrate the decent mechanical properties, reliable self-healing capability, and high sensing sensitivity together for assembling a high-performance, flexible, and wearable strain sensor.

Dynamic changes in neural circuit topology following mild mechanical injury in vitro.

PubMed

Patel, Tapan P; Ventre, Scott C; Meaney, David F

2012-01-01

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this article, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We estimated the imaging conditions needed to achieve accurate measures of network properties, and applied these methodologies to evaluate if mild mechanical injury to cortical neurons produces graded changes to either spontaneous network activity or altered network topology. We found that modest injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology. A calcium-activated neutral protease (calpain) was a key intermediary in these changes; blocking calpain activation restored the network nearly completely to its pre-injury state. Together, these findings show a more complex change in neural circuit behavior than previously reported for mild mechanical injury, and highlight at least one important early mechanism responsible for these changes.

Integrative Analysis of Genetic, Genomic, and Phenotypic Data for Ethanol Behaviors: A Network-Based Pipeline for Identifying Mechanisms and Potential Drug Targets.

PubMed

Bogenpohl, James W; Mignogna, Kristin M; Smith, Maren L; Miles, Michael F

2017-01-01

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce nonbiased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA, and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease.

INTEGRATIVE ANALYSIS OF GENETIC, GENOMIC AND PHENOTYPIC DATA FOR ETHANOL BEHAVIORS: A NETWORK-BASED PIPELINE FOR IDENTIFYING MECHANISMS AND POTENTIAL DRUG TARGETS

PubMed Central

Bogenpohl, James W.; Mignogna, Kristin M.; Smith, Maren L.; Miles, Michael F.

2016-01-01

Complex behavioral traits, such as alcohol abuse, are caused by an interplay of genetic and environmental factors, producing deleterious functional adaptations in the central nervous system. The long-term behavioral consequences of such changes are of substantial cost to both the individual and society. Substantial progress has been made in the last two decades in understanding elements of brain mechanisms underlying responses to ethanol in animal models and risk factors for alcohol use disorder (AUD) in humans. However, treatments for AUD remain largely ineffective and few medications for this disease state have been licensed. Genome-wide genetic polymorphism analysis (GWAS) in humans, behavioral genetic studies in animal models and brain gene expression studies produced by microarrays or RNA-seq have the potential to produce non-biased and novel insight into the underlying neurobiology of AUD. However, the complexity of such information, both statistical and informational, has slowed progress toward identifying new targets for intervention in AUD. This chapter describes one approach for integrating behavioral, genetic, and genomic information across animal model and human studies. The goal of this approach is to identify networks of genes functioning in the brain that are most relevant to the underlying mechanisms of a complex disease such as AUD. We illustrate an example of how genomic studies in animal models can be used to produce robust gene networks that have functional implications, and to integrate such animal model genomic data with human genetic studies such as GWAS for AUD. We describe several useful analysis tools for such studies: ComBAT, WGCNA and EW_dmGWAS. The end result of this analysis is a ranking of gene networks and identification of their cognate hub genes, which might provide eventual targets for future therapeutic development. Furthermore, this combined approach may also improve our understanding of basic mechanisms underlying gene x environmental interactions affecting brain functioning in health and disease. PMID:27933543

Common medial frontal mechanisms of adaptive control in humans and rodents

PubMed Central

Frank, Michael J.; Laubach, Mark

2013-01-01

In this report, we describe how common brain networks within the medial frontal cortex facilitate adaptive behavioral control in rodents and humans. We demonstrate that low frequency oscillations below 12 Hz are dramatically modulated after errors in humans over mid-frontal cortex and in rats within prelimbic and anterior cingulate regions of medial frontal cortex. These oscillations were phase-locked between medial frontal cortex and motor areas in both rats and humans. In rats, single neurons that encoded prior behavioral outcomes were phase-coherent with low-frequency field oscillations particularly after errors. Inactivating medial frontal regions in rats led to impaired behavioral adjustments after errors, eliminated the differential expression of low frequency oscillations after errors, and increased low-frequency spike-field coupling within motor cortex. Our results describe a novel mechanism for behavioral adaptation via low-frequency oscillations and elucidate how medial frontal networks synchronize brain activity to guide performance. PMID:24141310

Parallel processing by cortical inhibition enables context-dependent behavior.

PubMed

Kuchibhotla, Kishore V; Gill, Jonathan V; Lindsay, Grace W; Papadoyannis, Eleni S; Field, Rachel E; Sten, Tom A Hindmarsh; Miller, Kenneth D; Froemke, Robert C

2017-01-01

Physical features of sensory stimuli are fixed, but sensory perception is context dependent. The precise mechanisms that govern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones and performing a recognition task for the same stimuli. Two-photon imaging showed that many excitatory neurons in auditory cortex were suppressed during behavior, while some cells became more active. Whole-cell recordings showed that excitatory inputs were affected only modestly by context, but inhibition was more sensitive, with PV + , SOM + , and VIP + interneurons balancing inhibition and disinhibition within the network. Cholinergic modulation was involved in context switching, with cholinergic axons increasing activity during behavior and directly depolarizing inhibitory cells. Network modeling captured these findings, but only when modulation coincidently drove all three interneuron subtypes, ruling out either inhibition or disinhibition alone as sole mechanism for active engagement. Parallel processing of cholinergic modulation by cortical interneurons therefore enables context-dependent behavior.

Deciding where to attend: Large-scale network mechanisms underlying attention and intention revealed by graph-theoretic analysis.

PubMed

Liu, Yuelu; Hong, Xiangfei; Bengson, Jesse J; Kelley, Todd A; Ding, Mingzhou; Mangun, George R

2017-08-15

The neural mechanisms by which intentions are transformed into actions remain poorly understood. We investigated the network mechanisms underlying spontaneous voluntary decisions about where to focus visual-spatial attention (willed attention). Graph-theoretic analysis of two independent datasets revealed that regions activated during willed attention form a set of functionally-distinct networks corresponding to the frontoparietal network, the cingulo-opercular network, and the dorsal attention network. Contrasting willed attention with instructed attention (where attention is directed by external cues), we observed that the dorsal anterior cingulate cortex was allied with the dorsal attention network in instructed attention, but shifted connectivity during willed attention to interact with the cingulo-opercular network, which then mediated communications between the frontoparietal network and the dorsal attention network. Behaviorally, greater connectivity in network hubs, including the dorsolateral prefrontal cortex, the dorsal anterior cingulate cortex, and the inferior parietal lobule, was associated with faster reaction times. These results, shown to be consistent across the two independent datasets, uncover the dynamic organization of functionally-distinct networks engaged to support intentional acts. Copyright © 2017 Elsevier Inc. All rights reserved.

Interactions between neural networks: a mechanism for tuning chaos and oscillations.

PubMed

Wang, Lipo

2007-06-01

We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability.

Relationship between disease-specific structures of amyloid fibrils and their mechanical properties

NASA Astrophysics Data System (ADS)

Yoon, Gwonchan; Kab Kim, Young; Eom, Kilho; Na, Sungsoo

2013-01-01

It has recently been reported that the mechanical behavior of prion nanofibrils may play a critical role in expression of neurodegenerative diseases. In this work, we have studied the mechanical behavior of HET-s prion nanofibrils using an elastic network model. We have shown that the mechanical properties of prion nanofibrils formed as left-handed β-helices are different from those of non-prion nanofibrils formed as right-handed β-helices. In particular, the bending behavior of prion nanofibrils depends on the length of the nanofibril and that the bending rigidity of the prion nanofibril is larger than that of the non-prion nanofibril.

Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder.

PubMed

Worhunsky, Patrick D; Potenza, Marc N; Rogers, Robert D

2017-09-01

Continued, persistent gambling to recover accumulating losses, or 'loss-chasing', is a behavioral pattern linked particularly closely to gambling disorder (GD) but may reflect impaired decision-making processes relevant to drug addictions like cocaine-use disorder (CUD). However, little is known regarding the neurocognitive mechanisms of this complex, maladaptive behavior, particularly in individuals with addictive disorders. Seventy participants (25 GD, 18 CUD, and 27 healthy comparison (HC)) completed a loss-chase task during fMRI. Engagement of functional brain networks in response to losing outcomes and during decision-making periods preceding choices to loss-chase or to quit chasing losses were investigated using independent component analysis (ICA). An exploratory factor analysis was performed to examine patterns of coordinated engagement across identified networks. In GD relative to HC and CUD participants, choices to quit chasing were associated with greater engagement of a medial frontal executive-processing network. By comparison, CUD participants exhibited altered engagement of a striato-amygdala motivational network in response to losing outcomes as compared to HC, and during decision-making as compared to GD. Several other networks were differentially engaged during loss-chase relative to quit-chasing choices, but did not differ across participant groups. Exploratory factor analysis identified a system of coordinated activity across prefrontal executive-control networks that was greater in GD and CUD relative to HC participants and was associated with increased chasing persistence across all participants. Results provide evidence of shared and distinct neurobiological mechanisms in substance and behavioral addictions, and lend insight into potential cognitive interventions targeting loss-chasing behavior in GD. Copyright © 2017 Elsevier B.V. All rights reserved.

Spatiotemporal properties of microsaccades: Model predictions and experimental tests

NASA Astrophysics Data System (ADS)

Zhou, Jian-Fang; Yuan, Wu-Jie; Zhou, Zhao

2016-10-01

Microsaccades are involuntary and very small eye movements during fixation. Recently, the microsaccade-related neural dynamics have been extensively investigated both in experiments and by constructing neural network models. Experimentally, microsaccades also exhibit many behavioral properties. It’s well known that the behavior properties imply the underlying neural dynamical mechanisms, and so are determined by neural dynamics. The behavioral properties resulted from neural responses to microsaccades, however, are not yet understood and are rarely studied theoretically. Linking neural dynamics to behavior is one of the central goals of neuroscience. In this paper, we provide behavior predictions on spatiotemporal properties of microsaccades according to microsaccade-induced neural dynamics in a cascading network model, which includes both retinal adaptation and short-term depression (STD) at thalamocortical synapses. We also successfully give experimental tests in the statistical sense. Our results provide the first behavior description of microsaccades based on neural dynamics induced by behaving activity, and so firstly link neural dynamics to behavior of microsaccades. These results indicate strongly that the cascading adaptations play an important role in the study of microsaccades. Our work may be useful for further investigations of the microsaccadic behavioral properties and of the underlying neural dynamical mechanisms responsible for the behavioral properties.

Iterative design of peptide-based hydrogels and the effect of network electrostatics on primary chondrocyte behavior

PubMed Central

Sinthuvanich, Chomdao; Haines-Butterick, Lisa A.; Nagy, Katelyn J.; Schneider, Joel P.

2012-01-01

Iterative peptide design was used to generate two peptide-based hydrogels to study the effect of network electrostatics on primary chondrocyte behavior. MAX8 and HLT2 peptides have formal charge states of +7 and +5 per monomer, respectively. These peptides undergo triggered folding and self-assembly to afford hydrogel networks having similar rheological behavior and local network morphologies, yet different electrostatic character. Each gel can be used to directly encapsulate and syringe-deliver cells. The influence of network electrostatics on cell viability after encapsulation and delivery, extracellular matrix deposition, gene expression, and the bulk mechanical properties of the gel-cell constructs as a function of culture time was assessed. The less electropositive HLT2 gel provides a microenvironment more conducive to chondrocyte encapsulation, delivery, and phenotype maintenance. Cell viability was higher for this gel and although a moderate number of cells dedifferentiated to a fibroblast-like phenotype, many retained their chondrocytic behavior. As a result, gel-cell constructs prepared with HLT2, cultured under static in vitro conditions, contained more GAG and type II collagen resulting in mechanically superior constructs. Chondrocytes delivered in the more electropositive MAX8 gel experienced a greater degree of cell death during encapsulation and delivery and the remaining viable cells were less prone to maintain their phenotype. As a result, MAX8 gel-cell constructs had fewer cells, of which a limited number were capable of laying down cartilage-specific ECM. PMID:22841922

Iterative design of peptide-based hydrogels and the effect of network electrostatics on primary chondrocyte behavior.

PubMed

Sinthuvanich, Chomdao; Haines-Butterick, Lisa A; Nagy, Katelyn J; Schneider, Joel P

2012-10-01

Iterative peptide design was used to generate two peptide-based hydrogels to study the effect of network electrostatics on primary chondrocyte behavior. MAX8 and HLT2 peptides have formal charge states of +7 and +5 per monomer, respectively. These peptides undergo triggered folding and self-assembly to afford hydrogel networks having similar rheological behavior and local network morphologies, yet different electrostatic character. Each gel can be used to directly encapsulate and syringe-deliver cells. The influence of network electrostatics on cell viability after encapsulation and delivery, extracellular matrix deposition, gene expression, and the bulk mechanical properties of the gel-cell constructs as a function of culture time was assessed. The less electropositive HLT2 gel provides a microenvironment more conducive to chondrocyte encapsulation, delivery, and phenotype maintenance. Cell viability was higher for this gel and although a moderate number of cells dedifferentiated to a fibroblast-like phenotype, many retained their chondrocytic behavior. As a result, gel-cell constructs prepared with HLT2, cultured under static in vitro conditions, contained more GAG and type II collagen resulting in mechanically superior constructs. Chondrocytes delivered in the more electropositive MAX8 gel experienced a greater degree of cell death during encapsulation and delivery and the remaining viable cells were less prone to maintain their phenotype. As a result, MAX8 gel-cell constructs had fewer cells, of which a limited number were capable of laying down cartilage-specific ECM. Published by Elsevier Ltd.

Physics, stability, and dynamics of supply networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefan; Seidel, Thomas; Šeba, Pétr; Płatkowski, Tadeusz

2004-12-01

We show how to treat supply networks as physical transport problems governed by balance equations and equations for the adaptation of production speeds. Although the nonlinear behavior is different, the linearized set of coupled differential equations is formally related to those of mechanical or electrical oscillator networks. Supply networks possess interesting features due to their complex topology and directed links. We derive analytical conditions for absolute and convective instabilities. The empirically observed “bullwhip effect” in supply chains is explained as a form of convective instability based on resonance effects. Moreover, it is generalized to arbitrary supply networks. Their related eigenvalues are usually complex, depending on the network structure (even without loops). Therefore, their generic behavior is characterized by damped or growing oscillations. We also show that regular distribution networks possess two negative eigenvalues only, but perturbations generate a spectrum of complex eigenvalues.

Social Network Influences on Adolescent Substance Use: Disentangling Structural Equivalence from Cohesion

PubMed Central

Fujimoto, Kayo; Valente, Thomas W.

2012-01-01

This study investigates two contagion mechanisms of peer influence based on direct communication (cohesion) versus comparison through peers who occupy similar network positions (structural equivalence) in the context of adolescents' drinking alcohol and smoking. To date, the two contagion mechanisms have been considered observationally inseparable, but this study attempts to disentangle structural equivalence from cohesion as a contagion mechanism by examining the extent to which the transmission of drinking and smoking behaviors attenuates as a function of social distance (i.e., from immediate friends to indirectly connected peers). Using the U.S. Add Health data consisting of a nationally representative sample of American adolescents (Grades 7-12), this study measured peer risk-taking up to four steps away from the adolescent (friends of friends of friends of friends) using a network exposure model. Peer influence was tested using a logistic regression model of alcohol drinking and cigarette smoking. Results indicate that influence based on structural equivalence tended to be stronger than influence based on cohesion in general, and that the magnitude of the effect decreased up to three steps away from the adolescent (friends of friends of friends). Further analysis indicated that structural equivalence acted as a mechanism of contagion for drinking and cohesion acted as one for smoking. These results indicate that the two transmission mechanisms with differing network proximities can differentially affect drinking and smoking behaviors in American adolescents. PMID:22475405

Fast Flux Watch: A mechanism for online detection of fast flux networks.

PubMed

Al-Duwairi, Basheer N; Al-Hammouri, Ahmad T

2014-07-01

Fast flux networks represent a special type of botnets that are used to provide highly available web services to a backend server, which usually hosts malicious content. Detection of fast flux networks continues to be a challenging issue because of the similar behavior between these networks and other legitimate infrastructures, such as CDNs and server farms. This paper proposes Fast Flux Watch (FF-Watch), a mechanism for online detection of fast flux agents. FF-Watch is envisioned to exist as a software agent at leaf routers that connect stub networks to the Internet. The core mechanism of FF-Watch is based on the inherent feature of fast flux networks: flux agents within stub networks take the role of relaying client requests to point-of-sale websites of spam campaigns. The main idea of FF-Watch is to correlate incoming TCP connection requests to flux agents within a stub network with outgoing TCP connection requests from the same agents to the point-of-sale website. Theoretical and traffic trace driven analysis shows that the proposed mechanism can be utilized to efficiently detect fast flux agents within a stub network.

A generalized theory of preferential linking

NASA Astrophysics Data System (ADS)

Hu, Haibo; Guo, Jinli; Liu, Xuan; Wang, Xiaofan

2014-12-01

There are diverse mechanisms driving the evolution of social networks. A key open question dealing with understanding their evolution is: How do various preferential linking mechanisms produce networks with different features? In this paper we first empirically study preferential linking phenomena in an evolving online social network, find and validate the linear preference. We propose an analyzable model which captures the real growth process of the network and reveals the underlying mechanism dominating its evolution. Furthermore based on preferential linking we propose a generalized model reproducing the evolution of online social networks, and present unified analytical results describing network characteristics for 27 preference scenarios. We study the mathematical structure of degree distributions and find that within the framework of preferential linking analytical degree distributions can only be the combinations of finite kinds of functions which are related to rational, logarithmic and inverse tangent functions, and extremely complex network structure will emerge even for very simple sublinear preferential linking. This work not only provides a verifiable origin for the emergence of various network characteristics in social networks, but bridges the micro individuals' behaviors and the global organization of social networks.

Interactions between neural networks: a mechanism for tuning chaos and oscillations

PubMed Central

2007-01-01

We show that chaos and oscillations in a higher-order binary neural network can be tuned effectively using interactions between neural networks. Our results suggest that network interactions may be useful as a means of adjusting the level of dynamic activities in systems that employ chaos and oscillations for information processing, or as a means of suppressing oscillatory behaviors in systems that require stability. PMID:19003511

Topology and evolution of technology innovation networks

NASA Astrophysics Data System (ADS)

Valverde, Sergi; Solé, Ricard V.; Bedau, Mark A.; Packard, Norman

2007-11-01

The web of relations linking technological innovation can be fairly described in terms of patent citations. The resulting patent citation network provides a picture of the large-scale organization of innovations and its time evolution. Here we study the patterns of change of patents registered by the U.S. Patent and Trademark Office. We show that the scaling behavior exhibited by this network is consistent with a preferential attachment mechanism together with a Weibull-shaped aging term. Such an attachment kernel is shared by scientific citation networks, thus indicating a universal type of mechanism linking ideas and designs and their evolution. The implications for evolutionary theory of innovation are discussed.

Empirical Models of Social Learning in a Large, Evolving Network.

PubMed

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals' access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends.

Empirical Models of Social Learning in a Large, Evolving Network

PubMed Central

Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

2016-01-01

This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. PMID:27701430

Understanding metropolitan patterns of daily encounters.

PubMed

Sun, Lijun; Axhausen, Kay W; Lee, Der-Horng; Huang, Xianfeng

2013-08-20

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters' bounded nature. An individual's encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of "familiar strangers" in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or "structure of co-presence" across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and--particularly--disclosing the impact of human behavior on various diffusion/spreading processes.

Understanding metropolitan patterns of daily encounters

PubMed Central

Sun, Lijun; Axhausen, Kay W.; Lee, Der-Horng; Huang, Xianfeng

2013-01-01

Understanding of the mechanisms driving our daily face-to-face encounters is still limited; the field lacks large-scale datasets describing both individual behaviors and their collective interactions. However, here, with the help of travel smart card data, we uncover such encounter mechanisms and structures by constructing a time-resolved in-vehicle social encounter network on public buses in a city (about 5 million residents). Using a population scale dataset, we find physical encounters display reproducible temporal patterns, indicating that repeated encounters are regular and identical. On an individual scale, we find that collective regularities dominate distinct encounters’ bounded nature. An individual’s encounter capability is rooted in his/her daily behavioral regularity, explaining the emergence of “familiar strangers” in daily life. Strikingly, we find individuals with repeated encounters are not grouped into small communities, but become strongly connected over time, resulting in a large, but imperceptible, small-world contact network or “structure of co-presence” across the whole metropolitan area. Revealing the encounter pattern and identifying this large-scale contact network are crucial to understanding the dynamics in patterns of social acquaintances, collective human behaviors, and—particularly—disclosing the impact of human behavior on various diffusion/spreading processes. PMID:23918373

Exacerbated vulnerability of coupled socio-economic risk in complex networks

NASA Astrophysics Data System (ADS)

Zhang, Xin; Feng, Ling; Berman, Yonatan; Hu, Ning; Stanley, H. Eugene

2016-10-01

The study of risk contagion in economic networks has most often focused on the financial liquidities of institutions and assets. In practice the agents in a network affect each other through social contagion, i.e., through herd behavior and the tendency to follow leaders. We study the coupled risk between social and economic contagion and find it significantly more severe than when economic risk is considered alone. Using the empirical network from the China venture capital market we find that the system exhibits an extreme risk of abrupt phase transition and large-scale damage, which is in clear contrast to the smooth phase transition traditionally observed in economic contagion alone. We also find that network structure impacts market resilience and that the randomization of the social network of the market participants can reduce system fragility when there is herd behavior. Our work indicates that under coupled contagion mechanisms network resilience can exhibit a fundamentally different behavior, i.e., an abrupt transition. It also reveals the extreme risk when a system has coupled socio-economic risks, and this could be of interest to both policy makers and market practitioners.

Recurrence networks from multivariate signals for uncovering dynamic transitions of horizontal oil-water stratified flows

NASA Astrophysics Data System (ADS)

Gao, Zhong-Ke; Zhang, Xin-Wang; Jin, Ning-De; Donner, Reik V.; Marwan, Norbert; Kurths, Jürgen

2013-09-01

Characterizing the mechanism of drop formation at the interface of horizontal oil-water stratified flows is a fundamental problem eliciting a great deal of attention from different disciplines. We experimentally and theoretically investigate the formation and transition of horizontal oil-water stratified flows. We design a new multi-sector conductance sensor and measure multivariate signals from two different stratified flow patterns. Using the Adaptive Optimal Kernel Time-Frequency Representation (AOK TFR) we first characterize the flow behavior from an energy and frequency point of view. Then, we infer multivariate recurrence networks from the experimental data and investigate the cross-transitivity for each constructed network. We find that the cross-transitivity allows quantitatively uncovering the flow behavior when the stratified flow evolves from a stable state to an unstable one and recovers deeper insights into the mechanism governing the formation of droplets at the interface of stratified flows, a task that existing methods based on AOK TFR fail to work. These findings present a first step towards an improved understanding of the dynamic mechanism leading to the transition of horizontal oil-water stratified flows from a complex-network perspective.

Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model.

PubMed

Papasavvas, Christoforos A; Wang, Yujiang; Trevelyan, Andrew J; Kaiser, Marcus

2015-09-01

Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics.

Shear-thickening behavior of Fe-ZSM5 zeolite slurry and its removal with alumina/boehmites

NASA Astrophysics Data System (ADS)

Liu, Xiao-guang; Li, Yan; Xue, Wen-dong; Sun, Jia-lin; Tang, Qian

2018-06-01

A cryogenic scanning electron microscopy (cryo-SEM) technique was used to explore the shear-thickening behavior of Fe-ZSM5 zeolite pastes and to discover its underlying mechanism. Bare Fe-ZSM5 zeolite samples were found to contain agglomerations, which may break the flow of the pastes and cause shear-thickening behaviors. However, the shear-thickening behaviors can be eliminated by the addition of halloysite and various boehmites because of improved particle packing. Furthermore, compared with pure Fe-ZSM5 zeolite samples and its composite samples with halloysite, the samples with boehmite (Pural SB or Disperal) additions exhibited network structures in their cryo-SEM images; these structures could facilitate the storage and release of flow water, smooth paste flow, and avoid shear-thickening. By contrast, another boehmite (Versal 250) formed agglomerations rather than network structures after being added to the Fe-ZSM5 zeolite paste and resulted in shear-thickening behavior. Consequently, the results suggest that these network structures play key roles in eliminating the shear-thickening behavior.

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma: Connectome Neurotrauma Mechanics

DTIC Science & Technology

2012-08-16

threshold of 18% strain, 161 edges were removed. Watts and Strogatz [66] define the small-world network based on the clustering coefficient of the network and...NeuroImage 52: 1059–1069. 65. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87: 198701. 66. Watts DJ, Strogatz SH

Strain-driven criticality underlies nonlinear mechanics of fibrous networks

NASA Astrophysics Data System (ADS)

Sharma, A.; Licup, A. J.; Rens, R.; Vahabi, M.; Jansen, K. A.; Koenderink, G. H.; MacKintosh, F. C.

2016-10-01

Networks with only central force interactions are floppy when their average connectivity is below an isostatic threshold. Although such networks are mechanically unstable, they can become rigid when strained. It was recently shown that the transition from floppy to rigid states as a function of simple shear strain is continuous, with hallmark signatures of criticality [Sharma et al., Nature Phys. 12, 584 (2016), 10.1038/nphys3628]. The nonlinear mechanical response of collagen networks was shown to be quantitatively described within the framework of such mechanical critical phenomenon. Here, we provide a more quantitative characterization of critical behavior in subisostatic networks. Using finite-size scaling we demonstrate the divergence of strain fluctuations in the network at well-defined critical strain. We show that the characteristic strain corresponding to the onset of strain stiffening is distinct from but related to this critical strain in a way that depends on critical exponents. We confirm this prediction experimentally for collagen networks. Moreover, we find that the apparent critical exponents are largely independent of the spatial dimensionality. With subisostaticity as the only required condition, strain-driven criticality is expected to be a general feature of biologically relevant fibrous networks.

Mitigating Distributed Denial of Service Attacks with Dynamic Resource Pricing

DTIC Science & Technology

2001-10-01

should be nearly comparable to a system that does not use the payment mechanisms. There is prior work on how pricing can be used to influence consumer ... behavior , how to integrate pricing mechanisms with OS and network resource management mechanisms. In this paper, we instead focus on how pricing

Encoding mechano-memories in filamentous-actin networks

NASA Astrophysics Data System (ADS)

Majumdar, Sayantan; Foucard, Louis; Levine, Alex; Gardel, Margaret L.

History-dependent adaptation is a central feature of learning and memory. Incorporating such features into `adaptable materials' that can modify their mechanical properties in response to external cues, remains an outstanding challenge in materials science. Here, we study a novel mechanism of mechano-memory in cross-linked F-actin networks, the essential determinants of the mechanical behavior of eukaryotic cells. We find that the non-linear mechanical response of such networks can be reversibly programmed through induction of mechano-memories. In particular, the direction, magnitude, and duration of previously applied shear stresses can be encoded into the network architecture. The `memory' of the forcing history is long-lived, but it can be erased by force applied in the opposite direction. These results demonstrate that F-actin networks can encode analog read-write mechano-memories which can be used for adaptation to mechanical stimuli. We further show that the mechano-memory arises from changes in the nematic order of the constituent filaments. Our results suggest a new mechanism of mechanical sensing in eukaryotic cells and provide a strategy for designing a novel class of materials. S.M. acknowledges U. Chicago MRSEC for support through a Kadanoff-Rice fellowship.

Self-organizing network services with evolutionary adaptation.

PubMed

Nakano, Tadashi; Suda, Tatsuya

2005-09-01

This paper proposes a novel framework for developing adaptive and scalable network services. In the proposed framework, a network service is implemented as a group of autonomous agents that interact in the network environment. Agents in the proposed framework are autonomous and capable of simple behaviors (e.g., replication, migration, and death). In this paper, an evolutionary adaptation mechanism is designed using genetic algorithms (GAs) for agents to evolve their behaviors and improve their fitness values (e.g., response time to a service request) to the environment. The proposed framework is evaluated through simulations, and the simulation results demonstrate the ability of autonomous agents to adapt to the network environment. The proposed framework may be suitable for disseminating network services in dynamic and large-scale networks where a large number of data and services need to be replicated, moved, and deleted in a decentralized manner.

Application of neural networks to prediction of advanced composite structures mechanical response and behavior

NASA Technical Reports Server (NTRS)

Cios, K. J.; Vary, A.; Berke, L.; Kautz, H. E.

1992-01-01

Two types of neural networks were used to evaluate acousto-ultrasonic (AU) data for material characterization and mechanical reponse prediction. The neural networks included a simple feedforward network (backpropagation) and a radial basis functions network. Comparisons of results in terms of accuracy and training time are given. Acousto-ultrasonic (AU) measurements were performed on a series of tensile specimens composed of eight laminated layers of continuous, SiC fiber reinforced Ti-15-3 matrix. The frequency spectrum was dominated by frequencies of longitudinal wave resonance through the thickness of the specimen at the sending transducer. The magnitude of the frequency spectrum of the AU signal was used for calculating a stress-wave factor based on integrating the spectral distribution function and used for comparison with neural networks results.

Implications of behavioral architecture for the evolution of self-organized division of labor.

PubMed

Duarte, A; Scholtens, E; Weissing, F J

2012-01-01

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization.

Implications of Behavioral Architecture for the Evolution of Self-Organized Division of Labor

PubMed Central

Duarte, A.; Scholtens, E.; Weissing, F. J.

2012-01-01

Division of labor has been studied separately from a proximate self-organization and an ultimate evolutionary perspective. We aim to bring together these two perspectives. So far this has been done by choosing a behavioral mechanism a priori and considering the evolution of the properties of this mechanism. Here we use artificial neural networks to allow for a more open architecture. We study whether emergent division of labor can evolve in two different network architectures; a simple feedforward network, and a more complex network that includes the possibility of self-feedback from previous experiences. We focus on two aspects of division of labor; worker specialization and the ratio of work performed for each task. Colony fitness is maximized by both reducing idleness and achieving a predefined optimal work ratio. Our results indicate that architectural constraints play an important role for the outcome of evolution. With the simplest network, only genetically determined specialization is possible. This imposes several limitations on worker specialization. Moreover, in order to minimize idleness, networks evolve a biased work ratio, even when an unbiased work ratio would be optimal. By adding self-feedback to the network we increase the network's flexibility and worker specialization evolves under a wider parameter range. Optimal work ratios are more easily achieved with the self-feedback network, but still provide a challenge when combined with worker specialization. PMID:22457609

Tunneling Conductivity and Piezoresistivity of Composites Containing Randomly Dispersed Conductive Nano-Platelets

PubMed Central

Oskouyi, Amirhossein Biabangard; Sundararaj, Uttandaraman; Mertiny, Pierre

2014-01-01

In this study, a three-dimensional continuum percolation model was developed based on a Monte Carlo simulation approach to investigate the percolation behavior of an electrically insulating matrix reinforced with conductive nano-platelet fillers. The conductivity behavior of composites rendered conductive by randomly dispersed conductive platelets was modeled by developing a three-dimensional finite element resistor network. Parameters related to the percolation threshold and a power-low describing the conductivity behavior were determined. The piezoresistivity behavior of conductive composites was studied employing a reoriented resistor network emulating a conductive composite subjected to mechanical strain. The effects of the governing parameters, i.e., electron tunneling distance, conductive particle aspect ratio and size effects on conductivity behavior were examined. PMID:28788580

The Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres.

PubMed

Ince, Robin A A; Jaworska, Katarzyna; Gross, Joachim; Panzeri, Stefano; van Rijsbergen, Nicola J; Rousselet, Guillaume A; Schyns, Philippe G

2016-08-22

A key to understanding visual cognition is to determine "where", "when", and "how" brain responses reflect the processing of the specific visual features that modulate categorization behavior-the "what". The N170 is the earliest Event-Related Potential (ERP) that preferentially responds to faces. Here, we demonstrate that a paradigmatic shift is necessary to interpret the N170 as the product of an information processing network that dynamically codes and transfers face features across hemispheres, rather than as a local stimulus-driven event. Reverse-correlation methods coupled with information-theoretic analyses revealed that visibility of the eyes influences face detection behavior. The N170 initially reflects coding of the behaviorally relevant eye contralateral to the sensor, followed by a causal communication of the other eye from the other hemisphere. These findings demonstrate that the deceptively simple N170 ERP hides a complex network information processing mechanism involving initial coding and subsequent cross-hemispheric transfer of visual features. © The Author 2016. Published by Oxford University Press.

Bio-Inspired Networking — Self-Organizing Networked Embedded Systems

NASA Astrophysics Data System (ADS)

Dressler, Falko

The turn to nature has brought us many unforeseen great concepts and solutions. This course seems to hold on for many research domains. In this article, we study the applicability of biological mechanisms and techniques in the domain of communications. In particular, we study the behavior and the challenges in networked embedded systems that are meant to self-organize in large groups of nodes. Application examples include wireless sensor networks and sensor/actuator networks. Based on a review of the needs and requirements in such networks, we study selected bio-inspired networking approaches that claim to outperform other methods in specific domains. We study mechanisms in swarm intelligence, the artificial immune system, and approaches based on investigations on the cellular signaling pathways. As a major conclusion, we derive that bio-inspired networking techniques do have advantages compared to engineering methods. Nevertheless, selection and employment must be done carefully to achieve the desired performance gains.

Coupled Multi-physics analysis of Caprock Integrity and Fault Reactivation during CO2 Sequestration*

NASA Astrophysics Data System (ADS)

Newell, P.; Martinez, M. J.; Bishop, J.

2012-12-01

Structural/stratigraphic trapping beneath a low-permeable caprock layer is the primary trapping mechanism for long-term subsurface sequestration of CO2. Pre-existing fracture networks, injection induced fractures, and faults are of concern for possible CO2 leakage both during and after injection. In this work we model the effects of both caprock jointing and a fault on the caprock sealing integrity during various injection scenarios. The modeling effort uses a three-dimensional finite-element based coupled multiphase flow and geomechanics simulator. The joints within the caprock are idealized as equally spaced and parallel. Both the mechanical and flow behavior of the joint network are treated within an effective continuum formulation. The mechanical behavior of the joint network is linear elastic in shear and nonlinear elastic in the normal direction. The flow behavior of the joint network is treated using the classical cubic-law relating flow rate and aperture. The flow behavior is then upscaled to obtain an effective permeability. The fault is modeled as a finite-thickness layer with multiple joint sets. The joint sets within the fault region are modeled following the same mechanical and flow formulation as the joints within the caprock. Various injection schedules as well as fault and caprock jointing configurations within a proto-typical sequestration site have been investigated. The resulting leakage rates through the caprock and fault are compared to those assuming intact material. The predicted leakage rates are a strong nonlinear function of the injection rate. *This material is based upon work supported as part of the Center for Frontiers of Subsurface Energy Security, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0001114. Sandia is a multi-program laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energys National Nuclear Security Administration under Contract DE-AC04-94AL85000.

Universal bursty behavior in the air transportation system.

PubMed

Ito, Hidetaka; Nishinari, Katsuhiro

2015-12-01

Social activities display bursty behavior characterized by heavy-tailed interevent time distributions. We examine the bursty behavior of airplanes' arrivals in hub airports. The analysis indicates that the air transportation system universally follows a power-law interarrival time distribution with an exponent α=2.5 and an exponential cutoff. Moreover, we investigate the mechanism of this bursty behavior by introducing a simple model to describe it. In addition, we compare the extent of the hub-and-spoke structure and the burstiness of various airline networks in the system. Remarkably, the results suggest that the hub-and-spoke network of the system and the carriers' strategy to facilitate transit are the origins of this universality.

Biobased, self-healable, high strength rubber with tunicate cellulose nanocrystals.

PubMed

Cao, Liming; Yuan, Daosheng; Xu, Chuanhui; Chen, Yukun

2017-10-19

Cellulose nanocrystals represent a promising and environmentally friendly reinforcing nanofiller for polymers, especially for rubbers and elastomers. Here, a simple approach via latex mixing is used to fabricate biobased, healable rubber with high strength based on epoxidized natural rubber (ENR). Tunicate cellulose nanocrystals (t-CNs) isolated from marine biomass with a high aspect ratio are used to improve both mechanical properties and self-healing behavior of the material. By introducing dynamic hydrogen bond supramolecular networks between oxygenous groups of ENR and hydroxyl groups on the t-CN surface, together with chain interdiffusion in permanently but slightly cross-linked rubber, self-healing and mechanical properties are facilitated significantly in the resulting materials. Macroscopic tensile healing behavior and microscopic morphology analyses are carried out to evaluate the performance of the materials. Both t-CN content and healing time have significant influence on healing behavior. The results indicate that a synergistic effect between molecular interdiffusion and dynamic hydrogen bond supramolecular networks leads to the improved self-healing behavior.

Analytical solution for a class of network dynamics with mechanical and financial applications

NASA Astrophysics Data System (ADS)

Krejčí, P.; Lamba, H.; Melnik, S.; Rachinskii, D.

2014-09-01

We show that for a certain class of dynamics at the nodes the response of a network of any topology to arbitrary inputs is defined in a simple way by its response to a monotone input. The nodes may have either a discrete or continuous set of states and there is no limit on the complexity of the network. The results provide both an efficient numerical method and the potential for accurate analytic approximation of the dynamics on such networks. As illustrative applications, we introduce a quasistatic mechanical model with objects interacting via frictional forces and a financial market model with avalanches and critical behavior that are generated by momentum trading strategies.

The Slow Oscillation in Cortical and Thalamic Networks: Mechanisms and Functions

PubMed Central

Neske, Garrett T.

2016-01-01

During even the most quiescent behavioral periods, the cortex and thalamus express rich spontaneous activity in the form of slow (<1 Hz), synchronous network state transitions. Throughout this so-called slow oscillation, cortical and thalamic neurons fluctuate between periods of intense synaptic activity (Up states) and almost complete silence (Down states). The two decades since the original characterization of the slow oscillation in the cortex and thalamus have seen considerable advances in deciphering the cellular and network mechanisms associated with this pervasive phenomenon. There are, nevertheless, many questions regarding the slow oscillation that await more thorough illumination, particularly the mechanisms by which Up states initiate and terminate, the functional role of the rhythmic activity cycles in unconscious or minimally conscious states, and the precise relation between Up states and the activated states associated with waking behavior. Given the substantial advances in multineuronal recording and imaging methods in both in vivo and in vitro preparations, the time is ripe to take stock of our current understanding of the slow oscillation and pave the way for future investigations of its mechanisms and functions. My aim in this Review is to provide a comprehensive account of the mechanisms and functions of the slow oscillation, and to suggest avenues for further exploration. PMID:26834569

Neural mechanisms of cue-approach training

PubMed Central

Bakkour, Akram; Lewis-Peacock, Jarrod A.; Poldrack, Russell A.; Schonberg, Tom

2016-01-01

Biasing choices may prove a useful way to implement behavior change. Previous work has shown that a simple training task (the cue-approach task), which does not rely on external reinforcement, can robustly influence choice behavior by biasing choice toward items that were targeted during training. In the current study, we replicate previous behavioral findings and explore the neural mechanisms underlying the shift in preferences following cue-approach training. Given recent successes in the development and application of machine learning techniques to task-based fMRI data, which have advanced understanding of the neural substrates of cognition, we sought to leverage the power of these techniques to better understand neural changes during cue-approach training that subsequently led to a shift in choice behavior. Contrary to our expectations, we found that machine learning techniques applied to fMRI data during non-reinforced training were unsuccessful in elucidating the neural mechanism underlying the behavioral effect. However, univariate analyses during training revealed that the relationship between BOLD and choices for Go items increases as training progresses compared to choices of NoGo items primarily in lateral prefrontal cortical areas. This new imaging finding suggests that preferences are shifted via differential engagement of task control networks that interact with value networks during cue-approach training. PMID:27677231

Principles and management of neuropsychiatric symptoms in Alzheimer's dementia.

PubMed

Nowrangi, Milap A; Lyketsos, Constantine G; Rosenberg, Paul B

2015-01-29

Neuropsychiatric symptoms of Alzheimer's disease (NPS-AD) are highly prevalent and lead to poor medical and functional outcomes. In spite of the burdensome nature of NPS-AD, we are continuing to refine the nosology and only beginning to understand the underlying pathophysiology. Cluster analyses have frequently identified three to five subsyndromes of NPS-AD: behavioral dysfunction (for example, agitation/aggressiveness), psychosis (for example, delusions and hallucinations), and mood disturbance (for example, depression or apathy). Recent neurobiological studies have used new neuroimaging techniques to elucidate behaviorally relevant circuits and networks associated with these subsyndromes. Several fronto-subcortical circuits, cortico-cortical networks, and neurotransmitter systems have been proposed as regions and mechanisms underlying NPS-AD. Common to most of these subsyndromes is the broad overlap of regions associated with the salience network (anterior cingulate and insula), mood regulation (amygdala), and motivated behavior (frontal cortex). Treatment strategies for dysregulated mood syndromes (depression and apathy) have primarily targeted serotonergic mechanisms with antidepressants or dopaminergic mechanisms with psychostimulants. Psychotic symptoms have largely been targeted with anti-psychotic medications despite controversial risk/benefit tradeoffs. Management of behavioral dyscontrol, including agitation and aggression in AD, has encompassed a wide range of psychoactive medications as well as non-pharmacological approaches. Developing rational therapeutic approaches for NPS-AD will require a firmer understanding of the underlying etiology in order to improve nosology as well as provide the empirical evidence necessary to overcome regulatory and funding challenges to further study these debilitating symptoms.

Mechanical Behavior of Collagen-Fibrin Co-Gels Reflects Transition From Series to Parallel Interactions With Increasing Collagen Content

PubMed Central

Lai, Victor K.; Lake, Spencer P.; Frey, Christina R.; Tranquillo, Robert T.; Barocas, Victor H.

2012-01-01

Fibrin and collagen, biopolymers occurring naturally in the body, are biomaterials commonly-used as scaffolds for tissue engineering. How collagen and fibrin interact to confer macroscopic mechanical properties in collagen-fibrin composite systems remains poorly understood. In this study, we formulated collagen-fibrin co-gels at different collagen-tofibrin ratios to observe changes in the overall mechanical behavior and microstructure. A modeling framework of a two-network system was developed by modifying our micro-scale model, considering two forms of interaction between the networks: (a) two interpenetrating but noninteracting networks (“parallel”), and (b) a single network consisting of randomly alternating collagen and fibrin fibrils (“series”). Mechanical testing of our gels show that collagen-fibrin co-gels exhibit intermediate properties (UTS, strain at failure, tangent modulus) compared to those of pure collagen and fibrin. The comparison with model predictions show that the parallel and series model cases provide upper and lower bounds, respectively, for the experimental data, suggesting that a combination of such interactions exists between the collagen and fibrin in co-gels. A transition from the series model to the parallel model occurs with increasing collagen content, with the series model best describing predominantly fibrin co-gels, and the parallel model best describing predominantly collagen co-gels. PMID:22482659

Kinetics of Social Contagion

NASA Astrophysics Data System (ADS)

Ruan, Zhongyuan; Iñiguez, Gerardo; Karsai, Márton; Kertész, János

2015-11-01

Diffusion of information, behavioral patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporates threshold mechanism capturing sensitivity to peer pressure, the effect of "immune" nodes who never adopt, and a perpetual flow of external information. While any constant, nonzero rate of dynamically introduced spontaneous adopters leads to global spreading, the kinetics by which the asymptotic state is approached shows rich behavior. In particular, we find that, as a function of the immune node density, there is a transition from fast to slow spreading governed by entirely different mechanisms. This transition happens below the percolation threshold of network fragmentation, and has its origin in the competition between cascading behavior induced by adopters and blocking due to immune nodes. This change is accompanied by a percolation transition of the induced clusters.

Cortical network reorganization guided by sensory input features.

PubMed

Kilgard, Michael P; Pandya, Pritesh K; Engineer, Navzer D; Moucha, Raluca

2002-12-01

Sensory experience alters the functional organization of cortical networks. Previous studies using behavioral training motivated by aversive or rewarding stimuli have demonstrated that cortical plasticity is specific to salient inputs in the sensory environment. Sensory experience associated with electrical activation of the basal forebrain (BasF) generates similar input specific plasticity. By directly engaging plasticity mechanisms and avoiding extensive behavioral training, BasF stimulation makes it possible to efficiently explore how specific sensory features contribute to cortical plasticity. This review summarizes our observations that cortical networks employ a variety of strategies to improve the representation of the sensory environment. Different combinations of receptive-field, temporal, and spectrotemporal plasticity were generated in primary auditory cortex neurons depending on the pitch, modulation rate, and order of sounds paired with BasF stimulation. Simple tones led to map expansion, while modulated tones altered the maximum cortical following rate. Exposure to complex acoustic sequences led to the development of combination-sensitive responses. This remodeling of cortical response characteristics may reflect changes in intrinsic cellular mechanisms, synaptic efficacy, and local neuronal connectivity. The intricate relationship between the pattern of sensory activation and cortical plasticity suggests that network-level rules alter the functional organization of the cortex to generate the most behaviorally useful representation of the sensory environment.

Evolution of weighted complex bus transit networks with flow

NASA Astrophysics Data System (ADS)

Huang, Ailing; Xiong, Jie; Shen, Jinsheng; Guan, Wei

2016-02-01

Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.

Visual Working Memory Load-Related Changes in Neural Activity and Functional Connectivity

PubMed Central

Li, Ling; Zhang, Jin-Xiang; Jiang, Tao

2011-01-01

Background Visual working memory (VWM) helps us store visual information to prepare for subsequent behavior. The neuronal mechanisms for sustaining coherent visual information and the mechanisms for limited VWM capacity have remained uncharacterized. Although numerous studies have utilized behavioral accuracy, neural activity, and connectivity to explore the mechanism of VWM retention, little is known about the load-related changes in functional connectivity for hemi-field VWM retention. Methodology/Principal Findings In this study, we recorded electroencephalography (EEG) from 14 normal young adults while they performed a bilateral visual field memory task. Subjects had more rapid and accurate responses to the left visual field (LVF) memory condition. The difference in mean amplitude between the ipsilateral and contralateral event-related potential (ERP) at parietal-occipital electrodes in retention interval period was obtained with six different memory loads. Functional connectivity between 128 scalp regions was measured by EEG phase synchronization in the theta- (4–8 Hz), alpha- (8–12 Hz), beta- (12–32 Hz), and gamma- (32–40 Hz) frequency bands. The resulting matrices were converted to graphs, and mean degree, clustering coefficient and shortest path length was computed as a function of memory load. The results showed that brain networks of theta-, alpha-, beta-, and gamma- frequency bands were load-dependent and visual-field dependent. The networks of theta- and alpha- bands phase synchrony were most predominant in retention period for right visual field (RVF) WM than for LVF WM. Furthermore, only for RVF memory condition, brain network density of theta-band during the retention interval were linked to the delay of behavior reaction time, and the topological property of alpha-band network was negative correlation with behavior accuracy. Conclusions/Significance We suggest that the differences in theta- and alpha- bands between LVF and RVF conditions in functional connectivity and topological properties during retention period may result in the decline of behavioral performance in RVF task. PMID:21789253

Large strain deformation behavior of polymeric gels in shear- and cavitation rheology

NASA Astrophysics Data System (ADS)

Hashemnejad, Seyed Meysam; Kundu, Santanu

Polymeric gels are used in many applications including in biomedical and in food industries. Investigation of mechanical responses of swollen polymer gels and linking that to the polymer chain dynamics are of significant interest. Here, large strain deformation behavior of two different gel systems and with different network architecture will be presented. We consider biologically relevant polysaccharide hydrogels, formed through ionic and covalent crosslinking, and physically associating triblock copolymer gels in a midblock selective solvent. Gels with similar low-strain shear modulus display distinctly different non-linear rheological behavior in large strain shear deformation. Both these gels display strain-stiffening behavior in shear-deformation prior to macroscopic fracture of the network, however, only the alginate gels display negative normal stress. The cavitation rheology data show that the critical pressure for cavitation is higher for alginate gels than that observed for triblock gels. These distinctly different large-strain deformation behavior has been related to the gel network structure, as alginate chains are much stiffer than the triblock polymer chains.

An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

PubMed Central

2012-01-01

Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

Complex Networks in Psychological Models

NASA Astrophysics Data System (ADS)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Origin of the spike-timing-dependent plasticity rule

NASA Astrophysics Data System (ADS)

Cho, Myoung Won; Choi, M. Y.

2016-08-01

A biological synapse changes its efficacy depending on the difference between pre- and post-synaptic spike timings. Formulating spike-timing-dependent interactions in terms of the path integral, we establish a neural-network model, which makes it possible to predict relevant quantities rigorously by means of standard methods in statistical mechanics and field theory. In particular, the biological synaptic plasticity rule is shown to emerge as the optimal form for minimizing the free energy. It is further revealed that maximization of the entropy of neural activities gives rise to the competitive behavior of biological learning. This demonstrates that statistical mechanics helps to understand rigorously key characteristic behaviors of a neural network, thus providing the possibility of physics serving as a useful and relevant framework for probing life.

The phenotype of cancer cell invasion controlled by fibril diameter and pore size of 3D collagen networks.

PubMed

Sapudom, Jiranuwat; Rubner, Stefan; Martin, Steve; Kurth, Tony; Riedel, Stefanie; Mierke, Claudia T; Pompe, Tilo

2015-06-01

The behavior of cancer cells is strongly influenced by the properties of extracellular microenvironments, including topology, mechanics and composition. As topological and mechanical properties of the extracellular matrix are hard to access and control for in-depth studies of underlying mechanisms in vivo, defined biomimetic in vitro models are needed. Herein we show, how pore size and fibril diameter of collagen I networks distinctively regulate cancer cell morphology and invasion. Three-dimensional collagen I matrices with a tight control of pore size, fibril diameter and stiffness were reconstituted by adjustment of concentration and pH value during matrix reconstitution. At first, a detailed analysis of topology and mechanics of matrices using confocal laser scanning microscopy, image analysis tools and force spectroscopy indicate pore size and not fibril diameter as the major determinant of matrix elasticity. Secondly, by using two different breast cancer cell lines (MDA-MB-231 and MCF-7), we demonstrate collagen fibril diameter--and not pore size--to primarily regulate cell morphology, cluster formation and invasion. Invasiveness increased and clustering decreased with increasing fibril diameter for both, the highly invasive MDA-MB-231 cells with mesenchymal migratory phenotype and the MCF-7 cells with amoeboid migratory phenotype. As this behavior was independent of overall pore size, matrix elasticity is shown to be not the major determinant of the cell characteristics. Our work emphasizes the complex relationship between structural-mechanical properties of the extracellular matrix and invasive behavior of cancer cells. It suggests a correlation of migratory and invasive phenotype of cancer cells in dependence on topological and mechanical features of the length scale of single fibrils and not on coarse-grained network properties. Copyright © 2015 Elsevier Ltd. All rights reserved.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers.

PubMed

Eggebrecht, Adam T; Elison, Jed T; Feczko, Eric; Todorov, Alexandre; Wolff, Jason J; Kandala, Sridhar; Adams, Chloe M; Snyder, Abraham Z; Lewis, John D; Estes, Annette M; Zwaigenbaum, Lonnie; Botteron, Kelly N; McKinstry, Robert C; Constantino, John N; Evans, Alan; Hazlett, Heather C; Dager, Stephen; Paterson, Sarah J; Schultz, Robert T; Styner, Martin A; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L; Petersen, Steven E; Piven, Joseph; Pruett, John R

2017-03-01

Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. © The Author 2017. Published by Oxford University Press.

Joint Attention and Brain Functional Connectivity in Infants and Toddlers

PubMed Central

Eggebrecht, Adam T.; Elison, Jed T.; Feczko, Eric; Todorov, Alexandre; Wolff, Jason J.; Kandala, Sridhar; Adams, Chloe M.; Snyder, Abraham Z.; Lewis, John D.; Estes, Annette M.; Zwaigenbaum, Lonnie; Botteron, Kelly N.; McKinstry, Robert C.; Constantino, John N.; Evans, Alan; Hazlett, Heather C.; Dager, Stephen; Paterson, Sarah J.; Schultz, Robert T.; Styner, Martin A.; Gerig, Guido; Das, Samir; Kostopoulos, Penelope; Schlaggar, Bradley L.; Petersen, Steven E.; Piven, Joseph; Pruett, John R.

2017-01-01

Abstract Initiating joint attention (IJA), the behavioral instigation of coordinated focus of 2 people on an object, emerges over the first 2 years of life and supports social-communicative functioning related to the healthy development of aspects of language, empathy, and theory of mind. Deficits in IJA provide strong early indicators for autism spectrum disorder, and therapies targeting joint attention have shown tremendous promise. However, the brain systems underlying IJA in early childhood are poorly understood, due in part to significant methodological challenges in imaging localized brain function that supports social behaviors during the first 2 years of life. Herein, we show that the functional organization of the brain is intimately related to the emergence of IJA using functional connectivity magnetic resonance imaging and dimensional behavioral assessments in a large semilongitudinal cohort of infants and toddlers. In particular, though functional connections spanning the brain are involved in IJA, the strongest brain-behavior associations cluster within connections between a small subset of functional brain networks; namely between the visual network and dorsal attention network and between the visual network and posterior cingulate aspects of the default mode network. These observations mark the earliest known description of how functional brain systems underlie a burgeoning fundamental social behavior, may help improve the design of targeted therapies for neurodevelopmental disorders, and, more generally, elucidate physiological mechanisms essential to healthy social behavior development. PMID:28062515

A game theory-based trust measurement model for social networks.

PubMed

Wang, Yingjie; Cai, Zhipeng; Yin, Guisheng; Gao, Yang; Tong, Xiangrong; Han, Qilong

2016-01-01

In social networks, trust is a complex social network. Participants in online social networks want to share information and experiences with as many reliable users as possible. However, the modeling of trust is complicated and application dependent. Modeling trust needs to consider interaction history, recommendation, user behaviors and so on. Therefore, modeling trust is an important focus for online social networks. We propose a game theory-based trust measurement model for social networks. The trust degree is calculated from three aspects, service reliability, feedback effectiveness, recommendation credibility, to get more accurate result. In addition, to alleviate the free-riding problem, we propose a game theory-based punishment mechanism for specific trust and global trust, respectively. We prove that the proposed trust measurement model is effective. The free-riding problem can be resolved effectively through adding the proposed punishment mechanism.

Effect of cellulose nanocrystals (CNC) on rheological and mechanical properties and crystallization behavior of PLA/CNC nanocomposites.

PubMed

Kamal, Musa R; Khoshkava, Vahid

2015-06-05

In earlier work, we reported that spray freeze drying of cellulose nanocrystals (CNC) yields porous agglomerate structures. On the other hand, the conventional spray dried CNC (CNCSD) and the freeze dried CNC (CNCFD) produce compact solid structures with very low porosity. As it is rather difficult to obtain direct microscopic evidence of the quality of dispersion of CNC in polymer nanocomposites, it was shown that supporting evidence of the quality and influence of dispersion in a polypropylene (PP)/CNC nanocomposite could be obtained by studying the rheological behavior, mechanical properties and crystallization characteristics of PP/CNC nanocomposites. In an effort to produce a sustainable, fully biosourced, biodegradable nanocomposite, this manuscript presents the results of a study of the rheological, mechanical and crystallization behavior of PLA/CNCSFD nanocomposites obtained by melt processing. The results are analyzed to determine CNC network formation, rheological percolation threshold concentrations, mechanical properties in the rubbery and glassy states, and the effect of CNCSFD on crystalline nucleation and crystallization rates of PLA. These results suggest that the porosity and network structure of CNCSFD agglomerates contribute significantly to good dispersion of CNC in the PLA matrix. Copyright © 2015 Elsevier Ltd. All rights reserved.

Signaling mechanisms underlying the robustness and tunability of the plant immune network

PubMed Central

Kim, Yungil; Tsuda, Kenichi; Igarashi, Daisuke; Hillmer, Rachel A.; Sakakibara, Hitoshi; Myers, Chad L.; Katagiri, Fumiaki

2014-01-01

Summary How does robust and tunable behavior emerge in a complex biological network? We sought to understand this for the signaling network controlling pattern-triggered immunity (PTI) in Arabidopsis. A dynamic network model containing four major signaling sectors, the jasmonate, ethylene, PAD4, and salicylate sectors, which together explain up to 80% of the PTI level, was built using data for dynamic sector activities and PTI levels under exhaustive combinatorial sector perturbations. Our regularized multiple regression model had a high level of predictive power and captured known and unexpected signal flows in the network. The sole inhibitory sector in the model, the ethylene sector, was central to the network robustness via its inhibition of the jasmonate sector. The model's multiple input sites linked specific signal input patterns varying in strength and timing to different network response patterns, indicating a mechanism enabling tunability. PMID:24439900

Gain control through divisive inhibition prevents abrupt transition to chaos in a neural mass model

PubMed Central

Papasavvas, Christoforos A.; Wang, Yujiang; Trevelyan, Andrew J.; Kaiser, Marcus

2016-01-01

Experimental results suggest that there are two distinct mechanisms of inhibition in cortical neuronal networks: subtractive and divisive inhibition. They modulate the input-output function of their target neurons either by increasing the input that is needed to reach maximum output or by reducing the gain and the value of maximum output itself, respectively. However, the role of these mechanisms on the dynamics of the network is poorly understood. We introduce a novel population model and numerically investigate the influence of divisive inhibition on network dynamics. Specifically, we focus on the transitions from a state of regular oscillations to a state of chaotic dynamics via period-doubling bifurcations. The model with divisive inhibition exhibits a universal transition rate to chaos (Feigenbaum behavior). In contrast, in an equivalent model without divisive inhibition, transition rates to chaos are not bounded by the universal constant (non-Feigenbaum behavior). This non-Feigenbaum behavior, when only subtractive inhibition is present, is linked to the interaction of bifurcation curves in the parameter space. Indeed, searching the parameter space showed that such interactions are impossible when divisive inhibition is included. Therefore, divisive inhibition prevents non-Feigenbaum behavior and, consequently, any abrupt transition to chaos. The results suggest that the divisive inhibition in neuronal networks could play a crucial role in keeping the states of order and chaos well separated and in preventing the onset of pathological neural dynamics. PMID:26465514

Incentivizing and Evaluating Internet-Wide Network Measurements

DTIC Science & Technology

2014-03-01

Behavior research methods, vol. 44, no. 1, pp. 1–23, 2012. [21] J. Oh and G. Wang, “ Evaluating crowdsourcing through amazon mechanical turk as a...NAVAL POSTGRADUATE SCHOOL MONTEREY, CALIFORNIA THESIS INCENTIVIZING AND EVALUATING INTERNET-WIDE NETWORK MEASUREMENTS by Gokay Huz March 2014 Thesis...Thesis 2012-04-02—2014-03-28 INCENTIVIZING AND EVALUATING INTERNET-WIDE NETWORK MEASUREMENTS Gokay Huz Naval Postgraduate School Monterey, CA 93943

Neuromodulation of Behavioral and Cognitive Development across the Life Span

ERIC Educational Resources Information Center

Li, Shu-Chen

2012-01-01

Among other mechanisms, behavioral and cognitive development entail, on the one hand, contextual scaffolding and, on the other hand, neuromodulation of adaptive neurocognitive representations across the life span. Key brain networks underlying cognition, emotion, and motivation are innervated by major transmitter systems (e.g., the catecholamines…

The Use of End-to-End Multicast Measurements for Characterizing Internal Network Behavior

DTIC Science & Technology

2002-08-01

dropping on the basis Random Early Detection ( RED ) [17] is another mechanism by which packet loss may become decorrelated. It remains to be seen whether...this mechanism will be widely deployed in communications networks. On the other hand, the use of RED to merely mark packets will not break correlations...Tail and Random Early Detection ( RED ) buffer discard methods, [17]. We compared the inferred loss and delay with actual probe loss and delay. We found

Effect of pH on chitosan hydrogel polymer network structure.

PubMed

Xu, Hongcheng; Matysiak, Silvina

2017-06-29

Chitosan is a molecule that can form water-filled 3D polymer networks with a wide range of applications. A new coarse-grained model for chitosan hydrogel was developed to explore its pH-dependent self-assembly behavior and mechanical properties. Our results indicate that the underlying polymer physical crosslinking pattern induced by solution pH has a significant effect on hydrogel elastic moduli. With this model, we obtain pH-dependent structural and mechanical property changes in agreement with experimental observations, and provide a molecular mechanism behind the changes in polymer crosslinking patterns.

An Architecture for SCADA Network Forensics

NASA Astrophysics Data System (ADS)

Kilpatrick, Tim; Gonzalez, Jesus; Chandia, Rodrigo; Papa, Mauricio; Shenoi, Sujeet

Supervisory control and data acquisition (SCADA) systems are widely used in industrial control and automation. Modern SCADA protocols often employ TCP/IP to transport sensor data and control signals. Meanwhile, corporate IT infrastructures are interconnecting with previously isolated SCADA networks. The use of TCP/IP as a carrier protocol and the interconnection of IT and SCADA networks raise serious security issues. This paper describes an architecture for SCADA network forensics. In addition to supporting forensic investigations of SCADA network incidents, the architecture incorporates mechanisms for monitoring process behavior, analyzing trends and optimizing plant performance.

Affective mentalizing and brain activity at rest in the behavioral variant of frontotemporal dementia.

PubMed

Caminiti, Silvia P; Canessa, Nicola; Cerami, Chiara; Dodich, Alessandra; Crespi, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Falini, Andrea; Cappa, Stefano F

2015-01-01

bvFTD patients display an impairment in the attribution of cognitive and affective states to others, reflecting GM atrophy in brain regions associated with social cognition, such as amygdala, superior temporal cortex and posterior insula. Distinctive patterns of abnormal brain functioning at rest have been reported in bvFTD, but their relationship with defective attribution of affective states has not been investigated. To investigate the relationship among resting-state brain activity, gray matter (GM) atrophy and the attribution of mental states in the behavioral variant of fronto-temporal degeneration (bvFTD). We compared 12 bvFTD patients with 30 age- and education-matched healthy controls on a) performance in a task requiring the attribution of affective vs. cognitive mental states; b) metrics of resting-state activity in known functional networks; and c) the relationship between task-performances and resting-state metrics. In addition, we assessed a connection between abnormal resting-state metrics and GM atrophy. Compared with controls, bvFTD patients showed a reduction of intra-network coherent activity in several components, as well as decreased strength of activation in networks related to attentional processing. Anomalous resting-state activity involved networks which also displayed a significant reduction of GM density. In patients, compared with controls, higher affective mentalizing performance correlated with stronger functional connectivity between medial prefrontal sectors of the default-mode and attentional/performance monitoring networks, as well as with increased coherent activity in components of the executive, sensorimotor and fronto-limbic networks. Some of the observed effects may reflect specific compensatory mechanisms for the atrophic changes involving regions in charge of affective mentalizing. The analysis of specific resting-state networks thus highlights an intermediate level of analysis between abnormal brain structure and impaired behavioral performance in bvFTD, reflecting both dysfunction and compensation mechanisms.

Generalized memory associativity in a network model for the neuroses

NASA Astrophysics Data System (ADS)

Wedemann, Roseli S.; Donangelo, Raul; de Carvalho, Luís A. V.

2009-03-01

We review concepts introduced in earlier work, where a neural network mechanism describes some mental processes in neurotic pathology and psychoanalytic working-through, as associative memory functioning, according to the findings of Freud. We developed a complex network model, where modules corresponding to sensorial and symbolic memories interact, representing unconscious and conscious mental processes. The model illustrates Freud's idea that consciousness is related to symbolic and linguistic memory activity in the brain. We have introduced a generalization of the Boltzmann machine to model memory associativity. Model behavior is illustrated with simulations and some of its properties are analyzed with methods from statistical mechanics.

Reward-based training of recurrent neural networks for cognitive and value-based tasks

PubMed Central

Song, H Francis; Yang, Guangyu R; Wang, Xiao-Jing

2017-01-01

Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal’s internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task. DOI: http://dx.doi.org/10.7554/eLife.21492.001 PMID:28084991

Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men.

PubMed

Kapadia, F; Siconolfi, D E; Barton, S; Olivieri, B; Lombardo, L; Halkitis, P N

2013-06-01

Associations between social support network characteristics and sexual risk among racially/ethnically diverse young men who have sex with men (YMSM) were examined using egocentric network data from a prospective cohort study of YMSM (n = 501) recruited in New York City. Bivariate and multivariable logistic regression analyses examined associations between social support network characteristics and sexual risk taking behaviors in Black, Hispanic/Latino, and White YMSM. Bivariate analyses indicated key differences in network size, composition, communication frequency and average relationship duration by race/ethnicity. In multivariable analyses, controlling for individual level sociodemographic, psychosocial and relationship factors, having a sexual partner in one's social support network was associated with unprotected sexual behavior for both Hispanic/Latino (AOR = 3.90) and White YMSM (AOR = 4.93). Further examination of key network characteristics across racial/ethnic groups are warranted in order to better understand the extant mechanisms for provision of HIV prevention programming to racially/ethnically diverse YMSM at risk for HIV.

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer.

PubMed

Li, Site; Zhu, Xiaomei; Liu, Bingya; Wang, Gaowei; Ao, Ping

2015-05-30

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity.

Endogenous molecular network reveals two mechanisms of heterogeneity within gastric cancer

PubMed Central

Li, Site; Zhu, Xiaomei; Liu, Bingya; Wang, Gaowei; Ao, Ping

2015-01-01

Intratumor heterogeneity is a common phenomenon and impedes cancer therapy and research. Gastric cancer (GC) cells have generally been classified into two heterogeneous cellular phenotypes, the gastric and intestinal types, yet the mechanisms of maintaining two phenotypes and controlling phenotypic transition are largely unknown. A qualitative systematic framework, the endogenous molecular network hypothesis, has recently been proposed to understand cancer genesis and progression. Here, a minimal network corresponding to such framework was found for GC and was quantified via a stochastic nonlinear dynamical system. We then further extended the framework to address the important question of intratumor heterogeneity quantitatively. The working network characterized main known features of normal gastric epithelial and GC cell phenotypes. Our results demonstrated that four positive feedback loops in the network are critical for GC cell phenotypes. Moreover, two mechanisms that contribute to GC cell heterogeneity were identified: particular positive feedback loops are responsible for the maintenance of intestinal and gastric phenotypes; GC cell progression routes that were revealed by the dynamical behaviors of individual key components are heterogeneous. In this work, we constructed an endogenous molecular network of GC that can be expanded in the future and would broaden the known mechanisms of intratumor heterogeneity. PMID:25962957

Pairing Increases Activation of V1aR, but not OTR, in Auditory Regions of Zebra Finches: The Importance of Signal Modality in Nonapeptide-Social Behavior Relationships.

PubMed

Tomaszycki, Michelle L; Atchley, Derek

2017-10-01

Social relationships are complex, involving the production and comprehension of signals, individual recognition, and close coordination of behavior between two or more individuals. The nonapeptides oxytocin and vasopressin are widely believed to regulate social relationships. These findings come largely from prairie voles, in which nonapeptide receptors in olfactory neural circuits drive pair bonding. This research is assumed to apply to all species. Previous reviews have offered two competing hypotheses. The work of Sarah Newman has implicated a common neural network across species, the Social Behavior Network. In contrast, others have suggested that there are signal modality-specific networks that regulate social behavior. Our research focuses on evaluating these two competing hypotheses in the zebra finch, a species that relies heavily on vocal/auditory signals for communication, specifically the neural circuits underlying singing in males and song perception in females. We have demonstrated that the quality of vocal interactions is highly important for the formation of long-term monogamous bonds in zebra finches. Qualitative evidence at first suggests that nonapeptide receptor distributions are very different between monogamous rodents (olfactory species) and monogamous birds (vocal/auditory species). However, we have demonstrated that social bonding behaviors are not only correlated with activation of nonapeptide receptors in vocal and auditory circuits, but also involve regions of the common Social Behavior Network. Here, we show increased Vasopressin 1a receptor, but not oxytocin receptor, activation in two auditory regions following formation of a pair bond. To our knowledge, this is the first study to suggest a role of nonapeptides in the auditory circuit in pair bonding. Thus, we highlight converging mechanisms of social relationships and also point to the importance of studying multiple species to understand mechanisms of behavior. © The Author 2017. Published by Oxford University Press on behalf of the Society for Integrative and Comparative Biology. All rights reserved. For permissions please email: journals.permissions@oup.com.

A disassembly-driven mechanism explains F-actin-mediated chromosome transport in starfish oocytes

PubMed Central

Bun, Philippe; Dmitrieff, Serge; Belmonte, Julio M

2018-01-01

While contraction of sarcomeric actomyosin assemblies is well understood, this is not the case for disordered networks of actin filaments (F-actin) driving diverse essential processes in animal cells. For example, at the onset of meiosis in starfish oocytes a contractile F-actin network forms in the nuclear region transporting embedded chromosomes to the assembling microtubule spindle. Here, we addressed the mechanism driving contraction of this 3D disordered F-actin network by comparing quantitative observations to computational models. We analyzed 3D chromosome trajectories and imaged filament dynamics to monitor network behavior under various physical and chemical perturbations. We found no evidence of myosin activity driving network contractility. Instead, our observations are well explained by models based on a disassembly-driven contractile mechanism. We reconstitute this disassembly-based contractile system in silico revealing a simple architecture that robustly drives chromosome transport to prevent aneuploidy in the large oocyte, a prerequisite for normal embryonic development. PMID:29350616

Dynamic behavior of acrylic acid clusters as quasi-mobile nodes in a model of hydrogel network

NASA Astrophysics Data System (ADS)

Zidek, Jan; Milchev, Andrey; Vilgis, Thomas A.

2012-12-01

Using a molecular dynamics simulation, we study the thermo-mechanical behavior of a model hydrogel subject to deformation and change in temperature. The model is found to describe qualitatively poly-lactide-glycolide hydrogels in which acrylic acid (AA)-groups are believed to play the role of quasi-mobile nodes in the formation of a network. From our extensive analysis of the structure, formation, and disintegration of the AA-groups, we are able to elucidate the relationship between structure and viscous-elastic behavior of the model hydrogel. Thus, in qualitative agreement with observations, we find a softening of the mechanical response at large deformations, which is enhanced by growing temperature. Several observables as the non-affinity parameter A and the network rearrangement parameter V indicate the existence of a (temperature-dependent) threshold degree of deformation beyond which the quasi-elastic response of the model system turns over into plastic (ductile) one. The critical stretching when the affinity of the deformation is lost can be clearly located in terms of A and V as well as by analysis of the energy density of the system. The observed stress-strain relationship matches that of known experimental systems.

Speed and segmentation control mechanisms characterized in rhythmically-active circuits created from spinal neurons produced from genetically-tagged embryonic stem cells

PubMed Central

Sternfeld, Matthew J; Hinckley, Christopher A; Moore, Niall J; Pankratz, Matthew T; Hilde, Kathryn L; Driscoll, Shawn P; Hayashi, Marito; Amin, Neal D; Bonanomi, Dario; Gifford, Wesley D; Sharma, Kamal; Goulding, Martyn; Pfaff, Samuel L

2017-01-01

Flexible neural networks, such as the interconnected spinal neurons that control distinct motor actions, can switch their activity to produce different behaviors. Both excitatory (E) and inhibitory (I) spinal neurons are necessary for motor behavior, but the influence of recruiting different ratios of E-to-I cells remains unclear. We constructed synthetic microphysical neural networks, called circuitoids, using precise combinations of spinal neuron subtypes derived from mouse stem cells. Circuitoids of purified excitatory interneurons were sufficient to generate oscillatory bursts with properties similar to in vivo central pattern generators. Inhibitory V1 neurons provided dual layers of regulation within excitatory rhythmogenic networks - they increased the rhythmic burst frequency of excitatory V3 neurons, and segmented excitatory motor neuron activity into sub-networks. Accordingly, the speed and pattern of spinal circuits that underlie complex motor behaviors may be regulated by quantitatively gating the intra-network cellular activity ratio of E-to-I neurons. DOI: http://dx.doi.org/10.7554/eLife.21540.001 PMID:28195039

PDMS Network Structure-Property Relationships: Influence of Molecular Architecture on Mechanical and Wetting Properties

NASA Astrophysics Data System (ADS)

Melillo, Matthew Joseph

Poly(dimethylsiloxane) (PDMS) is one of the most common elastomers, with applications ranging from sealants and marine-antifouling coatings to medical devices and absorbents for water treatment. Fundamental understanding of how liquids spread on the surface of and absorb into and leach out of PDMS networks is of critical importance for the design and use in another application - microfluidic devices. The growing use of PDMS in microfluidic devices raises the concern that some researchers may use this material without fully understanding all of its advantages, drawbacks, and intricacies. The primary goal of this Ph.D. dissertation is to elucidate PDMS network molecular structure to macroscopic property relationships and to demonstrate how molecular architecture can alter dynamic mechanical and wetting characteristics. We prepare PDMS materials by using vinyl/ tetrakis(dimethylsiloxy)silane (TDSS) and silanol/ tetraethylorthosilicate (TEOS) combinations of PDMS end-groups and crosslinkers as two model systems. Under constant curing conditions, we systematically study the effects of polymer molecular weight, loading of crosslinker, and end-group chemical functionality on the extent of gelation and the dynamic mechanical and water wetting properties of end-linked PDMS networks. The extent of the gelation reaction is determined using the Soxhlet extraction to quantify the amount of material that did and did not participate in the crosslinking reactions, termed the gel and sol fractions, respectively. We use the Miller-Macosko model in conjunction with the gel fraction and precise chemical composition (i.e., stoichiometric ratio and molecular weight) to determine the fractions of elastic and pendant material, the molecular weight between chemical crosslinks, and the average effective functionality of the crosslinker molecule. Based on dynamic mechanical testing, we find that the maximum storage moduli are achieved at optimal stoichiometric conditions in the vinyl/TDSS and commercial PDMS-based Sylgard 184 composite, but only keep improving with additional crosslinker in the silanol/TEOS systems due to in situ TEOS aggregation. We relate molecular network topology to mechanical properties using outputs from the Miller-Macosko model in the vinyl/TDSS system. The elastic fraction and storage modulus correlate well, as do the pendant fraction and the loss tangent, demonstrating the importance of each fraction in bulk mechanical properties. By studying the dynamic behavior of water droplets wetting PDMS substrates, we observe non-linear wetting behaviors that are markedly different from linear behaviors seen on glassy polymer substrates. The non-linear behavior is only observed prior to extraction, while after extraction, both systems demonstrate behavior similar to glassy polymers. This reveals the dramatic role small amounts of uncrosslinked materials present in the sol fraction play in the surface wetting dynamics of PDMS materials. We further demonstrate the role of uncrosslinked material by adding silicone oils into otherwise fully crosslinked PDMS networks and study their wetting properties. Through careful formulation and preparation of PDMS materials, compared to simply mixing two formulations present in Sylgard 184, one can apply polymer network models to glean useful information about network topology. The benefits of doing so outweigh the costs. We stress the importance of performing Soxhlet extraction to remove unreacted components from PDMS materials, even when using optimal stoichiometry. These mobile molecules that remain after crosslinking can alter significantly wetting behavior and readily leach into liquid environments. However, it is equally important to stress that Soxhlet extraction will not remove all unreacted material. Some will always remain in PDMS, which is often the practice in preparing microfluidic devices. While Sylgard 184 is very well suited for some applications, the results presented in this dissertation demonstrate to researchers that the material does have its limitations and that other options are available. These findings will aid in the design and implementation of reliable microfluidic devices and other PDMS-based materials that encounter liquid interfaces.

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

Model and Dynamic Behavior of Malware Propagation over Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Song, Yurong; Jiang, Guo-Ping

Based on the inherent characteristics of wireless sensor networks (WSN), the dynamic behavior of malware propagation in flat WSN is analyzed and investigated. A new model is proposed using 2-D cellular automata (CA), which extends the traditional definition of CA and establishes whole transition rules for malware propagation in WSN. Meanwhile, the validations of the model are proved through theoretical analysis and simulations. The theoretical analysis yields closed-form expressions which show good agreement with the simulation results of the proposed model. It is shown that the malware propaga-tion in WSN unfolds neighborhood saturation, which dominates the effects of increasing infectivity and limits the spread of the malware. MAC mechanism of wireless sensor networks greatly slows down the speed of malware propagation and reduces the risk of large-scale malware prevalence in these networks. The proposed model can describe accurately the dynamic behavior of malware propagation over WSN, which can be applied in developing robust and efficient defense system on WSN.

Competing dynamic phases of active polymer networks

NASA Astrophysics Data System (ADS)

Freedman, Simon; Banerjee, Shiladitya; Dinner, Aaron R.

Recent experiments on in-vitro reconstituted assemblies of F-actin, myosin-II motors, and cross-linking proteins show that tuning local network properties can changes the fundamental biomechanical behavior of the system. For example, by varying cross-linker density and actin bundle rigidity, one can switch between contractile networks useful for reshaping cells, polarity sorted networks ideal for directed molecular transport, and frustrated networks with robust structural properties. To efficiently investigate the dynamic phases of actomyosin networks, we developed a coarse grained non-equilibrium molecular dynamics simulation of model semiflexible filaments, molecular motors, and cross-linkers with phenomenologically defined interactions. The simulation's accuracy was verified by benchmarking the mechanical properties of its individual components and collective behavior against experimental results at the molecular and network scales. By adjusting the model's parameters, we can reproduce the qualitative phases observed in experiment and predict the protein characteristics where phase crossovers could occur in collective network dynamics. Our model provides a framework for understanding cells' multiple uses of actomyosin networks and their applicability in materials research. Supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program.

[Network structures in biological systems].

PubMed

Oleskin, A V

2013-01-01

Network structures (networks) that have been extensively studied in the humanities are characterized by cohesion, a lack of a central control unit, and predominantly fractal properties. They are contrasted with structures that contain a single centre (hierarchies) as well as with those whose elements predominantly compete with one another (market-type structures). As far as biological systems are concerned, their network structures can be subdivided into a number of types involving different organizational mechanisms. Network organization is characteristic of various structural levels of biological systems ranging from single cells to integrated societies. These networks can be classified into two main subgroups: (i) flat (leaderless) network structures typical of systems that are composed of uniform elements and represent modular organisms or at least possess manifest integral properties and (ii) three-dimensional, partly hierarchical structures characterized by significant individual and/or intergroup (intercaste) differences between their elements. All network structures include an element that performs structural, protective, and communication-promoting functions. By analogy to cell structures, this element is denoted as the matrix of a network structure. The matrix includes a material and an immaterial component. The material component comprises various structures that belong to the whole structure and not to any of its elements per se. The immaterial (ideal) component of the matrix includes social norms and rules regulating network elements' behavior. These behavioral rules can be described in terms of algorithms. Algorithmization enables modeling the behavior of various network structures, particularly of neuron networks and their artificial analogs.

The Deceptively Simple N170 Reflects Network Information Processing Mechanisms Involving Visual Feature Coding and Transfer Across Hemispheres

PubMed Central

Ince, Robin A. A.; Jaworska, Katarzyna; Gross, Joachim; Panzeri, Stefano; van Rijsbergen, Nicola J.; Rousselet, Guillaume A.; Schyns, Philippe G.

2016-01-01

A key to understanding visual cognition is to determine “where”, “when”, and “how” brain responses reflect the processing of the specific visual features that modulate categorization behavior—the “what”. The N170 is the earliest Event-Related Potential (ERP) that preferentially responds to faces. Here, we demonstrate that a paradigmatic shift is necessary to interpret the N170 as the product of an information processing network that dynamically codes and transfers face features across hemispheres, rather than as a local stimulus-driven event. Reverse-correlation methods coupled with information-theoretic analyses revealed that visibility of the eyes influences face detection behavior. The N170 initially reflects coding of the behaviorally relevant eye contralateral to the sensor, followed by a causal communication of the other eye from the other hemisphere. These findings demonstrate that the deceptively simple N170 ERP hides a complex network information processing mechanism involving initial coding and subsequent cross-hemispheric transfer of visual features. PMID:27550865

Mechanisms of Developmental Regression in Autism and the Broader Phenotype: A Neural Network Modeling Approach

ERIC Educational Resources Information Center

Thomas, Michael S. C.; Knowland, Victoria C. P.; Karmiloff-Smith, Annette

2011-01-01

Loss of previously established behaviors in early childhood constitutes a markedly atypical developmental trajectory. It is found almost uniquely in autism and its cause is currently unknown (Baird et al., 2008). We present an artificial neural network model of developmental regression, exploring the hypothesis that regression is caused by…

The power of (Mis)perception: Rethinking suicide contagion in youth friendship networks.

PubMed

Zimmerman, Gregory M; Rees, Carter; Posick, Chad; Zimmerman, Lori A

2016-05-01

Suicide is a leading cause of death among youth. In the wake of peer suicide, youth are vulnerable to suicide contagion. But, questions remain about the mechanisms through which suicide spreads and the accuracy of youths' estimates of friends' suicidal behaviors. This study addresses these questions within school-aged youths' friendship networks. Social network data were drawn from two schools in the National Longitudinal Study of Adolescent to Adult Health, from which 2180 youth in grades 7-12 nominated up to ten friends. A measure of "perceived" friends' attempted suicide was constructed based on respondents' reports of their friends' attempted suicide. This measure was broader than a "true" measure of friends' attempted suicide, constructed from self-reports of nominated friends who attended respondents' schools. Sociograms graphically represented the accuracy with which suicide attempters estimated friends' suicide attempts. Results from cross-tabulation with Chi-square analysis indicated that approximately 4% of youth (88/2180) attempted suicide, and these youth disproportionately misperceived (predominantly overestimated) friends' suicidal behaviors, compared to non-suicide-attempters. Penalized logistic regression models indicated that friends' self-reported attempted suicide was unrelated to respondent attempted suicide. But, the odds of respondent attempted suicide were 2.54 times higher (95% CI, 1.06-6.10) among youth who accurately perceived friends' attempted suicide, and 5.40 times higher (95% CI, 3.34-8.77) among youth who overestimated friends' attempted suicide. The results suggest that at-risk youth overestimate their friends' suicidal behaviors, which exacerbates their own risk of suicidal behavior. Methodologically, this suggests that a continued collaboration among network scientists, suicide researchers, and medical providers is necessary to further examine the mechanisms surrounding this phenomenon. Practically, it is important to screen at-risk youth for exposure to peer suicide and to use the social environment created by adolescent friendship networks to empower and support youth who are susceptible to suicidal thoughts and behaviors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Dynamic model of time-dependent complex networks.

PubMed

Hill, Scott A; Braha, Dan

2010-10-01

The characterization of the "most connected" nodes in static or slowly evolving complex networks has helped in understanding and predicting the behavior of social, biological, and technological networked systems, including their robustness against failures, vulnerability to deliberate attacks, and diffusion properties. However, recent empirical research of large dynamic networks (characterized by irregular connections that evolve rapidly) has demonstrated that there is little continuity in degree centrality of nodes over time, even when their degree distributions follow a power law. This unexpected dynamic centrality suggests that the connections in these systems are not driven by preferential attachment or other known mechanisms. We present an approach to explain real-world dynamic networks and qualitatively reproduce these dynamic centrality phenomena. This approach is based on a dynamic preferential attachment mechanism, which exhibits a sharp transition from a base pure random walk scheme.

A network approach to discerning the identities of C. elegans in a free moving population

NASA Astrophysics Data System (ADS)

Winter, Peter B.; Brielmann, Renee M.; Timkovich, Nicholas P.; Navarro, Helio T.; Teixeira-Castro, Andreia; Morimoto, Richard I.; Amaral, Luis A. N.

2016-10-01

The study of C. elegans has led to ground-breaking discoveries in gene-function, neuronal circuits, and physiological responses. Subtle behavioral phenotypes, however, are often difficult to measure reproducibly. We have developed an experimental and computational infrastructure to simultaneously record and analyze the physical characteristics, movement, and social behaviors of dozens of interacting free-moving nematodes. Our algorithm implements a directed acyclic network that reconstructs the complex behavioral trajectories generated by individual C. elegans in a free moving population by chaining hundreds to thousands of short tracks into long contiguous trails. This technique allows for the high-throughput quantification of behavioral characteristics that require long-term observation of individual animals. The graphical interface we developed will enable researchers to uncover, in a reproducible manner, subtle time-dependent behavioral phenotypes that will allow dissection of the molecular mechanisms that give rise to organism-level behavior.

Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia.

PubMed

Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya F; Musen, Mark A

The need to examine the behavior of different user groups is a fundamental requirement when building information systems. In this paper, we present Ontology-based Decentralized Search (OBDS), a novel method to model the navigation behavior of users equipped with different types of background knowledge. Ontology-based Decentralized Search combines decentralized search, an established method for navigation in social networks, and ontologies to model navigation behavior in information networks. The method uses ontologies as an explicit representation of background knowledge to inform the navigation process and guide it towards navigation targets. By using different ontologies, users equipped with different types of background knowledge can be represented. We demonstrate our method using four biomedical ontologies and their associated Wikipedia articles. We compare our simulation results with base line approaches and with results obtained from a user study. We find that our method produces click paths that have properties similar to those originating from human navigators. The results suggest that our method can be used to model human navigation behavior in systems that are based on information networks, such as Wikipedia. This paper makes the following contributions: (i) To the best of our knowledge, this is the first work to demonstrate the utility of ontologies in modeling human navigation and (ii) it yields new insights and understanding about the mechanisms of human navigation in information networks.

Using ontologies to model human navigation behavior in information networks: A study based on Wikipedia

PubMed Central

Lamprecht, Daniel; Strohmaier, Markus; Helic, Denis; Nyulas, Csongor; Tudorache, Tania; Noy, Natalya F.; Musen, Mark A.

2015-01-01

The need to examine the behavior of different user groups is a fundamental requirement when building information systems. In this paper, we present Ontology-based Decentralized Search (OBDS), a novel method to model the navigation behavior of users equipped with different types of background knowledge. Ontology-based Decentralized Search combines decentralized search, an established method for navigation in social networks, and ontologies to model navigation behavior in information networks. The method uses ontologies as an explicit representation of background knowledge to inform the navigation process and guide it towards navigation targets. By using different ontologies, users equipped with different types of background knowledge can be represented. We demonstrate our method using four biomedical ontologies and their associated Wikipedia articles. We compare our simulation results with base line approaches and with results obtained from a user study. We find that our method produces click paths that have properties similar to those originating from human navigators. The results suggest that our method can be used to model human navigation behavior in systems that are based on information networks, such as Wikipedia. This paper makes the following contributions: (i) To the best of our knowledge, this is the first work to demonstrate the utility of ontologies in modeling human navigation and (ii) it yields new insights and understanding about the mechanisms of human navigation in information networks. PMID:26568745

Altered Behavioral and Autonomic Pain Responses in Alzheimer’s Disease Are Associated with Dysfunctional Affective, Self-Reflective and Salience Network Resting-State Connectivity

PubMed Central

Beach, Paul A.; Huck, Jonathan T.; Zhu, David C.; Bozoki, Andrea C.

2017-01-01

While pain behaviors are increased in Alzheimer’s disease (AD) patients compared to healthy seniors (HS) across multiple disease stages, autonomic responses are reduced with advancing AD. To better understand the neural mechanisms underlying these phenomena, we undertook a controlled cross-sectional study examining behavioral (Pain Assessment in Advanced Dementia, PAINAD scores) and autonomic (heart rate, HR) pain responses in 24 HS and 20 AD subjects using acute pressure stimuli. Resting-state fMRI was utilized to investigate how group connectivity differences were related to altered pain responses. Pain behaviors (slope of PAINAD score change and mean PAINAD score) were increased in patients vs. controls. Autonomic measures (HR change intercept and mean HR change) were reduced in severe vs. mildly affected AD patients. Group functional connectivity differences associated with greater pain behavior reactivity in patients included: connectivity within a temporal limbic network (TLN) and between the TLN and ventromedial prefrontal cortex (vmPFC); between default mode network (DMN) subcomponents; between the DMN and ventral salience network (vSN). Reduced HR responses within the AD group were associated with connectivity changes within the DMN and vSN—specifically the precuneus and vmPFC. Discriminant classification indicated HR-related connectivity within the vSN to the vmPFC best distinguished AD severity. Thus, altered behavioral and autonomic pain responses in AD reflects dysfunction of networks and structures subserving affective, self-reflective, salience and autonomic regulation. PMID:28959201

A simple generative model of collective online behavior.

PubMed

Gleeson, James P; Cellai, Davide; Onnela, Jukka-Pekka; Porter, Mason A; Reed-Tsochas, Felix

2014-07-22

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates--even when using purely observational data without experimental design--that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior.

A simple generative model of collective online behavior

PubMed Central

Gleeson, James P.; Cellai, Davide; Onnela, Jukka-Pekka; Porter, Mason A.; Reed-Tsochas, Felix

2014-01-01

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviors to population-level outcomes. In this paper, we introduce a simple generative model for the collective behavior of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct mechanisms: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behavior that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates—even when using purely observational data without experimental design—that temporal data-driven modeling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover previously unidentified aspects of collective online behavior. PMID:25002470

Synthesizing animal and human behavior research via neural network learning theory.

PubMed

Tryon, W W

1995-12-01

Animal and human research have been "divorced" since approximately 1968. Several recent articles have tried to persuade behavior therapists of the merits of animal research. Three reasons are given concerning why disinterest in animal research is so widespread: (1) functional explanations are given for animals, and cognitive explanations are given for humans; (2) serial symbol manipulating models are used to explain human behavior; and (3) human learning was assumed, thereby removing it as something to be explained. Brain-inspired connectionist neural networks, collectively referred to as neural network learning theory (NNLT), are briefly described, and a spectrum of their accomplishments from simple conditioning through speech is outlined. Five benefits that behavior therapists can derive from NNLT are described. They include (a) enhanced professional identity derived from a comprehensive learning theory, (b) improved interdisciplinary collaboration both clinically and scientifically, (c) renewed perceived relevance of animal research, (d) access to plausible proximal causal mechanisms capable of explaining operant conditioning, and (e) an inherently developmental perspective.

The Influence of Gender, Age, Matriline and Hierarchical Rank on Individual Social Position, Role and Interactional Patterns in Macaca sylvanus at ‘La Forêt des Singes’: A Multilevel Social Network Approach

PubMed Central

Sosa, Sebastian

2016-01-01

A society is a complex system composed of individuals that can be characterized by their own attributes that influence their behaviors. In this study, a specific analytical protocol based on social network analysis was adopted to investigate the influence of four attributes (gender, age, matriline, and hierarchical rank) on affiliative (allogrooming) and agonistic networks in a non-human primate species, Macaca sylvanus, at the park La Forêt des Singes in France. The results show significant differences with respect to the position (i.e., centric, peripheral) and role (i.e., implication in the network cohesiveness) of an individual within a social network and hence interactional patterns. Females are more central, more active, and have a denser ego network in the affiliative social network tan males; thus, they contribute in a greater way to the cohesive structure of the network. High-ranking individuals are likely to receive fewer agonistic behaviors than low-ranking individuals, and high-ranking females receive more allogrooming. I also observe homophily for affiliative interactions regarding all attributes and homophily for agonistic interactions regarding gender and age. Revealing the positions, the roles, and the interactional behavioral patterns of individuals can help understand the mechanisms that shape the overall structure of a social network. PMID:27148137

The Influence of Gender, Age, Matriline and Hierarchical Rank on Individual Social Position, Role and Interactional Patterns in Macaca sylvanus at 'La Forêt des Singes': A Multilevel Social Network Approach.

PubMed

Sosa, Sebastian

2016-01-01

A society is a complex system composed of individuals that can be characterized by their own attributes that influence their behaviors. In this study, a specific analytical protocol based on social network analysis was adopted to investigate the influence of four attributes (gender, age, matriline, and hierarchical rank) on affiliative (allogrooming) and agonistic networks in a non-human primate species, Macaca sylvanus, at the park La Forêt des Singes in France. The results show significant differences with respect to the position (i.e., centric, peripheral) and role (i.e., implication in the network cohesiveness) of an individual within a social network and hence interactional patterns. Females are more central, more active, and have a denser ego network in the affiliative social network tan males; thus, they contribute in a greater way to the cohesive structure of the network. High-ranking individuals are likely to receive fewer agonistic behaviors than low-ranking individuals, and high-ranking females receive more allogrooming. I also observe homophily for affiliative interactions regarding all attributes and homophily for agonistic interactions regarding gender and age. Revealing the positions, the roles, and the interactional behavioral patterns of individuals can help understand the mechanisms that shape the overall structure of a social network.

Fast social-like learning of complex behaviors based on motor motifs.

PubMed

Calvo Tapia, Carlos; Tyukin, Ivan Y; Makarov, Valeri A

2018-05-01

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n-1)! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n-1) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

Fast social-like learning of complex behaviors based on motor motifs

NASA Astrophysics Data System (ADS)

Calvo Tapia, Carlos; Tyukin, Ivan Y.; Makarov, Valeri A.

2018-05-01

Social learning is widely observed in many species. Less experienced agents copy successful behaviors exhibited by more experienced individuals. Nevertheless, the dynamical mechanisms behind this process remain largely unknown. Here we assume that a complex behavior can be decomposed into a sequence of n motor motifs. Then a neural network capable of activating motor motifs in a given sequence can drive an agent. To account for (n -1 )! possible sequences of motifs in a neural network, we employ the winnerless competition approach. We then consider a teacher-learner situation: one agent exhibits a complex movement, while another one aims at mimicking the teacher's behavior. Despite the huge variety of possible motif sequences we show that the learner, equipped with the provided learning model, can rewire "on the fly" its synaptic couplings in no more than (n -1 ) learning cycles and converge exponentially to the durations of the teacher's motifs. We validate the learning model on mobile robots. Experimental results show that the learner is indeed capable of copying the teacher's behavior composed of six motor motifs in a few learning cycles. The reported mechanism of learning is general and can be used for replicating different functions, including, for example, sound patterns or speech.

GoDisco: Selective Gossip Based Dissemination of Information in Social Community Based Overlays

NASA Astrophysics Data System (ADS)

Datta, Anwitaman; Sharma, Rajesh

We propose and investigate a gossip based, social principles and behavior inspired decentralized mechanism (GoDisco) to disseminate information in online social community networks, using exclusively social links and exploiting semantic context to keep the dissemination process selective to relevant nodes. Such a designed dissemination scheme using gossiping over a egocentric social network is unique and is arguably a concept whose time has arrived, emulating word of mouth behavior and can have interesting applications like probabilistic publish/subscribe, decentralized recommendation and contextual advertisement systems, to name a few. Simulation based experiments show that despite using only local knowledge and contacts, the system has good global coverage and behavior.

Stochastic Dynamics Underlying Cognitive Stability and Flexibility

PubMed Central

Ueltzhöffer, Kai; Armbruster-Genç, Diana J. N.; Fiebach, Christian J.

2015-01-01

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences. PMID:26068119

Universal behavior of the osmotically compressed cell and its analogy to the colloidal glass transition

PubMed Central

Zhou, E. H.; Trepat, X.; Park, C. Y.; Lenormand, G.; Oliver, M. N.; Mijailovich, S. M.; Hardin, C.; Weitz, D. A.; Butler, J. P.; Fredberg, J. J.

2009-01-01

Mechanical robustness of the cell under different modes of stress and deformation is essential to its survival and function. Under tension, mechanical rigidity is provided by the cytoskeletal network; with increasing stress, this network stiffens, providing increased resistance to deformation. However, a cell must also resist compression, which will inevitably occur whenever cell volume is decreased during such biologically important processes as anhydrobiosis and apoptosis. Under compression, individual filaments can buckle, thereby reducing the stiffness and weakening the cytoskeletal network. However, the intracellular space is crowded with macromolecules and organelles that can resist compression. A simple picture describing their behavior is that of colloidal particles; colloids exhibit a sharp increase in viscosity with increasing volume fraction, ultimately undergoing a glass transition and becoming a solid. We investigate the consequences of these 2 competing effects and show that as a cell is compressed by hyperosmotic stress it becomes progressively more rigid. Although this stiffening behavior depends somewhat on cell type, starting conditions, molecular motors, and cytoskeletal contributions, its dependence on solid volume fraction is exponential in every instance. This universal behavior suggests that compression-induced weakening of the network is overwhelmed by crowding-induced stiffening of the cytoplasm. We also show that compression dramatically slows intracellular relaxation processes. The increase in stiffness, combined with the slowing of relaxation processes, is reminiscent of a glass transition of colloidal suspensions, but only when comprised of deformable particles. Our work provides a means to probe the physical nature of the cytoplasm under compression, and leads to results that are universal across cell type. PMID:19520830

Neighborhoods and HIV: A Social Ecological Approach to Prevention and Care

PubMed Central

Latkin, Carl A.; German, Danielle; Vlahov, David

2013-01-01

Neighborhood factors have been linked to HIV risk behaviors, HIV counseling and testing, and HIV medical care. However, the social–psychological mechanisms that connect neighborhood factors to HIV-related behaviors have not been fully determined. In this paper we review the research on neighborhood factors and HIV-related behaviors, approaches to measuring neighborhoods, and mechanism that may help to explain how the physical and social environment within neighborhoods may lead to HIV related behaviors. We then discuss organizational, geographic, and social network approaches to intervene in neighborhoods to reduce HIV transmission and facilitate HIV medical care with the goal of reducing morbidity and mortality and increasing social and psychological well-being. PMID:23688089

Mathematical modelling of complex contagion on clustered networks

NASA Astrophysics Data System (ADS)

O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

2015-09-01

The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

Toward negative Poisson's ratio composites: Investigation of the auxetic behavior of fibrous networks

NASA Astrophysics Data System (ADS)

Tatlier, Mehmet Seha

Random fibrous can be found among natural and synthetic materials. Some of these random fibrous networks possess negative Poisson's ratio and they are extensively called auxetic materials. The governing mechanisms behind this counter intuitive property in random networks are yet to be understood and this kind of auxetic material remains widely under-explored. However, most of synthetic auxetic materials suffer from their low strength. This shortcoming can be rectified by developing high strength auxetic composites. The process of embedding auxetic random fibrous networks in a polymer matrix is an attractive alternate route to the manufacture of auxetic composites, however before such an approach can be developed, a methodology for designing fibrous networks with the desired negative Poisson's ratios must first be established. This requires an understanding of the factors which bring about negative Poisson's ratios in these materials. In this study, a numerical model is presented in order to investigate the auxetic behavior in compressed random fiber networks. Finite element analyses of three-dimensional stochastic fiber networks were performed to gain insight into the effects of parameters such as network anisotropy, network density, and degree of network compression on the out-of-plane Poisson's ratio and Young's modulus. The simulation results suggest that the compression is the critical parameter that gives rise to negative Poisson's ratio while anisotropy significantly promotes the auxetic behavior. This model can be utilized to design fibrous auxetic materials and to evaluate feasibility of developing auxetic composites by using auxetic fibrous networks as the reinforcing layer.

Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information.

PubMed

Xue, Angli; Wang, Hongcheng; Zhu, Jun

2017-09-28

Startle behavior is important for survival, and abnormal startle responses are related to several neurological diseases. Drosophila melanogaster provides a powerful system to investigate the genetic underpinnings of variation in startle behavior. Since mechanically induced, startle responses and environmental conditions can be readily quantified and precisely controlled. The 156 wild-derived fully sequenced lines of the Drosophila Genetic Reference Panel (DGRP) were used to identify SNPs and transcripts associated with variation in startle behavior. The results validated highly significant effects of 33 quantitative trait SNPs (QTSs) and 81 quantitative trait transcripts (QTTs) directly associated with phenotypic variation of startle response. We also detected QTT variation controlled by 20 QTSs (tQTSs) and 73 transcripts (tQTTs). Association mapping based on genomic and transcriptomic data enabled us to construct a complex genetic network that underlies variation in startle behavior. Based on principles of evolutionary conservation, human orthologous genes could be superimposed on this network. This study provided both genetic and biological insights into the variation of startle response behavior of Drosophila melanogaster, and highlighted the importance of genetic network to understand the genetic architecture of complex traits.

DANGEROUS LIAISONS? DATING AND DRINKING DIFFUSION IN ADOLESCENT PEER NETWORKS*

PubMed Central

Kreager, Derek A.; Haynie, Dana L.

2014-01-01

The onset and escalation of alcohol consumption and romantic relationships are hallmarks of adolescence, yet only recently have these domains jointly been the focus of sociological inquiry. We extend this literature by connecting alcohol use, dating and peers to understand the diffusion of drinking behavior in school-based friendship networks. Drawing on Granovetter’s classic concept of weak ties, we argue that adolescent romantic partners are likely to be network bridges, or liaisons, connecting daters to new peer contexts which, in turn, promote changes in individual drinking behaviors and allow these behaviors to spread across peer networks. Using longitudinal data of 459 couples from the National Longitudinal Study of Adolescent Health, we estimate Actor-Partner Interdependence Models and identify the unique contributions of partners’ drinking, friends’ drinking, and friends-of-partners’ drinking to daters’ own future binge drinking and drinking frequency. Findings support the liaison hypothesis and suggest that friends-of-partners’ drinking have net associations with adolescent drinking patterns. Moreover, the coefficient for friends-of-partners drinking is larger than the coefficient for one’s own peers and generally immune to prior selection. Our findings suggest that romantic relationships are important mechanisms for understanding the diffusion of emergent problem behaviors in adolescent peer networks. PMID:25328162

Effects of behavioral activation on default mode network connectivity in subthreshold depression: A preliminary resting-state fMRI study.

PubMed

Yokoyama, Satoshi; Okamoto, Yasumasa; Takagaki, Koki; Okada, Go; Takamura, Masahiro; Mori, Asako; Shiota, Syouichi; Ichikawa, Naho; Jinnin, Ran; Yamawaki, Shigeto

2018-02-01

Subthreshold depression is a risk factor for major depressive disorder, and it is known to have a negative impact on quality of life (QOL). Although behavioral activation, which is one type of cognitive behavioral therapy, is an effective psychological intervention for subthreshold depression, neural mechanisms of behavioral activation are unclear. Enhanced functional connectivity between default mode network (DMN) and the other regions has been demonstrated in participants with subthreshold depression. The purpose of this study was to examine the effects of behavioral activation on DMN abnormalities by using resting-state functional MRI (rs-fMRI). Participants with subthreshold depression (N =40) were randomly assigned to either an intervention group or a non-intervention group. They were scanned using rs-fMRI before and after the intervention. Independent component analysis indicated three subnetworks of the DMN. Analyzing intervention effects on functional connectivity of each subnetwork indicated that connectivity of the anterior DMN subnetwork with the dorsal anterior cingulate was reduced after the intervention. Moreover, this reduction was correlated with an increase in health-related QOL. We did not compare the findings with healthy participants. Further research should be conducted by including healthy controls to verify the results of this study. Mechanisms of behavioral activation might be related to enhanced ability to independently use the dACC and the DMN, which increases an attention control to positive external stimuli. This is the first study to investigate neural mechanisms of behavioral activation using rs-fMRI. Copyright © 2017 Elsevier B.V. All rights reserved.

Stochastic dynamics for idiotypic immune networks

NASA Astrophysics Data System (ADS)

Barra, Adriano; Agliari, Elena

2010-12-01

In this work we introduce and analyze the stochastic dynamics obeyed by a model of an immune network recently introduced by the authors. We develop Fokker-Planck equations for the single lymphocyte behavior and coarse grained Langevin schemes for the averaged clone behavior. After showing agreement with real systems (as a short path Jerne cascade), we suggest, both with analytical and numerical arguments, explanations for the generation of (metastable) memory cells, improvement of the secondary response (both in the quality and quantity) and bell shaped modulation against infections as a natural behavior. The whole emerges from the model without being postulated a-priori as it often occurs in second generation immune networks: so the aim of the work is to present some out-of-equilibrium features of this model and to highlight mechanisms which can replace a-priori assumptions in view of further detailed analysis in theoretical systemic immunology.

Behavioral and multimodal neuroimaging evidence for a deficit in brain timing networks in stuttering: a hypothesis and theory

PubMed Central

Etchell, Andrew C.; Johnson, Blake W.; Sowman, Paul F.

2014-01-01

The fluent production of speech requires accurately timed movements. In this article, we propose that a deficit in brain timing networks is one of the core neurophysiological deficits in stuttering. We first discuss the experimental evidence supporting the involvement of the basal ganglia and supplementary motor area (SMA) in stuttering and the involvement of the cerebellum as a possible mechanism for compensating for the neural deficits that underlie stuttering. Next, we outline the involvement of the right inferior frontal gyrus (IFG) as another putative compensatory locus in stuttering and suggest a role for this structure in an expanded core timing-network. Subsequently, we review behavioral studies of timing in people who stutter and examine their behavioral performance as compared to people who do not stutter. Finally, we highlight challenges to existing research and provide avenues for future research with specific hypotheses. PMID:25009487

A Digital Image-Based Discrete Fracture Network Model and Its Numerical Investigation of Direct Shear Tests

NASA Astrophysics Data System (ADS)

Wang, Peitao; Cai, Meifeng; Ren, Fenhua; Li, Changhong; Yang, Tianhong

2017-07-01

This paper develops a numerical approach to determine the mechanical behavior of discrete fractures network (DFN) models based on digital image processing technique and particle flow code (PFC2D). A series of direct shear tests of jointed rocks were numerically performed to study the effect of normal stress, friction coefficient and joint bond strength on the mechanical behavior of joint rock and evaluate the influence of micro-parameters on the shear properties of jointed rocks using the proposed approach. The complete shear stress-displacement curve of the DFN model under direct shear tests was presented to evaluate the failure processes of jointed rock. The results show that the peak and residual strength are sensitive to normal stress. A higher normal stress has a greater effect on the initiation and propagation of cracks. Additionally, an increase in the bond strength ratio results in an increase in the number of both shear and normal cracks. The friction coefficient was also found to have a significant influence on the shear strength and shear cracks. Increasing in the friction coefficient resulted in the decreasing in the initiation of normal cracks. The unique contribution of this paper is the proposed modeling technique to simulate the mechanical behavior of jointed rock mass based on particle mechanics approaches.

A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation.

PubMed

Camuñas-Mesa, Luis A; Domínguez-Cordero, Yaisel L; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2018-01-01

Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network.

A Configurable Event-Driven Convolutional Node with Rate Saturation Mechanism for Modular ConvNet Systems Implementation

PubMed Central

Camuñas-Mesa, Luis A.; Domínguez-Cordero, Yaisel L.; Linares-Barranco, Alejandro; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2018-01-01

Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli. On the other hand, hardware implementations show difficulties to be used for different applications, due to their reduced flexibility. In this paper, we propose a fully configurable event-driven convolutional node with rate saturation mechanism that can be used to implement arbitrary ConvNets on FPGAs. This node includes a convolutional processing unit and a routing element which allows to build large 2D arrays where any multilayer structure can be implemented. The rate saturation mechanism emulates the refractory behavior in biological neurons, guaranteeing a minimum separation in time between consecutive events. A 4-layer ConvNet with 22 convolutional nodes trained for poker card symbol recognition has been implemented in a Spartan6 FPGA. This network has been tested with a stimulus where 40 poker cards were observed by a Dynamic Vision Sensor (DVS) in 1 s time. Different slow-down factors were applied to characterize the behavior of the system for high speed processing. For slow stimulus play-back, a 96% recognition rate is obtained with a power consumption of 0.85 mW. At maximum play-back speed, a traffic control mechanism downsamples the input stimulus, obtaining a recognition rate above 63% when less than 20% of the input events are processed, demonstrating the robustness of the network. PMID:29515349

Quantum Monte Carlo study of the transverse-field quantum Ising model on infinite-dimensional structures

NASA Astrophysics Data System (ADS)

Baek, Seung Ki; Um, Jaegon; Yi, Su Do; Kim, Beom Jun

2011-11-01

In a number of classical statistical-physical models, there exists a characteristic dimensionality called the upper critical dimension above which one observes the mean-field critical behavior. Instead of constructing high-dimensional lattices, however, one can also consider infinite-dimensional structures, and the question is whether this mean-field character extends to quantum-mechanical cases as well. We therefore investigate the transverse-field quantum Ising model on the globally coupled network and on the Watts-Strogatz small-world network by means of quantum Monte Carlo simulations and the finite-size scaling analysis. We confirm that both of the structures exhibit critical behavior consistent with the mean-field description. In particular, we show that the existing cumulant method has difficulty in estimating the correct dynamic critical exponent and suggest that an order parameter based on the quantum-mechanical expectation value can be a practically useful numerical observable to determine critical behavior when there is no well-defined dimensionality.

Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

PubMed

Calvin, Nicholas T; J McDowell, J

2015-11-01

For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respondent learning occurs via the same neural mechanisms. As part of a larger project to evaluate the operant behavior predicted by the theory, this project was the first replication of neural network models based on the unified theory of reinforcement. In the process of replicating these neural network models it became apparent that a previously published finding, namely, that the networks simulate the blocking phenomenon (Donahoe et al., 1993), was a misinterpretation of the data. We show that the apparent blocking produced by these networks is an artifact of the inability of these networks to generate the same conditioned response to multiple stimuli. The piecemeal approach to evaluate the unified theory of reinforcement via simulation is critiqued and alternatives are discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Reinforcement Learning of Two-Joint Virtual Arm Reaching in a Computer Model of Sensorimotor Cortex

PubMed Central

Neymotin, Samuel A.; Chadderdon, George L.; Kerr, Cliff C.; Francis, Joseph T.; Lytton, William W.

2014-01-01

Neocortical mechanisms of learning sensorimotor control involve a complex series of interactions at multiple levels, from synaptic mechanisms to cellular dynamics to network connectomics. We developed a model of sensory and motor neocortex consisting of 704 spiking model neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a two-joint virtual arm to reach to a fixed target. For each of 125 trained networks, we used 200 training sessions, each involving 15 s reaches to the target from 16 starting positions. Learning altered network dynamics, with enhancements to neuronal synchrony and behaviorally relevant information flow between neurons. After learning, networks demonstrated retention of behaviorally relevant memories by using proprioceptive information to perform reach-to-target from multiple starting positions. Networks dynamically controlled which joint rotations to use to reach a target, depending on current arm position. Learning-dependent network reorganization was evident in both sensory and motor populations: learned synaptic weights showed target-specific patterning optimized for particular reach movements. Our model embodies an integrative hypothesis of sensorimotor cortical learning that could be used to interpret future electrophysiological data recorded in vivo from sensorimotor learning experiments. We used our model to make the following predictions: learning enhances synchrony in neuronal populations and behaviorally relevant information flow across neuronal populations, enhanced sensory processing aids task-relevant motor performance and the relative ease of a particular movement in vivo depends on the amount of sensory information required to complete the movement. PMID:24047323

A distributed incentive compatible pricing mechanism for P2P networks

NASA Astrophysics Data System (ADS)

Zhang, Jie; Zhao, Zheng; Xiong, Xiao; Shi, Qingwei

2007-09-01

Peer-to-Peer (P2P) systems are currently receiving considerable interest. However, as experience with P2P networks shows, the selfish behaviors of peers may lead to serious problems of P2P network, such as free-riding and white-washing. In order to solve these problems, there are increasing considerations on reputation system design in the study of P2P networks. Most of the existing works is concerning probabilistic estimation or social networks to evaluate the trustworthiness for a peer to others. However, these models can not be efficient all the time. In this paper, our aim is to provide a general mechanism that can maximize P2P networks social welfare in a way of Vickrey-Clarke-Groves family, while assuming every peer in P2P networks is rational and selfish, which means they only concern about their own outcome. This mechanism has some desirable properties using an O(n) algorithm: (1) incentive compatibility, every peer truly report its connection type; (2) individually rationality; and (3) fully decentralized, we design a multiple-principal multiple-agent model, concerning about the service provider and service requester individually.

The effects of cognitive-behavioral therapy on intrinsic functional brain networks in adults with attention-deficit/hyperactivity disorder.

PubMed

Wang, Xiaoli; Cao, Qingjiu; Wang, Jinhui; Wu, Zhaomin; Wang, Peng; Sun, Li; Cai, Taisheng; Wang, Yufeng

2016-01-01

Cognitive-behavioral therapy (CBT) is an efficacious psychological treatment for adults with attention-deficit/hyperactivity disorder (ADHD), but the neural processes underlying the benefits of CBT are not well understood. This study aims to unravel psychosocial mechanisms for treatment ADHD by exploring the effects of CBT on functional brain networks. Ten adults with ADHD were enrolled and resting-state functional magnetic resonance imaging scans were acquired before and after a 12-session CBT. Twelve age- and gender-matched healthy controls were also scanned. We constructed whole-brain functional connectivity networks using graph-theory approaches and further computed the changes of regional functional connectivity strength (rFCS) between pre- and post-CBT in ADHD for measuring the effects of CBT. The results showed that rFCS was increased in the fronto-parietal network and cerebellum, the brain regions that were most often affected by medication, in adults with ADHD following CBT. Furthermore, the enhanced functional coupling between bilateral superior parietal gyrus was positively correlated with the improvement of ADHD symptoms following CBT. Together, these findings provide evidence that CBT can selectively modulate the intrinsic network connectivity in the fronto-parietal network and cerebellum and suggest that the CBT may share common brain mechanism with the pharmacology in adults with ADHD. Copyright © 2015 Elsevier Ltd. All rights reserved.

Expert Game experiment predicts emergence of trust in professional communication networks.

PubMed

Bendtsen, Kristian Moss; Uekermann, Florian; Haerter, Jan O

2016-10-25

Strong social capital is increasingly recognized as an organizational advantage. Better knowledge sharing and reduced transaction costs increase work efficiency. To mimic the formation of the associated communication network, we propose the Expert Game, where each individual must find a specific expert and receive her help. Participants act in an impersonal environment and under time constraints that provide short-term incentives for noncooperative behavior. Despite these constraints, we observe cooperation between individuals and the self-organization of a sustained trust network, which facilitates efficient communication channels with increased information flow. We build a behavioral model that explains the experimental dynamics. Analysis of the model reveals an exploitation protection mechanism and measurable social capital, which quantitatively describe the economic utility of trust.

Emergent structure-function relations in emphysema and asthma.

PubMed

Winkler, Tilo; Suki, Béla

2011-01-01

Structure-function relationships in the respiratory system are often a result of the emergence of self-organized patterns or behaviors that are characteristic of certain respiratory diseases. Proper description of such self-organized behavior requires network models that include nonlinear interactions among different parts of the system. This review focuses on 2 models that exhibit self-organized behavior: a network model of the lung parenchyma during the progression of emphysema that is driven by mechanical force-induced breakdown, and an integrative model of bronchoconstriction in asthma that describes interactions among airways within the bronchial tree. Both models suggest that the transition from normal to pathologic states is a nonlinear process that includes a tipping point beyond which interactions among the system components are reinforced by positive feedback, further promoting the progression of pathologic changes. In emphysema, the progressive destruction of tissue is irreversible, while in asthma, it is possible to recover from a severe bronchoconstriction. These concepts may have implications for pulmonary medicine. Specifically, we suggest that structure-function relationships emerging from network behavior across multiple scales should be taken into account when the efficacy of novel treatments or drug therapy is evaluated. Multiscale, computational, network models will play a major role in this endeavor.

The interplay of friendship networks and social networking sites: longitudinal analysis of selection and influence effects on adolescent smoking and alcohol use.

PubMed

Huang, Grace C; Soto, Daniel; Fujimoto, Kayo; Valente, Thomas W

2014-08-01

We examined the coevolution of adolescent friendships and peer influences with respect to their risk behaviors and social networking site use. Investigators of the Social Network Study collected longitudinal data during fall 2010 and spring 2011 from 10th-grade students in 5 Southern California high schools (n = 1434). We used meta-analyses of stochastic actor-based models to estimate changes in friendship ties and risk behaviors and the effects of Facebook and MySpace use. Significant shifts in adolescent smoking and drinking occurred despite little change in overall prevalence rates. Students with higher levels of alcohol use were more likely to send and receive friendship nominations and become friends with other drinkers. They were also more likely to increase alcohol use if their friends drank more. Adolescents selected friends with similar Facebook and MySpace use habits. Exposure to friends' risky online pictures increased smoking behaviors but had no significant effects on alcohol use. Our findings support a greater focus on friendship selection mechanisms in school-based alcohol use interventions. Social media platforms may help identify at-risk adolescent groups and foster positive norms about risk behaviors.

Combining the Finite Element Method with Structural Connectome-based Analysis for Modeling Neurotrauma:Connectome Neurotrauma Mechanics

DTIC Science & Technology

2012-08-16

death threshold. Using an injury threshold of 18% strain, 161 edges were removed. Watts and Strogatz [66] define the small-world network based on the...NeuroImage 52: 1059–1069. 65. Latora V, Marchiori M (2001) Efficient behavior of small-world networks. Phys Rev Lett 87: 198701. 66. Watts DJ, Strogatz SH

Social power, conflict policing, and the role of subordination signals in rhesus macaque society

PubMed Central

Beisner, Brianne A.; Hannibal, Darcy L.; Finn, Kelly R.; Fushing, Hsieh; McCowan, Brenda

2017-01-01

Objectives Policing is a conflict-limiting mechanism observed in many primate species. It is thought to require a skewed distribution of social power for some individuals to have sufficiently high social power to stop others’ fights, yet social power has not been examined in most species with policing behavior. We examined networks of subordination signals as a source of social power that permits policing behavior in rhesus macaques. Materials and Methods For each of seven captive groups of rhesus macaques, we (a) examined the structure of subordination signal networks and used GLMs to examine the relationship between (b) pairwise dominance certainty and subordination network pathways and (c) policing frequency and social power (group-level convergence in subordination signaling pathways). Results Networks of subordination signals had perfect linear transitivity, and pairs connected by both direct and indirect pathways of signals had more certain dominance relationships than pairs with no such network connection. Social power calculated using both direct and indirect network pathways showed a heavy-tailed distribution and positively predicted conflict policing. Conclusions Our results empirically substantiate that subordination signaling is associated with greater dominance relationship certainty and further show that pairs who signal rarely (or not at all) may use information from others’ signaling interactions to infer or reaffirm the relative certainty of their own relationships. We argue that the network of formal dominance relationships is central to societal stability because it is important for relationship stability and also supports the additional stabilizing mechanism of policing. PMID:26801956

Social power, conflict policing, and the role of subordination signals in rhesus macaque society.

PubMed

Beisner, Brianne A; Hannibal, Darcy L; Finn, Kelly R; Fushing, Hsieh; McCowan, Brenda

2016-05-01

Policing is a conflict-limiting mechanism observed in many primate species. It is thought to require a skewed distribution of social power for some individuals to have sufficiently high social power to stop others' fights, yet social power has not been examined in most species with policing behavior. We examined networks of subordination signals as a source of social power that permits policing behavior in rhesus macaques. For each of seven captive groups of rhesus macaques, we (a) examined the structure of subordination signal networks and used GLMs to examine the relationship between (b) pairwise dominance certainty and subordination network pathways and (c) policing frequency and social power (group-level convergence in subordination signaling pathways). Networks of subordination signals had perfect linear transitivity, and pairs connected by both direct and indirect pathways of signals had more certain dominance relationships than pairs with no such network connection. Social power calculated using both direct and indirect network pathways showed a heavy-tailed distribution and positively predicted conflict policing. Our results empirically substantiate that subordination signaling is associated with greater dominance relationship certainty and further show that pairs who signal rarely (or not at all) may use information from others' signaling interactions to infer or reaffirm the relative certainty of their own relationships. We argue that the network of formal dominance relationships is central to societal stability because it is important for relationship stability and also supports the additional stabilizing mechanism of policing. © 2016 Wiley Periodicals, Inc.

Cycle oxidation behavior and anti-oxidation mechanism of hot-dipped aluminum coating on TiBw/Ti6Al4V composites with network microstructure.

PubMed

Li, X T; Huang, L J; Wei, S L; An, Q; Cui, X P; Geng, L

2018-04-10

Controlled and compacted TiAl 3 coating was successfully fabricated on the network structured TiBw/Ti6Al4V composites by hot-dipping aluminum and subsequent interdiffusion treatment. The network structure of the composites was inherited to the TiAl 3 coating, which effectively reduces the thermal stress and avoids the cracks appeared in the coating. Moreover, TiB reinforcements could pin the TiAl 3 coating which can effectively improve the bonding strength between the coating and composite substrate. The cycle oxidation behavior of the network structured coating on 873 K, 973 K and 1073 K for 100 h were investigated. The results showed the coating can remarkably improve the high temperature oxidation resistance of the TiBw/Ti6Al4V composites. The network structure was also inherited to the Al 2 O 3 oxide scale, which effectively decreases the tendency of cracking even spalling about the oxide scale. Certainly, no crack was observed in the coating after long-term oxidation due to the division effect of network structured coating and pinning effect of TiB reinforcements. Interfacial reaction between the coating and the composite substrate occurred and a bilayer structure of TiAl/TiAl 2 formed next to the substrate after oxidation at 973 K and 1073 K. The anti-oxidation mechanism of the network structured coating was also discussed.

If it walks like a duck: nanosensor threat assessment

NASA Astrophysics Data System (ADS)

Chachis, George C.

2003-09-01

A convergence of technologies is making deployment of unattended ground nanosensors operationally feasible in terms of energy, communications for both arbitrated and self-organizing distributed, collective behaviors. A number of nano communications technologies are already making network-centric systems possible for MicroElectrical Mechanical (MEM) sensor devices today. Similar technologies may make NanoElectrical Mechanical (NEM) sensor devices operationally feasible a few years from now. Just as organizational behaviors of large numbers of nanodevices can derive strategies from social insects and other group-oriented animals, bio-inspired heuristics for threat assessment provide a conceptual approach for successful integration of nanosensors into unattended smart sensor networks. Biological models such as the organization of social insects or the dynamics of immune systems show promise as biologically-inspired paradigms for protecting nanosensor networks for security scene analysis and battlespace awareness. The paradox of nanosensors is that the smaller the device is the more useful it is but the smaller it is the more vulnerable it is to a variety of threats. In other words simpler means networked nanosensors are more likely to fall prey to a wide-range of attacks including jamming, spoofing, Janisserian recruitment, Pied-Piper distraction, as well as typical attacks computer network security. Thus, unattended sensor technologies call for network architectures that include security and countermeasures to provide reliable scene analysis or battlespace awareness information. Such network centric architectures may well draw upon a variety of bio-inspired approaches to safeguard, validate and make sense of large quantities of information.

Birth of an abstraction: a dynamical systems account of the discovery of an elsewhere principle in a category learning task.

PubMed

Tabor, Whitney; Cho, Pyeong W; Dankowicz, Harry

2013-01-01

Human participants and recurrent ("connectionist") neural networks were both trained on a categorization system abstractly similar to natural language systems involving irregular ("strong") classes and a default class. Both the humans and the networks exhibited staged learning and a generalization pattern reminiscent of the Elsewhere Condition (Kiparsky, 1973). Previous connectionist accounts of related phenomena have often been vague about the nature of the networks' encoding systems. We analyzed our network using dynamical systems theory, revealing topological and geometric properties that can be directly compared with the mechanisms of non-connectionist, rule-based accounts. The results reveal that the networks "contain" structures related to mechanisms posited by rule-based models, partly vindicating the insights of these models. On the other hand, they support the one mechanism (OM), as opposed to the more than one mechanism (MOM), view of symbolic abstraction by showing how the appearance of MOM behavior can arise emergently from one underlying set of principles. The key new contribution of this study is to show that dynamical systems theory can allow us to explicitly characterize the relationship between the two perspectives in implemented models. © 2013 Cognitive Science Society, Inc.

Fractal analysis on human dynamics of library loans

NASA Astrophysics Data System (ADS)

Fan, Chao; Guo, Jin-Li; Zha, Yi-Long

2012-12-01

In this paper, the fractal characteristic of human behaviors is investigated from the perspective of time series constructed with the amount of library loans. The values of the Hurst exponent and length of non-periodic cycle calculated through rescaled range analysis indicate that the time series of human behaviors and their sub-series are fractal with self-similarity and long-range dependence. Then the time series are converted into complex networks by the visibility algorithm. The topological properties of the networks such as scale-free property and small-world effect imply that there is a close relationship among the numbers of repetitious behaviors performed by people during certain periods of time. Our work implies that there is intrinsic regularity in the human collective repetitious behaviors. The conclusions may be helpful to develop some new approaches to investigate the fractal feature and mechanism of human dynamics, and provide some references for the management and forecast of human collective behaviors.

Modular cell biology: retroactivity and insulation

PubMed Central

Del Vecchio, Domitilla; Ninfa, Alexander J; Sontag, Eduardo D

2008-01-01

Modularity plays a fundamental role in the prediction of the behavior of a system from the behavior of its components, guaranteeing that the properties of individual components do not change upon interconnection. Just as electrical, hydraulic, and other physical systems often do not display modularity, nor do many biochemical systems, and specifically, genetic networks. Here, we study the effect of interconnections on the input–output dynamic characteristics of transcriptional components, focusing on a property, which we call ‘retroactivity', that plays a role analogous to non-zero output impedance in electrical systems. In transcriptional networks, retroactivity is large when the amount of transcription factor is comparable to, or smaller than, the amount of promoter-binding sites, or when the affinity of such binding sites is high. To attenuate the effect of retroactivity, we propose a feedback mechanism inspired by the design of amplifiers in electronics. We introduce, in particular, a mechanism based on a phosphorylation–dephosphorylation cycle. This mechanism enjoys a remarkable insulation property, due to the fast timescales of the phosphorylation and dephosphorylation reactions. PMID:18277378

Interlayer shear behaviors of graphene-carbon nanotube network

NASA Astrophysics Data System (ADS)

Qin, Huasong; Liu, Yilun

2017-09-01

The interlayer shear resistance plays an important role in graphene related applications, and different mechanisms have been proposed to enhance its interlayer load capacity. In this work, we performed molecular dynamics (MD) simulations and theoretical analysis to study interlayer shear behaviors of three dimensional graphene-carbon (3D-GC) nanotube networks. The shear mechanical properties of carbon nanotubes (CNTs) crosslink with different diameters are obtained which is one order of magnitude larger than that of other types of crosslinks. Under shear loading, 3D-GC exhibits two failure modes, i.e., fracture of graphene sheet and failure of CNT crosslink, determined by the diameter of CNT crosslink, crosslink density, and length of 3D-GC. A modified tension-shear chain model is proposed to predict the shear mechanical properties and failure mode of 3D-GC, which agrees well with MD simulation results. The results presented in this work may provide useful insights for future development of high-performance 3D-GC materials.

Infection dynamics on spatial small-world network models

NASA Astrophysics Data System (ADS)

Iotti, Bryan; Antonioni, Alberto; Bullock, Seth; Darabos, Christian; Tomassini, Marco; Giacobini, Mario

2017-11-01

The study of complex networks, and in particular of social networks, has mostly concentrated on relational networks, abstracting the distance between nodes. Spatial networks are, however, extremely relevant in our daily lives, and a large body of research exists to show that the distances between nodes greatly influence the cost and probability of establishing and maintaining a link. A random geometric graph (RGG) is the main type of synthetic network model used to mimic the statistical properties and behavior of many social networks. We propose a model, called REDS, that extends energy-constrained RGGs to account for the synergic effect of sharing the cost of a link with our neighbors, as is observed in real relational networks. We apply both the standard Watts-Strogatz rewiring procedure and another method that conserves the degree distribution of the network. The second technique was developed to eliminate unwanted forms of spatial correlation between the degree of nodes that are affected by rewiring, limiting the effect on other properties such as clustering and assortativity. We analyze both the statistical properties of these two network types and their epidemiological behavior when used as a substrate for a standard susceptible-infected-susceptible compartmental model. We consider and discuss the differences in properties and behavior between RGGs and REDS as rewiring increases and as infection parameters are changed. We report considerable differences both between the network types and, in the case of REDS, between the two rewiring schemes. We conclude that REDS represent, with the application of these rewiring mechanisms, extremely useful and interesting tools in the study of social and epidemiological phenomena in synthetic complex networks.

Network Events on Multiple Space and Time Scales in Cultured Neural Networks and in a Stochastic Rate Model

PubMed Central

Gigante, Guido; Deco, Gustavo; Marom, Shimon; Del Giudice, Paolo

2015-01-01

Cortical networks, in-vitro as well as in-vivo, can spontaneously generate a variety of collective dynamical events such as network spikes, UP and DOWN states, global oscillations, and avalanches. Though each of them has been variously recognized in previous works as expression of the excitability of the cortical tissue and the associated nonlinear dynamics, a unified picture of the determinant factors (dynamical and architectural) is desirable and not yet available. Progress has also been partially hindered by the use of a variety of statistical measures to define the network events of interest. We propose here a common probabilistic definition of network events that, applied to the firing activity of cultured neural networks, highlights the co-occurrence of network spikes, power-law distributed avalanches, and exponentially distributed ‘quasi-orbits’, which offer a third type of collective behavior. A rate model, including synaptic excitation and inhibition with no imposed topology, synaptic short-term depression, and finite-size noise, accounts for all these different, coexisting phenomena. We find that their emergence is largely regulated by the proximity to an oscillatory instability of the dynamics, where the non-linear excitable behavior leads to a self-amplification of activity fluctuations over a wide range of scales in space and time. In this sense, the cultured network dynamics is compatible with an excitation-inhibition balance corresponding to a slightly sub-critical regime. Finally, we propose and test a method to infer the characteristic time of the fatigue process, from the observed time course of the network’s firing rate. Unlike the model, possessing a single fatigue mechanism, the cultured network appears to show multiple time scales, signalling the possible coexistence of different fatigue mechanisms. PMID:26558616

Effect of chain rigidity on network architecture and deformation behavior of glassy polymer networks

NASA Astrophysics Data System (ADS)

Knowles, Kyler Reser

Processing carbon fiber composite laminates creates molecular-level strains in the thermoset matrix upon curing and cooling which can lead to failures such as geometry deformations, micro-cracking, and other issues. It is known strain creation is attributed to the significant volume and physical state changes undergone by the polymer matrix throughout the curing process, though storage and relaxation of cure-induced strains remain poorly understood. This dissertation establishes two approaches to address the issue. The first establishes testing methods to simultaneously measure key volumetric properties of a carbon fiber composite laminate and its polymer matrix. The second approach considers the rigidity of the polymer matrix in regards to strain storage and relaxation mechanisms which ultimately control composite performance throughout manufacturing and use. Through the use of a non-contact, full-field strain measurement technique known as digital image correlation (DIC), we describe and implement useful experiments which quantify matrix and composite parameters necessary for simulation efforts and failure models. The methods are compared to more traditional techniques and show excellent correlation. Further, we established relationships which represent matrix-fiber compatibility in regards to critical processing constraints. The second approach involves a systematic study of epoxy-amine networks which are chemically-similar but differ in chain segment rigidity. Prior research has investigated the isomer effect of glassy polymers, showing sizeable differences in thermal, volumetric, physical, and mechanical properties. This work builds on these themes and shows the apparent isomer effect is rather an effect of chain rigidity. Indeed, it was found that structurally-dissimilar polymer networks exhibit very similar properties as a consequence of their shared average network rigidity. Differences in chain packing, as a consequence of chain rigidity, were shown to alter the physical, volumetric, and mechanical properties of the glassy networks. Chain rigidity was found to directly control deformation mechanisms, which were related to the yielding behavior of the epoxy network series. The unique benefit to our approach is the ability to separate the role of rigidity - an intramolecular parameter - from intermolecular phenomena which otherwise influence network properties.

Feasibility of silica-hybridized collagen hydrogels as three-dimensional cell matrices for hard tissue engineering.

PubMed

Yu, Hye-Sun; Lee, Eun-Jung; Seo, Seog-Jin; Knowles, Jonathan C; Kim, Hae-Won

2015-09-01

Exploiting hydrogels for the cultivation of stem cells, aiming to provide them with physico-chemical cues suitable for osteogenesis, is a critical demand for bone engineering. Here, we developed hybrid compositions of collagen and silica into hydrogels via a simple sol-gel process. The physico-chemical and mechanical properties, degradation behavior, and bone-bioactivity were characterized in-depth; furthermore, the in vitro mesenchymal stem cell growth and osteogenic differentiation behaviors within the 3D hybrid gel matrices were communicated for the first time. The hydrolyzed and condensed silica phase enabled chemical links with the collagen fibrils to form networked hybrid gels. The hybrid gels showed improved chemical stability and greater resistance to enzymatic degradation. The in vitro apatite-forming ability was enhanced by the hybrid composition. The viscoelastic mechanical properties of the hybrid gels were significantly improved in terms of the deformation resistance to an applied load and the modulus values under a dynamic oscillation. Mesenchymal stem cells adhered well to the hybrid networks and proliferated actively with substantial cytoskeletal extensions within the gel matrices. Of note, the hybrid gels substantially reduced the cell-mediated gel contraction behaviors, possibly due to the stiffer networks and higher resistance to cell-mediated degradation. Furthermore, the osteogenic differentiation of cells, including the expression of bone-associated genes and protein, was significantly upregulated within the hybrid gel matrices. Together with the physico-chemical and mechanical properties, the cellular behaviors observed within 3D gel matrices, being different from the previous approaches reported on 2D substrates, provide new information on the feasibility and usefulness of the silica-collagen system for stem cell culture and tissue engineering of hard tissues. © The Author(s) 2015.

Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men

PubMed Central

Kapadia, F; Siconolfi, DE; Barton, S; Olivieri, B; Lombardo, L; Halkitis, PN

2013-01-01

Associations between social support network characteristics and sexual risk among racially/ethnically diverse young men who have sex with men (YMSM) were examined using egocentric network data from a prospective cohort study of YMSM (n=501) recruited in New York City. Bivariate and multivariable logistic regression analyses examined associations between social support network characteristics and sexual risk taking behaviors in Black, Hispanic/Latino, and White YMSM. Bivariate analyses indicated key differences in network size, composition, communication frequency and average relationship duration by race/ethnicity. In multivariable analyses, controlling for individual level sociodemographic, psychosocial and relationship factors, having a sexual partner in one’s social support network was associated with unprotected sexual behavior for both Hispanic/Latino (AOR=3.90) and White YMSM (AOR=4.93). Further examination of key network characteristics across racial/ethnic groups are warranted in order to better understand the extant mechanisms for provision of HIV prevention programming to racially/ethnically diverse YMSM at risk for HIV. PMID:23553346

Finite plateau in spectral gap of polychromatic constrained random networks

NASA Astrophysics Data System (ADS)

Avetisov, V.; Gorsky, A.; Nechaev, S.; Valba, O.

2017-12-01

We consider critical behavior in the ensemble of polychromatic Erdős-Rényi networks and regular random graphs, where network vertices are painted in different colors. The links can be randomly removed and added to the network subject to the condition of the vertex degree conservation. In these constrained graphs we run the Metropolis procedure, which favors the connected unicolor triads of nodes. Changing the chemical potential, μ , of such triads, for some wide region of μ , we find the formation of a finite plateau in the number of intercolor links, which exactly matches the finite plateau in the network algebraic connectivity (the value of the first nonvanishing eigenvalue of the Laplacian matrix, λ2). We claim that at the plateau the spontaneously broken Z2 symmetry is restored by the mechanism of modes collectivization in clusters of different colors. The phenomena of a finite plateau formation holds also for polychromatic networks with M ≥2 colors. The behavior of polychromatic networks is analyzed via the spectral properties of their adjacency and Laplacian matrices.

Modeling the Coupled Chemo-Thermo-Mechanical Behavior of Amorphous Polymer Networks.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zimmerman, Jonathan A.; Nguyen, Thao D.; Xiao, Rui

2015-02-01

Amorphous polymers exhibit a rich landscape of time-dependent behavior including viscoelasticity, structural relaxation, and viscoplasticity. These time-dependent mechanisms can be exploited to achieve shape-memory behavior, which allows the material to store a programmed deformed shape indefinitely and to recover entirely the undeformed shape in response to specific environmental stimulus. The shape-memory performance of amorphous polymers depends on the coordination of multiple physical mechanisms, and considerable opportunities exist to tailor the polymer structure and shape-memory programming procedure to achieve the desired performance. The goal of this project was to use a combination of theoretical, numerical and experimental methods to investigate themore » effect of shape memory programming, thermo-mechanical properties, and physical and environmental aging on the shape memory performance. Physical and environmental aging occurs during storage and through exposure to solvents, such as water, and can significantly alter the viscoelastic behavior and shape memory behavior of amorphous polymers. This project – executed primarily by Professor Thao Nguyen and Graduate Student Rui Xiao at Johns Hopkins University in support of a DOE/NNSA Presidential Early Career Award in Science and Engineering (PECASE) – developed a theoretical framework for chemothermo- mechanical behavior of amorphous polymers to model the effects of physical aging and solvent-induced environmental factors on their thermoviscoelastic behavior.« less

In Silico Reconstitution of Actin-Based Symmetry Breaking and Motility

PubMed Central

Dayel, Mark J.; Akin, Orkun; Landeryou, Mark; Risca, Viviana; Mogilner, Alex; Mullins, R. Dyche

2009-01-01

Eukaryotic cells assemble viscoelastic networks of crosslinked actin filaments to control their shape, mechanical properties, and motility. One important class of actin network is nucleated by the Arp2/3 complex and drives both membrane protrusion at the leading edge of motile cells and intracellular motility of pathogens such as Listeria monocytogenes. These networks can be reconstituted in vitro from purified components to drive the motility of spherical micron-sized beads. An Elastic Gel model has been successful in explaining how these networks break symmetry, but how they produce directed motile force has been less clear. We have combined numerical simulations with in vitro experiments to reconstitute the behavior of these motile actin networks in silico using an Accumulative Particle-Spring (APS) model that builds on the Elastic Gel model, and demonstrates simple intuitive mechanisms for both symmetry breaking and sustained motility. The APS model explains observed transitions between smooth and pulsatile motion as well as subtle variations in network architecture caused by differences in geometry and conditions. Our findings also explain sideways symmetry breaking and motility of elongated beads, and show that elastic recoil, though important for symmetry breaking and pulsatile motion, is not necessary for smooth directional motility. The APS model demonstrates how a small number of viscoelastic network parameters and construction rules suffice to recapture the complex behavior of motile actin networks. The fact that the model not only mirrors our in vitro observations, but also makes novel predictions that we confirm by experiment, suggests that the model captures much of the essence of actin-based motility in this system. PMID:19771152

Deep Belief Networks Learn Context Dependent Behavior

PubMed Central

Raudies, Florian; Zilli, Eric A.; Hasselmo, Michael E.

2014-01-01

With the goal of understanding behavioral mechanisms of generalization, we analyzed the ability of neural networks to generalize across context. We modeled a behavioral task where the correct responses to a set of specific sensory stimuli varied systematically across different contexts. The correct response depended on the stimulus (A,B,C,D) and context quadrant (1,2,3,4). The possible 16 stimulus-context combinations were associated with one of two responses (X,Y), one of which was correct for half of the combinations. The correct responses varied symmetrically across contexts. This allowed responses to previously unseen stimuli (probe stimuli) to be generalized from stimuli that had been presented previously. By testing the simulation on two or more stimuli that the network had never seen in a particular context, we could test whether the correct response on the novel stimuli could be generated based on knowledge of the correct responses in other contexts. We tested this generalization capability with a Deep Belief Network (DBN), Multi-Layer Perceptron (MLP) network, and the combination of a DBN with a linear perceptron (LP). Overall, the combination of the DBN and LP had the highest success rate for generalization. PMID:24671178

In vivo dynamical behavior of yeast chromatin modeled as an entangled polymer network with constraint release

NASA Astrophysics Data System (ADS)

Wang, Chenxi; Kilfoil, Maria L.

2013-03-01

The high fidelity segregation of chromatin is the central problem in cell mitosis. The role of mechanics underlying this, however, is undetermined. Work in this area has largely focused on cytoskeletal elements of the process. Preliminary work in our lab suggests the mechanical properties of chromatin are fundamental in this process. Nevertheless, the mechanical properties of chromatin in the cellular context are not well-characterized. For better understanding of the role of mechanics in this cellular process, and of the chromatin mechanics in vivo generally, a systematic dynamical description of chromatin in vivo is required. Accordingly, we label specific sites on chromatin with fluorescent proteins of different wave lengths, enabling us to detect multiple spots separately in 3D and track their displacements in time inside living yeast cells. We analyze the pairwise cross-correlated motion between spots as a function of relative distance along the DNA contour. Comparison between the reptation model and our data serves to test our conjecture that chromatin in the cell is basically an entangled polymer network under constraints to thermal motion, and removal of constraints by non-thermal cellular processes is expected to affect its dynamic behavior.

Putting age-related task activation into large-scale brain networks: A meta-analysis of 114 fMRI studies on healthy aging.

PubMed

Li, Hui-Jie; Hou, Xiao-Hui; Liu, Han-Hui; Yue, Chun-Lin; Lu, Guang-Ming; Zuo, Xi-Nian

2015-10-01

Normal aging is associated with cognitive decline and underlying brain dysfunction. Previous studies concentrated less on brain network changes at a systems level. Our goal was to examine these age-related changes of fMRI-derived activation with a common network parcellation of the human brain function, offering a systems-neuroscience perspective of healthy aging. We conducted a series of meta-analyses on a total of 114 studies that included 2035 older adults and 1845 young adults. Voxels showing significant age-related changes in activation were then overlaid onto seven commonly referenced neuronal networks. Older adults present moderate cognitive decline in behavioral performance during fMRI scanning, and hypo-activate the visual network and hyper-activate both the frontoparietal control and default mode networks. The degree of increased activation in frontoparietal network was associated with behavioral performance in older adults. Age-related changes in activation present different network patterns across cognitive domains. The systems neuroscience approach used here may be useful for elucidating the underlying network mechanisms of various brain plasticity processes during healthy aging. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

The influence of self-exempting beliefs and social networks on daily smoking: a mediation relationship explored.

PubMed

Yang, Xiaozhao Y; Kelly, Brian C; Yang, Tingzhong

2014-09-01

The decision to initiate, maintain, or quit cigarette smoking is structured by both social networks and health beliefs. Self-exempting beliefs affect people's decisions in favor of a behavior even when they recognize the harm associated with it. This study incorporated the literatures on social networks and self-exempting beliefs to study the problem of daily smoking by exploring their mediatory relationships and the mechanisms of how smoking behavior is developed and maintained. Specifically, this article hypothesizes that social networks affect daily smoking directly as well as indirectly by facilitating the formation of self-exempting beliefs. The sample comes from urban male residents in Hangzhou, China randomly selected and interviewed through multistage sampling in 2011. Using binary mediation analysis with logistic regression to test the hypotheses, the authors found that (a) daily smoking is associated with having smokers in several social network arenas and (b) self-exempting beliefs about smoking mediate the association between coworker network and daily smoking, but not for family network and friend network. The role of social network at work place in the creation and maintenance of self-exempting beliefs should be considered by policymakers, prevention experts, and interventionists.

Memory-induced mechanism for self-sustaining activity in networks

NASA Astrophysics Data System (ADS)

Allahverdyan, A. E.; Steeg, G. Ver; Galstyan, A.

2015-12-01

We study a mechanism of activity sustaining on networks inspired by a well-known model of neuronal dynamics. Our primary focus is the emergence of self-sustaining collective activity patterns, where no single node can stay active by itself, but the activity provided initially is sustained within the collective of interacting agents. In contrast to existing models of self-sustaining activity that are caused by (long) loops present in the network, here we focus on treelike structures and examine activation mechanisms that are due to temporal memory of the nodes. This approach is motivated by applications in social media, where long network loops are rare or absent. Our results suggest that under a weak behavioral noise, the nodes robustly split into several clusters, with partial synchronization of nodes within each cluster. We also study the randomly weighted version of the models where the nodes are allowed to change their connection strength (this can model attention redistribution) and show that it does facilitate the self-sustained activity.

Network architectures and circuit function: testing alternative hypotheses in multifunctional networks.

PubMed

Leonard, J L

2000-05-01

Understanding how species-typical movement patterns are organized in the nervous system is a central question in neurobiology. The current explanations involve 'alphabet' models in which an individual neuron may participate in the circuit for several behaviors but each behavior is specified by a specific neural circuit. However, not all of the well-studied model systems fit the 'alphabet' model. The 'equation' model provides an alternative possibility, whereby a system of parallel motor neurons, each with a unique (but overlapping) field of innervation, can account for the production of stereotyped behavior patterns by variable circuits. That is, it is possible for such patterns to arise as emergent properties of a generalized neural network in the absence of feedback, a simple version of a 'self-organizing' behavioral system. Comparison of systems of identified neurons suggest that the 'alphabet' model may account for most observations where CPGs act to organize motor patterns. Other well-known model systems, involving architectures corresponding to feed-forward neural networks with a hidden layer, may organize patterned behavior in a manner consistent with the 'equation' model. Such architectures are found in the Mauthner and reticulospinal circuits, 'escape' locomotion in cockroaches, CNS control of Aplysia gill, and may also be important in the coordination of sensory information and motor systems in insect mushroom bodies and the vertebrate hippocampus. The hidden layer of such networks may serve as an 'internal representation' of the behavioral state and/or body position of the animal, allowing the animal to fine-tune oriented, or particularly context-sensitive, movements to the prevalent conditions. Experiments designed to distinguish between the two models in cases where they make mutually exclusive predictions provide an opportunity to elucidate the neural mechanisms by which behavior is organized in vivo and in vitro. Copyright 2000 S. Karger AG, Basel

How to identify the most effective control measures based on disease-behavior coupled mechanisms?. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Sun, Gui-Quan; Jin, Zhen

2015-12-01

Modelling infectious diseases on complex networks is a significant tool to understand the transmission of epidemics in human society, and consequently it has commanded increasing attention in the community of mathematicians, physicists, epidemiologists, public health policy-makers and so on [1-4]. Human behavior responses are associated with the emergence of infectious disease, for instance, wearing masks [5], staying away from a thick crowd [6], cutting contacts with infected individuals [7] and receiving a vaccination [8]. However, infectious diseases and human behavior were often modeled as independent systems in the literature, despite the fact that in the real world they are often mutually influential on each other, and hence their coupling exerts significant impacts on disease spread [9,10].

Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons.

PubMed

Toutounji, Hazem; Pasemann, Frank

2014-01-01

The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot.

Behavior control in the sensorimotor loop with short-term synaptic dynamics induced by self-regulating neurons

PubMed Central

Toutounji, Hazem; Pasemann, Frank

2014-01-01

The behavior and skills of living systems depend on the distributed control provided by specialized and highly recurrent neural networks. Learning and memory in these systems is mediated by a set of adaptation mechanisms, known collectively as neuronal plasticity. Translating principles of recurrent neural control and plasticity to artificial agents has seen major strides, but is usually hampered by the complex interactions between the agent's body and its environment. One of the important standing issues is for the agent to support multiple stable states of behavior, so that its behavioral repertoire matches the requirements imposed by these interactions. The agent also must have the capacity to switch between these states in time scales that are comparable to those by which sensory stimulation varies. Achieving this requires a mechanism of short-term memory that allows the neurocontroller to keep track of the recent history of its input, which finds its biological counterpart in short-term synaptic plasticity. This issue is approached here by deriving synaptic dynamics in recurrent neural networks. Neurons are introduced as self-regulating units with a rich repertoire of dynamics. They exhibit homeostatic properties for certain parameter domains, which result in a set of stable states and the required short-term memory. They can also operate as oscillators, which allow them to surpass the level of activity imposed by their homeostatic operation conditions. Neural systems endowed with the derived synaptic dynamics can be utilized for the neural behavior control of autonomous mobile agents. The resulting behavior depends also on the underlying network structure, which is either engineered or developed by evolutionary techniques. The effectiveness of these self-regulating units is demonstrated by controlling locomotion of a hexapod with 18 degrees of freedom, and obstacle-avoidance of a wheel-driven robot. PMID:24904403

An integrate-and-fire model for synchronized bursting in a network of cultured cortical neurons.

PubMed

French, D A; Gruenstein, E I

2006-12-01

It has been suggested that spontaneous synchronous neuronal activity is an essential step in the formation of functional networks in the central nervous system. The key features of this type of activity consist of bursts of action potentials with associated spikes of elevated cytoplasmic calcium. These features are also observed in networks of rat cortical neurons that have been formed in culture. Experimental studies of these cultured networks have led to several hypotheses for the mechanisms underlying the observed synchronized oscillations. In this paper, bursting integrate-and-fire type mathematical models for regular spiking (RS) and intrinsic bursting (IB) neurons are introduced and incorporated through a small-world connection scheme into a two-dimensional excitatory network similar to those in the cultured network. This computer model exhibits spontaneous synchronous activity through mechanisms similar to those hypothesized for the cultured experimental networks. Traces of the membrane potential and cytoplasmic calcium from the model closely match those obtained from experiments. We also consider the impact on network behavior of the IB neurons, the geometry and the small world connection scheme.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

NASA Astrophysics Data System (ADS)

Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

2016-04-01

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules.

Uncovering disease mechanisms through network biology in the era of Next Generation Sequencing

PubMed Central

Piñero, Janet; Berenstein, Ariel; Gonzalez-Perez, Abel; Chernomoretz, Ariel; Furlong, Laura I.

2016-01-01

Characterizing the behavior of disease genes in the context of biological networks has the potential to shed light on disease mechanisms, and to reveal both new candidate disease genes and therapeutic targets. Previous studies addressing the network properties of disease genes have produced contradictory results. Here we have explored the causes of these discrepancies and assessed the relationship between the network roles of disease genes and their tolerance to deleterious germline variants in human populations leveraging on: the abundance of interactome resources, a comprehensive catalog of disease genes and exome variation data. We found that the most salient network features of disease genes are driven by cancer genes and that genes related to different types of diseases play network roles whose centrality is inversely correlated to their tolerance to likely deleterious germline mutations. This proved to be a multiscale signature, including global, mesoscopic and local network centrality features. Cancer driver genes, the most sensitive to deleterious variants, occupy the most central positions, followed by dominant disease genes and then by recessive disease genes, which are tolerant to variants and isolated within their network modules. PMID:27080396

Hippocampal mechanisms for the context-dependent retrieval of episodes

PubMed Central

Hasselmo, Michael E.; Eichenbaum, Howard B.

2008-01-01

Behaviors ranging from delivering newspapers to waiting tables depend on remembering previous episodes to avoid incorrect repetition. Physiologically, this requires mechanisms for long-term storage and selective retrieval of episodes based on time of occurrence, despite variable intervals and similarity of events in a familiar environment. Here, this process has been modeled based on physiological properties of the hippocampal formation, including mechanisms for sustained activity in entorhinal cortex and theta rhythm oscillations in hippocampal subregions. The model simulates the context-sensitive firing properties of hippocampal neurons including trial specific firing during spatial alternation and trial by trial changes in theta phase precession on a linear track. This activity is used to guide behavior, and lesions of the hippocampal network impair memory-guided behavior. The model links data at the cellular level to behavior at the systems level, describing a physiologically plausible mechanism for the brain to recall a given episode which occurred at a specific place and time. PMID:16263240

A model of individualized canonical microcircuits supporting cognitive operations

PubMed Central

Peterson, Andre D. H.; Haueisen, Jens; Knösche, Thomas R.

2017-01-01

Major cognitive functions such as language, memory, and decision-making are thought to rely on distributed networks of a large number of basic elements, called canonical microcircuits. In this theoretical study we propose a novel canonical microcircuit model and find that it supports two basic computational operations: a gating mechanism and working memory. By means of bifurcation analysis we systematically investigate the dynamical behavior of the canonical microcircuit with respect to parameters that govern the local network balance, that is, the relationship between excitation and inhibition, and key intrinsic feedback architectures of canonical microcircuits. We relate the local behavior of the canonical microcircuit to cognitive processing and demonstrate how a network of interacting canonical microcircuits enables the establishment of spatiotemporal sequences in the context of syntax parsing during sentence comprehension. This study provides a framework for using individualized canonical microcircuits for the construction of biologically realistic networks supporting cognitive operations. PMID:29200435

A mathematical model for adaptive transport network in path finding by true slime mold.

PubMed

Tero, Atsushi; Kobayashi, Ryo; Nakagaki, Toshiyuki

2007-02-21

We describe here a mathematical model of the adaptive dynamics of a transport network of the true slime mold Physarum polycephalum, an amoeboid organism that exhibits path-finding behavior in a maze. This organism possesses a network of tubular elements, by means of which nutrients and signals circulate through the plasmodium. When the organism is put in a maze, the network changes its shape to connect two exits by the shortest path. This process of path-finding is attributed to an underlying physiological mechanism: a tube thickens as the flux through it increases. The experimental evidence for this is, however, only qualitative. We constructed a mathematical model of the general form of the tube dynamics. Our model contains a key parameter corresponding to the extent of the feedback regulation between the thickness of a tube and the flux through it. We demonstrate the dependence of the behavior of the model on this parameter.

Cell-cell recognition and social networking in bacteria

PubMed Central

Troselj, Vera; Cao, Pengbo; Wall, Daniel

2018-01-01

SUMMARY The ability to recognize self and to recognize partnering cells allows microorganisms to build social networks that perform functions beyond the capabilities of the individual. In bacteria, recognition typically involves genetic determinants that provide cell surface receptors or diffusible signaling chemicals to identify proximal cells at the molecular level that can participate in cooperative processes. Social networks also rely on discriminating mechanisms to exclude competing cells from joining and exploiting their groups. In addition to their appropriate genotypes, cell-cell recognition also requires compatible phenotypes, which vary according to environmental cues or exposures as well as stochastic processes that leads to heterogeneity and potential disharmony in the population. Understanding how bacteria identify their social partners and how they synchronize their behaviors to conduct multicellular functions is an expanding field of research. Here we review recent progress in the field and contrast the various strategies used in recognition and behavioral networking. PMID:29194914

Changes in neural network homeostasis trigger neuropsychiatric symptoms.

PubMed

Winkelmann, Aline; Maggio, Nicola; Eller, Joanna; Caliskan, Gürsel; Semtner, Marcus; Häussler, Ute; Jüttner, René; Dugladze, Tamar; Smolinsky, Birthe; Kowalczyk, Sarah; Chronowska, Ewa; Schwarz, Günter; Rathjen, Fritz G; Rechavi, Gideon; Haas, Carola A; Kulik, Akos; Gloveli, Tengis; Heinemann, Uwe; Meier, Jochen C

2014-02-01

The mechanisms that regulate the strength of synaptic transmission and intrinsic neuronal excitability are well characterized; however, the mechanisms that promote disease-causing neural network dysfunction are poorly defined. We generated mice with targeted neuron type-specific expression of a gain-of-function variant of the neurotransmitter receptor for glycine (GlyR) that is found in hippocampectomies from patients with temporal lobe epilepsy. In this mouse model, targeted expression of gain-of-function GlyR in terminals of glutamatergic cells or in parvalbumin-positive interneurons persistently altered neural network excitability. The increased network excitability associated with gain-of-function GlyR expression in glutamatergic neurons resulted in recurrent epileptiform discharge, which provoked cognitive dysfunction and memory deficits without affecting bidirectional synaptic plasticity. In contrast, decreased network excitability due to gain-of-function GlyR expression in parvalbumin-positive interneurons resulted in an anxiety phenotype, but did not affect cognitive performance or discriminative associative memory. Our animal model unveils neuron type-specific effects on cognition, formation of discriminative associative memory, and emotional behavior in vivo. Furthermore, our data identify a presynaptic disease-causing molecular mechanism that impairs homeostatic regulation of neural network excitability and triggers neuropsychiatric symptoms.

Self-Organized Information Processing in Neuronal Networks: Replacing Layers in Deep Networks by Dynamics

NASA Astrophysics Data System (ADS)

Kirst, Christoph

It is astonishing how the sub-parts of a brain co-act to produce coherent behavior. What are mechanism that coordinate information processing and communication and how can those be changed flexibly in order to cope with variable contexts? Here we show that when information is encoded in the deviations around a collective dynamical reference state of a recurrent network the propagation of these fluctuations is strongly dependent on precisely this underlying reference. Information here 'surfs' on top of the collective dynamics and switching between states enables fast and flexible rerouting of information. This in turn affects local processing and consequently changes in the global reference dynamics that re-regulate the distribution of information. This provides a generic mechanism for self-organized information processing as we demonstrate with an oscillatory Hopfield network that performs contextual pattern recognition. Deep neural networks have proven to be very successful recently. Here we show that generating information channels via collective reference dynamics can effectively compress a deep multi-layer architecture into a single layer making this mechanism a promising candidate for the organization of information processing in biological neuronal networks.

Affective mentalizing and brain activity at rest in the behavioral variant of frontotemporal dementia

PubMed Central

Caminiti, Silvia P.; Canessa, Nicola; Cerami, Chiara; Dodich, Alessandra; Crespi, Chiara; Iannaccone, Sandro; Marcone, Alessandra; Falini, Andrea; Cappa, Stefano F.

2015-01-01

Background bvFTD patients display an impairment in the attribution of cognitive and affective states to others, reflecting GM atrophy in brain regions associated with social cognition, such as amygdala, superior temporal cortex and posterior insula. Distinctive patterns of abnormal brain functioning at rest have been reported in bvFTD, but their relationship with defective attribution of affective states has not been investigated. Objective To investigate the relationship among resting-state brain activity, gray matter (GM) atrophy and the attribution of mental states in the behavioral variant of fronto-temporal degeneration (bvFTD). Methods We compared 12 bvFTD patients with 30 age- and education-matched healthy controls on a) performance in a task requiring the attribution of affective vs. cognitive mental states; b) metrics of resting-state activity in known functional networks; and c) the relationship between task-performances and resting-state metrics. In addition, we assessed a connection between abnormal resting-state metrics and GM atrophy. Results Compared with controls, bvFTD patients showed a reduction of intra-network coherent activity in several components, as well as decreased strength of activation in networks related to attentional processing. Anomalous resting-state activity involved networks which also displayed a significant reduction of GM density. In patients, compared with controls, higher affective mentalizing performance correlated with stronger functional connectivity between medial prefrontal sectors of the default-mode and attentional/performance monitoring networks, as well as with increased coherent activity in components of the executive, sensorimotor and fronto-limbic networks. Conclusions Some of the observed effects may reflect specific compensatory mechanisms for the atrophic changes involving regions in charge of affective mentalizing. The analysis of specific resting-state networks thus highlights an intermediate level of analysis between abnormal brain structure and impaired behavioral performance in bvFTD, reflecting both dysfunction and compensation mechanisms. PMID:26594631

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Dr. Li; Cui, Xiaohui; Cemerlic, Alma

Ad hoc networks are very helpful in situations when no fixed network infrastructure is available, such as natural disasters and military conflicts. In such a network, all wireless nodes are equal peers simultaneously serving as both senders and routers for other nodes. Therefore, how to route packets through reliable paths becomes a fundamental problems when behaviors of certain nodes deviate from wireless ad hoc routing protocols. We proposed a novel Dirichlet reputation model based on Bayesian inference theory which evaluates reliability of each node in terms of packet delivery. Our system offers a way to predict and select a reliablemore » path through combination of first-hand observation and second-hand reputation reports. We also proposed moving window mechanism which helps to adjust ours responsiveness of our system to changes of node behaviors. We integrated the Dirichlet reputation into routing protocol of wireless ad hoc networks. Our extensive simulation indicates that our proposed reputation system can improve good throughput of the network and reduce negative impacts caused by misbehaving nodes.« less

Influence of mechanically-induced dilatation on the shape memory behavior of amorphous polymers at large deformation

NASA Astrophysics Data System (ADS)

Hanzon, Drew W.; Lu, Haibao; Yakacki, Christopher M.; Yu, Kai

2018-01-01

In this study, we explore the influence of mechanically-induced dilatation on the thermomechanical and shape memory behavior of amorphous shape memory polymers (SMPs) at large deformation. The uniaxial tension, glass transition, stress relaxation and free recovery behaviors are examined with different strain levels (up to 340% engineering strain). A multi-branched constitutive model that incorporates dilatational effects on the polymer relaxation time is established and applied to assist in discussions and understand the nonlinear viscoelastic behaviors of SMPs. It is shown that the volumetric dilatation results in an SMP network with lower viscosity, faster relaxation, and lower Tg. The influence of the dilatational effect on the thermomechanical behaviors is significant when the polymers are subject to large deformation or in a high viscosity state. The dilation also increases the free recovery rate of SMP at a given recovery temperature. Even though the tested SMPs are far beyond their linear viscoelastic region when a large programming strain is applied, the free recovery behavior still follows the time-temperature superposition (TTSP) if the dilatational effect is considered during the transformation of time scales; however, if the programming strain is different, TTSP fails in predicting the recovery behavior of SMPs because the network has different entropy state and driving force during shape recovery. Since most soft active polymers are subject to large deformation in practice, this study provides a theoretical basis to better understand their nonlinear viscoelastic behaviors, and optimize their performance in engineering applications.

Distributed recurrent neural forward models with synaptic adaptation and CPG-based control for complex behaviors of walking robots

PubMed Central

Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain a variety of movements, the neural mechanisms generate movements while making appropriate predictions crucial for achieving adaptation. Such predictions or planning ahead can be achieved by way of internal models that are grounded in the overall behavior of the animal. Inspired by these findings, we present here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated movements, (2) distributed (at each leg) recurrent neural network based adaptive forward models with efference copies as internal models for sensory predictions and instantaneous state estimations, and (3) searching and elevation control for adapting the movement of an individual leg to deal with different environmental conditions. Using simulations we show that this bio-inspired approach with adaptive internal models allows the walking robot to perform complex locomotive behaviors as observed in insects, including walking on undulated terrains, crossing large gaps, leg damage adaptations, as well as climbing over high obstacles. Furthermore, we demonstrate that the newly developed recurrent network based approach to online forward models outperforms the adaptive neuron forward models, which have hitherto been the state of the art, to model a subset of similar walking behaviors in walking robots. PMID:26441629

Frontal glutamate and reward processing in adolescence and adulthood.

PubMed

Gleich, Tobias; Lorenz, Robert C; Pöhland, Lydia; Raufelder, Diana; Deserno, Lorenz; Beck, Anne; Heinz, Andreas; Kühn, Simone; Gallinat, Jürgen

2015-11-01

The fronto-limbic network interaction, driven by glutamatergic and dopaminergic neurotransmission, represents a core mechanism of motivated behavior and personality traits. Reward seeking behavior undergoes tremendous changes in adolescence paralleled by neurobiological changes of this network including the prefrontal cortex, striatum and amygdala. Since fronto-limbic dysfunctions also underlie major psychiatric diseases beginning in adolescence, this investigation focuses on network characteristics separating adolescents from adults. To investigate differences in network interactions, the brain reward system activity (slot machine task) together with frontal glutamate concentration (anterior cingulate cortex, ACC) was measured in 28 adolescents and 26 adults employing functional magnetic resonance imaging and magnetic resonance spectroscopy, respectively. An inverse coupling of glutamate concentrations in the ACC and activation of the ventral striatum was observed in adolescents. Further, amygdala response in adolescents was negatively correlated with the personality trait impulsivity. For adults, no significant associations of network components or correlations with impulsivity were found. The inverse association between frontal glutamate concentration and striatal activation in adolescents is in line with the triadic model of motivated behavior stressing the important role of frontal top-down inhibition on limbic structures. Our data identified glutamate as the mediating neurotransmitter of this inhibitory process and demonstrates the relevance of glutamate on the reward system and related behavioral traits like impulsivity. This fronto-limbic coupling may represent a vulnerability factor for psychiatric disorders starting in adolescence but not in adulthood.

Brain intrinsic network connectivity in individuals with frequent tanning behavior.

PubMed

Ketcherside, Ariel; Filbey, Francesca M; Aubert, Pamela M; Seibyl, John P; Price, Julianne L; Adinoff, Bryon

2018-05-01

Emergent studies suggest a bidirectional relationship between brain functioning and the skin. This neurocutaneous connection may be responsible for the reward response to tanning and, thus, may contribute to excessive tanning behavior. To date, however, this association has not yet been examined. To explore whether intrinsic brain functional connectivity within the default mode network (DMN) is related to indoor tanning behavior. Resting state functional connectivity (rsFC) was obtained in twenty adults (16 females) with a history of indoor tanning. Using a seed-based [(posterior cingulate cortex (PCC)] approach, the relationship between tanning severity and FC strength was assessed. Tanning severity was measured with symptom count from the Structured Clinical Interview for Tanning Abuse and Dependence (SITAD) and tanning intensity (lifetime indoor tanning episodes/years tanning). rsFC strength between the PCC and other DMN regions (left globus pallidus, left medial frontal gyrus, left superior frontal gyrus) is positively correlated with tanning symptom count. rsFC strength between the PCC and salience network regions (right anterior cingulate cortex, left inferior parietal lobe, left inferior temporal gyrus) is correlated with tanning intensity. Greater connectivity between tanning severity and DMN and salience network connectivity suggests that heightened self-awareness of salient stimuli may be a mechanism that underlies frequent tanning behavior. These findings add to the growing evidence of brain-skin connection and reflect dysregulation in the reward processing networks in those with frequent tanning.

Neurobiological Mechanisms for the Regulation of Mammalian Sleep-Wake Behavior: Reinterpretation of Historical Evidence and Inclusion of Contemporary Cellular and Molecular Evidence

PubMed Central

Datta, Subimal; MacLean, Robert Ross

2007-01-01

At its most basic level, the function of mammalian sleep can be described as a restorative process of the brain and body; recently, however, progressive research has revealed a host of vital functions to which sleep is essential. Although many excellent reviews on sleep behavior have been published, none have incorporated contemporary studies examining the molecular mechanisms that govern the various stages of sleep. Utilizing a holistic approach, this review is focused on the basic mechanisms involved in the transition from wakefulness, initiation of sleep and the subsequent generation of slow-wave sleep and rapid eye movement (REM) sleep. Additionally, using recent molecular studies and experimental evidence that provides a direct link to sleep as a behavior, we have developed a new model, the Cellular-Molecular-Network model, explaining the mechanisms responsible for regulating REM sleep. By analyzing the fundamental neurobiological mechanisms responsible for the generation and maintenance of sleep-wake behavior in mammals, we intend to provide a broader understanding of our present knowledge in the field of sleep research. PMID:17445891

A dynamic network model for interbank market

NASA Astrophysics Data System (ADS)

Xu, Tao; He, Jianmin; Li, Shouwei

2016-12-01

In this paper, a dynamic network model based on agent behavior is introduced to explain the formation mechanism of interbank market network. We investigate the impact of credit lending preference on interbank market network topology, the evolution of interbank market network and stability of interbank market. Experimental results demonstrate that interbank market network is a small-world network and cumulative degree follows the power-law distribution. We find that the interbank network structure keeps dynamic stability in the network evolution process. With the increase of bank credit lending preference, network clustering coefficient increases and average shortest path length decreases monotonously, which improves the stability of the network structure. External shocks are main threats for the interbank market and the reduction of bank external investment yield rate and deposits fluctuations contribute to improve the resilience of the banking system.

Factors affecting the mechanical behavior of collagen hydrogels for skin tissue engineering.

PubMed

Pensalfini, Marco; Ehret, Alexander E; Stüdeli, Silvia; Marino, Daniela; Kaech, Andres; Reichmann, Ernst; Mazza, Edoardo

2017-05-01

The effect of the production factors yielding a functional dermal substitute was investigated by means of monotonic and cyclic uniaxial tensile tests, as well as electron microscopy visualizations. The role of (i) plastic compression, (ii) product incubation, and (iii) cell permanence in the collagenous matrix in order to achieve a skin-like behavior were characterized in terms of material and structural stiffness, in-plane kinematics, and cyclic response, as well as pore size and network density. The plastic compression resulted in a denser and stiffer material, while no corresponding change was observed in the behavior of the entire structure. This was related to the progressive reduction in product thickness and amount of excess water, rather than to formation of new crosslinks between fibers. Contrary, irrespective of the presence of human fibroblasts, the product incubation induced both material and structural stiffening, indicating the formation of a denser network. These results were confirmed by similar evolutions in the construct in-plane kinematics and cyclic stress reduction. Finally, comparison of constructs incubated in different culture media indicated a determinant contribution of the biochemical environment, rather than of the seeded cells, to the achieved mechanical properties. The observed features are relevant in terms of mechanical biocompatibility of the implant and might direct future optimizations of the production process in order to rapidly attain the desired mechanical properties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Self-Healing Nanocomposite Hydrogel with Well-Controlled Dynamic Mechanics

NASA Astrophysics Data System (ADS)

Li, Qiaochu; Mishra, Sumeet; Chen, Pangkuan; Tracy, Joseph; Holten-Andersen, Niels

Network dynamics is a crucial factor that determines the macroscopic self-healing rate and efficiency in polymeric hydrogel materials, yet its controllability is seldom studied in most reported self-healing hydrogel systems. Inspired by mussel's adhesion chemistry, we developed a novel approach to assemble inorganic nanoparticles and catechol-decorated PEG polymer into a hydrogel network. When utilized as reversible polymer-particle crosslinks, catechol-metal coordination bonds yield a unique gel network with dynamic mechanics controlled directly by interfacial crosslink structure. Taking advantage of this structure-property relationship at polymer-particle interfaces, we next designed a hierarchically structured hybrid gel with two distinct relaxation timescales. By tuning the relative contribution of the two hierarchical relaxation modes, we are able to finely control the gel's dynamic mechanical behavior from a viscoelastic fluid to a stiff solid, yet preserving its fast self-healing property without the need for external stimuli.

Rapid Self-healing Nanocomposite Hydrogel with Tunable Dynamic Mechanics

NASA Astrophysics Data System (ADS)

Li, Qiaochu; Mishra, Sumeet; Chapman, Brian; Chen, Pangkuan; Tracy, Joseph; Holten-Andersen, Niels

The macroscopic healing rate and efficiency in self-repairing hydrogel materials are largely determined by the dissociation dynamics of their polymer network, which is hardly achieved in a controllable manner. Inspired by mussel's adhesion chemistry, we developed a novel approach to assemble inorganic nanoparticles and catechol-decorated PEG polymer into a hydrogel network. When utilized as reversible polymer-particle crosslinks, catechol-metal coordination bonds yield a unique gel network with dynamic mechanics controlled directly by interfacial crosslink structure. Taking advantage of this structure-property relationship at polymer-particle interfaces, we designed a hierarchically structured hybrid gel with two distinct relaxation timescales. By tuning the relative contribution of the two relaxation modes, we are able to finely control the gel's dynamic mechanical behavior from a viscoelastic fluid to a stiff solid, yet preserving its rapid self-healing property without the need for external stimuli.

Deconstructing Memory in Drosophila

PubMed Central

Margulies, Carla; Tully, Tim; Dubnau, Josh

2011-01-01

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level. This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across three levels of biological organization: behavior, neural circuitry and biochemical pathways. We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field. PMID:16139203

Parsing learning in networks using brain-machine interfaces.

PubMed

Orsborn, Amy L; Pesaran, Bijan

2017-10-01

Brain-machine interfaces (BMIs) define new ways to interact with our environment and hold great promise for clinical therapies. Motor BMIs, for instance, re-route neural activity to control movements of a new effector and could restore movement to people with paralysis. Increasing experience shows that interfacing with the brain inevitably changes the brain. BMIs engage and depend on a wide array of innate learning mechanisms to produce meaningful behavior. BMIs precisely define the information streams into and out of the brain, but engage wide-spread learning. We take a network perspective and review existing observations of learning in motor BMIs to show that BMIs engage multiple learning mechanisms distributed across neural networks. Recent studies demonstrate the advantages of BMI for parsing this learning and its underlying neural mechanisms. BMIs therefore provide a powerful tool for studying the neural mechanisms of learning that highlights the critical role of learning in engineered neural therapies. Copyright © 2017 Elsevier Ltd. All rights reserved.

Artificial Neural Network Based Mission Planning Mechanism for Spacecraft

NASA Astrophysics Data System (ADS)

Li, Zhaoyu; Xu, Rui; Cui, Pingyuan; Zhu, Shengying

2018-04-01

The ability to plan and react fast in dynamic space environments is central to intelligent behavior of spacecraft. For space and robotic applications, many planners have been used. But it is difficult to encode the domain knowledge and directly use existing techniques such as heuristic to improve the performance of the application systems. Therefore, regarding planning as an advanced control problem, this paper first proposes an autonomous mission planning and action selection mechanism through a multiple layer perceptron neural network approach to select actions in planning process and improve efficiency. To prove the availability and effectiveness, we use autonomous mission planning problems of the spacecraft, which is a sophisticated system with complex subsystems and constraints as an example. Simulation results have shown that artificial neural networks (ANNs) are usable for planning problems. Compared with the existing planning method in EUROPA, the mechanism using ANNs is more efficient and can guarantee stable performance. Therefore, the mechanism proposed in this paper is more suitable for planning problems of spacecraft that require real time and stability.

Linking Resting-State Networks in the Prefrontal Cortex to Executive Function: A Functional Near Infrared Spectroscopy Study.

PubMed

Zhao, Jia; Liu, Jiangang; Jiang, Xin; Zhou, Guifei; Chen, Guowei; Ding, Xiao P; Fu, Genyue; Lee, Kang

2016-01-01

Executive function (EF) plays vital roles in our everyday adaptation to the ever-changing environment. However, limited existing studies have linked EF to the resting-state brain activity. The functional connectivity in the resting state between the sub-regions of the brain can reveal the intrinsic neural mechanisms involved in cognitive processing of EF without disturbance from external stimuli. The present study investigated the relations between the behavioral executive function (EF) scores and the resting-state functional network topological properties in the Prefrontal Cortex (PFC). We constructed complex brain functional networks in the PFC from 90 healthy young adults using functional near infrared spectroscopy (fNIRS). We calculated the correlations between the typical network topological properties (regional topological properties and global topological properties) and the scores of both the Total EF and components of EF measured by computer-based Cambridge Neuropsychological Test Automated Battery (CANTAB). We found that the Total EF scores were positively correlated with regional properties in the right dorsal superior frontal gyrus (SFG), whereas the opposite pattern was found in the right triangular inferior frontal gyrus (IFG). Different EF components were related to different regional properties in various PFC areas, such as planning in the right middle frontal gyrus (MFG), working memory mainly in the right MFG and triangular IFG, short-term memory in the left dorsal SFG, and task switch in the right MFG. In contrast, there were no significant findings for global topological properties. Our findings suggested that the PFC plays an important role in individuals' behavioral performance in the executive function tasks. Further, the resting-state functional network can reveal the intrinsic neural mechanisms involved in behavioral EF abilities.

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tourret, D.; Mertens, J. C. E.; Lieberman, E.

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure,more » supported by quantitative simulations of microstructure formation and its mechanical behavior.« less

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

DOE PAGES

Tourret, D.; Mertens, J. C. E.; Lieberman, E.; ...

2017-09-13

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure,more » supported by quantitative simulations of microstructure formation and its mechanical behavior.« less

From Solidification Processing to Microstructure to Mechanical Properties: A Multi-scale X-ray Study of an Al-Cu Alloy Sample

NASA Astrophysics Data System (ADS)

Tourret, D.; Mertens, J. C. E.; Lieberman, E.; Imhoff, S. D.; Gibbs, J. W.; Henderson, K.; Fezzaa, K.; Deriy, A. L.; Sun, T.; Lebensohn, R. A.; Patterson, B. M.; Clarke, A. J.

2017-11-01

We follow an Al-12 at. pct Cu alloy sample from the liquid state to mechanical failure, using in situ X-ray radiography during directional solidification and tensile testing, as well as three-dimensional computed tomography of the microstructure before and after mechanical testing. The solidification processing stage is simulated with a multi-scale dendritic needle network model, and the micromechanical behavior of the solidified microstructure is simulated using voxelized tomography data and an elasto-viscoplastic fast Fourier transform model. This study demonstrates the feasibility of direct in situ monitoring of a metal alloy microstructure from the liquid processing stage up to its mechanical failure, supported by quantitative simulations of microstructure formation and its mechanical behavior.

Neuroendocrine control of seasonal plasticity in the auditory and vocal systems of fish

PubMed Central

Forlano, Paul M.; Sisneros, Joseph A.; Rohmann, Kevin N.; Bass, Andrew H.

2014-01-01

Seasonal changes in reproductive-related vocal behavior are widespread among fishes. This review highlights recent studies of the vocal plainfin midshipman fish, Porichthys notatus, a neuroethological model system used for the past two decades to explore neural and endocrine mechanisms of vocal-acoustic social behaviors shared with tetrapods. Integrative approaches combining behavior, neurophysiology, neuropharmacology, neuroanatomy, and gene expression methodologies have taken advantage of simple, stereotyped and easily quantifiable behaviors controlled by discrete neural networks in this model system to enable discoveries such as the first demonstration of adaptive seasonal plasticity in the auditory periphery of a vertebrate as well as rapid steroid and neuropeptide effects on vocal physiology and behavior. This simple model system has now revealed cellular and molecular mechanisms underlying seasonal and steroid-driven auditory and vocal plasticity in the vertebrate brain. PMID:25168757

Healthy and pathological cerebellar Spiking Neural Networks in Vestibulo-Ocular Reflex.

PubMed

Antonietti, Alberto; Casellato, Claudia; Geminiani, Alice; D'Angelo, Egidio; Pedrocchi, Alessandra

2015-01-01

Since the Marr-Albus model, computational neuroscientists have been developing a variety of models of the cerebellum, with different approaches and features. In this work, we developed and tested realistic artificial Spiking Neural Networks inspired to this brain region. We tested in computational simulations of the Vestibulo-Ocular Reflex protocol three different models: a network equipped with a single plasticity site, at the cortical level; a network equipped with a distributed plasticity, at both cortical and nuclear levels; a network with a pathological plasticity mechanism at the cortical level. We analyzed the learning performance of the three different models, highlighting the behavioral differences among them. We proved that the model with a distributed plasticity produces a faster and more accurate cerebellar response, especially during a second session of acquisition, compared with the single plasticity model. Furthermore, the pathological model shows an impaired learning capability in Vestibulo-Ocular Reflex acquisition, as found in neurophysiological studies. The effect of the different plasticity conditions, which change fast and slow dynamics, memory consolidation and, in general, learning capabilities of the cerebellar network, explains differences in the behavioral outcome.

Innovation flow through social networks: productivity distribution in France and Italy

NASA Astrophysics Data System (ADS)

di Matteo, T.; Aste, T.; Gallegati, M.

2005-10-01

From a detailed empirical analysis of the productivity of non financial firms across several countries and years we show that productivity follows a non-Gaussian distribution with `fat tails' in the large productivity region which are well mimicked by power law behaviors. We discuss how these empirical findings can be linked to a mechanism of exchanges in a social network where firms improve their productivity by direct innovation and/or by imitation of other firm's technological and organizational solutions. The type of network-connectivity determines how fast and how efficiently information can diffuse and how quickly innovation will permeate or behaviors will be imitated. From a model for innovation flow through a complex network we show that the expectation values of the productivity of each firm are proportional to its connectivity in the network of links between firms. The comparison with the empirical distributions in France and Italy reveals that in this model, such a network must be of a scale-free type with a power-law degree distribution in the large connectivity range.

Irregular behavior in an excitatory-inhibitory neuronal network

NASA Astrophysics Data System (ADS)

Park, Choongseok; Terman, David

2010-06-01

Excitatory-inhibitory networks arise in many regions throughout the central nervous system and display complex spatiotemporal firing patterns. These neuronal activity patterns (of individual neurons and/or the whole network) are closely related to the functional status of the system and differ between normal and pathological states. For example, neurons within the basal ganglia, a group of subcortical nuclei that are responsible for the generation of movement, display a variety of dynamic behaviors such as correlated oscillatory activity and irregular, uncorrelated spiking. Neither the origins of these firing patterns nor the mechanisms that underlie the patterns are well understood. We consider a biophysical model of an excitatory-inhibitory network in the basal ganglia and explore how specific biophysical properties of the network contribute to the generation of irregular spiking. We use geometric dynamical systems and singular perturbation methods to systematically reduce the model to a simpler set of equations, which is suitable for analysis. The results specify the dependence on the strengths of synaptic connections and the intrinsic firing properties of the cells in the irregular regime when applied to the subthalamopallidal network of the basal ganglia.

Coevolution of strategy-selection time scale and cooperation in spatial prisoner's dilemma game

NASA Astrophysics Data System (ADS)

Rong, Zhihai; Wu, Zhi-Xi; Chen, Guanrong

2013-06-01

In this paper, we investigate a networked prisoner's dilemma game where individuals' strategy-selection time scale evolves based on their historical learning information. We show that the more times the current strategy of an individual is learnt by his neighbors, the longer time he will stick on the successful behavior by adaptively adjusting the lifetime of the adopted strategy. Through characterizing the extent of success of the individuals with normalized payoffs, we show that properly using the learned information can form a positive feedback mechanism between cooperative behavior and its lifetime, which can boost cooperation on square lattices and scale-free networks.

Optical and mechanical behaviors of glassy silicone networks derived from linear siloxane precursors

NASA Astrophysics Data System (ADS)

Jang, Heejun; Seo, Wooram; Kim, Hyungsun; Lee, Yoonjoo; Kim, Younghee

2016-01-01

Silicon-based inorganic polymers are promising materials as matrix materials for glass fiber composites because of their good process ability, transparency, and thermal property. In this study, for utilization as a matrix precursor for a glass-fiber-reinforced composite, glassy silicone networks were prepared via hydrosilylation of linear/pendant Si-H polysiloxanes and the C=C bonds of viny-lterminated linear/cyclic polysiloxanes. 13C nuclear magnetic resonance spectroscopy was used to determine the structure of the cross-linked states, and a thermal analysis was performed. To assess the mechanical properties of the glassy silicone networks, we performed nanoindentation and 4-point bending tests. Cross-linked networks derived from siloxane polymers are thermally and optically more stable at high temperatures. Different cross-linking agents led to final networks with different properties due to differences in the molecular weights and structures. After stepped postcuring, the Young's modulus and the hardness of the glassy silicone networks increased; however, the brittleness also increased. The characteristics of the cross-linking agent played an important role in the functional glassy silicone networks.

Punishment diminishes the benefits of network reciprocity in social dilemma experiments.

PubMed

Li, Xuelong; Jusup, Marko; Wang, Zhen; Li, Huijia; Shi, Lei; Podobnik, Boris; Stanley, H Eugene; Havlin, Shlomo; Boccaletti, Stefano

2018-01-02

Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism-costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks. Copyright © 2017 the Author(s). Published by PNAS.

OpenFlow arbitrated programmable network channels for managing quantum metadata

DOE PAGES

Dasari, Venkat R.; Humble, Travis S.

2016-10-10

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-definedmore » network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.« less

OpenFlow arbitrated programmable network channels for managing quantum metadata

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dasari, Venkat R.; Humble, Travis S.

Quantum networks must classically exchange complex metadata between devices in order to carry out information for protocols such as teleportation, super-dense coding, and quantum key distribution. Demonstrating the integration of these new communication methods with existing network protocols, channels, and data forwarding mechanisms remains an open challenge. Software-defined networking (SDN) offers robust and flexible strategies for managing diverse network devices and uses. We adapt the principles of SDN to the deployment of quantum networks, which are composed from unique devices that operate according to the laws of quantum mechanics. We show how quantum metadata can be managed within a software-definedmore » network using the OpenFlow protocol, and we describe how OpenFlow management of classical optical channels is compatible with emerging quantum communication protocols. We next give an example specification of the metadata needed to manage and control quantum physical layer (QPHY) behavior and we extend the OpenFlow interface to accommodate this quantum metadata. Here, we conclude by discussing near-term experimental efforts that can realize SDN’s principles for quantum communication.« less

The Interplay of Friendship Networks and Social Networking Sites: Longitudinal Analysis of Selection and Influence Effects on Adolescent Smoking and Alcohol Use

PubMed Central

Soto, Daniel; Fujimoto, Kayo; Valente, Thomas W.

2014-01-01

Objectives. We examined the coevolution of adolescent friendships and peer influences with respect to their risk behaviors and social networking site use. Methods. Investigators of the Social Network Study collected longitudinal data during fall 2010 and spring 2011 from 10th-grade students in 5 Southern California high schools (n = 1434). We used meta-analyses of stochastic actor-based models to estimate changes in friendship ties and risk behaviors and the effects of Facebook and MySpace use. Results. Significant shifts in adolescent smoking and drinking occurred despite little change in overall prevalence rates. Students with higher levels of alcohol use were more likely to send and receive friendship nominations and become friends with other drinkers. They were also more likely to increase alcohol use if their friends drank more. Adolescents selected friends with similar Facebook and MySpace use habits. Exposure to friends’ risky online pictures increased smoking behaviors but had no significant effects on alcohol use. Conclusions. Our findings support a greater focus on friendship selection mechanisms in school-based alcohol use interventions. Social media platforms may help identify at-risk adolescent groups and foster positive norms about risk behaviors. PMID:24922126

3D printing of an interpenetrating network hydrogel material with tunable viscoelastic properties.

PubMed

Bootsma, Katherine; Fitzgerald, Martha M; Free, Brandon; Dimbath, Elizabeth; Conjerti, Joe; Reese, Greg; Konkolewicz, Dominik; Berberich, Jason A; Sparks, Jessica L

2017-06-01

Interpenetrating network (IPN) hydrogel materials are recognized for their unique mechanical properties. While IPN elasticity and toughness properties have been explored in previous studies, the factors that impact the time-dependent stress relaxation behavior of IPN materials are not well understood. Time-dependent (i.e. viscoelastic) mechanical behavior is a critical design parameter in the development of materials for a variety of applications, such as medical simulation devices, flexible substrate materials, cellular mechanobiology substrates, or regenerative medicine applications. This study reports a novel technique for 3D printing alginate-polyacrylamide IPN gels with tunable elastic and viscoelastic properties. The viscoelastic stress relaxation behavior of the 3D printed alginate-polyacrylamide IPN hydrogels was influenced most strongly by varying the concentration of the acrylamide cross-linker (MBAA), while the elastic modulus was affected most by varying the concentration of total monomer material. The material properties of our 3D printed IPN constructs were consistent with those reported in the biomechanics literature for soft tissues such as skeletal muscle, cardiac muscle, skin and subcutaneous tissue. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure and mechanics of aegagropilae fiber network.

PubMed

Verhille, Gautier; Moulinet, Sébastien; Vandenberghe, Nicolas; Adda-Bedia, Mokhtar; Le Gal, Patrice

2017-05-02

Fiber networks encompass a wide range of natural and manmade materials. The threads or filaments from which they are formed span a wide range of length scales: from nanometers, as in biological tissues and bundles of carbon nanotubes, to millimeters, as in paper and insulation materials. The mechanical and thermal behavior of these complex structures depends on both the individual response of the constituent fibers and the density and degree of entanglement of the network. A question of paramount importance is how to control the formation of a given fiber network to optimize a desired function. The study of fiber clustering of natural flocs could be useful for improving fabrication processes, such as in the paper and textile industries. Here, we use the example of aegagropilae that are the remains of a seagrass ( Posidonia oceanica ) found on Mediterranean beaches. First, we characterize different aspects of their structure and mechanical response, and second, we draw conclusions on their formation process. We show that these natural aggregates are formed in open sea by random aggregation and compaction of fibers held together by friction forces. Although formed in a natural environment, thus under relatively unconstrained conditions, the geometrical and mechanical properties of the resulting fiber aggregates are quite robust. This study opens perspectives for manufacturing complex fiber network materials.

Mechanical Network in Titin Immunoglobulin from Force Distribution Analysis

PubMed Central

Wilmanns, Matthias; Gräter, Frauke

2009-01-01

The role of mechanical force in cellular processes is increasingly revealed by single molecule experiments and simulations of force-induced transitions in proteins. How the applied force propagates within proteins determines their mechanical behavior yet remains largely unknown. We present a new method based on molecular dynamics simulations to disclose the distribution of strain in protein structures, here for the newly determined high-resolution crystal structure of I27, a titin immunoglobulin (IG) domain. We obtain a sparse, spatially connected, and highly anisotropic mechanical network. This allows us to detect load-bearing motifs composed of interstrand hydrogen bonds and hydrophobic core interactions, including parts distal to the site to which force was applied. The role of the force distribution pattern for mechanical stability is tested by in silico unfolding of I27 mutants. We then compare the observed force pattern to the sparse network of coevolved residues found in this family. We find a remarkable overlap, suggesting the force distribution to reflect constraints for the evolutionary design of mechanical resistance in the IG family. The force distribution analysis provides a molecular interpretation of coevolution and opens the road to the study of the mechanism of signal propagation in proteins in general. PMID:19282960

A thermo-chemo-mechanically coupled constitutive model for curing of glassy polymers

NASA Astrophysics Data System (ADS)

Sain, Trisha; Loeffel, Kaspar; Chester, Shawn

2018-07-01

Curing of a polymer is the process through which a polymer liquid transitions into a solid polymer, capable of bearing mechanical loads. The curing process is a coupled thermo-chemo-mechanical conversion process which requires a thorough understanding of the system behavior to predict the cure dependent mechanical behavior of the solid polymer. In this paper, a thermodynamically consistent, frame indifferent, thermo-chemo-mechanically coupled continuum level constitutive framework is proposed for thermally cured glassy polymers. The constitutive framework considers the thermodynamics of chemical reactions, as well as the material behavior for a glassy polymer. A stress-free intermediate configuration is introduced within a finite deformation setting to capture the formation of the network in a stress-free configuration. This work considers a definition for the degree of cure based on the chemistry of the curing reactions. A simplified version of the proposed model has been numerically implemented, and simulations are used to understand the capabilities of the model and framework.

Network Stability Is a Balancing Act of Personality, Power, and Conflict Dynamics in Rhesus Macaque Societies

PubMed Central

McCowan, Brenda; Beisner, Brianne A.; Capitanio, John P.; Jackson, Megan E.; Cameron, Ashley N.; Seil, Shannon; Atwill, Edward R.; Fushing, Hsieh

2011-01-01

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups. PMID:21857922

Network stability is a balancing act of personality, power, and conflict dynamics in rhesus macaque societies.

PubMed

McCowan, Brenda; Beisner, Brianne A; Capitanio, John P; Jackson, Megan E; Cameron, Ashley N; Seil, Shannon; Atwill, Edward R; Fushing, Hsieh

2011-01-01

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups.

Checkpoints couple transcription network oscillator dynamics to cell-cycle progression.

PubMed

Bristow, Sara L; Leman, Adam R; Simmons Kovacs, Laura A; Deckard, Anastasia; Harer, John; Haase, Steven B

2014-09-05

The coupling of cyclin dependent kinases (CDKs) to an intrinsically oscillating network of transcription factors has been proposed to control progression through the cell cycle in budding yeast, Saccharomyces cerevisiae. The transcription network regulates the temporal expression of many genes, including cyclins, and drives cell-cycle progression, in part, by generating successive waves of distinct CDK activities that trigger the ordered program of cell-cycle events. Network oscillations continue autonomously in mutant cells arrested by depletion of CDK activities, suggesting the oscillator can be uncoupled from cell-cycle progression. It is not clear what mechanisms, if any, ensure that the network oscillator is restrained when progression in normal cells is delayed or arrested. A recent proposal suggests CDK acts as a master regulator of cell-cycle processes that have the potential for autonomous oscillatory behavior. Here we find that mitotic CDK is not sufficient for fully inhibiting transcript oscillations in arrested cells. We do find that activation of the DNA replication and spindle assembly checkpoints can fully arrest the network oscillator via overlapping but distinct mechanisms. Further, we demonstrate that the DNA replication checkpoint effector protein, Rad53, acts to arrest a portion of transcript oscillations in addition to its role in halting cell-cycle progression. Our findings indicate that checkpoint mechanisms, likely via phosphorylation of network transcription factors, maintain coupling of the network oscillator to progression during cell-cycle arrest.

Fibrin-polyethylene oxide interpenetrating polymer networks: new self-supported biomaterials combining the properties of both protein gel and synthetic polymer.

PubMed

Akpalo, E; Bidault, L; Boissière, M; Vancaeyzeele, C; Fichet, O; Larreta-Garde, V

2011-06-01

Interpenetrating polymer network (IPN) architectures were conceived to improve the mechanical properties of a fibrin gel. Conditions allowing an enzymatic reaction to create one of the two networks in IPN architecture were included in the synthesis pathway. Two IPN series were carried out, starting from two polyethylene oxide (PEO) network precursors leading to different cross-linking densities of the PEO phase. The fibrin concentration varied from 5 to 20 wt.% in each series. The behavior of these materials during dehydration/hydration cycles was also studied. The mechanical properties of the resulting IPN were characterized in the wet and dry states. These self-supported biomaterials combine the properties of both a protein gel and a synthetic polymer. Finally, cells were grown on PEO/fibrin IPN, indicating that they are non-cytotoxic. Copyright © 2011 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo (Editor)

1990-01-01

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Deep Belief Networks Learn Context Dependent Behavior

DTIC Science & Technology

2014-03-26

cortical mechanisms for goal- directed behavior. J Cogn Neurosci 17: 1115–1129. 13. Koene RA, Hasselmo ME (2005) An integrate-and-fire model of prefrontal... Neuroscience and Neural Technology, Boston University, Boston, Massachusetts, United States of America, 2 Center of Excellence for Learning in Education...America, 4 Department of Psychology and Graduate Program for Neuroscience , Boston University, Boston, Massachusetts, United States of America Abstract With

A mechanism design approach to bandwidth allocation in tactical data networks

NASA Astrophysics Data System (ADS)

Mour, Ankur

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems'. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today's software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches.

Animal Hairs as Water-stimulated Shape Memory Materials: Mechanism and Structural Networks in Molecular Assemblies

NASA Astrophysics Data System (ADS)

Xiao, Xueliang; Hu, Jinlian

2016-05-01

Animal hairs consisting of α-keratin biopolymers existing broadly in nature may be responsive to water for recovery to the innate shape from their fixed deformation, thus possess smart behavior, namely shape memory effect (SME). In this article, three typical animal hair fibers were first time investigated for their water-stimulated SME, and therefrom to identify the corresponding net-points and switches in their molecular and morphological structures. Experimentally, the SME manifested a good stability of high shape fixation ratio and reasonable recovery rate after many cycles of deformation programming under water stimulation. The effects of hydration on hair lateral size, recovery kinetics, dynamic mechanical behaviors and structural components (crystal, disulfide and hydrogen bonds) were then systematically studied. SME mechanisms were explored based on the variations of structural components in molecular assemblies of such smart fibers. A hybrid structural network model with single-switch and twin-net-points was thereafter proposed to interpret the water-stimulated shape memory mechanism of animal hairs. This original work is expected to provide inspiration for exploring other natural materials to reveal their smart functions and natural laws in animals including human as well as making more remarkable synthetic smart materials.

Animal Hairs as Water-stimulated Shape Memory Materials: Mechanism and Structural Networks in Molecular Assemblies

PubMed Central

Xiao, Xueliang; Hu, Jinlian

2016-01-01

Animal hairs consisting of α-keratin biopolymers existing broadly in nature may be responsive to water for recovery to the innate shape from their fixed deformation, thus possess smart behavior, namely shape memory effect (SME). In this article, three typical animal hair fibers were first time investigated for their water-stimulated SME, and therefrom to identify the corresponding net-points and switches in their molecular and morphological structures. Experimentally, the SME manifested a good stability of high shape fixation ratio and reasonable recovery rate after many cycles of deformation programming under water stimulation. The effects of hydration on hair lateral size, recovery kinetics, dynamic mechanical behaviors and structural components (crystal, disulfide and hydrogen bonds) were then systematically studied. SME mechanisms were explored based on the variations of structural components in molecular assemblies of such smart fibers. A hybrid structural network model with single-switch and twin-net-points was thereafter proposed to interpret the water-stimulated shape memory mechanism of animal hairs. This original work is expected to provide inspiration for exploring other natural materials to reveal their smart functions and natural laws in animals including human as well as making more remarkable synthetic smart materials. PMID:27230823

Environmentally induced amplitude death and firing provocation in large-scale networks of neuronal systems

NASA Astrophysics Data System (ADS)

Pankratova, Evgeniya V.; Kalyakulina, Alena I.

2016-12-01

We study the dynamics of multielement neuronal systems taking into account both the direct interaction between the cells via linear coupling and nondiffusive cell-to-cell communication via common environment. For the cells exhibiting individual bursting behavior, we have revealed the dependence of the network activity on its scale. Particularly, we show that small-scale networks demonstrate the inability to maintain complicated oscillations: for a small number of elements in an ensemble, the phenomenon of amplitude death is observed. The existence of threshold network scales and mechanisms causing firing in artificial and real multielement neural networks, as well as their significance for biological applications, are discussed.

Incoherence-Mediated Remote Synchronization

NASA Astrophysics Data System (ADS)

Zhang, Liyue; Motter, Adilson E.; Nishikawa, Takashi

2017-04-01

In previously identified forms of remote synchronization between two nodes, the intermediate portion of the network connecting the two nodes is not synchronized with them but generally exhibits some coherent dynamics. Here we report on a network phenomenon we call incoherence-mediated remote synchronization (IMRS), in which two noncontiguous parts of the network are identically synchronized while the dynamics of the intermediate part is statistically and information-theoretically incoherent. We identify mirror symmetry in the network structure as a mechanism allowing for such behavior, and show that IMRS is robust against dynamical noise as well as against parameter changes. IMRS may underlie neuronal information processing and potentially lead to network solutions for encryption key distribution and secure communication.

Modelling the public opinion transmission on social networks under opinion leaders

NASA Astrophysics Data System (ADS)

Li, Zuozhi; Li, Meng; Ji, Wanwan

2017-06-01

In this paper, based on Social Network Analysis (SNA), the social network model of opinion leaders influencing the public opinion transmission is explored. The hot event, A Female Driver Was Beaten Due To Lane Change, has characteristics of individual short-term and non-government intervention, which is used to data extraction, and formed of the network structure on opinion leaders influencing the public opinion transmission. And the evolution mechanism are analyzed in the three evolutionary situations. Opinion leaders influence micro-blogging public opinion on social network evolution model shows that this type of network public opinion transmission is largely constrained by opinion leaders, so the opinion leaders behavior supervising on the spread of this public opinion is pivotal, and which has a guiding significance.

A Large Scale Code Resolution Service Network in the Internet of Things

PubMed Central

Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

2012-01-01

In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT's advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS. PMID:23202207

A large scale code resolution service network in the Internet of Things.

PubMed

Yu, Haining; Zhang, Hongli; Fang, Binxing; Yu, Xiangzhan

2012-11-07

In the Internet of Things a code resolution service provides a discovery mechanism for a requester to obtain the information resources associated with a particular product code immediately. In large scale application scenarios a code resolution service faces some serious issues involving heterogeneity, big data and data ownership. A code resolution service network is required to address these issues. Firstly, a list of requirements for the network architecture and code resolution services is proposed. Secondly, in order to eliminate code resolution conflicts and code resolution overloads, a code structure is presented to create a uniform namespace for code resolution records. Thirdly, we propose a loosely coupled distributed network consisting of heterogeneous, independent; collaborating code resolution services and a SkipNet based code resolution service named SkipNet-OCRS, which not only inherits DHT’s advantages, but also supports administrative control and autonomy. For the external behaviors of SkipNet-OCRS, a novel external behavior mode named QRRA mode is proposed to enhance security and reduce requester complexity. For the internal behaviors of SkipNet-OCRS, an improved query algorithm is proposed to increase query efficiency. It is analyzed that integrating SkipNet-OCRS into our resolution service network can meet our proposed requirements. Finally, simulation experiments verify the excellent performance of SkipNet-OCRS.

Collective learning for the emergence of social norms in networked multiagent systems.

PubMed

Yu, Chao; Zhang, Minjie; Ren, Fenghui

2014-12-01

Social norms such as social rules and conventions play a pivotal role in sustaining system order by regulating and controlling individual behaviors toward a global consensus in large-scale distributed systems. Systematic studies of efficient mechanisms that can facilitate the emergence of social norms enable us to build and design robust distributed systems, such as electronic institutions and norm-governed sensor networks. This paper studies the emergence of social norms via learning from repeated local interactions in networked multiagent systems. A collective learning framework, which imitates the opinion aggregation process in human decision making, is proposed to study the impact of agent local collective behaviors on the emergence of social norms in a number of different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action toward each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. Extensive experiments are carried out to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, influences of nonlearning agents, and so on. Experimental results reveal some significant insights into the manipulation and control of norm emergence in networked multiagent systems achieved through local collective behaviors.

Realistic computer network simulation for network intrusion detection dataset generation

NASA Astrophysics Data System (ADS)

Payer, Garrett

2015-05-01

The KDD-99 Cup dataset is dead. While it can continue to be used as a toy example, the age of this dataset makes it all but useless for intrusion detection research and data mining. Many of the attacks used within the dataset are obsolete and do not reflect the features important for intrusion detection in today's networks. Creating a new dataset encompassing a large cross section of the attacks found on the Internet today could be useful, but would eventually fall to the same problem as the KDD-99 Cup; its usefulness would diminish after a period of time. To continue research into intrusion detection, the generation of new datasets needs to be as dynamic and as quick as the attacker. Simply examining existing network traffic and using domain experts such as intrusion analysts to label traffic is inefficient, expensive, and not scalable. The only viable methodology is simulation using technologies including virtualization, attack-toolsets such as Metasploit and Armitage, and sophisticated emulation of threat and user behavior. Simulating actual user behavior and network intrusion events dynamically not only allows researchers to vary scenarios quickly, but enables online testing of intrusion detection mechanisms by interacting with data as it is generated. As new threat behaviors are identified, they can be added to the simulation to make quicker determinations as to the effectiveness of existing and ongoing network intrusion technology, methodology and models.

Proposed correlation of structure network inherited from producing techniques and deformation behavior for Ni-Ti-Mo metallic glasses via atomistic simulations

PubMed Central

Yang, M. H.; Li, J. H.; Liu, B. X.

2016-01-01

Based on the newly constructed n-body potential of Ni-Ti-Mo system, Molecular Dynamics and Monte Carlo simulations predict an energetically favored glass formation region and an optimal composition sub-region with the highest glass-forming ability. In order to compare the producing techniques between liquid melt quenching (LMQ) and solid-state amorphization (SSA), inherent hierarchical structure and its effect on mechanical property were clarified via atomistic simulations. It is revealed that both producing techniques exhibit no pronounced differences in the local atomic structure and mechanical behavior, while the LMQ method makes a relatively more ordered structure and a higher intrinsic strength. Meanwhile, it is found that the dominant short-order clusters of Ni-Ti-Mo metallic glasses obtained by LMQ and SSA are similar. By analyzing the structural evolution upon uniaxial tensile deformation, it is concluded that the gradual collapse of the spatial structure network is intimately correlated to the mechanical response of metallic glasses and acts as a structural signature of the initiation and propagation of shear bands. PMID:27418115

Concurrent enhancement of percolation and synchronization in adaptive networks

PubMed Central

Eom, Young-Ho; Boccaletti, Stefano; Caldarelli, Guido

2016-01-01

Co-evolutionary adaptive mechanisms are not only ubiquitous in nature, but also beneficial for the functioning of a variety of systems. We here consider an adaptive network of oscillators with a stochastic, fitness-based, rule of connectivity, and show that it self-organizes from fragmented and incoherent states to connected and synchronized ones. The synchronization and percolation are associated to abrupt transitions, and they are concurrently (and significantly) enhanced as compared to the non-adaptive case. Finally we provide evidence that only partial adaptation is sufficient to determine these enhancements. Our study, therefore, indicates that inclusion of simple adaptive mechanisms can efficiently describe some emergent features of networked systems’ collective behaviors, and suggests also self-organized ways to control synchronization and percolation in natural and social systems. PMID:27251577

The Cellular Building Blocks of Breathing

PubMed Central

Ramirez, J.M.; Doi, A.; Garcia, A.J.; Elsen, F.P.; Koch, H.; Wei, A.D.

2013-01-01

Respiratory brainstem neurons fulfill critical roles in controlling breathing: they generate the activity patterns for breathing and contribute to various sensory responses including changes in O2 and CO2. These complex sensorimotor tasks depend on the dynamic interplay between numerous cellular building blocks that consist of voltage-, calcium-, and ATP-dependent ionic conductances, various ionotropic and metabotropic synaptic mechanisms, as well as neuromodulators acting on G-protein coupled receptors and second messenger systems. As described in this review, the sensorimotor responses of the respiratory network emerge through the state-dependent integration of all these building blocks. There is no known respiratory function that involves only a small number of intrinsic, synaptic, or modulatory properties. Because of the complex integration of numerous intrinsic, synaptic, and modulatory mechanisms, the respiratory network is capable of continuously adapting to changes in the external and internal environment, which makes breathing one of the most integrated behaviors. Not surprisingly, inspiration is critical not only in the control of ventilation, but also in the context of “inspiring behaviors” such as arousal of the mind and even creativity. Far-reaching implications apply also to the underlying network mechanisms, as lessons learned from the respiratory network apply to network functions in general. PMID:23720262

Training Excitatory-Inhibitory Recurrent Neural Networks for Cognitive Tasks: A Simple and Flexible Framework

PubMed Central

Wang, Xiao-Jing

2016-01-01

The ability to simultaneously record from large numbers of neurons in behaving animals has ushered in a new era for the study of the neural circuit mechanisms underlying cognitive functions. One promising approach to uncovering the dynamical and computational principles governing population responses is to analyze model recurrent neural networks (RNNs) that have been optimized to perform the same tasks as behaving animals. Because the optimization of network parameters specifies the desired output but not the manner in which to achieve this output, “trained” networks serve as a source of mechanistic hypotheses and a testing ground for data analyses that link neural computation to behavior. Complete access to the activity and connectivity of the circuit, and the ability to manipulate them arbitrarily, make trained networks a convenient proxy for biological circuits and a valuable platform for theoretical investigation. However, existing RNNs lack basic biological features such as the distinction between excitatory and inhibitory units (Dale’s principle), which are essential if RNNs are to provide insights into the operation of biological circuits. Moreover, trained networks can achieve the same behavioral performance but differ substantially in their structure and dynamics, highlighting the need for a simple and flexible framework for the exploratory training of RNNs. Here, we describe a framework for gradient descent-based training of excitatory-inhibitory RNNs that can incorporate a variety of biological knowledge. We provide an implementation based on the machine learning library Theano, whose automatic differentiation capabilities facilitate modifications and extensions. We validate this framework by applying it to well-known experimental paradigms such as perceptual decision-making, context-dependent integration, multisensory integration, parametric working memory, and motor sequence generation. Our results demonstrate the wide range of neural activity patterns and behavior that can be modeled, and suggest a unified setting in which diverse cognitive computations and mechanisms can be studied. PMID:26928718

Thermomagnetic processing of liquid-crystalline epoxy resins and their mechanical characterization using nanoindentation.

PubMed

Li, Yuzhan; Rios, Orlando; Kessler, Michael R

2014-11-12

A thermomagnetic processing method was used to produce a biphenyl-based liquid-crystalline epoxy resin (LCER) with oriented liquid-crystalline (LC) domains. The orientation of the LCER was confirmed and quantified using two-dimensional X-ray diffraction. The effect of molecular alignment on the mechanical and thermomechanical properties of the LCER was investigated using nanoindentation and thermomechanical analysis, respectively. The effect of the orientation on the fracture behavior was also examined. The results showed that macroscopic orientation of the LC domains was achieved, resulting in an epoxy network with an anisotropic modulus, hardness, creep behavior, and thermal expansion.

From cognitive networks to seizures: Stimulus evoked dynamics in a coupled cortical network

NASA Astrophysics Data System (ADS)

Lee, Jaejin; Ermentrout, Bard; Bodner, Mark

2013-12-01

Epilepsy is one of the most common neuropathologies worldwide. Seizures arising in epilepsy or in seizure disorders are characterized generally by uncontrolled spread of excitation and electrical activity to a limited region or even over the entire cortex. While it is generally accepted that abnormal excessive firing and synchronization of neuron populations lead to seizures, little is known about the precise mechanisms underlying human epileptic seizures, the mechanisms of transitions from normal to paroxysmal activity, or about how seizures spread. Further complication arises in that seizures do not occur with a single type of dynamics but as many different phenotypes and genotypes with a range of patterns, synchronous oscillations, and time courses. The concept of preventing, terminating, or modulating seizures and/or paroxysmal activity through stimulation of brain has also received considerable attention. The ability of such stimulation to prevent or modulate such pathological activity may depend on identifiable parameters. In this work, firing rate networks with inhibitory and excitatory populations were modeled. Network parameters were chosen to model normal working memory behaviors. Two different models of cognitive activity were developed. The first model consists of a single network corresponding to a local area of the brain. The second incorporates two networks connected through sparser recurrent excitatory connectivity with transmission delays ranging from approximately 3 ms within local populations to 15 ms between populations residing in different cortical areas. The effect of excitatory stimulation to activate working memory behavior through selective persistent activation of populations is examined in the models, and the conditions and transition mechanisms through which that selective activation breaks down producing spreading paroxysmal activity and seizure states are characterized. Specifically, we determine critical parameters and architectural changes that produce the different seizure dynamics in the networks. This provides possible mechanisms for seizure generation. Because seizures arise as attractors in a multi-state system, the system may possibly be returned to its baseline state through some particular stimulation. The ability of stimulation to terminate seizure dynamics in the local and distributed models is studied. We systematically examine when this may occur and the form of the stimulation necessary for the range of seizure dynamics. In both the local and distributed network models, termination is possible for all seizure types observed by stimulation possessing some particular configuration of spatial and temporal characteristics.

Network public opinion space sentiment tendency analyze based on recurrent convolution neural network

NASA Astrophysics Data System (ADS)

Zhang, Gaowei; Xu, Lingyu; Wang, Lei

2018-04-01

The purpose of this chapter is to analyze the investor's psychological characteristics and investment decision-making behavior characteristics, to study the investor sentiment under the network public opinion, and then analyze from three aspects: First, investor sentiment analysis and how to spread in the online media; The influence mechanism of investor's emotion on the stock market and its effect; the third one is to measure the investor's emotion based on the degree of attention, trying hard to sort out the internal relations between the investor's sentiment and the network public opinion and the stock market, in order to lay the theoretical foundation of this article.

Controlling extreme events on complex networks

NASA Astrophysics Data System (ADS)

Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

2014-08-01

Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

From Actions to Habits

PubMed Central

Yin, Henry H.

2008-01-01

Recent work on the role of overlapping cerebral networks in action selection and habit formation has important implications for alcohol addiction research. As reviewed below, (1) these networks, which all involve a group of deep-brain structures called the basal ganglia, are associated with distinct behavioral control processes, such as reward-guided Pavlovian conditional responses, goal-directed instrumental actions, and stimulus-driven habits; (2) different stages of action learning are associated with different networks, which have the ability to change (i.e., plasticity); and (3) exposure to alcohol and other addictive drugs can have profound effects on these networks by influencing the mechanisms underlying neural plasticity. PMID:23584008

Cellular mechanisms underlying behavioral state-dependent bidirectional modulation of motor cortex output.

PubMed

Schiemann, Julia; Puggioni, Paolo; Dacre, Joshua; Pelko, Miha; Domanski, Aleksander; van Rossum, Mark C W; Duguid, Ian

2015-05-26

Neuronal activity in primary motor cortex (M1) correlates with behavioral state, but the cellular mechanisms underpinning behavioral state-dependent modulation of M1 output remain largely unresolved. Here, we performed in vivo patch-clamp recordings from layer 5B (L5B) pyramidal neurons in awake mice during quiet wakefulness and self-paced, voluntary movement. We show that L5B output neurons display bidirectional (i.e., enhanced or suppressed) firing rate changes during movement, mediated via two opposing subthreshold mechanisms: (1) a global decrease in membrane potential variability that reduced L5B firing rates (L5Bsuppressed neurons), and (2) a coincident noradrenaline-mediated increase in excitatory drive to a subpopulation of L5B neurons (L5Benhanced neurons) that elevated firing rates. Blocking noradrenergic receptors in forelimb M1 abolished the bidirectional modulation of M1 output during movement and selectively impaired contralateral forelimb motor coordination. Together, our results provide a mechanism for how noradrenergic neuromodulation and network-driven input changes bidirectionally modulate M1 output during motor behavior. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Development of pacemaker properties and rhythmogenic mechanisms in the mouse embryonic respiratory network

PubMed Central

Chevalier, Marc; Toporikova, Natalia; Simmers, John; Thoby-Brisson, Muriel

2016-01-01

Breathing is a vital rhythmic behavior generated by hindbrain neuronal circuitry, including the preBötzinger complex network (preBötC) that controls inspiration. The emergence of preBötC network activity during prenatal development has been described, but little is known regarding inspiratory neurons expressing pacemaker properties at embryonic stages. Here, we combined calcium imaging and electrophysiological recordings in mouse embryo brainstem slices together with computational modeling to reveal the existence of heterogeneous pacemaker oscillatory properties relying on distinct combinations of burst-generating INaP and ICAN conductances. The respective proportion of the different inspiratory pacemaker subtypes changes during prenatal development. Concomitantly, network rhythmogenesis switches from a purely INaP/ICAN-dependent mechanism at E16.5 to a combined pacemaker/network-driven process at E18.5. Our results provide the first description of pacemaker bursting properties in embryonic preBötC neurons and indicate that network rhythmogenesis undergoes important changes during prenatal development through alterations in both circuit properties and the biophysical characteristics of pacemaker neurons. DOI: http://dx.doi.org/10.7554/eLife.16125.001 PMID:27434668

Tonic nanomolar dopamine enables an activity-dependent phase recovery mechanism that persistently alters the maximal conductance of the hyperpolarization-activated current in a rhythmically active neuron.

PubMed

Rodgers, Edmund W; Fu, Jing Jing; Krenz, Wulf-Dieter C; Baro, Deborah J

2011-11-09

The phases at which network neurons fire in rhythmic motor outputs are critically important for the proper generation of motor behaviors. The pyloric network in the crustacean stomatogastric ganglion generates a rhythmic motor output wherein neuronal phase relationships are remarkably invariant across individuals and throughout lifetimes. The mechanisms for maintaining these robust phase relationships over the long-term are not well described. Here we show that tonic nanomolar dopamine (DA) acts at type 1 DA receptors (D1Rs) to enable an activity-dependent mechanism that can contribute to phase maintenance in the lateral pyloric (LP) neuron. The LP displays continuous rhythmic bursting. The activity-dependent mechanism was triggered by a prolonged decrease in LP burst duration, and it generated a persistent increase in the maximal conductance (G(max)) of the LP hyperpolarization-activated current (I(h)), but only in the presence of steady-state DA. Interestingly, micromolar DA produces an LP phase advance accompanied by a decrease in LP burst duration that abolishes normal LP network function. During a 1 h application of micromolar DA, LP phase recovered over tens of minutes because, the activity-dependent mechanism enabled by steady-state DA was triggered by the micromolar DA-induced decrease in LP burst duration. Presumably, this mechanism restored normal LP network function. These data suggest steady-state DA may enable homeostatic mechanisms that maintain motor network output during protracted neuromodulation. This DA-enabled, activity-dependent mechanism to preserve phase may be broadly relevant, as diminished dopaminergic tone has recently been shown to reduce I(h) in rhythmically active neurons in the mammalian brain.

Active Tension Network model reveals an exotic mechanical state realized in epithelial tissues

NASA Astrophysics Data System (ADS)

Noll, Nicholas; Mani, Madhav; Heemskerk, Idse; Streicha, Sebastian; Shraiman, Boris

Mechanical interactions play a crucial role in epithelial morphogenesis, yet understanding the complex mechanisms through which stress and deformation affect cell behavior remains an open problem. Here we formulate and analyze the Active Tension Network (ATN) model, which assumes that mechanical balance of cells is dominated by cortical tension and introduces tension dependent active remodeling of the cortex. We find that ATNs exhibit unusual mechanical properties: i) ATN behaves as a fluid at short times, but at long times it supports external tension, like a solid; ii) its mechanical equilibrium state has extensive degeneracy associated with a discrete conformal - ''isogonal'' - deformation of cells. ATN model predicts a constraint on equilibrium cell geometry, which we demonstrate to hold in certain epithelial tissues. We further show that isogonal modes are observed in a fruit fly embryo, accounting for the striking variability of apical area of ventral cells and helping understand the early phase of gastrulation. Living matter realizes new and exotic mechanical states, understanding which helps understand biological phenomena.

Cortical Networks for Visual Self-Recognition

NASA Astrophysics Data System (ADS)

Sugiura, Motoaki

This paper briefly reviews recent developments regarding the brain mechanisms of visual self-recognition. A special cognitive mechanism for visual self-recognition has been postulated based on behavioral and neuropsychological evidence, but its neural substrate remains controversial. Recent functional imaging studies suggest that multiple cortical mechanisms play self-specific roles during visual self-recognition, reconciling the existing controversy. Respective roles for the left occipitotemporal, right parietal, and frontal cortices in symbolic, visuospatial, and conceptual aspects of self-representation have been proposed.

In situ TEM study of lithiation behavior of silicon nanoparticles attached to and embedded in a carbon matrix.

PubMed

Gu, Meng; Li, Ying; Li, Xiaolin; Hu, Shenyang; Zhang, Xiangwu; Xu, Wu; Thevuthasan, Suntharampillai; Baer, Donald R; Zhang, Ji-Guang; Liu, Jun; Wang, Chongmin

2012-09-25

Rational design of silicon and carbon nanocomposite with a special topological feature has been demonstrated to be a feasible way for mitigating the capacity fading associated with the large volume change of silicon anode in lithium ion batteries. Although the lithiation behavior of silicon and carbon as individual components has been well understood, lithium ion transport behavior across a network of silicon and carbon is still lacking. In this paper, we probe the lithiation behavior of silicon nanoparticles attached to and embedded in a carbon nanofiber using in situ TEM and continuum mechanical calculation. We found that aggregated silicon nanoparticles show contact flattening upon initial lithiation, which is characteristically analogous to the classic sintering of powder particles by a neck-growth mechanism. As compared with the surface-attached silicon particles, particles embedded in the carbon matrix show delayed lithiation. Depending on the strength of the carbon matrix, lithiation of the embedded silicon nanoparticles can lead to the fracture of the carbon fiber. These observations provide insights on lithium ion transport in the network-structured composite of silicon and carbon and ultimately provide fundamental guidance for mitigating the failure of batteries due to the large volume change of silicon anodes.

An Architectural Concept for Intrusion Tolerance in Air Traffic Networks

NASA Technical Reports Server (NTRS)

Maddalon, Jeffrey M.; Miner, Paul S.

2003-01-01

The goal of an intrusion tolerant network is to continue to provide predictable and reliable communication in the presence of a limited num ber of compromised network components. The behavior of a compromised network component ranges from a node that no longer responds to a nod e that is under the control of a malicious entity that is actively tr ying to cause other nodes to fail. Most current data communication ne tworks do not include support for tolerating unconstrained misbehavio r of components in the network. However, the fault tolerance communit y has developed protocols that provide both predictable and reliable communication in the presence of the worst possible behavior of a limited number of nodes in the system. One may view a malicious entity in a communication network as a node that has failed and is behaving in an arbitrary manner. NASA/Langley Research Center has developed one such fault-tolerant computing platform called SPIDER (Scalable Proces sor-Independent Design for Electromagnetic Resilience). The protocols and interconnection mechanisms of SPIDER may be adapted to large-sca le, distributed communication networks such as would be required for future Air Traffic Management systems. The predictability and reliabi lity guarantees provided by the SPIDER protocols have been formally v erified. This analysis can be readily adapted to similar network stru ctures.

Transient response of nonlinear polymer networks: A kinetic theory

NASA Astrophysics Data System (ADS)

Vernerey, Franck J.

2018-06-01

Dynamic networks are found in a majority of natural materials, but also in engineering materials, such as entangled polymers and physically cross-linked gels. Owing to their transient bond dynamics, these networks display a rich class of behaviors, from elasticity, rheology, self-healing, or growth. Although classical theories in rheology and mechanics have enabled us to characterize these materials, there is still a gap in our understanding on how individuals (i.e., the mechanics of each building blocks and its connection with others) affect the emerging response of the network. In this work, we introduce an alternative way to think about these networks from a statistical point of view. More specifically, a network is seen as a collection of individual polymer chains connected by weak bonds that can associate and dissociate over time. From the knowledge of these individual chains (elasticity, transient attachment, and detachment events), we construct a statistical description of the population and derive an evolution equation of their distribution based on applied deformation and their local interactions. We specifically concentrate on nonlinear elastic response that follows from the strain stiffening response of individual chains of finite size. Upon appropriate averaging operations and using a mean field approximation, we show that the distribution can be replaced by a so-called chain distribution tensor that is used to determine important macroscopic measures such as stress, energy storage and dissipation in the network. Prediction of the kinetic theory are then explored against known experimental measurement of polymer responses under uniaxial loading. It is found that even under the simplest assumptions of force-independent chain kinetics, the model is able to reproduce complex time-dependent behaviors of rubber and self-healing supramolecular polymers.

Memory Transmission in Small Groups and Large Networks: An Agent-Based Model.

PubMed

Luhmann, Christian C; Rajaram, Suparna

2015-12-01

The spread of social influence in large social networks has long been an interest of social scientists. In the domain of memory, collaborative memory experiments have illuminated cognitive mechanisms that allow information to be transmitted between interacting individuals, but these experiments have focused on small-scale social contexts. In the current study, we took a computational approach, circumventing the practical constraints of laboratory paradigms and providing novel results at scales unreachable by laboratory methodologies. Our model embodied theoretical knowledge derived from small-group experiments and replicated foundational results regarding collaborative inhibition and memory convergence in small groups. Ultimately, we investigated large-scale, realistic social networks and found that agents are influenced by the agents with which they interact, but we also found that agents are influenced by nonneighbors (i.e., the neighbors of their neighbors). The similarity between these results and the reports of behavioral transmission in large networks offers a major theoretical insight by linking behavioral transmission to the spread of information. © The Author(s) 2015.

Converging models of schizophrenia - Network alterations of prefrontal cortex underlying cognitive impairments

PubMed Central

Sakurai, Takeshi; Gamo, Nao J; Hikida, Takatoshi; Kim, Sun-Hong; Murai, Toshiya; Tomoda, Toshifumi; Sawa, Akira

2015-01-01

The prefrontal cortex (PFC) and its connections with other brain areas are crucial for cognitive function. Cognitive impairments are one of the core symptoms associated with schizophrenia, and manifest even before the onset of the disorder. Altered neural networks involving PFC contribute to cognitive impairments in schizophrenia. Both genetic and environmental risk factors affect the development of the local circuitry within PFC as well as development of broader brain networks, and make the system vulnerable to further insults during adolescence, leading to the onset of the disorder in young adulthood. Since spared cognitive functions correlate with functional outcome and prognosis, a better understanding of the mechanisms underlying cognitive impairments will have important implications for novel therapeutics for schizophrenia focusing on cognitive functions. Multidisciplinary approaches, from basic neuroscience to clinical studies, are required to link molecules, circuitry, networks, and behavioral phenotypes. Close interactions among such fields by sharing a common language on connectomes, behavioral readouts, and other concepts are crucial for this goal. PMID:26408506

Cortisol, salivary alpha-amylase and children's perceptions of their social networks.

PubMed

Ponzi, Davide; Muehlenbein, Michael P; Geary, David C; Flinn, Mark V

2016-01-01

In recent years there has been a growing interest in the use of social network analysis in biobehavioral research. Despite the well-established importance of social relationships in influencing human behavior and health, little is known about how children's perception of their immediate social relationships correlates with biological parameters of stress. In this study we explore the association between two measures of children's personal social networks, perceived network size and perceived network density, with two biomarkers of stress, cortisol and salivary alpha-amylase. Forty children (mean age = 8.30, min age = 5, and max age = 12) were interviewed to collect information about their friendships and three samples of saliva were collected. Our results show that children characterized by a lower pre-interview cortisol concentration and a lower salivary alpha-amylase reactivity to the interview reported the highest density of friendships. We discuss this result in light of the multisystem approach to the study of children's behavioral outcomes, emphasizing that future work of this kind is needed in order to understand the cognitive and biological mechanisms underlying children's and adolescents' social perceptual biases.

Mechanism of signal propagation in Physarum polycephalum.

PubMed

Alim, Karen; Andrew, Natalie; Pringle, Anne; Brenner, Michael P

2017-05-16

Complex behaviors are typically associated with animals, but the capacity to integrate information and function as a coordinated individual is also a ubiquitous but poorly understood feature of organisms such as slime molds and fungi. Plasmodial slime molds grow as networks and use flexible, undifferentiated body plans to forage for food. How an individual communicates across its network remains a puzzle, but Physarum polycephalum has emerged as a novel model used to explore emergent dynamics. Within P. polycephalum , cytoplasm is shuttled in a peristaltic wave driven by cross-sectional contractions of tubes. We first track P. polycephalum 's response to a localized nutrient stimulus and observe a front of increased contraction. The front propagates with a velocity comparable to the flow-driven dispersion of particles. We build a mathematical model based on these data and in the aggregate experiments and model identify the mechanism of signal propagation across a body: The nutrient stimulus triggers the release of a signaling molecule. The molecule is advected by fluid flows but simultaneously hijacks flow generation by causing local increases in contraction amplitude as it travels. The molecule is initiating a feedback loop to enable its own movement. This mechanism explains previously puzzling phenomena, including the adaptation of the peristaltic wave to organism size and P. polycephalum 's ability to find the shortest route between food sources. A simple feedback seems to give rise to P. polycephalum 's complex behaviors, and the same mechanism is likely to function in the thousands of additional species with similar behaviors.

Hybrid networks based on epoxidized camelina oil

PubMed Central

Balanuca, Brindusa; Stan, Raluca; Lungu, Adriana; Vasile, Eugeniu; Iovu, Horia

2017-01-01

Abstract Lately, renewable resources received great attention in the macromolecular compounds area, regarding the design of the monomers and polymers with different applications. In this study the capacity of several modified vegetable oil-based monomers to build competitive hybrid networks was investigate, taking into account thermal and mechanical behavior of the designed materials. In order to synthesize such competitive nanocomposites, the selected renewable raw material, camelina oil, was employed due to the non-toxicity and biodegradability behavior. General properties of epoxidized camelina oil-based materials were improved by loading of different types of organic-inorganic hybrid compounds – polyhedral oligomeric silsesquioxane (POSS) bearing one (POSS1Ep) or eight (POSS8Ep) epoxy rings on the cages. In order to identify the chemical changes occurring after the thermal curing reactions, FT-IR spectrometry was employed. The new synthesized nanocomposites based on epoxidized camelina oil (ECO) were characterized by dynamic mechanical analyze and thermogravimetric analyze. The morphology of the ECO-based materials was investigate by scanning electron microscopy and supplementary information regarding the presence of the POSS compounds were establish by energy dispersive X-ray analysis and X-ray photoelectron spectroscopy. The smooth materials without any separation phase indicates a well dispersion of the Si–O–Si cages within the organic matrix and the incorporation of this hybrid compounds into the ECO network demonstrates to be a well strategy to improve the thermal and mechanical properties, simultaneously. PMID:29491775

Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

PubMed

Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

PubMed Central

Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

2014-01-01

The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

Quantitative analysis of bloggers' collective behavior powered by emotions

NASA Astrophysics Data System (ADS)

Mitrović, Marija; Paltoglou, Georgios; Tadić, Bosiljka

2011-02-01

Large-scale data resulting from users' online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms involving emotions expressed in the posted text play a role. Here we combine approaches of statistical physics with machine-learning methods of text analysis to study the emergence of emotional behavior among Web users. Mapping the high-resolution data from digg.com onto bipartite networks of users and their comments onto posted stories, we identify user communities centered around certain popular posts and determine emotional contents of the related comments by the emotion classifier developed for this type of text. Applied over different time periods, this framework reveals strong correlations between the excess of negative emotions and the evolution of communities. We observe avalanches of emotional comments exhibiting significant self-organized critical behavior and temporal correlations. To explore the robustness of these critical states, we design a network-automaton model on realistic network connections and several control parameters, which can be inferred from the dataset. Dissemination of emotions by a small fraction of very active users appears to critically tune the collective states.

Cognitive conflict without explicit conflict monitoring in a dynamical agent.

PubMed

Ward, Robert; Ward, Ronnie

2006-11-01

We examine mechanisms for resolving cognitive conflict in an embodied, situated, and dynamic agent, developed through an evolutionary learning process. The agent was required to solve problems of response conflict in a dual-target "catching" task, focusing response on one of the targets while ignoring the other. Conflict in the agent was revealed at the behavioral level in terms of increased latencies to the second target. This behavioral interference was correlated to peak violations of the network's stable state equation. At the level of the agent's neural network, peak violations were also correlated to periods of disagreement in source inputs to the agent's motor effectors. Despite observing conflict at these numerous levels, we did not find any explicit conflict monitoring mechanisms within the agent. We instead found evidence of a distributed conflict management system, characterized by competitive sources within the network. In contrast to the conflict monitoring hypothesis [Botvinick, M. M., Braver, T. S., Barch, D. M., Carter, C. S., & Cohen, J. D. (2001). Conflict monitoring and cognitive control. Psychological Review, 108(3), 624-652], this agent demonstrates that resolution of cognitive conflict does not require explicit conflict monitoring. We consider the implications of our results for the conflict monitoring hypothesis.

Frontal Theta Dynamics during Response Conflict in Long-Term Mindfulness Meditators

PubMed Central

Jo, Han-Gue; Malinowski, Peter; Schmidt, Stefan

2017-01-01

Mindfulness meditators often show greater efficiency in resolving response conflicts than non-meditators. However, the neural mechanisms underlying the improved behavioral efficiency are unclear. Here, we investigated frontal theta dynamics—a neural mechanism involved in cognitive control processes—in long-term mindfulness meditators. The dynamics of EEG theta oscillations (4–8 Hz) recorded over the medial frontal cortex (MFC) were examined in terms of their power (MFC theta power) and their functional connectivity with other brain areas (the MFC-centered theta network). Using a flanker-type paradigm, EEG data were obtained from 22 long-term mindfulness meditators and compared to those from 23 matched controls without meditation experience. Meditators showed more efficient cognitive control after conflicts, evidenced by fewer error responses irrespective of response timing. Furthermore, meditators exhibited enhanced conflict modulations of the MFC-centered theta network shortly before the response, in particular for the functional connection between the MFC and the motor cortex. In contrast, MFC theta power was comparable between groups. These results suggest that the higher behavioral efficiency after conflicts in mindfulness meditators could be a function of increased engagement to control the motor system in association with the MFC-centered theta network. PMID:28638334

A discrete mathematical model applied to genetic regulation and metabolic networks.

PubMed

Asenjo, A J; Ramirez, P; Rapaport, I; Aracena, J; Goles, E; Andrews, B A

2007-03-01

This paper describes the use of a discrete mathematical model to represent the basic mechanisms of regulation of the bacteria E. coli in batch fermentation. The specific phenomena studied were the changes in metabolism and genetic regulation when the bacteria use three different carbon substrates (glucose, glycerol, and acetate). The model correctly predicts the behavior of E. coli vis-à-vis substrate mixtures. In a mixture of glucose, glycerol, and acetate, it prefers glucose, then glycerol, and finally acetate. The model included 67 nodes; 28 were genes, 20 enzymes, and 19 regulators/biochemical compounds. The model represents both the genetic regulation and metabolic networks in an inrtegrated form, which is how they function biologically. This is one of the first attempts to include both of these networks in one model. Previously, discrete mathematical models were used only to describe genetic regulation networks. The study of the network dynamics generated 8 (2(3)) fixed points, one for each nutrient configuration (substrate mixture) in the medium. The fixed points of the discrete model reflect the phenotypes described. Gene expression and the patterns of the metabolic fluxes generated are described accurately. The activation of the gene regulation network depends basically on the presence of glucose and glycerol. The model predicts the behavior when mixed carbon sources are utilized as well as when there is no carbon source present. Fictitious jokers (Joker1, Joker2, and Repressor SdhC) had to be created to control 12 genes whose regulation mechanism is unknown, since glycerol and glucose do not act directly on the genes. The approach presented in this paper is particularly useful to investigate potential unknown gene regulation mechanisms; such a novel approach can also be used to describe other gene regulation situations such as the comparison between non-recombinant and recombinant yeast strain, producing recombinant proteins, presently under investigation in our group.

From Physician to Consumer: The Effectiveness of Strategies to Manage Health Care Utilization

PubMed Central

Flynn, Kathryn E.; Smith, Maureen A.; Davis, Margaret K.

2006-01-01

Many strategies are commonly used to influence physician behavior in managed care organizations. This review examines the effectiveness of three mechanisms to influence physician behavior: financial incentives directed at providers or patients, policies/procedures for managing care, and the selection/education of both providers and patients. We reach three conclusions. First, all health care systems use financial incentives, but these mechanisms are shifting away from financial incentives directed at the physician to those directed at the consumer. Second, heavily procedural strategies such as utilization review and gatekeeping show some evidence of effectiveness but are highly unpopular due to their restrictions on physician and patient choice. Third, a future system built on consumer choice is contradicted by mechanisms that rely solely on narrow networks of providers or the education of physicians. If patients become the new locus of decision-making in health care, provider-focused mechanisms to influence physician behavior will not disappear but are likely to decline in importance. PMID:12508705

Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment

PubMed Central

Yamashita, Yuichi; Tani, Jun

2008-01-01

It is generally thought that skilled behavior in human beings results from a functional hierarchy of the motor control system, within which reusable motor primitives are flexibly integrated into various sensori-motor sequence patterns. The underlying neural mechanisms governing the way in which continuous sensori-motor flows are segmented into primitives and the way in which series of primitives are integrated into various behavior sequences have, however, not yet been clarified. In earlier studies, this functional hierarchy has been realized through the use of explicit hierarchical structure, with local modules representing motor primitives in the lower level and a higher module representing sequences of primitives switched via additional mechanisms such as gate-selecting. When sequences contain similarities and overlap, however, a conflict arises in such earlier models between generalization and segmentation, induced by this separated modular structure. To address this issue, we propose a different type of neural network model. The current model neither makes use of separate local modules to represent primitives nor introduces explicit hierarchical structure. Rather than forcing architectural hierarchy onto the system, functional hierarchy emerges through a form of self-organization that is based on two distinct types of neurons, each with different time properties (“multiple timescales”). Through the introduction of multiple timescales, continuous sequences of behavior are segmented into reusable primitives, and the primitives, in turn, are flexibly integrated into novel sequences. In experiments, the proposed network model, coordinating the physical body of a humanoid robot through high-dimensional sensori-motor control, also successfully situated itself within a physical environment. Our results suggest that it is not only the spatial connections between neurons but also the timescales of neural activity that act as important mechanisms leading to functional hierarchy in neural systems. PMID:18989398

Cognitive Vulnerability to Major Depression: View from the Intrinsic Network and Cross-network Interactions

PubMed Central

Wang, Xiang; Öngür, Dost; Auerbach, Randy P.; Yao, Shuqiao

2016-01-01

Abstract Although it is generally accepted that cognitive factors contribute to the pathogenesis of major depressive disorder (MDD), there are missing links between behavioral and biological models of depression. Nevertheless, research employing neuroimaging technologies has elucidated some of the neurobiological mechanisms related to cognitive-vulnerability factors, especially from a whole-brain, dynamic perspective. In this review, we integrate well-established cognitive-vulnerability factors for MDD and corresponding neural mechanisms in intrinsic networks using a dual-process framework. We propose that the dynamic alteration and imbalance among the intrinsic networks, both in the resting-state and the rest-task transition stages, contribute to the development of cognitive vulnerability and MDD. Specifically, we propose that abnormally increased resting-state default mode network (DMN) activity and connectivity (mainly in anterior DMN regions) contribute to the development of cognitive vulnerability. Furthermore, when subjects confront negative stimuli in the period of rest-to-task transition, the following three kinds of aberrant network interactions have been identified as facilitators of vulnerability and dysphoric mood, each through a different cognitive mechanism: DMN dominance over the central executive network (CEN), an impaired salience network–mediated switching between the DMN and CEN, and ineffective CEN modulation of the DMN. This focus on interrelated networks and brain-activity changes between rest and task states provides a neural-system perspective for future research on cognitive vulnerability and resilience, and may potentially guide the development of new intervention strategies for MDD. PMID:27148911

Analysis of Network Topologies Underlying Ethylene Growth Response Kinetics

PubMed Central

Prescott, Aaron M.; McCollough, Forest W.; Eldreth, Bryan L.; Binder, Brad M.; Abel, Steven M.

2016-01-01

Most models for ethylene signaling involve a linear pathway. However, measurements of seedling growth kinetics when ethylene is applied and removed have resulted in more complex network models that include coherent feedforward, negative feedback, and positive feedback motifs. The dynamical responses of the proposed networks have not been explored in a quantitative manner. Here, we explore (i) whether any of the proposed models are capable of producing growth-response behaviors consistent with experimental observations and (ii) what mechanistic roles various parts of the network topologies play in ethylene signaling. To address this, we used computational methods to explore two general network topologies: The first contains a coherent feedforward loop that inhibits growth and a negative feedback from growth onto itself (CFF/NFB). In the second, ethylene promotes the cleavage of EIN2, with the product of the cleavage inhibiting growth and promoting the production of EIN2 through a positive feedback loop (PFB). Since few network parameters for ethylene signaling are known in detail, we used an evolutionary algorithm to explore sets of parameters that produce behaviors similar to experimental growth response kinetics of both wildtype and mutant seedlings. We generated a library of parameter sets by independently running the evolutionary algorithm many times. Both network topologies produce behavior consistent with experimental observations, and analysis of the parameter sets allows us to identify important network interactions and parameter constraints. We additionally screened these parameter sets for growth recovery in the presence of sub-saturating ethylene doses, which is an experimentally-observed property that emerges in some of the evolved parameter sets. Finally, we probed simplified networks maintaining key features of the CFF/NFB and PFB topologies. From this, we verified observations drawn from the larger networks about mechanisms underlying ethylene signaling. Analysis of each network topology results in predictions about changes that occur in network components that can be experimentally tested to give insights into which, if either, network underlies ethylene responses. PMID:27625669

Mechanical output of myosin II motors is regulated by myosin filament size and actin network mechanics

NASA Astrophysics Data System (ADS)

Stam, Samantha; Alberts, Jonathan; Gardel, Margaret; Munro, Edwin

2013-03-01

The interactions of bipolar myosin II filaments with actin arrays are a predominate means of generating forces in numerous physiological processes including muscle contraction and cell migration. However, how the spatiotemporal regulation of these forces depends on motor mechanochemistry, bipolar filament size, and local actin mechanics is unknown. Here, we simulate myosin II motors with an agent-based model in which the motors have been benchmarked against experimental measurements. Force generation occurs in two distinct regimes characterized either by stable tension maintenance or by stochastic buildup and release; transitions between these regimes occur by changes to duty ratio and myosin filament size. The time required for building force to stall scales inversely with the stiffness of a network and the actin gliding speed of a motor. Finally, myosin motors are predicted to contract a network toward stiffer regions, which is consistent with experimental observations. Our representation of myosin motors can be used to understand how their mechanical and biochemical properties influence their observed behavior in a variety of in vitro and in vivo contexts.

Finding Influential Spreaders from Human Activity beyond Network Location.

PubMed

Min, Byungjoon; Liljeros, Fredrik; Makse, Hernán A

2015-01-01

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.

Quality of service policy control in virtual private networks

NASA Astrophysics Data System (ADS)

Yu, Yiqing; Wang, Hongbin; Zhou, Zhi; Zhou, Dongru

2004-04-01

This paper studies the QoS of VPN in an environment where the public network prices connection-oriented services based on source, destination and grade of service, and advertises these prices to its VPN customers (users). As different QoS technologies can produce different QoS, there are according different traffic classification rules and priority rules. The internet service provider (ISP) may need to build complex mechanisms separately for each node. In order to reduce the burden of network configuration, we need to design policy control technologies. We considers mainly directory server, policy server, policy manager and policy enforcers. Policy decision point (PDP) decide its control according to policy rules. In network, policy enforce point (PEP) decide its network controlled unit. For InterServ and DiffServ, we will adopt different policy control methods as following: (1) In InterServ, traffic uses resource reservation protocol (RSVP) to guarantee the network resource. (2) In DiffServ, policy server controls the DiffServ code points and per hop behavior (PHB), its PDP distributes information to each network node. Policy server will function as following: information searching; decision mechanism; decision delivering; auto-configuration. In order to prove the effectiveness of QoS policy control, we make the corrective simulation.

Evolution of the social network of scientific collaborations

NASA Astrophysics Data System (ADS)

Barabási, A. L.; Jeong, H.; Néda, Z.; Ravasz, E.; Schubert, A.; Vicsek, T.

2002-08-01

The co-authorship network of scientists represents a prototype of complex evolving networks. In addition, it offers one of the most extensive database to date on social networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an 8-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. Three complementary approaches allow us to obtain a detailed characterization. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. In some limits the model can be solved analytically, predicting a two-regime scaling in agreement with the measurements. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically. The combined numerical and analytical results underline the important role internal links play in determining the observed scaling behavior and network topology. The results and methodologies developed in the context of the co-authorship network could be useful for a systematic study of other complex evolving networks as well, such as the world wide web, Internet, or other social networks.

Discrimination of Complex Human Behavior by Pigeons (Columba livia) and Humans

PubMed Central

Qadri, Muhammad A. J.; Sayde, Justin M.; Cook, Robert G.

2014-01-01

The cognitive and neural mechanisms for recognizing and categorizing behavior are not well understood in non-human animals. In the current experiments, pigeons and humans learned to categorize two non-repeating, complex human behaviors (“martial arts” vs. “Indian dance”). Using multiple video exemplars of a digital human model, pigeons discriminated these behaviors in a go/no-go task and humans in a choice task. Experiment 1 found that pigeons already experienced with discriminating the locomotive actions of digital animals acquired the discrimination more rapidly when action information was available than when only pose information was available. Experiments 2 and 3 found this same dynamic superiority effect with naïve pigeons and human participants. Both species used the same combination of immediately available static pose information and more slowly perceived dynamic action cues to discriminate the behavioral categories. Theories based on generalized visual mechanisms, as opposed to embodied, species-specific action networks, offer a parsimonious account of how these different animals recognize behavior across and within species. PMID:25379777

SAINT: A combined simulation language for modeling man-machine systems

NASA Technical Reports Server (NTRS)

Seifert, D. J.

1979-01-01

SAINT (Systems Analysis of Integrated Networks of Tasks) is a network modeling and simulation technique for design and analysis of complex man machine systems. SAINT provides the conceptual framework for representing systems that consist of discrete task elements, continuous state variables, and interactions between them. It also provides a mechanism for combining human performance models and dynamic system behaviors in a single modeling structure. The SAINT technique is described and applications of the SAINT are discussed.

High-alpha band synchronization across frontal, parietal and visual cortex mediates behavioral and neuronal effects of visuospatial attention.

PubMed

Lobier, Muriel; Palva, J Matias; Palva, Satu

2018-01-15

Visuospatial attention prioritizes processing of attended visual stimuli. It is characterized by lateralized alpha-band (8-14 Hz) amplitude suppression in visual cortex and increased neuronal activity in a network of frontal and parietal areas. It has remained unknown what mechanisms coordinate neuronal processing among frontoparietal network and visual cortices and implement the attention-related modulations of alpha-band amplitudes and behavior. We investigated whether large-scale network synchronization could be such a mechanism. We recorded human cortical activity with magnetoencephalography (MEG) during a visuospatial attention task. We then identified the frequencies and anatomical networks of inter-areal phase synchronization from source localized MEG data. We found that visuospatial attention is associated with robust and sustained long-range synchronization of cortical oscillations exclusively in the high-alpha (10-14 Hz) frequency band. This synchronization connected frontal, parietal and visual regions and was observed concurrently with amplitude suppression of low-alpha (6-9 Hz) band oscillations in visual cortex. Furthermore, stronger high-alpha phase synchronization was associated with decreased reaction times to attended stimuli and larger suppression of alpha-band amplitudes. These results thus show that high-alpha band phase synchronization is functionally significant and could coordinate the neuronal communication underlying the implementation of visuospatial attention. Copyright © 2017 Elsevier Inc. All rights reserved.

Integrated analysis of genetic, behavioral, and biochemical data implicates neural stem cell-induced changes in immunity, neurotransmission and mitochondrial function in Dementia with Lewy Body mice.

PubMed

Lakatos, Anita; Goldberg, Natalie R S; Blurton-Jones, Mathew

2017-03-10

We previously demonstrated that transplantation of murine neural stem cells (NSCs) can improve motor and cognitive function in a transgenic model of Dementia with Lewy Bodies (DLB). These benefits occurred without changes in human α-synuclein pathology and were mediated in part by stem cell-induced elevation of brain-derived neurotrophic factor (BDNF). However, instrastriatal NSC transplantation likely alters the brain microenvironment via multiple mechanisms that may synergize to promote cognitive and motor recovery. The underlying neurobiology that mediates such restoration no doubt involves numerous genes acting in concert to modulate signaling within and between host brain cells and transplanted NSCs. In order to identify functionally connected gene networks and additional mechanisms that may contribute to stem cell-induced benefits, we performed weighted gene co-expression network analysis (WGCNA) on striatal tissue isolated from NSC- and vehicle-injected wild-type and DLB mice. Combining continuous behavioral and biochemical data with genome wide expression via network analysis proved to be a powerful approach; revealing significant alterations in immune response, neurotransmission, and mitochondria function. Taken together, these data shed further light on the gene network and biological processes that underlie the therapeutic effects of NSC transplantation on α-synuclein induced cognitive and motor impairments, thereby highlighting additional therapeutic targets for synucleinopathies.

Reputation-Based Investment Helps to Optimize Group Behaviors in Spatial Lattice Networks.

PubMed

Ding, Hong; Cao, Lin; Ren, Yizhi; Choo, Kim-Kwang Raymond; Shi, Benyun

2016-01-01

Encouraging cooperation among selfish individuals is crucial in many real-world systems, where individuals' collective behaviors can be analyzed using evolutionary public goods game. Along this line, extensive studies have shown that reputation is an effective mechanism to investigate the evolution of cooperation. In most existing studies, participating individuals in a public goods game are assumed to contribute unconditionally into the public pool, or they can choose partners based on a common reputation standard (e.g., preferences or characters). However, to assign one reputation standard for all individuals is impractical in many real-world deployment. In this paper, we introduce a reputation tolerance mechanism that allows an individual to select its potential partners and decide whether or not to contribute an investment to the public pool based on its tolerance to other individuals' reputation. Specifically, an individual takes part in a public goods game only if the number of participants with higher reputation exceeds the value of its tolerance. Moreover, in this paper, an individual's reputation can increase or decrease in a bounded interval based on its historical behaviors. We explore the principle that how the reputation tolerance and conditional investment mechanisms can affect the evolution of cooperation in spatial lattice networks. Our simulation results demonstrate that a larger tolerance value can achieve an environment that promote the cooperation of participants.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model.

PubMed

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well.

Active, motor-driven mechanics in a DNA gel.

PubMed

Bertrand, Olivier J N; Fygenson, Deborah Kuchnir; Saleh, Omar A

2012-10-23

Cells are capable of a variety of dramatic stimuli-responsive mechanical behaviors. These capabilities are enabled by the pervading cytoskeletal network, an active gel composed of structural filaments (e.g., actin) that are acted upon by motor proteins (e.g., myosin). Here, we describe the synthesis and characterization of an active gel using noncytoskeletal components. We use methods of base-pair-templated DNA self assembly to create a hybrid DNA gel containing stiff tubes and flexible linkers. We then activate the gel by adding the motor FtsK50C, a construct derived from the bacterial protein FtsK that, in vitro, has a strong and processive DNA contraction activity. The motors stiffen the gel and create stochastic contractile events that affect the positions of attached beads. We quantify the fluctuations of the beads and show that they are comparable both to measurements of cytoskeletal systems and to theoretical predictions for active gels. Thus, we present a DNA-based active gel whose behavior highlights the universal aspects of nonequilibrium, motor-driven networks.

Chronic stress and brain plasticity: mechanisms underlying adaptive and maladaptive changes and implications for stress-related CNS disorders

PubMed Central

Radley, Jason; Morilak, David; Viau, Victor; Campeau, Serge

2015-01-01

Stress responses entail neuroendocrine, autonomic, and behavioral changes to promote effective coping with real or perceived threats to one’s safety. While these responses are critical for the survival of the individual, adverse effects of repeated exposure to stress are widely known to have deleterious effects on health. Thus, a considerable effort in the search for treatments to stress-related CNS disorders necessitates unraveling the brain mechanisms responsible for adaptation under acute conditions and their perturbations following chronic stress exposure. This paper is based upon a symposium from the 2014 International Behavioral Neuroscience Meeting, summarizing some recent advances in understanding the effects of stress on adaptive and maladaptive responses subserved by limbic forebrain networks. An important theme highlighted in this review is that the same networks mediating neuroendocrine, autonomic, and behavioral processes during adaptive coping also comprise targets of the effects of repeated stress exposure in the development of maladaptive states. Where possible, reference is made to the similarity of neurobiological substrates and effects observed following repeated exposure to stress in laboratory animals and the clinical features of stress-related disorders in humans. PMID:26116544

Long-term Behavior of Hydrocarbon Production Curves

NASA Astrophysics Data System (ADS)

Lovell, A.; Karra, S.; O'Malley, D.; Viswanathan, H. S.; Srinivasan, G.

2017-12-01

Recovering hydrocarbons (such as natural gas) from naturally-occurring formations with low permeability has had a huge impact on the energy sector, however, recovery rates are low due to poor understanding of recovery and transport mechanisms [1]. The physical mechanisms that control the production of hydrocarbon are only partially understood. Calculations have shown that the short-term behavior in the peak of the production curve is understood to come from the free hydrocarbons in the fracture networks, but the long-term behavior of these curves is often underpredicted [2]. This behavior is thought to be due to small scale processes - such as matrix diffusion, desorption, and connectivity in the damage region around the large fracture network. In this work, we explore some of these small-scale processes using discrete fracture networks (DFN) and the toolkit dfnWorks [3], the matrix diffusion, size of the damage region, and distribution of free gas between the fracture networks and rock matrix. Individual and combined parameter spaces are explored, and comparisons of the resulting production curves are made to experimental site data from the Haynesville formation [4]. We find that matrix diffusion significantly controls the shape of the tail of the production curve, while the distribution of free gas impacts the relative magnitude of the peak to the tail. The height of the damage region has no effect on the shape of the tail. Understanding the constrains of the parameter space based on site data is the first step in rigorously quantifying the uncertainties coming from these types of systems, which can in turn optimize and improve hydrocarbon recovery. [1] C. McGlade, et. al., (2013) Methods of estimating shale gas resources - comparison, evaluation, and implications, Energy, 59, 116-125 [2] S. Karra, et. al., (2015) Effect of advective flow in fractures and matrix diffusion on natural gas production, Water Resources Research, 51(10), 8646-8657 [3] J.D. Hyman, et. al., (2015) dfnworks: A discrete fracture network framework for modeling subsurface flow and transport, Computers & Geosciences, 84, 10-19 [4] E.J. Moniz, et. al., (2011) The future of natural gas, Cambridge, MA, Massachusetts Institute of Technology

Modeling Behavioral Experiment Interaction and Environmental Stimuli for a Synthetic C. elegans.

PubMed

Mujika, Andoni; Leškovský, Peter; Álvarez, Roberto; Otaduy, Miguel A; Epelde, Gorka

2017-01-01

This paper focusses on the simulation of the neural network of the Caenorhabditis elegans living organism, and more specifically in the modeling of the stimuli applied within behavioral experiments and the stimuli that is generated in the interaction of the C. elegans with the environment. To the best of our knowledge, all efforts regarding stimuli modeling for the C. elegans are focused on a single type of stimulus, which is usually tested with a limited subnetwork of the C. elegans neural system. In this paper, we follow a different approach where we model a wide-range of different stimuli, with more flexible neural network configurations and simulations in mind. Moreover, we focus on the stimuli sensation by different types of sensory organs or various sensory principles of the neurons. As part of this work, most common stimuli involved in behavioral assays have been modeled. It includes models for mechanical, thermal, chemical, electrical and light stimuli, and for proprioception-related self-sensed information exchange with the neural network. The developed models have been implemented and tested with the hardware-based Si elegans simulation platform.

Modeling Behavioral Experiment Interaction and Environmental Stimuli for a Synthetic C. elegans

PubMed Central

Mujika, Andoni; Leškovský, Peter; Álvarez, Roberto; Otaduy, Miguel A.; Epelde, Gorka

2017-01-01

This paper focusses on the simulation of the neural network of the Caenorhabditis elegans living organism, and more specifically in the modeling of the stimuli applied within behavioral experiments and the stimuli that is generated in the interaction of the C. elegans with the environment. To the best of our knowledge, all efforts regarding stimuli modeling for the C. elegansare focused on a single type of stimulus, which is usually tested with a limited subnetwork of the C. elegansneural system. In this paper, we follow a different approach where we model a wide-range of different stimuli, with more flexible neural network configurations and simulations in mind. Moreover, we focus on the stimuli sensation by different types of sensory organs or various sensory principles of the neurons. As part of this work, most common stimuli involved in behavioral assays have been modeled. It includes models for mechanical, thermal, chemical, electrical and light stimuli, and for proprioception-related self-sensed information exchange with the neural network. The developed models have been implemented and tested with the hardware-based Si elegans simulation platform. PMID:29276485

Modeling Citation Networks Based on Vigorousness and Dormancy

NASA Astrophysics Data System (ADS)

Wang, Xue-Wen; Zhang, Li-Jie; Yang, Guo-Hong; Xu, Xin-Jian

2013-08-01

In citation networks, the activity of papers usually decreases with age and dormant papers may be discovered and become fashionable again. To model this phenomenon, a competition mechanism is suggested which incorporates two factors: vigorousness and dormancy. Based on this idea, a citation network model is proposed, in which a node has two discrete stage: vigorous and dormant. Vigorous nodes can be deactivated and dormant nodes may be activated and become vigorous. The evolution of the network couples addition of new nodes and state transitions of old ones. Both analytical calculation and numerical simulation show that the degree distribution of nodes in generated networks displays a good right-skewed behavior. Particularly, scale-free networks are obtained as the deactivated vertex is target selected and exponential networks are realized for the random-selected case. Moreover, the measurement of four real-world citation networks achieves a good agreement with the stochastic model.

A model of the regulatory network involved in the control of the cell cycle and cell differentiation in the Caenorhabditis elegans vulva.

PubMed

Weinstein, Nathan; Ortiz-Gutiérrez, Elizabeth; Muñoz, Stalin; Rosenblueth, David A; Álvarez-Buylla, Elena R; Mendoza, Luis

2015-03-13

There are recent experimental reports on the cross-regulation between molecules involved in the control of the cell cycle and the differentiation of the vulval precursor cells (VPCs) of Caenorhabditis elegans. Such discoveries provide novel clues on how the molecular mechanisms involved in the cell cycle and cell differentiation processes are coordinated during vulval development. Dynamic computational models are helpful to understand the integrated regulatory mechanisms affecting these cellular processes. Here we propose a simplified model of the regulatory network that includes sufficient molecules involved in the control of both the cell cycle and cell differentiation in the C. elegans vulva to recover their dynamic behavior. We first infer both the topology and the update rules of the cell cycle module from an expected time series. Next, we use a symbolic algorithmic approach to find which interactions must be included in the regulatory network. Finally, we use a continuous-time version of the update rules for the cell cycle module to validate the cyclic behavior of the network, as well as to rule out the presence of potential artifacts due to the synchronous updating of the discrete model. We analyze the dynamical behavior of the model for the wild type and several mutants, finding that most of the results are consistent with published experimental results. Our model shows that the regulation of Notch signaling by the cell cycle preserves the potential of the VPCs and the three vulval fates to differentiate and de-differentiate, allowing them to remain completely responsive to the concentration of LIN-3 and lateral signal in the extracellular microenvironment.

Time-dependent deformation of polymer network in polymer-stabilized cholesteric liquid crystals (Conference Presentation)

NASA Astrophysics Data System (ADS)

Lee, Kyung Min; Tondiglia, Vincent P.; Bunning, Timothy J.; White, Timothy J.

2017-02-01

Recently, we reported direct current (DC) field controllable electro-optic (EO) responses of negative dielectric anisotropy polymer stabilized cholesteric liquid crystals (PSCLCs). A potential mechanism is: Ions in the liquid crystal mixtures are trapped in/on the polymer network during the fast photopolymerization process, and the movement of ions by the application of the DC field distorts polymer network toward the negative electrode, inducing pitch variation through the cell thickness, i.e., pitch compression on the negative electrode side and pitch expansion on positive electrode side. As the DC voltage is directly applied to a target voltage, charged polymer network is deformed and the reflection band is tuned. Interestingly, the polymer network deforms further (red shift of reflection band) with time when constantly applied DC voltage, illustrating DC field induced time dependent deformation of polymer network (creep-like behavior). This time dependent reflection band changes in PSCLCs are investigated by varying the several factors, such as type and concentration of photoinitiators, liquid crystal monomer content, and curing condition (UV intensity and curing time). In addition, simple linear viscoelastic spring-dashpot models, such as 2-parameter Kelvin and 3-parameter linear models, are used to investigate the time-dependent viscoelastic behaviors of polymer networks in PSCLC.

Individual heterogeneity generating explosive system network dynamics.

PubMed

Manrique, Pedro D; Johnson, Neil F

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Individual heterogeneity generating explosive system network dynamics

NASA Astrophysics Data System (ADS)

Manrique, Pedro D.; Johnson, Neil F.

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Divergent and convergent roles for insulin-like peptides in the worm, fly and mammalian nervous systems.

PubMed

Lau, Hiu E; Chalasani, Sreekanth H

2014-09-01

Insulin signaling plays a critical role in coupling external changes to animal physiology and behavior. Despite remarkable conservation in the insulin signaling pathway components across species, divergence in the mechanism and function of the signal is evident. Focusing on recent findings from C. elegans, D. melanogaster and mammals, we discuss the role of insulin signaling in regulating adult neuronal function and behavior. In particular, we describe the transcription-dependent and transcription-independent aspects of insulin signaling across these three species. Interestingly, we find evidence of diverse mechanisms underlying complex networks of peptide action in modulating nervous system function.

Hydrophobic networked PbO2 electrode for electrochemical oxidation of paracetamol drug and degradation mechanism kinetics.

PubMed

He, Yapeng; Wang, Xue; Huang, Weimin; Chen, Rongling; Zhang, Wenli; Li, Hongdong; Lin, Haibo

2018-02-01

A hydrophobic networked PbO 2 electrode was deposited on mesh titanium substrate and utilized for the electrochemical elimination towards paracetamol drug. Three dimensional growth mechanism of PbO 2 layer provided more loading capacity of active materials and network structure greatly reduced the mass transfer for the electrochemical degradation. The active electrochemical surface area based on voltammetric charge quantity of networked PbO 2 electrode is about 2.1 times for traditional PbO 2 electrode while lower charge transfer resistance (6.78 Ω cm 2 ) could be achieved on networked PbO 2 electrode. The electrochemical incineration kinetics of paracetamol drug followed a pseudo first-order behavior and the corresponding rate constant were 0.354, 0.658 and 0.880 h -1 for traditional, networked PbO 2 and boron doped diamond electrode. Higher electrochemical elimination kinetics could be achieved on networked PbO 2 electrode and the performance can be equal to boron doped diamond electrode in result. Based on the quantification of reactive oxidants (hydroxyl radicals), the utilization rate of hydroxyl radicals could reach as high as 90% on networked PbO 2 electrode. The enhancement of excellent electrochemical oxidation capacity towards paracetamol drug was related to the properties of higher loading capacity, enhanced mass transfer and hydrophobic surface. The possible degradation mechanism and pathway of paracetamol on networked PbO 2 electrode were proposed in details accordingly based on the intermediate products. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cognitive interventions for addiction medicine: Understanding the underlying neurobiological mechanisms

PubMed Central

Zilverstand, Anna; Parvaz, Muhammad A.; Moeller, Scott J.; Goldstein, Rita Z.

2016-01-01

Neuroimaging provides a tool for investigating the neurobiological mechanisms of cognitive interventions in addiction. The aim of this review was to describe the brain circuits that are recruited during cognitive interventions, examining differences between various treatment modalities while highlighting core mechanisms, in drug addicted individuals. Based on a systematic Medline search we reviewed neuroimaging studies on cognitive behavioral therapy, cognitive inhibition of craving, motivational interventions, emotion regulation, mindfulness, and neurofeedback training in addiction. Across intervention modalities, common results included the normalization of aberrant activity in the brain’s reward circuitry, and the recruitment and strengthening of the brain’s inhibitory control network. Results suggest that different cognitive interventions act, at least partly, through recruitment of a common inhibitory control network as a core mechanism. This implies potential transfer effects between training modalities. Overall, results confirm that chronically hypoactive prefrontal regions implicated in cognitive control in addiction can be normalized through cognitive means. PMID:26822363

Mechanism Design for Incentivizing Social Media Contributions

NASA Astrophysics Data System (ADS)

Singh, Vivek K.; Jain, Ramesh; Kankanhalli, Mohan

Despite recent advancements in user-driven social media platforms, tools for studying user behavior patterns and motivations remain primitive. We highlight the voluntary nature of user contributions and that users can choose when (and when not) to contribute to the common media pool. A Game theoretic framework is proposed to study the dynamics of social media networks where contribution costs are individual but gains are common. We model users as rational selfish agents, and consider domain attributes like voluntary participation, virtual reward structure, network effect, and public-sharing to model the dynamics of this interaction. The created model describes the most appropriate contribution strategy from each user's perspective and also highlights issues like 'free-rider' problem and individual rationality leading to irrational (i.e. sub-optimal) group behavior. We also consider the perspective of the system designer who is interested in finding the best incentive mechanisms to influence the selfish end-users so that the overall system utility is maximized. We propose and compare multiple mechanisms (based on optimal bonus payment, social incentive leveraging, and second price auction) to study how a system designer can exploit the selfishness of its users, to design incentive mechanisms which improve the overall task-completion probability and system performance, while possibly still benefiting the individual users.

Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior

PubMed Central

Picciotto, Marina R.; Higley, Michael J.; Mineur, Yann S.

2012-01-01

Acetylcholine in the brain alters neuronal excitability, influences synaptic transmission, induces synaptic plasticity and coordinates the firing of groups of neurons. As a result, it changes the state of neuronal networks throughout the brain and modifies their response to internal and external inputs: the classical role of a neuromodulator. Here we identify actions of cholinergic signaling on cellular and synaptic properties of neurons in several brain areas and discuss the consequences of this signaling on behaviors related to drug abuse, attention, food intake, and affect. The diverse effects of acetylcholine depend on the site of release, the receptor subtypes, and the target neuronal population, however, a common theme is that acetylcholine potentiates behaviors that are adaptive to environmental stimuli and decreases responses to ongoing stimuli that do not require immediate action. The ability of acetylcholine to coordinate the response of neuronal networks in many brain areas makes cholinergic modulation an essential mechanism underlying complex behaviors. PMID:23040810

The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog.

PubMed

Girdhar, Kiran; Gruebele, Martin; Chemla, Yann R

2015-01-01

How simple is the underlying control mechanism for the complex locomotion of vertebrates? We explore this question for the swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish "eigenshapes" as coordinates, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative "postural space" that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a "behavioral space" using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns-scoots, turns, rests-but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish.

The Behavioral Space of Zebrafish Locomotion and Its Neural Network Analog

PubMed Central

Girdhar, Kiran; Gruebele, Martin; Chemla, Yann R.

2015-01-01

How simple is the underlying control mechanism for the complex locomotion of vertebrates? We explore this question for the swimming behavior of zebrafish larvae. A parameter-independent method, similar to that used in studies of worms and flies, is applied to analyze swimming movies of fish. The motion itself yields a natural set of fish "eigenshapes" as coordinates, rather than the experimenter imposing a choice of coordinates. Three eigenshape coordinates are sufficient to construct a quantitative "postural space" that captures >96% of the observed zebrafish locomotion. Viewed in postural space, swim bouts are manifested as trajectories consisting of cycles of shapes repeated in succession. To classify behavioral patterns quantitatively and to understand behavioral variations among an ensemble of fish, we construct a "behavioral space" using multi-dimensional scaling (MDS). This method turns each cycle of a trajectory into a single point in behavioral space, and clusters points based on behavioral similarity. Clustering analysis reveals three known behavioral patterns—scoots, turns, rests—but shows that these do not represent discrete states, but rather extremes of a continuum. The behavioral space not only classifies fish by their behavior but also distinguishes fish by age. With the insight into fish behavior from postural space and behavioral space, we construct a two-channel neural network model for fish locomotion, which produces strikingly similar postural space and behavioral space dynamics compared to real zebrafish. PMID:26132396

Associative memory through rigid origami

NASA Astrophysics Data System (ADS)

Murugan, Arvind; Brenner, Michael

2015-03-01

Mechanisms such as Miura Ori have proven useful in diverse contexts since they have only one degree of freedom that is easily controlled. We combine the theory of rigid origami and associative memory in frustrated neural networks to create structures that can ``learn'' multiple generic folding mechanisms and yet can be robustly controlled. We show that such rigid origami structures can ``recall'' a specific learned mechanism when induced by a physical impulse that only need resemble the desired mechanism (i.e. robust recall through association). Such associative memory in matter, seen before in self-assembly, arises due to a balance between local promiscuity (i.e., many local degrees of freedom) and global frustration which minimizes interference between different learned behaviors. Origami with associative memory can lead to a new class of deployable structures and kinetic architectures with multiple context-dependent behaviors.

Hippocampal Network Modularity Is Associated With Relational Memory Dysfunction in Schizophrenia.

PubMed

Avery, Suzanne N; Rogers, Baxter P; Heckers, Stephan

2018-05-01

Functional dysconnectivity has been proposed as a major pathophysiological mechanism for cognitive dysfunction in schizophrenia. The hippocampus is a focal point of dysconnectivity in schizophrenia, with decreased hippocampal functional connectivity contributing to the marked memory deficits observed in patients. Normal memory function relies on the interaction of complex corticohippocampal networks. However, only recent technological advances have enabled the large-scale exploration of functional networks with accuracy and precision. We investigated the modularity of hippocampal resting-state functional networks in a sample of 45 patients with schizophrenia spectrum disorders and 38 healthy control subjects. Modularity was calculated for two distinct functional networks: a core hippocampal-medial temporal lobe cortex network and an extended hippocampal-cortical network. As hippocampal function differs along its longitudinal axis, follow-up analyses examined anterior and posterior networks separately. To explore effects of resting network function on behavior, we tested associations between modularity and relational memory ability. Age, sex, handedness, and parental education were similar between groups. Network modularity was lower in schizophrenia patients, especially in the posterior hippocampal network. Schizophrenia patients also showed markedly lower relational memory ability compared with control subjects. We found a distinct brain-behavior relationship in schizophrenia that differed from control subjects by network and anterior/posterior division-while relational memory in control subjects was associated with anterior hippocampal-cortical modularity, schizophrenia patients showed an association with posterior hippocampal-medial temporal lobe cortex network modularity. Our findings support a model of abnormal resting-state corticohippocampal network coherence in schizophrenia, which may contribute to relational memory deficits. Copyright © 2018 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Longitudinal social networks impacts on weight and weight-related behaviors assessed using mobile-based ecological momentary assessments: Study Protocols for the SPARC study.

PubMed

Bruening, Meg; Ohri-Vachaspati, Punam; Brewis, Alexandra; Laska, Melissa; Todd, Michael; Hruschka, Daniel; Schaefer, David R; Whisner, Corrie M; Dunton, Genevieve

2016-08-30

The transition from the home to college is a phase in which emerging adults shift toward more unhealthy eating and physical activity patterns, higher body mass indices, thus increasing risk of overweight/obesity. Currently, little is understood about how changing friendship networks shape weight gain behaviors. This paper describes the recruitment, data collection, and data analytic protocols for the SPARC (Social impact of Physical Activity and nutRition in College) study, a longitudinal examination of the mechanisms by which friends and friendship networks influence nutrition and physical activity behaviors and weight gain in the transition to college life. The SPARC study aims to follow 1450 university freshmen from a large university over an academic year, collecting data on multiple aspects of friends and friendship networks. Integrating multiple types of data related to student lives, ecological momentary assessments (EMAs) are administered via a cell phone application, devilSPARC. EMAs collected in four 1-week periods (a total of 4 EMA waves) are integrated with linked data from web-based surveys and anthropometric measurements conducted at four times points (for a total of eight data collection periods including EMAs, separated by ~1 month). University databases will provide student card data, allowing integration of both time-dated data on food purchasing, use of physical activity venues, and geographical information system (GIS) locations of these activities relative to other students in their social networks. Findings are intended to guide the development of more effective interventions to enhance behaviors among college students that protect against weight gain during college.

Synaptic Scaling in Combination with Many Generic Plasticity Mechanisms Stabilizes Circuit Connectivity

PubMed Central

Tetzlaff, Christian; Kolodziejski, Christoph; Timme, Marc; Wörgötter, Florentin

2011-01-01

Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks. PMID:22203799

Multiple Resting-State Networks Are Associated With Tremors and Cognitive Features in Essential Tremor.

PubMed

Fang, Weidong; Chen, Huiyue; Wang, Hansheng; Zhang, Han; Liu, Mengqi; Puneet, Munankami; Lv, Fajin; Cheng, Oumei; Wang, Xuefeng; Lu, Xiurong; Luo, Tianyou

2015-12-01

The heterogeneous clinical features of essential tremor indicate that the dysfunctions of this syndrome are not confined to motor networks, but extend to nonmotor networks. Currently, these neural network dysfunctions in essential tremor remain unclear. In this study, independent component analysis of resting-state functional MRI was used to study these neural network mechanisms. Thirty-five essential tremor patients and 35 matched healthy controls with clinical and neuropsychological tests were included, and eight resting-state networks were identified. After considering the structure and head-motion factors and testing the reliability of the selected resting-state networks, we assessed the functional connectivity changes within or between resting-state networks. Finally, image-behavior correlation analysis was performed. Compared to healthy controls, essential tremor patients displayed increased functional connectivity in the sensorimotor and salience networks and decreased functional connectivity in the cerebellum network. Additionally, increased functional network connectivity was observed between anterior and posterior default mode networks, and a decreased functional network connectivity was noted between the cerebellum network and the sensorimotor and posterior default mode networks. Importantly, the functional connectivity changes within and between these resting-state networks were correlated with the tremor severity and total cognitive scores of essential tremor patients. The findings of this study provide the first evidence that functional connectivity changes within and between multiple resting-state networks are associated with tremors and cognitive features of essential tremor, and this work demonstrates a potential approach for identifying the underlying neural network mechanisms of this syndrome. © 2015 International Parkinson and Movement Disorder Society.

Asymptotic theory of time varying networks with burstiness and heterogeneous activation patterns

NASA Astrophysics Data System (ADS)

Burioni, Raffaella; Ubaldi, Enrico; Vezzani, Alessandro

2017-05-01

The recent availability of large-scale, time-resolved and high quality digital datasets has allowed for a deeper understanding of the structure and properties of many real-world networks. The empirical evidence of a temporal dimension prompted the switch of paradigm from a static representation of networks to a time varying one. In this work we briefly review the framework of time-varying-networks in real world social systems, especially focusing on the activity-driven paradigm. We develop a framework that allows for the encoding of three generative mechanisms that seem to play a central role in the social networks’ evolution: the individual’s propensity to engage in social interactions, its strategy in allocate these interactions among its alters and the burstiness of interactions amongst social actors. The functional forms and probability distributions encoding these mechanisms are typically data driven. A natural question arises if different classes of strategies and burstiness distributions, with different local scale behavior and analogous asymptotics can lead to the same long time and large scale structure of the evolving networks. We consider the problem in its full generality, by investigating and solving the system dynamics in the asymptotic limit, for general classes of ties allocation mechanisms and waiting time probability distributions. We show that the asymptotic network evolution is driven by a few characteristics of these functional forms, that can be extracted from direct measurements on large datasets.

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Synthetic incoherent feedforward circuits show adaptation to the amount of their genetic template

PubMed Central

Bleris, Leonidas; Xie, Zhen; Glass, David; Adadey, Asa; Sontag, Eduardo; Benenson, Yaakov

2011-01-01

Natural and synthetic biological networks must function reliably in the face of fluctuating stoichiometry of their molecular components. These fluctuations are caused in part by changes in relative expression efficiency and the DNA template amount of the network-coding genes. Gene product levels could potentially be decoupled from these changes via built-in adaptation mechanisms, thereby boosting network reliability. Here, we show that a mechanism based on an incoherent feedforward motif enables adaptive gene expression in mammalian cells. We modeled, synthesized, and tested transcriptional and post-transcriptional incoherent loops and found that in all cases the gene product adapts to changes in DNA template abundance. We also observed that the post-transcriptional form results in superior adaptation behavior, higher absolute expression levels, and lower intrinsic fluctuations. Our results support a previously hypothesized endogenous role in gene dosage compensation for such motifs and suggest that their incorporation in synthetic networks will improve their robustness and reliability. PMID:21811230

Enhancement of electrical signaling in neural networks on graphene films.

PubMed

Tang, Mingliang; Song, Qin; Li, Ning; Jiang, Ziyun; Huang, Rong; Cheng, Guosheng

2013-09-01

One of the key challenges for neural tissue engineering is to exploit supporting materials with robust functionalities not only to govern cell-specific behaviors, but also to form functional neural network. The unique electrical and mechanical properties of graphene imply it as a promising candidate for neural interfaces, but little is known about the details of neural network formation on graphene as a scaffold material for tissue engineering. Therapeutic regenerative strategies aim to guide and enhance the intrinsic capacity of the neurons to reorganize by promoting plasticity mechanisms in a controllable manner. Here, we investigated the impact of graphene on the formation and performance in the assembly of neural networks in neural stem cell (NSC) culture. Using calcium imaging and electrophysiological recordings, we demonstrate the capabilities of graphene to support the growth of functional neural circuits, and improve neural performance and electrical signaling in the network. These results offer a better understanding of interactions between graphene and NSCs, also they clearly present the great potentials of graphene as neural interface in tissue engineering. Copyright © 2013 Elsevier Ltd. All rights reserved.

A neural network for intermale aggression to establish social hierarchy.

PubMed

Stagkourakis, Stefanos; Spigolon, Giada; Williams, Paul; Protzmann, Jil; Fisone, Gilberto; Broberger, Christian

2018-06-01

Intermale aggression is used to establish social rank. Several neuronal populations have been implicated in aggression, but the circuit mechanisms that shape this innate behavior and coordinate its different components (including attack execution and reward) remain elusive. We show that dopamine transporter-expressing neurons in the hypothalamic ventral premammillary nucleus (PMv DAT neurons) organize goal-oriented aggression in male mice. Activation of PMv DAT neurons triggers attack behavior; silencing these neurons interrupts attacks. Regenerative PMv DAT membrane conductances interacting with recurrent and reciprocal excitation explain how a brief trigger can elicit a long-lasting response (hysteresis). PMv DAT projections to the ventrolateral part of the ventromedial hypothalamic and the supramammillary nuclei control attack execution and aggression reward, respectively. Brief manipulation of PMv DAT activity switched the dominance relationship between males, an effect persisting for weeks. These results identify a network structure anchored in PMv DAT neurons that organizes aggressive behavior and, as a consequence, determines intermale hierarchy.

Controlled Shape Memory Behavior of a Smectic Main-Chain Liquid Crystalline Elastomer

DOE PAGES

Li, Yuzhan; Pruitt, Cole; Rios, Orlando; ...

2015-04-10

Here, we describe how a smectic main-chain liquid crystalline elastomer (LCE), with controlled shape memory behavior, is synthesized by polymerizing a biphenyl-based epoxy monomer with an aliphatic carboxylic acid curing agent. Microstructures of the LCEs, including their liquid crystallinity and cross-linking density, are modified by adjusting the stoichiometric ratio of the reactants to tailor the thermomechanical properties and shape memory behavior of the material. Thermal and liquid crystalline properties of the LCEs, characterized using differential scanning calorimetry and dynamic mechanical analysis, and structural analysis, performed using small-angle and wide-angle X-ray scattering, show that liquid crystallinity, cross-linking density, and network rigiditymore » are strongly affected by the stoichiometry of the curing reaction. With appropriate structural modifications it is possible to tune the thermal, dynamic mechanical, and thermomechanical properties as well as the shape memory and thermal degradation behavior of LCEs.« less

From homeostasis to behavior: Balanced activity in an exploration of embodied dynamic environmental-neural interaction.

PubMed

Hellyer, Peter John; Clopath, Claudia; Kehagia, Angie A; Turkheimer, Federico E; Leech, Robert

2017-08-01

In recent years, there have been many computational simulations of spontaneous neural dynamics. Here, we describe a simple model of spontaneous neural dynamics that controls an agent moving in a simple virtual environment. These dynamics generate interesting brain-environment feedback interactions that rapidly destabilize neural and behavioral dynamics demonstrating the need for homeostatic mechanisms. We investigate roles for homeostatic plasticity both locally (local inhibition adjusting to balance excitatory input) as well as more globally (regional "task negative" activity that compensates for "task positive", sensory input in another region) balancing neural activity and leading to more stable behavior (trajectories through the environment). Our results suggest complementary functional roles for both local and macroscale mechanisms in maintaining neural and behavioral dynamics and a novel functional role for macroscopic "task-negative" patterns of activity (e.g., the default mode network).

Controlled Shape Memory Behavior of a Smectic Main-Chain Liquid Crystalline Elastomer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yuzhan; Pruitt, Cole; Rios, Orlando

Here, we describe how a smectic main-chain liquid crystalline elastomer (LCE), with controlled shape memory behavior, is synthesized by polymerizing a biphenyl-based epoxy monomer with an aliphatic carboxylic acid curing agent. Microstructures of the LCEs, including their liquid crystallinity and cross-linking density, are modified by adjusting the stoichiometric ratio of the reactants to tailor the thermomechanical properties and shape memory behavior of the material. Thermal and liquid crystalline properties of the LCEs, characterized using differential scanning calorimetry and dynamic mechanical analysis, and structural analysis, performed using small-angle and wide-angle X-ray scattering, show that liquid crystallinity, cross-linking density, and network rigiditymore » are strongly affected by the stoichiometry of the curing reaction. With appropriate structural modifications it is possible to tune the thermal, dynamic mechanical, and thermomechanical properties as well as the shape memory and thermal degradation behavior of LCEs.« less

Vasotocinergic control of agonistic behavior told by Neotropical fishes.

PubMed

Silva, Ana C; Pandolfi, Matías

2018-04-24

The hypothalamic neuropeptides of the vasopressin-oxytocin family (and their homologs for non-mammalian species) are key modulators of the Social Brain Network, acting via specific receptors reported in all the nuclei of this network. Different conclusive examples have proven the context-dependency actions of hypothalamic nonapeptides on social behavior in several vertebrate taxa. Teleost fishes provide endless possibilities of experimental model systems to explore the underlying mechanisms of nonapeptide actions on social behavior given that they are the most diverse group of vertebrates. Although it has been difficult to identify commonalities of nonapeptide actions across species, indisputable evidence in many teleost species have demonstrated a clear role of vasotocin in the modulation of aggressive and sexual behaviors. Though Neotropical South American fish contribute an important percentage of teleost diversity, most native species remain unexplored as model systems for the study of the neuroendocrine bases of social behavior. In this review, we will revise recent data on the two model systems of Neotropical fish, South American cichlids and weakly electric fish that have contributed to this issue. Copyright © 2018 Elsevier Inc. All rights reserved.

Dynamical analysis of Parkinsonian state emulated by hybrid Izhikevich neuron models

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Li, Huiyan; Loparo, Kenneth A.; Fietkiewicz, Chris

2015-11-01

Computational models play a significant role in exploring novel theories to complement the findings of physiological experiments. Various computational models have been developed to reveal the mechanisms underlying brain functions. Particularly, in the development of therapies to modulate behavioral and pathological abnormalities, computational models provide the basic foundations to exhibit transitions between physiological and pathological conditions. Considering the significant roles of the intrinsic properties of the globus pallidus and the coupling connections between neurons in determining the firing patterns and the dynamical activities of the basal ganglia neuronal network, we propose a hypothesis that pathological behaviors under the Parkinsonian state may originate from combined effects of intrinsic properties of globus pallidus neurons and synaptic conductances in the whole neuronal network. In order to establish a computational efficient network model, hybrid Izhikevich neuron model is used due to its capacity of capturing the dynamical characteristics of the biological neuronal activities. Detailed analysis of the individual Izhikevich neuron model can assist in understanding the roles of model parameters, which then facilitates the establishment of the basal ganglia-thalamic network model, and contributes to a further exploration of the underlying mechanisms of the Parkinsonian state. Simulation results show that the hybrid Izhikevich neuron model is capable of capturing many of the dynamical properties of the basal ganglia-thalamic neuronal network, such as variations of the firing rates and emergence of synchronous oscillations under the Parkinsonian condition, despite the simplicity of the two-dimensional neuronal model. It may suggest that the computational efficient hybrid Izhikevich neuron model can be used to explore basal ganglia normal and abnormal functions. Especially it provides an efficient way of emulating the large-scale neuron network and potentially contributes to development of improved therapy for neurological disorders such as Parkinson's disease.

Promotion of cooperation induced by appropriate payoff aspirations in a small-world networked game

NASA Astrophysics Data System (ADS)

Chen, Xiaojie; Wang, Long

2008-01-01

Based on learning theory, we adopt a stochastic learning updating rule to investigate the evolution of cooperation in the Prisoner’s Dilemma game on Newman-Watts small-world networks with different payoff aspiration levels. Interestingly, simulation results show that the mechanism of intermediate aspiration promoting cooperation resembles a resonancelike behavior, and there exists a ping-pong vibration of cooperation for large payoff aspiration. To explain the nontrivial dependence of the cooperation level on the aspiration level, we investigate the fractions of links, provide analytical results of the cooperation level, and find that the simulation results are in close agreement with analytical ones. Our work may be helpful in understanding the cooperative behavior induced by the aspiration level in society.

Attention Network Dysfunction in Bulimia Nervosa - An fMRI Study

PubMed Central

Dahmen, Brigitte; Schulte-Rüther, Martin; Legenbauer, Tanja; Herpertz-Dahlmann, Beate; Konrad, Kerstin

2016-01-01

Objective Recent evidence has suggested an increased rate of comorbid ADHD and subclinical attentional impairments in bulimia nervosa (BN) patients. However, little is known regarding the underlying neural mechanisms of attentional functions in BN. Method Twenty BN patients and twenty age- and weight-matched healthy controls (HC) were investigated using a modified version of the Attention Network Task (ANT) in an fMRI study. This design enabled an investigation of the neural mechanisms associated with the three attention networks involved in alerting, reorienting and executive attention. Results The BN patients showed hyperactivation in parieto-occipital regions and reduced deactivation of default-mode-network (DMN) areas during alerting compared with HCs. Posterior cingulate activation during alerting correlated with the severity of eating-disorder symptoms within the patient group. Conversely, BN patients showed hypoactivation during reorienting and executive attention in anterior cingulate regions, the temporo-parietal junction (TPJ) and parahippocampus compared with HCs, which was negatively associated with global ADHD symptoms and impulsivity, respectively. Discussion Our findings demonstrate altered brain mechanisms in BN associated with all three attentional networks. Failure to deactivate the DMN and increased parieto-occipital activation required for alerting might be associated with a constant preoccupation with food or body image-related thoughts. Hypoactivation of executive control networks and TPJ might increase the likelihood of inattentive and impulsive behaviors and poor emotion regulation. Thus, dysfunction in the attentional network in BN goes beyond an altered executive attentional domain and needs to be considered in the diagnosis and treatment of BN. PMID:27607439

Micro-level dynamics of the online information propagation: A user behavior model based on noisy spiking neurons.

PubMed

Lymperopoulos, Ilias N; Ioannou, George D

2016-10-01

We develop and validate a model of the micro-level dynamics underlying the formation of macro-level information propagation patterns in online social networks. In particular, we address the dynamics at the level of the mechanism regulating a user's participation in an online information propagation process. We demonstrate that this mechanism can be realistically described by the dynamics of noisy spiking neurons driven by endogenous and exogenous, deterministic and stochastic stimuli representing the influence modulating one's intention to be an information spreader. Depending on the dynamically changing influence characteristics, time-varying propagation patterns emerge reflecting the temporal structure, strength, and signal-to-noise ratio characteristics of the stimulation driving the online users' information sharing activity. The proposed model constitutes an overarching, novel, and flexible approach to the modeling of the micro-level mechanisms whereby information propagates in online social networks. As such, it can be used for a comprehensive understanding of the online transmission of information, a process integral to the sociocultural evolution of modern societies. The proposed model is highly adaptable and suitable for the study of the propagation patterns of behavior, opinions, and innovations among others. Copyright © 2016 Elsevier Ltd. All rights reserved.

Shared developmental and evolutionary origins for neural basis of vocal–acoustic and pectoral–gestural signaling

PubMed Central

Bass, Andrew H.; Chagnaud, Boris P.

2012-01-01

Acoustic signaling behaviors are widespread among bony vertebrates, which include the majority of living fishes and tetrapods. Developmental studies in sound-producing fishes and tetrapods indicate that central pattern generating networks dedicated to vocalization originate from the same caudal hindbrain rhombomere (rh) 8-spinal compartment. Together, the evidence suggests that vocalization and its morphophysiological basis, including mechanisms of vocal–respiratory coupling that are widespread among tetrapods, are ancestral characters for bony vertebrates. Premotor-motor circuitry for pectoral appendages that function in locomotion and acoustic signaling develops in the same rh8-spinal compartment. Hence, vocal and pectoral phenotypes in fishes share both developmental origins and roles in acoustic communication. These findings lead to the proposal that the coupling of more highly derived vocal and pectoral mechanisms among tetrapods, including those adapted for nonvocal acoustic and gestural signaling, originated in fishes. Comparative studies further show that rh8 premotor populations have distinct neurophysiological properties coding for equally distinct behavioral attributes such as call duration. We conclude that neural network innovations in the spatiotemporal patterning of vocal and pectoral mechanisms of social communication, including forelimb gestural signaling, have their evolutionary origins in the caudal hindbrain of fishes. PMID:22723366

A Channel Network Evolution Model with Subsurface Saturation Mechanism and Analysis of the Chaotic Behavior of the Model

DTIC Science & Technology

1990-09-01

between basin shapes and hydrologic responses is fundamental for the purpose of hydrologic predictions , especially in ungaged basins. Another goal is...47] studied this model and showed analitically how very small differences in the c field generated completely different leaf vein network structures... predictability impossible. Complexity is by no means a requirement in order for a system to exhibit SIC. A system as simple as the logistic equation x,,,,=ax,,(l

Connecting a Connectome to Behavior: An Ensemble of Neuroanatomical Models of C. elegans Klinotaxis

PubMed Central

Izquierdo, Eduardo J.; Beer, Randall D.

2013-01-01

Increased efforts in the assembly and analysis of connectome data are providing new insights into the principles underlying the connectivity of neural circuits. However, despite these considerable advances in connectomics, neuroanatomical data must be integrated with neurophysiological and behavioral data in order to obtain a complete picture of neural function. Due to its nearly complete wiring diagram and large behavioral repertoire, the nematode worm Caenorhaditis elegans is an ideal organism in which to explore in detail this link between neural connectivity and behavior. In this paper, we develop a neuroanatomically-grounded model of salt klinotaxis, a form of chemotaxis in which changes in orientation are directed towards the source through gradual continual adjustments. We identify a minimal klinotaxis circuit by systematically searching the C. elegans connectome for pathways linking chemosensory neurons to neck motor neurons, and prune the resulting network based on both experimental considerations and several simplifying assumptions. We then use an evolutionary algorithm to find possible values for the unknown electrophsyiological parameters in the network such that the behavioral performance of the entire model is optimized to match that of the animal. Multiple runs of the evolutionary algorithm produce an ensemble of such models. We analyze in some detail the mechanisms by which one of the best evolved circuits operates and characterize the similarities and differences between this mechanism and other solutions in the ensemble. Finally, we propose a series of experiments to determine which of these alternatives the worm may be using. PMID:23408877

Technology Developments Integrating a Space Network Communications Testbed

NASA Technical Reports Server (NTRS)

Kwong, Winston; Jennings, Esther; Clare, Loren; Leang, Dee

2006-01-01

As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enable its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions. It can simulate entire networks and can interface with external (testbed) systems. The key technology developments enabling the integration of MACHETE into a distributed testbed are the Monitor and Control module and the QualNet IP Network Emulator module. Specifically, the Monitor and Control module establishes a standard interface mechanism to centralize the management of each testbed component. The QualNet IP Network Emulator module allows externally generated network traffic to be passed through MACHETE to experience simulated network behaviors such as propagation delay, data loss, orbital effects and other communications characteristics, including entire network behaviors. We report a successful integration of MACHETE with a space communication testbed modeling a lunar exploration scenario. This document is the viewgraph slides of the presentation.

An Evolutionary Game Theory Model of Spontaneous Brain Functioning.

PubMed

Madeo, Dario; Talarico, Agostino; Pascual-Leone, Alvaro; Mocenni, Chiara; Santarnecchi, Emiliano

2017-11-22

Our brain is a complex system of interconnected regions spontaneously organized into distinct networks. The integration of information between and within these networks is a continuous process that can be observed even when the brain is at rest, i.e. not engaged in any particular task. Moreover, such spontaneous dynamics show predictive value over individual cognitive profile and constitute a potential marker in neurological and psychiatric conditions, making its understanding of fundamental importance in modern neuroscience. Here we present a theoretical and mathematical model based on an extension of evolutionary game theory on networks (EGN), able to capture brain's interregional dynamics by balancing emulative and non-emulative attitudes among brain regions. This results in the net behavior of nodes composing resting-state networks identified using functional magnetic resonance imaging (fMRI), determining their moment-to-moment level of activation and inhibition as expressed by positive and negative shifts in BOLD fMRI signal. By spontaneously generating low-frequency oscillatory behaviors, the EGN model is able to mimic functional connectivity dynamics, approximate fMRI time series on the basis of initial subset of available data, as well as simulate the impact of network lesions and provide evidence of compensation mechanisms across networks. Results suggest evolutionary game theory on networks as a new potential framework for the understanding of human brain network dynamics.

Percolation mechanism drives actin gels to the critically connected state

NASA Astrophysics Data System (ADS)

Lee, Chiu Fan; Pruessner, Gunnar

2016-05-01

Cell motility and tissue morphogenesis depend crucially on the dynamic remodeling of actomyosin networks. An actomyosin network consists of an actin polymer network connected by cross-linker proteins and motor protein myosins that generate internal stresses on the network. A recent discovery shows that for a range of experimental parameters, actomyosin networks contract to clusters with a power-law size distribution [J. Alvarado, Nat. Phys. 9, 591 (2013), 10.1038/nphys2715]. Here, we argue that actomyosin networks can exhibit a robust critical signature without fine-tuning because the dynamics of the system can be mapped onto a modified version of percolation with trapping (PT), which is known to show critical behavior belonging to the static percolation universality class without the need for fine-tuning of a control parameter. We further employ our PT model to generate experimentally testable predictions.

Optimal Objective-Based Experimental Design for Uncertain Dynamical Gene Networks with Experimental Error.

PubMed

Mohsenizadeh, Daniel N; Dehghannasiri, Roozbeh; Dougherty, Edward R

2018-01-01

In systems biology, network models are often used to study interactions among cellular components, a salient aim being to develop drugs and therapeutic mechanisms to change the dynamical behavior of the network to avoid undesirable phenotypes. Owing to limited knowledge, model uncertainty is commonplace and network dynamics can be updated in different ways, thereby giving multiple dynamic trajectories, that is, dynamics uncertainty. In this manuscript, we propose an experimental design method that can effectively reduce the dynamics uncertainty and improve performance in an interaction-based network. Both dynamics uncertainty and experimental error are quantified with respect to the modeling objective, herein, therapeutic intervention. The aim of experimental design is to select among a set of candidate experiments the experiment whose outcome, when applied to the network model, maximally reduces the dynamics uncertainty pertinent to the intervention objective.

A bipartite fitness model for online music streaming services

NASA Astrophysics Data System (ADS)

Pongnumkul, Suchit; Motohashi, Kazuyuki

2018-01-01

This paper proposes an evolution model and an analysis of the behavior of music consumers on online music streaming services. While previous studies have observed power-law degree distributions of usage in online music streaming services, the underlying behavior of users has not been well understood. Users and songs can be described using a bipartite network where an edge exists between a user node and a song node when the user has listened that song. The growth mechanism of bipartite networks has been used to understand the evolution of online bipartite networks Zhang et al. (2013). Existing bipartite models are based on a preferential attachment mechanism László Barabási and Albert (1999) in which the probability that a user listens to a song is proportional to its current popularity. This mechanism does not allow for two types of real world phenomena. First, a newly released song with high quality sometimes quickly gains popularity. Second, the popularity of songs normally decreases as time goes by. Therefore, this paper proposes a new model that is more suitable for online music services by adding fitness and aging functions to the song nodes of the bipartite network proposed by Zhang et al. (2013). Theoretical analyses are performed for the degree distribution of songs. Empirical data from an online streaming service, Last.fm, are used to confirm the degree distribution of the object nodes. Simulation results show improvements from a previous model. Finally, to illustrate the application of the proposed model, a simplified royalty cost model for online music services is used to demonstrate how the changes in the proposed parameters can affect the costs for online music streaming providers. Managerial implications are also discussed.

Drosophila growth cones: a genetically tractable platform for the analysis of axonal growth dynamics.

PubMed

Sánchez-Soriano, Natalia; Gonçalves-Pimentel, Catarina; Beaven, Robin; Haessler, Ulrike; Ofner-Ziegenfuss, Lisa; Ballestrem, Christoph; Prokop, Andreas

2010-01-01

The formation of neuronal networks, during development and regeneration, requires outgrowth of axons along reproducible paths toward their appropriate postsynaptic target cells. Axonal extension occurs at growth cones (GCs) at the tips of axons. GC advance and navigation requires the activity of their cytoskeletal networks, comprising filamentous actin (F-actin) in lamellipodia and filopodia as well as dynamic microtubules (MTs) emanating from bundles of the axonal core. The molecular mechanisms governing these two cytoskeletal networks, their cross-talk, and their response to extracellular signaling cues are only partially understood, hindering our conceptual understanding of how regulated changes in GC behavior are controlled. Here, we introduce Drosophila GCs as a suitable model to address these mechanisms. Morphological and cytoskeletal readouts of Drosophila GCs are similar to those of other models, including mammals, as demonstrated here for MT and F-actin dynamics, axonal growth rates, filopodial structure and motility, organizational principles of MT networks, and subcellular marker localization. Therefore, we expect fundamental insights gained in Drosophila to be translatable into vertebrate biology. The advantage of the Drosophila model over others is its enormous amenability to combinatorial genetics as a powerful strategy to address the complexity of regulatory networks governing axonal growth. Thus, using pharmacological and genetic manipulations, we demonstrate a role of the actin cytoskeleton in a specific form of MT organization (loop formation), known to regulate GC pausing behavior. We demonstrate these events to be mediated by the actin-MT linking factor Short stop, thus identifying an essential molecular player in this context.

Symmetries and synchronization in multilayer random networks

NASA Astrophysics Data System (ADS)

Saa, Alberto

2018-04-01

In the light of the recently proposed scenario of asymmetry-induced synchronization (AISync), in which dynamical uniformity and consensus in a distributed system would demand certain asymmetries in the underlying network, we investigate here the influence of some regularities in the interlayer connection patterns on the synchronization properties of multilayer random networks. More specifically, by considering a Stuart-Landau model of complex oscillators with random frequencies, we report for multilayer networks a dynamical behavior that could be also classified as a manifestation of AISync. We show, namely, that the presence of certain symmetries in the interlayer connection pattern tends to diminish the synchronization capability of the whole network or, in other words, asymmetries in the interlayer connections would enhance synchronization in such structured networks. Our results might help the understanding not only of the AISync mechanism itself but also its possible role in the determination of the interlayer connection pattern of multilayer and other structured networks with optimal synchronization properties.

Physics of soft hyaluronic acid-collagen type II double network gels

NASA Astrophysics Data System (ADS)

Morozova, Svetlana; Muthukumar, Murugappan

2015-03-01

Many biological hydrogels are made up of multiple interpenetrating, charged components. We study the swelling, elastic diffusion, mechanical, and optical behaviors of 100 mol% ionizable hyaluronic acid (HA) and collagen type II fiber networks. Dilute, 0.05-0.5 wt% hyaluronic acid networks are extremely sensitive to solution salt concentration, but are stable at pH above 2. When swelled in 0.1M NaCl, single-network hyaluronic acid gels follow scaling laws relevant to high salt semidilute solutions; the elastic shear modulus G' and diffusion constant D scale with the volume fraction ϕ as G' ~ϕ 9 / 4 and D ~ϕ 3 / 4 , respectively. With the addition of a collagen fiber network, we find that the hyaluronic acid network swells to suspend the rigid collagen fibers, providing extra strength to the hydrogel. Results on swelling equilibria, elasticity, and collective diffusion on these double network hydrogels will be presented.

Evolution of the social network of scientific collaborations

NASA Astrophysics Data System (ADS)

Barabasi, Albert-Laszlo; Jeong, Hawoong; Neda, Zoltan; Ravasz, Erzsebet; Schubert, Andras; Vicsek, Tamas

2002-03-01

The co-authorship network of scientists represents a prototype of complex evolving networks. By mapping the electronic database containing all relevant journals in mathematics and neuro-science for an eight-year period (1991-98), we infer the dynamic and the structural mechanisms that govern the evolution and topology of this complex system. First, empirical measurements allow us to uncover the topological measures that characterize the network at a given moment, as well as the time evolution of these quantities. The results indicate that the network is scale-free, and that the network evolution is governed by preferential attachment, affecting both internal and external links. However, in contrast with most model predictions the average degree increases in time, and the node separation decreases. Second, we propose a simple model that captures the network's time evolution. Third, numerical simulations are used to uncover the behavior of quantities that could not be predicted analytically.

Mechanics of composite actin networks: in vitro and cellular perspectives

NASA Astrophysics Data System (ADS)

Upadhyaya, Arpita

2014-03-01

Actin filaments and associated actin binding proteins play an essential role in governing the mechanical properties of eukaryotic cells. Even though cells have multiple actin binding proteins (ABPs) that exist simultaneously to maintain the structural and mechanical integrity of the cellular cytoskeleton, how these proteins work together to determine the properties of actin networks is not well understood. The ABP, palladin, is essential for the integrity of cell morphology and movement during development. Palladin coexists with alpha-actinin in stress fibers and focal adhesions and binds to both actin and alpha-actinin. To obtain insight into how mutually interacting actin crosslinking proteins modulate the properties of actin networks, we have characterized the micro-structure and mechanics of actin networks crosslinked with palladin and alpha-actinin. Our studies on composite networks of alpha-actinin/palladin/actin show that palladin and alpha-actinin synergistically determine network viscoelasticity. We have further examined the role of palladin in cellular force generation and mechanosensing. Traction force microscopy revealed that TAFs are sensitive to substrate stiffness as they generate larger forces on substrates of increased stiffness. Contrary to expectations, knocking down palladin increased the forces generated by cells, and also inhibited the ability to sense substrate stiffness for very stiff gels. This was accompanied by significant differences in the actin organization and adhesion dynamics of palladin knock down cells. Perturbation experiments also suggest altered myosin activity in palladin KD cells. Our results suggest that the actin crosslinkers such as palladin and myosin motors coordinate for optimal cell function and to prevent aberrant behavior as in cancer metastasis.

DSGRN: Examining the Dynamics of Families of Logical Models.

PubMed

Cummins, Bree; Gedeon, Tomas; Harker, Shaun; Mischaikow, Konstantin

2018-01-01

We present a computational tool DSGRN for exploring the dynamics of a network by computing summaries of the dynamics of switching models compatible with the network across all parameters. The network can arise directly from a biological problem, or indirectly as the interaction graph of a Boolean model. This tool computes a finite decomposition of parameter space such that for each region, the state transition graph that describes the coarse dynamical behavior of a network is the same. Each of these parameter regions corresponds to a different logical description of the network dynamics. The comparison of dynamics across parameters with experimental data allows the rejection of parameter regimes or entire networks as viable models for representing the underlying regulatory mechanisms. This in turn allows a search through the space of perturbations of a given network for networks that robustly fit the data. These are the first steps toward discovering a network that optimally matches the observed dynamics by searching through the space of networks.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

PubMed

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-08-15

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

Size matters: concurrency and the epidemic potential of HIV in small networks.

PubMed

Carnegie, Nicole Bohme; Morris, Martina

2012-01-01

Generalized heterosexual epidemics are responsible for the largest share of the global burden of HIV. These occur in populations that do not have high rates of partner acquisition, and research suggests that a pattern of fewer, but concurrent, partnerships may be the mechanism that provides the connectivity necessary for sustained transmission. We examine how network size affects the impact of concurrency on network connectivity. We use a stochastic network model to generate a sample of networks, varying the size of the network and the level of concurrency, and compare the largest components for each scenario to the asymptotic expected values. While the threshold for the growth of a giant component does not change, the transition is more gradual in the smaller networks. As a result, low levels of concurrency generate more connectivity in small networks. Generalized HIV epidemics are by definition those that spread to a larger fraction of the population, but the mechanism may rely in part on the dynamics of transmission in a set of linked small networks. Examples include rural populations in sub-Saharan Africa and segregated minority populations in the US, where the effective size of the sexual network may well be in the hundreds, rather than thousands. Connectivity emerges at lower levels of concurrency in smaller networks, but these networks can still be disconnected with small changes in behavior. Concurrency remains a strategic target for HIV combination prevention programs in this context.

From single fiber to macro-level mechanics: A structural finite-element model for elastomeric fibrous biomaterials

PubMed Central

D'Amore, Antonio; Amoroso, Nicholas; Gottardi, Riccardo; Hobson, Christopher; Carruthers, Christopher; Watkins, Simon; Wagner, William R.; Sacks, Michael S.

2014-01-01

In the present work, we demonstrate that the mesoscopic in-plane mechanical behavior of membrane elastomeric scaffolds can be simulated by replication of actual quantified fibrous geometries. Elastomeric electrospun polyurethane (ES-PEUU) scaffolds, with and without particulate inclusions, were utilized. Simulations were developed from experimentally-derived fiber network geometries, based on a range of scaffold isotropic and anisotropic behaviors. These were chosen to evaluate the effects on macro-mechanics based on measurable geometric parameters such as fiber intersections, connectivity, orientation, and diameter. Simulations were conducted with only the fiber material model parameters adjusted to match the macro-level mechanical test data. Fiber model validation was performed at the microscopic level by individual fiber mechanical tests using AFM. Results demonstrated very good agreement to the experimental data, and revealed the formation of extended preferential fiber orientations spanning the entire model space. We speculate that these emergent structures may be responsible for the tissue-like macroscale behaviors observed in electrospun scaffolds. To conclude, the modeling approach has implications for (1) gaining insight on the intricate relationship between fabrication variables, structure, and mechanics to manufacture more functional devices/materials, (2) elucidating the effects of cell or particulate inclusions on global construct mechanics, and (3) fabricating better performing tissue surrogates that could recapitulate native tissue mechanics. PMID:25128869

Protein Kinase A Deregulation in the Medial Prefrontal Cortex Impairs Working Memory in Murine Oligophrenin-1 Deficiency.

PubMed

Zhang, Chun-Lei; Aime, Mattia; Laheranne, Emilie; Houbaert, Xander; El Oussini, Hajer; Martin, Christelle; Lepleux, Marilyn; Normand, Elisabeth; Chelly, Jamel; Herzog, Etienne; Billuart, Pierre; Humeau, Yann

2017-11-15

Classical and systems genetics have identified wide networks of genes associated with cognitive and neurodevelopmental diseases. In parallel to deciphering the role of each of these genes in neuronal or synaptic function, evaluating the response of neuronal and molecular networks to gene loss of function could reveal some pathophysiological mechanisms potentially accessible to nongenetic therapies. Loss of function of the Rho-GAP oligophrenin-1 is associated with cognitive impairments in both human and mouse. Upregulation of both PKA and ROCK has been reported in Ophn1 -/ y mice, but it remains unclear whether kinase hyperactivity contributes to the behavioral phenotypes. In this study, we thoroughly characterized a prominent perseveration phenotype displayed by Ophn1 -deficient mice using a Y-maze spatial working memory (SWM) test. We report that Ophn1 deficiency in the mouse generated severe cognitive impairments, characterized by both a high occurrence of perseverative behaviors and a lack of deliberation during the SWM test. In vivo and in vitro pharmacological experiments suggest that PKA dysregulation in the mPFC underlies cognitive dysfunction in Ophn1 -deficient mice, as assessed using a delayed spatial alternation task results. Functionally, mPFC neuronal networks appeared to be affected in a PKA-dependent manner, whereas hippocampal-PFC projections involved in SWM were not affected in Ophn1 -/y mice. Thus, we propose that discrete gene mutations in intellectual disability might generate "secondary" pathophysiological mechanisms, which are prone to become pharmacological targets for curative strategies in adult patients. SIGNIFICANCE STATEMENT Here we report that Ophn1 deficiency generates severe impairments in performance at spatial working memory tests, characterized by a high occurrence of perseverative behaviors and a lack of decision making. This cognitive deficit is consecutive to PKA deregulation in the mPFC that prevents Ophn1 KO mice to exploit a correctly acquired rule. Functionally, mPFC neuronal networks appear to be affected in a PKA-dependent manner, whereas behaviorally important hippocampal projections were preserved by the mutation. Thus, we propose that discrete gene mutations in intellectual disability can generate "secondary" pathophysiological mechanisms prone to become pharmacological targets for curative strategies in adults. Copyright © 2017 the authors 0270-6474/17/3711114-13$15.00/0.

Evolution and development of brain networks: from Caenorhabditis elegans to Homo sapiens.

PubMed

Kaiser, Marcus; Varier, Sreedevi

2011-01-01

Neural networks show a progressive increase in complexity during the time course of evolution. From diffuse nerve nets in Cnidaria to modular, hierarchical systems in macaque and humans, there is a gradual shift from simple processes involving a limited amount of tasks and modalities to complex functional and behavioral processing integrating different kinds of information from highly specialized tissue. However, studies in a range of species suggest that fundamental similarities, in spatial and topological features as well as in developmental mechanisms for network formation, are retained across evolution. 'Small-world' topology and highly connected regions (hubs) are prevalent across the evolutionary scale, ensuring efficient processing and resilience to internal (e.g. lesions) and external (e.g. environment) changes. Furthermore, in most species, even the establishment of hubs, long-range connections linking distant components, and a modular organization, relies on similar mechanisms. In conclusion, evolutionary divergence leads to greater complexity while following essential developmental constraints.

Micromechanics and poroelasticity of hydrated cellulose networks.

PubMed

Lopez-Sanchez, P; Rincon, Mauricio; Wang, D; Brulhart, S; Stokes, J R; Gidley, M J

2014-06-09

The micromechanics of cellulose hydrogels have been investigated using a new rheological experimental approach, combined with simulation using a poroelastic constitutive model. A series of mechanical compression steps at different strain rates were performed as a function of cellulose hydrogel thickness, combined with small amplitude oscillatory shear after each step to monitor the viscoelasticity of the sample. During compression, bacterial cellulose hydrogels behaved as anisotropic materials with near zero Poisson's ratio. The micromechanics of the hydrogels altered with each compression as water was squeezed out of the structure, and microstructural changes were strain rate-dependent, with increased densification of the cellulose network and increased cellulose fiber aggregation observed for slower compressive strain rates. A transversely isotropic poroelastic model was used to explain the observed micromechanical behavior, showing that the mechanical properties of cellulose networks in aqueous environments are mainly controlled by the rate of water movement within the structure.

Time Series Analysis of the Bacillus subtilis Sporulation Network Reveals Low Dimensional Chaotic Dynamics.

PubMed

Lecca, Paola; Mura, Ivan; Re, Angela; Barker, Gary C; Ihekwaba, Adaoha E C

2016-01-01

Chaotic behavior refers to a behavior which, albeit irregular, is generated by an underlying deterministic process. Therefore, a chaotic behavior is potentially controllable. This possibility becomes practically amenable especially when chaos is shown to be low-dimensional, i.e., to be attributable to a small fraction of the total systems components. In this case, indeed, including the major drivers of chaos in a system into the modeling approach allows us to improve predictability of the systems dynamics. Here, we analyzed the numerical simulations of an accurate ordinary differential equation model of the gene network regulating sporulation initiation in Bacillus subtilis to explore whether the non-linearity underlying time series data is due to low-dimensional chaos. Low-dimensional chaos is expectedly common in systems with few degrees of freedom, but rare in systems with many degrees of freedom such as the B. subtilis sporulation network. The estimation of a number of indices, which reflect the chaotic nature of a system, indicates that the dynamics of this network is affected by deterministic chaos. The neat separation between the indices obtained from the time series simulated from the model and those obtained from time series generated by Gaussian white and colored noise confirmed that the B. subtilis sporulation network dynamics is affected by low dimensional chaos rather than by noise. Furthermore, our analysis identifies the principal driver of the networks chaotic dynamics to be sporulation initiation phosphotransferase B (Spo0B). We then analyzed the parameters and the phase space of the system to characterize the instability points of the network dynamics, and, in turn, to identify the ranges of values of Spo0B and of the other drivers of the chaotic dynamics, for which the whole system is highly sensitive to minimal perturbation. In summary, we described an unappreciated source of complexity in the B. subtilis sporulation network by gathering evidence for the chaotic behavior of the system, and by suggesting candidate molecules driving chaos in the system. The results of our chaos analysis can increase our understanding of the intricacies of the regulatory network under analysis, and suggest experimental work to refine our behavior of the mechanisms underlying B. subtilis sporulation initiation control.

Functional connectivity correlates of response inhibition impairment in anorexia nervosa.

PubMed

Collantoni, Enrico; Michelon, Silvia; Tenconi, Elena; Degortes, Daniela; Titton, Francesca; Manara, Renzo; Clementi, Maurizio; Pinato, Claudia; Forzan, Monica; Cassina, Matteo; Santonastaso, Paolo; Favaro, Angela

2016-01-30

Anorexia nervosa (AN) is a disorder characterized by high levels of cognitive control and behavioral perseveration. The present study aims at exploring inhibitory control abilities and their functional connectivity correlates in patients with AN. Inhibitory control - an executive function that allows the realization of adaptive behavior according to environmental contingencies - has been assessed by means of the Stop-Signal paradigm. The study involved 155 patients with lifetime AN and 102 healthy women. A subsample underwent resting-state functional magnetic resonance imaging and was genotyped for COMT and 5-HTTLPR polymorphisms. AN patients showed an impaired response inhibition and a disruption of the functional connectivity of the ventral attention circuit, a neural network implicated in behavioral response when a stimulus occurs unexpected. The 5-HTTLPR genotype appears to significantly interact with the functional connectivity of ventral attention network in explaining task performance in both patients and controls, suggesting a role of the serotoninergic system in mechanisms of response selection. The disruption of the ventral attention network in patients with AN suggests lower efficiency of bottom-up signal filtering, which might be involved in difficulties to adapt behavioral responses to environmental needs. Our findings deserve further research to confirm their scientific and therapeutic implications. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A spread willingness computing-based information dissemination model.

PubMed

Huang, Haojing; Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

A Spread Willingness Computing-Based Information Dissemination Model

PubMed Central

Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network. PMID:25110738

Lateral Prefrontal Cortex Subregions Make Dissociable Contributions during Fluid Reasoning

PubMed Central

Thompson, Russell; Duncan, John; Owen, Adrian M.

2011-01-01

Reasoning is a key component of adaptable “executive” behavior and is known to depend on a network of frontal and parietal brain regions. However, the mechanisms by which this network supports reasoning and adaptable behavior remain poorly defined. Here, we examine the relationship between reasoning, executive control, and frontoparietal function in a series of nonverbal reasoning experiments. Our results demonstrate that, in accordance with previous studies, a network of frontal and parietal brain regions is recruited during reasoning. Our results also reveal that this network can be fractionated according to how different subregions respond when distinct reasoning demands are manipulated. While increased rule complexity modulates activity within a right lateralized network including the middle frontal gyrus and the superior parietal cortex, analogical reasoning demand—or the requirement to remap rules on to novel features—recruits the left inferior rostrolateral prefrontal cortex and the lateral occipital complex. In contrast, the posterior extent of the inferior frontal gyrus, associated with simpler executive demands, is not differentially sensitive to rule complexity or analogical demand. These findings accord well with the hypothesis that different reasoning demands are supported by different frontal and parietal subregions. PMID:20483908

Altered Intrinsic Hippocmapus Declarative Memory Network and Its Association with Impulsivity in Abstinent Heroin Dependent Subjects

PubMed Central

Zhai, Tian-Ye; Shao, Yong-Cong; Xie, Chun-Ming; Ye, En-Mao; Zou, Feng; Fu, Li-Ping; Li, Wen-Jun; Chen, Gang; Chen, Guang-Yu; Zhang, Zheng-Guo; Li, Shi-Jiang; Yang, Zheng

2014-01-01

Converging evidence suggests that addiction can be considered a disease of aberrant learning and memory with impulsive decision-making. In the past decades, numerous studies have demonstrated that drug addiction is involved in multiple memory systems such as classical conditioned drug memory, instrumental learning memory and the habitual learning memory. However, most of these studies have focused on the contributions of non-declarative memory, and declarative memory has largely been neglected in the research of addiction. Based on a recent finding that hippocampus, as a core functioning region of declarative memory, was proved biased the decision-making process based on past experiences by spreading associated reward values throughout memory. Our present study focused on the hippocampus. By utilizing seed-based network analysis on the resting-state functional MRI datasets with the seed hippocampus we tested how the intrinsic hippocampal memory network altered towards drug addiction, and examined how the functional connectivity strength within the altered hippocampal network correlated with behavioral index ‘impulsivity’. Our results demonstrated that HD group showed enhanced coherence between hippocampus which represents declarative memory system and non-declarative rewardguided learning memory system, and also showed attenuated intrinsic functional link between hippocampus and top-down control system, compared to the CN group. This alteration was furthered found to have behavioral significance over the behavioral index ‘impulsivity’ measured with Barratt Impulsiveness Scale (BIS). These results provide insights into the mechanism of declarative memory underlying the impulsive behavior in drug addiction. PMID:25008351

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

PubMed Central

Tse, Margaret J.; Chu, Brian K.; Roy, Mahua; Read, Elizabeth L.

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

Neural adaptation accounts for the dynamic resizing of peripersonal space: evidence from a psychophysical-computational approach.

PubMed

Noel, Jean-Paul; Blanke, Olaf; Magosso, Elisa; Serino, Andrea

2018-06-01

Interactions between the body and the environment occur within the peripersonal space (PPS), the space immediately surrounding the body. The PPS is encoded by multisensory (audio-tactile, visual-tactile) neurons that possess receptive fields (RFs) anchored on the body and restricted in depth. The extension in depth of PPS neurons' RFs has been documented to change dynamically as a function of the velocity of incoming stimuli, but the underlying neural mechanisms are still unknown. Here, by integrating a psychophysical approach with neural network modeling, we propose a mechanistic explanation behind this inherent dynamic property of PPS. We psychophysically mapped the size of participant's peri-face and peri-trunk space as a function of the velocity of task-irrelevant approaching auditory stimuli. Findings indicated that the peri-trunk space was larger than the peri-face space, and, importantly, as for the neurophysiological delineation of RFs, both of these representations enlarged as the velocity of incoming sound increased. We propose a neural network model to mechanistically interpret these findings: the network includes reciprocal connections between unisensory areas and higher order multisensory neurons, and it implements neural adaptation to persistent stimulation as a mechanism sensitive to stimulus velocity. The network was capable of replicating the behavioral observations of PPS size remapping and relates behavioral proxies of PPS size to neurophysiological measures of multisensory neurons' RF size. We propose that a biologically plausible neural adaptation mechanism embedded within the network encoding for PPS can be responsible for the dynamic alterations in PPS size as a function of the velocity of incoming stimuli. NEW & NOTEWORTHY Interactions between body and environment occur within the peripersonal space (PPS). PPS neurons are highly dynamic, adapting online as a function of body-object interactions. The mechanistic underpinning PPS dynamic properties are unexplained. We demonstrate with a psychophysical approach that PPS enlarges as incoming stimulus velocity increases, efficiently preventing contacts with faster approaching objects. We present a neurocomputational model of multisensory PPS implementing neural adaptation to persistent stimulation to propose a neurophysiological mechanism underlying this effect.

Optimization behavior of brainstem respiratory neurons. A cerebral neural network model.

PubMed

Poon, C S

1991-01-01

A recent model of respiratory control suggested that the steady-state respiratory responses to CO2 and exercise may be governed by an optimal control law in the brainstem respiratory neurons. It was not certain, however, whether such complex optimization behavior could be accomplished by a realistic biological neural network. To test this hypothesis, we developed a hybrid computer-neural model in which the dynamics of the lung, brain and other tissue compartments were simulated on a digital computer. Mimicking the "controller" was a human subject who pedalled on a bicycle with varying speed (analog of ventilatory output) with a view to minimize an analog signal of the total cost of breathing (chemical and mechanical) which was computed interactively and displayed on an oscilloscope. In this manner, the visuomotor cortex served as a proxy (homolog) of the brainstem respiratory neurons in the model. Results in 4 subjects showed a linear steady-state ventilatory CO2 response to arterial PCO2 during simulated CO2 inhalation and a nearly isocapnic steady-state response during simulated exercise. Thus, neural optimization is a plausible mechanism for respiratory control during exercise and can be achieved by a neural network with cognitive computational ability without the need for an exercise stimulus.

The co-evolution of networks and prisoner’s dilemma game by considering sensitivity and visibility

NASA Astrophysics Data System (ADS)

Li, Dandan; Ma, Jing; Han, Dun; Sun, Mei; Tian, Lixin; Stanley, H. Eugene

2017-03-01

Strategies adopted by individuals in a social network significantly impact the network, and they strongly affect relationships between individuals in the network. Links between individuals also heavily influence their levels of cooperation. Taking into account the evolution of each individual’s connection, we explore how sensitivity and visibility affect the prisoner’s dilemma game. The so-called ‘sensitivity’ and ‘visibility’ respectively present one’s self-protection consciousness and the ability of gaining information. We find that at moderate levels of player sensitivity cooperative behavior increases, but that at high levels it is inhibited. We also find that the heterogeneity of the weight of individuals at the end of the game is higher when sensitivity and visibility are increased, but that the successful-defection-payoff has less impact on the weight of individuals and on the relationship between the heterogeneity of the weight of individuals and the density of cooperators. This framework can be used to clarify the interaction mechanism between the micro-level of individual behavior and the macro-level of individual co-evolutionary processes.

The co-evolution of networks and prisoner's dilemma game by considering sensitivity and visibility.

PubMed

Li, Dandan; Ma, Jing; Han, Dun; Sun, Mei; Tian, Lixin; Stanley, H Eugene

2017-03-24

Strategies adopted by individuals in a social network significantly impact the network, and they strongly affect relationships between individuals in the network. Links between individuals also heavily influence their levels of cooperation. Taking into account the evolution of each individual's connection, we explore how sensitivity and visibility affect the prisoner's dilemma game. The so-called 'sensitivity' and 'visibility' respectively present one's self-protection consciousness and the ability of gaining information. We find that at moderate levels of player sensitivity cooperative behavior increases, but that at high levels it is inhibited. We also find that the heterogeneity of the weight of individuals at the end of the game is higher when sensitivity and visibility are increased, but that the successful-defection-payoff has less impact on the weight of individuals and on the relationship between the heterogeneity of the weight of individuals and the density of cooperators. This framework can be used to clarify the interaction mechanism between the micro-level of individual behavior and the macro-level of individual co-evolutionary processes.

The co-evolution of networks and prisoner’s dilemma game by considering sensitivity and visibility

PubMed Central

Li, Dandan; Ma, Jing; Han, Dun; Sun, Mei; Tian, Lixin; Stanley, H. Eugene

2017-01-01

Strategies adopted by individuals in a social network significantly impact the network, and they strongly affect relationships between individuals in the network. Links between individuals also heavily influence their levels of cooperation. Taking into account the evolution of each individual’s connection, we explore how sensitivity and visibility affect the prisoner’s dilemma game. The so-called ‘sensitivity’ and ‘visibility’ respectively present one’s self-protection consciousness and the ability of gaining information. We find that at moderate levels of player sensitivity cooperative behavior increases, but that at high levels it is inhibited. We also find that the heterogeneity of the weight of individuals at the end of the game is higher when sensitivity and visibility are increased, but that the successful-defection-payoff has less impact on the weight of individuals and on the relationship between the heterogeneity of the weight of individuals and the density of cooperators. This framework can be used to clarify the interaction mechanism between the micro-level of individual behavior and the macro-level of individual co-evolutionary processes. PMID:28338070

Multi-channels coupling-induced pattern transition in a tri-layer neuronal network

NASA Astrophysics Data System (ADS)

Wu, Fuqiang; Wang, Ya; Ma, Jun; Jin, Wuyin; Hobiny, Aatef

2018-03-01

Neurons in nerve system show complex electrical behaviors due to complex connection types and diversity in excitability. A tri-layer network is constructed to investigate the signal propagation and pattern formation by selecting different coupling channels between layers. Each layer is set as different states, and the local kinetics is described by Hindmarsh-Rose neuron model. By changing the number of coupling channels between layers and the state of the first layer, the collective behaviors of each layer and synchronization pattern of network are investigated. A statistical factor of synchronization on each layer is calculated. It is found that quiescent state in the second layer can be excited and disordered state in the third layer is suppressed when the first layer is controlled by a pacemaker, and the developed state is dependent on the number of coupling channels. Furthermore, the collapse in the first layer can cause breakdown of other layers in the network, and the mechanism is that disordered state in the third layer is enhanced when sampled signals from the collapsed layer can impose continuous disturbance on the next layer.

Parents, friends, and romantic partners: enmeshment in deviant networks and adolescent delinquency involvement.

PubMed

Lonardo, Robert A; Giordano, Peggy C; Longmore, Monica A; Manning, Wendy D

2009-03-01

Adolescent networks include parents, friends, and romantic partners, but research on the social learning mechanisms related to delinquency has not typically examined the characteristics of all three domains simultaneously. Analyses draw on data from the Toledo Adolescent Relationships Study (n = 957), and our analytic sample contains 51% male and 49% female as well as 69% white, 24% African-American, and 7% Latino respondents. Parents,' peers,' and partners' deviance are each related to respondents' delinquency, and affiliation with a greater number of deviant networks is associated with higher self-reported involvement. Analyses that consider enmeshment type indicate that those with both above average romantic partner and friend delinquency report especially high levels of self-reported involvement. In all comparisons, adolescents with deviant romantic partners are more delinquent than those youths with more prosocial partners, regardless of friends' and parents' behavior. Findings highlight the importance of capturing the adolescent's entire network of affiliations, rather than viewing these in isolation, and suggest the need for additional research on romantic partner influences on delinquent behavior and other adolescent outcomes.

Subgenual anterior cingulate cortex controls sadness-induced modulations of cognitive and emotional network hubs.

PubMed

Ramirez-Mahaluf, Juan P; Perramon, Joan; Otal, Begonya; Villoslada, Pablo; Compte, Albert

2018-06-04

The regulation of cognitive and emotional processes is critical for proper executive functions and social behavior, but its specific mechanisms remain unknown. Here, we addressed this issue by studying with functional magnetic resonance imaging the changes in network topology that underlie competitive interactions between emotional and cognitive networks in healthy participants. Our behavioral paradigm contrasted periods with high emotional and cognitive demands by including a sadness provocation task followed by a spatial working memory task. The sharp contrast between successive tasks was designed to enhance the separability of emotional and cognitive networks and reveal areas that regulate the flow of information between them (hubs). By applying graph analysis methods on functional connectivity between 20 regions of interest in 22 participants we identified two main brain network modules, one dorsal and one ventral, and their hub areas: the left dorsolateral prefrontal cortex (dlPFC) and the left medial frontal pole (mFP). These hub areas did not modulate their mutual functional connectivity following sadness but they did so through an interposed area, the subgenual anterior cingulate cortex (sACC). Our results identify dlPFC and mFP as areas regulating interactions between emotional and cognitive networks, and suggest that their modulation by sadness experience is mediated by sACC.

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions.

PubMed

Blank, Idan A; Fedorenko, Evelina

2017-10-11

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this "multiple demand" (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people ( n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized "core language network", whereas domain-general mechanisms are implemented in the bilateral "multiple demand" (MD) network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking. Copyright © 2017 the authors 0270-6474/17/3710000-13$15.00/0.

The research on user behavior evaluation method for network state

NASA Astrophysics Data System (ADS)

Zhang, Chengyuan; Xu, Haishui

2017-08-01

Based on the correlation between user behavior and network running state, this paper proposes a method of user behavior evaluation based on network state. Based on the analysis and evaluation methods in other fields of study, we introduce the theory and tools of data mining. Based on the network status information provided by the trusted network view, the user behavior data and the network state data are analysed. Finally, we construct the user behavior evaluation index and weight, and on this basis, we can accurately quantify the influence degree of the specific behavior of different users on the change of network running state, so as to provide the basis for user behavior control decision.

Cluster Synchronization of Diffusively Coupled Nonlinear Systems: A Contraction-Based Approach

NASA Astrophysics Data System (ADS)

Aminzare, Zahra; Dey, Biswadip; Davison, Elizabeth N.; Leonard, Naomi Ehrich

2018-04-01

Finding the conditions that foster synchronization in networked nonlinear systems is critical to understanding a wide range of biological and mechanical systems. However, the conditions proved in the literature for synchronization in nonlinear systems with linear coupling, such as has been used to model neuronal networks, are in general not strict enough to accurately determine the system behavior. We leverage contraction theory to derive new sufficient conditions for cluster synchronization in terms of the network structure, for a network where the intrinsic nonlinear dynamics of each node may differ. Our result requires that network connections satisfy a cluster-input-equivalence condition, and we explore the influence of this requirement on network dynamics. For application to networks of nodes with FitzHugh-Nagumo dynamics, we show that our new sufficient condition is tighter than those found in previous analyses that used smooth or nonsmooth Lyapunov functions. Improving the analytical conditions for when cluster synchronization will occur based on network configuration is a significant step toward facilitating understanding and control of complex networked systems.

Mechanisms of transgenerational inheritance of addictive-like behaviors.

PubMed

Vassoler, F M; Sadri-Vakili, G

2014-04-04

Genetic factors are implicated in the heritability of drug abuse. However, even with advances in current technology no specific genes have been identified that are critical for the transmission of drug-induced phenotypes to subsequent generations. It is now evident that epigenetic factors contribute to disease heritability and represent a link between genes and the environment. Recently, epigenetic mechanisms have been shown to underlie drug-induced structural, synaptic, and behavioral plasticity by coordinating the expression of gene networks within the brain. Therefore, the epigenome provides a direct mechanism for drugs of abuse to influence the genetic events involved in the development of addiction as well as its heritability to subsequent generations. In this review we discuss the mechanisms underlying intergenerational epigenetic transmission, highlight studies that demonstrate this phenomenon with particular attention to the field of addiction, and identify gaps for future studies. Copyright © 2013 IBRO. Published by Elsevier Ltd. All rights reserved.

Collective helping and bystander effects in coevolving helping networks.

PubMed

Jo, Hang-Hyun; Lee, Hyun Keun; Park, Hyunggyu

2010-06-01

We study collective helping behavior and bystander effects in a coevolving helping network model. A node and a link of the network represents an agent who renders or receives help and a friendly relation between agents, respectively. A helping trial of an agent depends on relations with other involved agents and its result (success or failure) updates the relation between the helper and the recipient. We study the network link dynamics and its steady states analytically and numerically. The full phase diagram is presented with various kinds of active and inactive phases and the nature of phase transitions are explored. We find various interesting bystander effects, consistent with the field study results, of which the underlying mechanism is proposed.

FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model

PubMed Central

Yaghini Bonabi, Safa; Asgharian, Hassan; Safari, Saeed; Nili Ahmadabadi, Majid

2014-01-01

A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the network behavior is the subject of study. To solve the problem, we used computational techniques such as CORDIC (Coordinate Rotation Digital Computer) algorithm and step-by-step integration in the implementation of arithmetic circuits. In addition, we employed different techniques such as sharing resources to preserve the details of model as well as increasing the network size in addition to keeping the network execution speed close to real time while having high precision. Implementation of a two mini-columns network with 120/30 excitatory/inhibitory neurons is provided to investigate the characteristic of our method in practice. The implementation techniques provide an opportunity to construct large FPGA-based network models to investigate the effect of different neurophysiological mechanisms, like voltage-gated channels and synaptic activities, on the behavior of a neural network in an appropriate execution time. Additional to inherent properties of FPGA, like parallelism and re-configurability, our approach makes the FPGA-based system a proper candidate for study on neural control of cognitive robots and systems as well. PMID:25484854

Tensegrity I. Cell structure and hierarchical systems biology

NASA Technical Reports Server (NTRS)

Ingber, Donald E.

2003-01-01

In 1993, a Commentary in this journal described how a simple mechanical model of cell structure based on tensegrity architecture can help to explain how cell shape, movement and cytoskeletal mechanics are controlled, as well as how cells sense and respond to mechanical forces (J. Cell Sci. 104, 613-627). The cellular tensegrity model can now be revisited and placed in context of new advances in our understanding of cell structure, biological networks and mechanoregulation that have been made over the past decade. Recent work provides strong evidence to support the use of tensegrity by cells, and mathematical formulations of the model predict many aspects of cell behavior. In addition, development of the tensegrity theory and its translation into mathematical terms are beginning to allow us to define the relationship between mechanics and biochemistry at the molecular level and to attack the larger problem of biological complexity. Part I of this two-part article covers the evidence for cellular tensegrity at the molecular level and describes how this building system may provide a structural basis for the hierarchical organization of living systems--from molecule to organism. Part II, which focuses on how these structural networks influence information processing networks, appears in the next issue.

The Internet As a Large-Scale Complex System

NASA Astrophysics Data System (ADS)

Park, Kihong; Willinger, Walter

2005-06-01

The Internet may be viewed as a "complex system" with diverse features and many components that can give rise to unexpected emergent phenomena, revealing much about its own engineering. This book brings together chapter contributions from a workshop held at the Santa Fe Institute in March 2001. This volume captures a snapshot of some features of the Internet that may be fruitfully approached using a complex systems perspective, meaning using interdisciplinary tools and methods to tackle the subject area. The Internet penetrates the socioeconomic fabric of everyday life; a broader and deeper grasp of the Internet may be needed to meet the challenges facing the future. The resulting empirical data have already proven to be invaluable for gaining novel insights into the network's spatio-temporal dynamics, and can be expected to become even more important when tryin to explain the Internet's complex and emergent behavior in terms of elementary networking-based mechanisms. The discoveries of fractal or self-similar network traffic traces, power-law behavior in network topology and World Wide Web connectivity are instances of unsuspected, emergent system traits. Another important factor at the heart of fair, efficient, and stable sharing of network resources is user behavior. Network systems, when habited by selfish or greedy users, take on the traits of a noncooperative multi-party game, and their stability and efficiency are integral to understanding the overall system and its dynamics. Lastly, fault-tolerance and robustness of large-scale network systems can exhibit spatial and temporal correlations whose effective analysis and management may benefit from rescaling techniques applied in certain physical and biological systems. The present book will bring together several of the leading workers involved in the analysis of complex systems with the future development of the Internet.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity.

PubMed

Wang, Quan; Rothkopf, Constantin A; Triesch, Jochen

2017-08-01

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network's changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network's sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Transition of the functional brain network related to increasing cognitive demands.

PubMed

Finc, Karolina; Bonna, Kamil; Lewandowska, Monika; Wolak, Tomasz; Nikadon, Jan; Dreszer, Joanna; Duch, Włodzisław; Kühn, Simone

2017-04-22

Network neuroscience provides tools that can easily be used to verify main assumptions of the global workspace theory (GWT), such as the existence of highly segregated information processing during effortless tasks performance, engagement of multiple distributed networks during effortful tasks and the critical role of long-range connections in workspace formation. A number of studies support the assumptions of GWT by showing the reorganization of the whole-brain functional network during cognitive task performance; however, the involvement of specific large scale networks in the formation of workspace is still not well-understood. (1) to examine changes in the whole-brain functional network under increased cognitive demands of working memory during an n-back task, and their relationship with behavioral outcomes; and (2) to provide a comprehensive description of local changes that may be involved in the formation of the global workspace, using hub detection and network-based statistic. Our results show that network modularity decreased with increasing cognitive demands, and this change allowed us to predict behavioral performance. The number of connector hubs increased, whereas the number of provincial hubs decreased when the task became more demanding. We also found that the default mode network (DMN) increased its connectivity to other networks while decreasing connectivity between its own regions. These results, apart from replicating previous findings, provide a valuable insight into the mechanisms of the formation of the global workspace, highlighting the role of the DMN in the processes of network integration. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A game-theoretical pricing mechanism for multiuser rate allocation for video over WiMAX

NASA Astrophysics Data System (ADS)

Chen, Chao-An; Lo, Chi-Wen; Lin, Chia-Wen; Chen, Yung-Chang

2010-07-01

In multiuser rate allocation in a wireless network, strategic users can bias the rate allocation by misrepresenting their bandwidth demands to a base station, leading to an unfair allocation. Game-theoretical approaches have been proposed to address the unfair allocation problems caused by the strategic users. However, existing approaches rely on a timeconsuming iterative negotiation process. Besides, they cannot completely prevent unfair allocations caused by inconsistent strategic behaviors. To address these problems, we propose a Search Based Pricing Mechanism to reduce the communication time and to capture a user's strategic behavior. Our simulation results show that the proposed method significantly reduce the communication time as well as converges stably to an optimal allocation.

Rheological behaviors of doughs reconstituted from wheat gluten and starch.

PubMed

Yang, Yanyan; Song, Yihu; Zheng, Qiang

2011-08-01

Hydrated starch-gluten reconstituted doughs were prepared and dynamic rheological tests of the reconstituted doughs were performed using dynamic strain and dynamic frequency sweep modes. Influence of starch/gluten ratio on rheological behaviors of the reconstituted doughs was investigated. The results showed that the reconstituted doughs exhibited nonlinear rheological behavior with increasing strain. The mechanical spectra revealed predominantly elastic characteristics in frequency range from 10(-1) rad s(-1) to 10(2) rad s(-1). Cole-Cole functions were applied to fit the mechanical spectra to reveal the influence of starch/gluten ratio on Plateau modulus and longest relaxation time of the dough network. The time-temperature superposition principle was applicable to a narrow temperature range of 25°C ~40°C while it failed at 50°C due to swelling and gelatinization of the starch.

Stochastic amplification and signaling in enzymatic futile cycles through noise-induced bistability with oscillations

NASA Astrophysics Data System (ADS)

Samoilov, Michael; Plyasunov, Sergey; Arkin, Adam P.

2005-02-01

Stochastic effects in biomolecular systems have now been recognized as a major physiologically and evolutionarily important factor in the development and function of many living organisms. Nevertheless, they are often thought of as providing only moderate refinements to the behaviors otherwise predicted by the classical deterministic system description. In this work we show by using both analytical and numerical investigation that at least in one ubiquitous class of (bio)chemical-reaction mechanisms, enzymatic futile cycles, the external noise may induce a bistable oscillatory (dynamic switching) behavior that is both quantitatively and qualitatively different from what is predicted or possible deterministically. We further demonstrate that the noise required to produce these distinct properties can itself be caused by a set of auxiliary chemical reactions, making it feasible for biological systems of sufficient complexity to generate such behavior internally. This new stochastic dynamics then serves to confer additional functional modalities on the enzymatic futile cycle mechanism that include stochastic amplification and signaling, the characteristics of which could be controlled by both the type and parameters of the driving noise. Hence, such noise-induced phenomena may, among other roles, potentially offer a novel type of control mechanism in pathways that contain these cycles and the like units. In particular, observations of endogenous or externally driven noise-induced dynamics in regulatory networks may thus provide additional insight into their topology, structure, and kinetics. network motif | signal transduction | chemical reaction | synthetic biology | systems biology

THE FUNCTIONAL ARCHITECTURE OF DEHYDRATION-ANOREXIA

PubMed Central

Watts, Alan G.; Boyle, Christina N.

2010-01-01

The anorexia that accompanies the drinking of hypertonic saline (DE-anorexia) is a critical adaptive behavioral mechanism that helps protect the integrity of fluid compartments during extended periods of cellular dehydration. Feeding is rapidly reinstated once drinking water is made available again. The relative simplicity and reproducibility of these behaviors makes DE-anorexia a very useful model for investigating how the various neural networks that control ingestive behaviors first suppress and then reinstate feeding. We show that DE-anorexia develops primarily because the mechanisms that terminate ongoing meals are upregulated in such a way as to significantly reduce meal size. At the same time however, signals generated by the ensuing negative energy balance appropriately activate neural mechanisms that can increase food intake. But as the output from these two competing processes is integrated, the net result is an increasing reduction of nocturnal food intake, despite the fact that spontaneous meals are initiated with the same frequency as in control animals. Furthermore, hypothalamic NPY injections also stimulate feeding in DE-anorexic animals with the same latency as controls, but again meals are prematurely terminated. Comparing Fos expression patterns across the brain following 2-deoxyglucose administration to control and DE-anorexic animals implicates neurons in the descending part of the parvicellular paraventricular nucleus of the hypothalamus and the lateral hypothalamic areas as key components of the networks that control DE-anorexia. Finally, DE-anorexia generates multiple inhibitory processes to suppress feeding. These are differentially disengaged once drinking water is reinstated. PMID:20399797

Domain-General Brain Regions Do Not Track Linguistic Input as Closely as Language-Selective Regions

PubMed Central

Fedorenko, Evelina

2017-01-01

Language comprehension engages a cortical network of left frontal and temporal regions. Activity in this network is language-selective, showing virtually no modulation by nonlinguistic tasks. In addition, language comprehension engages a second network consisting of bilateral frontal, parietal, cingulate, and insular regions. Activity in this “multiple demand” (MD) network scales with comprehension difficulty, but also with cognitive effort across a wide range of nonlinguistic tasks in a domain-general fashion. Given the functional dissociation between the language and MD networks, their respective contributions to comprehension are likely distinct, yet such differences remain elusive. Prior neuroimaging studies have suggested that activity in each network covaries with some linguistic features that, behaviorally, influence on-line processing and comprehension. This sensitivity of the language and MD networks to local input characteristics has often been interpreted, implicitly or explicitly, as evidence that both networks track linguistic input closely, and in a manner consistent across individuals. Here, we used fMRI to directly test this assumption by comparing the BOLD signal time courses in each network across different people (n = 45, men and women) listening to the same story. Language network activity showed fewer individual differences, indicative of closer input tracking, whereas MD network activity was more idiosyncratic and, moreover, showed lower reliability within an individual across repetitions of a story. These findings constrain cognitive models of language comprehension by suggesting a novel distinction between the processes implemented in the language and MD networks. SIGNIFICANCE STATEMENT Language comprehension recruits both language-specific mechanisms and domain-general mechanisms that are engaged in many cognitive processes. In the human cortex, language-selective mechanisms are implemented in the left-lateralized “core language network”, whereas domain-general mechanisms are implemented in the bilateral “multiple demand” (MD) network. Here, we report the first direct comparison of the respective contributions of these networks to naturalistic story comprehension. Using a novel combination of neuroimaging approaches we find that MD regions track stories less closely than language regions. This finding constrains the possible contributions of the MD network to comprehension, contrasts with accounts positing that this network has continuous access to linguistic input, and suggests a new typology of comprehension processes based on their extent of input tracking. PMID:28871034

Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

PubMed Central

Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

2016-01-01

Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

Predicting language diversity with complex networks.

PubMed

Raducha, Tomasz; Gubiec, Tomasz

2018-01-01

We analyze the model of social interactions with coevolution of the topology and states of the nodes. This model can be interpreted as a model of language change. We propose different rewiring mechanisms and perform numerical simulations for each. Obtained results are compared with the empirical data gathered from two online databases and anthropological study of Solomon Islands. We study the behavior of the number of languages for different system sizes and we find that only local rewiring, i.e. triadic closure, is capable of reproducing results for the empirical data in a qualitative manner. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change.

A model for compression-weakening materials and the elastic fields due to contractile cells

NASA Astrophysics Data System (ADS)

Rosakis, Phoebus; Notbohm, Jacob; Ravichandran, Guruswami

2015-12-01

We construct a homogeneous, nonlinear elastic constitutive law that models aspects of the mechanical behavior of inhomogeneous fibrin networks. Fibers in such networks buckle when in compression. We model this as a loss of stiffness in compression in the stress-strain relations of the homogeneous constitutive model. Problems that model a contracting biological cell in a finite matrix are solved. It is found that matrix displacements and stresses induced by cell contraction decay slower (with distance from the cell) in a compression weakening material than linear elasticity would predict. This points toward a mechanism for long-range cell mechanosensing. In contrast, an expanding cell would induce displacements that decay faster than in a linear elastic matrix.

The neurobiology of the emotional adolescent: From the inside out

PubMed Central

Guyer, Amanda E.; Silk, Jennifer S.; Nelson, Eric E.

2016-01-01

Adolescents are commonly portrayed as highly emotional, with their behaviors often hijacked by their emotions. Research on the neural substrates of adolescent affective behavior is beginning to paint a more nuanced picture of how neurodevelopmental changes in brain function influence affective behavior, and how these influences are modulated by external factors in the environment. Recent neurodevelopmental models suggest that the brain is designed to promote emotion regulation, learning, and affiliation across development, and that affective behavior reciprocally interacts with age-specific social demands and different social contexts. In this review, we discuss current findings on neurobiological mechanisms of adolescents’ affective behavior and highlight individual differences in and social-contextual influences on adolescents’ emotionality. Neurobiological mechanisms of affective processes related to anxiety and depression are also discussed as examples. As the field progresses, it will be critical to test new hypotheses generated from the foundational empirical and conceptual work and to focus on identifying more precisely how and when neural networks change in ways that promote or thwart adaptive affective behavior during adolescence. PMID:27506384

Theory of mind mediates the prospective relationship between abnormal social brain network morphology and chronic behavior problems after pediatric traumatic brain injury.

PubMed

Ryan, Nicholas P; Catroppa, Cathy; Beare, Richard; Silk, Timothy J; Crossley, Louise; Beauchamp, Miriam H; Yeates, Keith Owen; Anderson, Vicki A

2016-04-01

Childhood and adolescence coincide with rapid maturation and synaptic reorganization of distributed neural networks that underlie complex cognitive-affective behaviors. These regions, referred to collectively as the 'social brain network' (SBN) are commonly vulnerable to disruption from pediatric traumatic brain injury (TBI); however, the mechanisms that link morphological changes in the SBN to behavior problems in this population remain unclear. In 98 children and adolescents with mild to severe TBI, we acquired 3D T1-weighted MRIs at 2-8 weeks post-injury. For comparison, 33 typically developing controls of similar age, sex and education were scanned. All participants were assessed on measures of Theory of Mind (ToM) at 6 months post-injury and parents provided ratings of behavior problems at 24-months post-injury. Severe TBI was associated with volumetric reductions in the overall SBN package, as well as regional gray matter structural change in multiple component regions of the SBN. When compared with TD controls and children with milder injuries, the severe TBI group had significantly poorer ToM, which was associated with more frequent behavior problems and abnormal SBN morphology. Mediation analysis indicated that impaired theory of mind mediated the prospective relationship between abnormal SBN morphology and more frequent chronic behavior problems. Our findings suggest that sub-acute alterations in SBN morphology indirectly contribute to long-term behavior problems via their influence on ToM. Volumetric change in the SBN and its putative hub regions may represent useful imaging biomarkers for prediction of post-acute social cognitive impairment, which may in turn elevate risk for chronic behavior problems. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Theoretical study of a thermo-acousto-electric generator equipped with an electroacoustic feedback loop

NASA Astrophysics Data System (ADS)

Olivier, Come; Penelet, Guillaume; Poignand, Gaelle; Lotton, Pierrick

2015-10-01

A simplified model of a Stirling-type thermoacoustic engine coupled to a resonant mechanical system is presented. The acoustic network is presented as its temperature-dependent lumped element equivalent, and the nonlinear effects involved in such engines are accounted for in a nonlinear heat equation governing the temperature distribution through the thermoacoustic core. The low-order model is sufficient to capture the behavior of the engine, both in terms of stability and dynamic behavior.

Advances in the mechanical modeling of filamentous actin and its cross-linked networks on multiple scales.

PubMed

Unterberger, Michael J; Holzapfel, Gerhard A

2014-11-01

The protein actin is a part of the cytoskeleton and, therefore, responsible for the mechanical properties of the cells. Starting with the single molecule up to the final structure, actin creates a hierarchical structure of several levels exhibiting a remarkable behavior. The hierarchy spans several length scales and limitations in computational power; therefore, there is a call for different mechanical modeling approaches for the different scales. On the molecular level, we may consider each atom in molecular dynamics simulations. Actin forms filaments by combining the molecules into a double helix. In a model, we replace molecular subdomains using coarse-graining methods, allowing the investigation of larger systems of several atoms. These models on the nanoscale inform continuum mechanical models of large filaments, which are based on worm-like chain models for polymers. Assemblies of actin filaments are connected with cross-linker proteins. Models with discrete filaments, so-called Mikado models, allow us to investigate the dependence of the properties of networks on the parameters of the constituents. Microstructurally motivated continuum models of the networks provide insights into larger systems containing cross-linked actin networks. Modeling of such systems helps to gain insight into the processes on such small scales. On the other hand, they call for verification and hence trigger the improvement of established experiments and the development of new methods.

Cell-ECM Interactions During Cancer Invasion

NASA Astrophysics Data System (ADS)

Jiang, Yi

The extracellular matrix (ECM), a fibrous material that forms a network in a tissue, significantly affects many aspects of cellular behavior, including cell movement and proliferation. Transgenic mouse tumor studies indicate that excess collagen, a major component of ECM, enhances tumor formation and invasiveness. Clinically, tumor associated collagen signatures are strong markers for breast cancer survival. However, the underlying mechanisms are unclear since the properties of ECM are complex, with diverse structural and mechanical properties depending on various biophysical parameters. We have developed a three-dimensional elastic fiber network model, and parameterized it with in vitro collagen mechanics. Using this model, we study ECM remodeling as a result of local deformation and cell migration through the ECM as a network percolation problem. We have also developed a three-dimensional, multiscale model of cell migration and interaction with ECM. Our model reproduces quantitative single cell migration experiments. This model is a first step toward a fully biomechanical cell-matrix interaction model and may shed light on tumor associated collagen signatures in breast cancer. This work was partially supported by NIH-U01CA143069.

Mechanisms and neuronal networks involved in reactive and proactive cognitive control of interference in working memory.

PubMed

Irlbacher, Kerstin; Kraft, Antje; Kehrer, Stefanie; Brandt, Stephan A

2014-10-01

Cognitive control can be reactive or proactive in nature. Reactive control mechanisms, which support the resolution of interference, start after its onset. Conversely, proactive control involves the anticipation and prevention of interference prior to its occurrence. The interrelation of both types of cognitive control is currently under debate: Are they mediated by different neuronal networks? Or are there neuronal structures that have the potential to act in a proactive as well as in a reactive manner? This review illustrates the way in which integrating knowledge gathered from behavioral studies, functional imaging, and human electroencephalography proves useful in answering these questions. We focus on studies that investigate interference resolution at the level of working memory representations. In summary, different mechanisms are instrumental in supporting reactive and proactive control. Distinct neuronal networks are involved, though some brain regions, especially pre-SMA, possess functions that are relevant to both control modes. Therefore, activation of these brain areas could be observed in reactive, as well as proactive control, but at different times during information processing. Copyright © 2014 Elsevier Ltd. All rights reserved.

Promoting cooperation by preventing exploitation: The role of network structure

NASA Astrophysics Data System (ADS)

Utkovski, Zoran; Stojkoski, Viktor; Basnarkov, Lasko; Kocarev, Ljupco

2017-08-01

A growing body of empirical evidence indicates that social and cooperative behavior can be affected by cognitive and neurological factors, suggesting the existence of state-based decision-making mechanisms that may have emerged by evolution. Motivated by these observations, we propose a simple mechanism of anonymous network interactions identified as a form of generalized reciprocity—a concept organized around the premise "help anyone if helped by someone'—and study its dynamics on random graphs. In the presence of such a mechanism, the evolution of cooperation is related to the dynamics of the levels of investments (i.e., probabilities of cooperation) of the individual nodes engaging in interactions. We demonstrate that the propensity for cooperation is determined by a network centrality measure here referred to as neighborhood importance index and discuss relevant implications to natural and artificial systems. To address the robustness of the state-based strategies to an invasion of defectors, we additionally provide an analysis which redefines the results for the case when a fraction of the nodes behave as unconditional defectors.

An Appraisal of Social Network Theory and Analysis as Applied to Public Health: Challenges and Opportunities.

PubMed

Valente, Thomas W; Pitts, Stephanie R

2017-03-20

The use of social network theory and analysis methods as applied to public health has expanded greatly in the past decade, yielding a significant academic literature that spans almost every conceivable health issue. This review identifies several important theoretical challenges that confront the field but also provides opportunities for new research. These challenges include (a) measuring network influences, (b) identifying appropriate influence mechanisms, (c) the impact of social media and computerized communications, (d) the role of networks in evaluating public health interventions, and (e) ethics. Next steps for the field are outlined and the need for funding is emphasized. Recently developed network analysis techniques, technological innovations in communication, and changes in theoretical perspectives to include a focus on social and environmental behavioral influences have created opportunities for new theory and ever broader application of social networks to public health topics.

Modeling semiflexible polymer networks

NASA Astrophysics Data System (ADS)

Broedersz, C. P.; MacKintosh, F. C.

2014-07-01

This is an overview of theoretical approaches to semiflexible polymers and their networks. Such semiflexible polymers have large bending rigidities that can compete with the entropic tendency of a chain to crumple up into a random coil. Many studies on semiflexible polymers and their assemblies have been motivated by their importance in biology. Indeed, cross-linked networks of semiflexible polymers form a major structural component of tissue and living cells. Reconstituted networks of such biopolymers have emerged as a new class of biological soft matter systems with remarkable material properties, which have spurred many of the theoretical developments discussed here. Starting from the mechanics and dynamics of individual semiflexible polymers, the physics of semiflexible bundles, entangled solutions, and disordered cross-linked networks are reviewed. Finally, recent developments on marginally stable fibrous networks, which exhibit critical behavior similar to other marginal systems such as jammed soft matter, are discussed.

Experimental and modeling study of Newtonian and non-Newtonian fluid flow in pore network micromodels.

PubMed

Perrin, Christian L; Tardy, Philippe M J; Sorbie, Ken S; Crawshaw, John C

2006-03-15

The in situ rheology of polymeric solutions has been studied experimentally in etched silicon micromodels which are idealizations of porous media. The rectangular channels in these etched networks have dimensions typical of pore sizes in sandstone rocks. Pressure drop/flow rate relations have been measured for water and non-Newtonian hydrolyzed-polyacrylamide (HPAM) solutions in both individual straight rectangular capillaries and in networks of such capillaries. Results from these experiments have been analyzed using pore-scale network modeling incorporating the non-Newtonian fluid mechanics of a Carreau fluid. Quantitative agreement is seen between the experiments and the network calculations in the Newtonian and shear-thinning flow regions demonstrating that the 'shift factor,'alpha, can be calculated a priori. Shear-thickening behavior was observed at higher flow rates in the micromodel experiments as a result of elastic effects becoming important and this remains to be incorporated in the network model.

Recurrent Network models of sequence generation and memory

PubMed Central

Rajan, Kanaka; Harvey, Christopher D; Tank, David W

2016-01-01

SUMMARY Sequential activation of neurons is a common feature of network activity during a variety of behaviors, including working memory and decision making. Previous network models for sequences and memory emphasized specialized architectures in which a principled mechanism is pre-wired into their connectivity. Here, we demonstrate that starting from random connectivity and modifying a small fraction of connections, a largely disordered recurrent network can produce sequences and implement working memory efficiently. We use this process, called Partial In-Network training (PINning), to model and match cellular-resolution imaging data from the posterior parietal cortex during a virtual memory-guided two-alternative forced choice task [Harvey, Coen and Tank, 2012]. Analysis of the connectivity reveals that sequences propagate by the cooperation between recurrent synaptic interactions and external inputs, rather than through feedforward or asymmetric connections. Together our results suggest that neural sequences may emerge through learning from largely unstructured network architectures. PMID:26971945

Mechanically dynamic PDMS substrates to investigate changing cell environments

PubMed Central

Yeh, Yi-Cheun; Corbin, Elise A.; Caliari, Steven R.; Ouyang, Liu; Vega, Sebastián L.; Truitt, Rachel; Han, Lin; Margulies, Kenneth B.; Burdick, Jason A.

2018-01-01

Mechanics of the extracellular matrix (ECM) play a pivotal role in governing cell behavior, such as cell spreading and differentiation. ECM mechanics have been recapitulated primarily in elastic hydrogels, including with dynamic properties to mimic complex behaviors (e.g., fibrosis); however, these dynamic hydrogels fail to introduce the viscoelastic nature of many tissues. Here, we developed a two-step crosslinking strategy to first form (via platinum-catalyzed crosslinking) networks of polydimethylsiloxane (PDMS) and then to increase PDMS crosslinking (via thiol-ene click reaction) in a temporally-controlled manner. This photoinitiated reaction increased the compressive modulus of PDMS up to 10-fold within minutes and was conducted under cytocompatible conditions. With stiffening, cells displayed increased spreading, changing from ~1300 to 1900 μm2 and from ~2700 to 4600 μm2 for fibroblasts and mesenchymal stem cells, respectively. In addition, higher myofibroblast activation (from ~2 to 20%) for cardiac fibroblasts was observed with increasing PDMS substrate stiffness. These results indicate a cellular response to changes in PDMS substrate mechanics, along with a demonstration of a mechanically dynamic and photoresponsive PDMS substrate platform to model the dynamic behavior of ECM. PMID:28843064

Mechanically dynamic PDMS substrates to investigate changing cell environments.

PubMed

Yeh, Yi-Cheun; Corbin, Elise A; Caliari, Steven R; Ouyang, Liu; Vega, Sebastián L; Truitt, Rachel; Han, Lin; Margulies, Kenneth B; Burdick, Jason A

2017-11-01

Mechanics of the extracellular matrix (ECM) play a pivotal role in governing cell behavior, such as cell spreading and differentiation. ECM mechanics have been recapitulated primarily in elastic hydrogels, including with dynamic properties to mimic complex behaviors (e.g., fibrosis); however, these dynamic hydrogels fail to introduce the viscoelastic nature of many tissues. Here, we developed a two-step crosslinking strategy to first form (via platinum-catalyzed crosslinking) networks of polydimethylsiloxane (PDMS) and then to increase PDMS crosslinking (via thiol-ene click reaction) in a temporally-controlled manner. This photoinitiated reaction increased the compressive modulus of PDMS up to 10-fold within minutes and was conducted under cytocompatible conditions. With stiffening, cells displayed increased spreading, changing from ∼1300 to 1900 μm 2 and from ∼2700 to 4600 μm 2 for fibroblasts and mesenchymal stem cells, respectively. In addition, higher myofibroblast activation (from ∼2 to 20%) for cardiac fibroblasts was observed with increasing PDMS substrate stiffness. These results indicate a cellular response to changes in PDMS substrate mechanics, along with a demonstration of a mechanically dynamic and photoresponsive PDMS substrate platform to model the dynamic behavior of ECM. Copyright © 2017 Elsevier Ltd. All rights reserved.

Possible relation of water structural relaxation to water anomalies

PubMed Central

Mallamace, Francesco; Corsaro, Carmelo; Stanley, H. Eugene

2013-01-01

The anomalous behavior of thermodynamic response functions is an unsolved problem in the physics of water. The mechanism that gives rise to the dramatic indefinite increase at low temperature in the heat capacity, the compressibility, and the coefficient of thermal expansion, is unknown. We explore this problem by analyzing both new and existing experimental data on the power spectrum S(Q, ω) of bulk and confined water at ambient pressure. When decreasing the temperature, we find that the liquid undergoes a structural transformation coinciding with the onset of an extended hydrogen bond network. This network onset seems to give rise to the marked viscoelastic behavior, consistent with the interesting possibility that the sound velocity and response functions of water depend upon both the frequency and wave vector. PMID:23483053

The Neurobiology of Moral Behavior: Review and Neuropsychiatric Implications

PubMed Central

Mendez, Mario F.

2011-01-01

Morality may be innate to the human brain. This review examines the neurobiological evidence from research involving functional magnetic resonance imaging of normal subjects, developmental sociopathy, acquired sociopathy from brain lesions, and frontotemporal dementia. These studies indicate a “neuromoral” network for responding to moral dilemmas centered in the ventromedial prefrontal cortex and its connections, particularly on the right. The neurobiological evidence indicates the existence of automatic “prosocial” mechanisms for identification with others that are part of the moral brain. Patients with disorders involving this moral network have attenuated emotional reactions to the possibility of harming others and may perform sociopathic acts. The existence of this neuromoral system has major clinical implications for the management of patients with dysmoral behavior from brain disorders and for forensic neuropsychiatry. PMID:20173686

Cortical–Subcortical Interactions in Hypersomnia Disorders: Mechanisms Underlying Cognitive and Behavioral Aspects of the Sleep–Wake Cycle

PubMed Central

Larson-Prior, Linda J.; Ju, Yo-El; Galvin, James E.

2014-01-01

Subcortical circuits mediating sleep–wake functions have been well characterized in animal models, and corroborated by more recent human studies. Disruptions in these circuits have been identified in hypersomnia disorders (HDs) such as narcolepsy and Kleine–Levin Syndrome, as well as in neurodegenerative disorders expressing excessive daytime sleepiness. However, the behavioral expression of sleep–wake functions is not a simple on-or-off state determined by subcortical circuits, but encompasses a complex range of behaviors determined by the interaction between cortical networks and subcortical circuits. While conceived as disorders of sleep, HDs are equally disorders of wake, representing a fundamental instability in neural state characterized by lapses of alertness during wake. These episodic lapses in alertness and wakefulness are also frequently seen in neurodegenerative disorders where electroencephalogram demonstrates abnormal function in cortical regions associated with cognitive fluctuations (CFs). Moreover, functional connectivity MRI shows instability of cortical networks in individuals with CFs. We propose that the inability to stabilize neural state due to disruptions in the sleep–wake control networks is common to the sleep and cognitive dysfunctions seen in hypersomnia and neurodegenerative disorders. PMID:25309500

Effect of microstructure on high-temperature mechanical behavior of nickel-base superalloys for turbine disc applications

NASA Astrophysics Data System (ADS)

Sharpe, Heather Joan

2007-05-01

Engineers constantly seek advancements in the performance of aircraft and power generation engines, including, lower costs and emissions, and improved fuel efficiency. Nickel-base superalloys are the material of choice for turbine discs, which experience some of the highest temperatures and stresses in the engine. Engine performance is proportional to operating temperatures. Consequently, the high-temperature capabilities of disc materials limit the performance of gas-turbine engines. Therefore, any improvements to engine performance necessitate improved alloy performance. In order to take advantage of improvements in high-temperature capabilities through tailoring of alloy microstructure, the overall objectives of this work were to establish relationships between alloy processing and microstructure, and between microstructure and mechanical properties. In addition, the projected aimed to demonstrate the applicability of neural network modeling to the field of Ni-base disc alloy development and behavior. The first phase of this work addressed the issue of how microstructure varies with heat treatment and by what mechanisms these structures are formed. Further it considered how superalloy composition could account for microstructural variations from the same heat treatment. To study this, four next-generation Ni-base disc alloys were subjected to various controlled heat-treatments and the resulting microstructures were then quantified. These quantitative results were correlated to chemistry and processing, including solution temperature, cooling rate, and intermediate hold temperature. A complex interaction of processing steps and chemistry was found to contribute to all features measured; grain size, precipitate distribution, grain boundary serrations. Solution temperature, above a certain threshold, and cooling rate controlled grain size, while cooling rate and intermediate hold temperature controlled precipitate formation and grain boundary serrations. Diffusion, both intergranular and grain boundary, was identified as the most pertinent mechanism. Variations in chemistry between alloys created different amounts of gamma/gamma' misfit strain, which affected precipitate size and morphology. Next the question of how a disc alloy with differing microstructures would respond to constant or cyclic stresses as a function of time was addressed. To this end, mechanical testing at elevated temperatures was conducted, including tensile, hardness, creep deformation, creep crack growth and fatigue crack growth. Overall, mechanical properties were primarily related to the cooling rate during processing with hold temperatures being secondary. Whether the impact was positive or negative depended on the behavior under consideration. Fast cooling rates improved yield strength and creep resistance, but were detrimental to creep crack growth rates. The ability of precipitate particles to impede dislocation motion was the most frequently cited mechanism behind structure-property interaction. Neural network models were successfully generated for processing-structure predictions, as well as for structure-property predictions. Training data was limited, none-the-less models were able to predict outputs with minimal relative errors. This was achieved through careful balance between the number of inputs and amount of training data. Despite the demonstrated correlation between microstructure and yield strength, microstructural quantities did not need to be directly inputted. Neural networks were sufficiently sensitive as to infer these effects from processing and chemistry inputs. This result improves the efficiency of this technique, while also demonstrating the capability of neural network techniques. A full program of heat-treatment, microstructure quantification, mechanical testing, and neural network modeling was successfully applied to next generation Ni-base disc alloys. From this work the mechanisms of processing-structure and structure-property relationships were studied. Further, testing results were used to demonstrate the applicability of machine-learning techniques to the development and optimization of this family of superalloys.

Spontaneous scale-free structure in adaptive networks with synchronously dynamical linking

NASA Astrophysics Data System (ADS)

Yuan, Wu-Jie; Zhou, Jian-Fang; Li, Qun; Chen, De-Bao; Wang, Zhen

2013-08-01

Inspired by the anti-Hebbian learning rule in neural systems, we study how the feedback from dynamical synchronization shapes network structure by adding new links. Through extensive numerical simulations, we find that an adaptive network spontaneously forms scale-free structure, as confirmed in many real systems. Moreover, the adaptive process produces two nontrivial power-law behaviors of deviation strength from mean activity of the network and negative degree correlation, which exists widely in technological and biological networks. Importantly, these scalings are robust to variation of the adaptive network parameters, which may have meaningful implications in the scale-free formation and manipulation of dynamical networks. Our study thus suggests an alternative adaptive mechanism for the formation of scale-free structure with negative degree correlation, which means that nodes of high degree tend to connect, on average, with others of low degree and vice versa. The relevance of the results to structure formation and dynamical property in neural networks is briefly discussed as well.

Mental health and social networks in early adolescence: a dynamic study of objectively-measured social interaction behaviors.

PubMed

Pachucki, Mark C; Ozer, Emily J; Barrat, Alain; Cattuto, Ciro

2015-01-01

How are social interaction dynamics associated with mental health during early stages of adolescence? The goal of this study is to objectively measure social interactions and evaluate the roles that multiple aspects of the social environment--such as physical activity and food choice--may jointly play in shaping the structure of children's relationships and their mental health. The data in this study are drawn from a longitudinal network-behavior study conducted in 2012 at a private K-8 school in an urban setting in California. We recruited a highly complete network sample of sixth-graders (n = 40, 91% of grade, mean age = 12.3), and examined how two measures of distressed mental health (self-esteem and depressive symptoms) are positionally distributed in an early adolescent interaction network. We ascertained how distressed mental health shapes the structure of relationships over a three-month period, adjusting for relevant dimensions of the social environment. Cross-sectional analyses of interaction networks revealed that self-esteem and depressive symptoms are differentially stratified by gender. Specifically, girls with more depressive symptoms have interactions consistent with social inhibition, while boys' interactions suggest robustness to depressive symptoms. Girls higher in self-esteem tended towards greater sociability. Longitudinal network behavior models indicate that gender similarity and perceived popularity are influential in the formation of social ties. Greater school connectedness predicts the development of self-esteem, though social ties contribute to more self-esteem improvement among students who identify as European-American. Cross-sectional evidence shows associations between distressed mental health and students' network peers. However, there is no evidence that connected students' mental health status becomes more similar in their over time because of their network interactions. These findings suggest that mental health during early adolescence may be less subject to mechanisms of social influence than network research in even slightly older adolescents currently indicates. Copyright © 2014. Published by Elsevier Ltd.

Viscoplastic fracture transition of a biopolymer gel.

PubMed

Frieberg, Bradley R; Garatsa, Ray-Shimry; Jones, Ronald L; Bachert, John O; Crawshaw, Benjamin; Liu, X Michael; Chan, Edwin P

2018-06-13

Physical gels are swollen polymer networks consisting of transient crosslink junctions associated with hydrogen or ionic bonds. Unlike covalently crosslinked gels, these physical crosslinks are reversible thus enabling these materials to display highly tunable and dynamic mechanical properties. In this work, we study the polymer composition effects on the fracture behavior of a gelatin gel, which is a thermoreversible biopolymer gel consisting of denatured collagen chains bridging physical network junctions formed from triple helices. Below the critical volume fraction for chain entanglement, which we confirm via neutron scattering measurements, we find that the fracture behavior is consistent with a viscoplastic type process characterized by hydrodynamic friction of individual polymer chains through the polymer mesh to show that the enhancement in fracture scales inversely with the squared of the mesh size of the gelatin gel network. Above this critical volume fraction, the fracture process can be described by the Lake-Thomas theory that considers fracture as a chain scission process due to chain entanglements.

MEART: The Semi-Living Artist

PubMed Central

Bakkum, Douglas J.; Gamblen, Philip M.; Ben-Ary, Guy; Chao, Zenas C.; Potter, Steve M.

2007-01-01

Here, we and others describe an unusual neurorobotic project, a merging of art and science called MEART, the semi-living artist. We built a pneumatically actuated robotic arm to create drawings, as controlled by a living network of neurons from rat cortex grown on a multi-electrode array (MEA). Such embodied cultured networks formed a real-time closed-loop system which could now behave and receive electrical stimulation as feedback on its behavior. We used MEART and simulated embodiments, or animats, to study the network mechanisms that produce adaptive, goal-directed behavior. This approach to neural interfacing will help instruct the design of other hybrid neural-robotic systems we call hybrots. The interfacing technologies and algorithms developed have potential applications in responsive deep brain stimulation systems and for motor prosthetics using sensory components. In a broader context, MEART educates the public about neuroscience, neural interfaces, and robotics. It has paved the way for critical discussions on the future of bio-art and of biotechnology. PMID:18958276

Prefrontal mediation of the reading network predicts intervention response in dyslexia.

PubMed

Aboud, Katherine S; Barquero, Laura A; Cutting, Laurie E

2018-04-01

A primary challenge facing the development of interventions for dyslexia is identifying effective predictors of intervention response. While behavioral literature has identified core cognitive characteristics of response, the distinction of reading versus executive cognitive contributions to response profiles remains unclear, due in part to the difficulty of segregating these constructs using behavioral outputs. In the current study we used functional neuroimaging to piece apart the mechanisms of how/whether executive and reading network relationships are predictive of intervention response. We found that readers who are responsive to intervention have more typical pre-intervention functional interactions between executive and reading systems compared to nonresponsive readers. These findings suggest that intervention response in dyslexia is influenced not only by domain-specific reading regions, but also by contributions from intervening domain-general networks. Our results make a significant gain in identifying predictive bio-markers of outcomes in dyslexia, and have important implications for the development of personalized clinical interventions. Copyright © 2018 Elsevier Ltd. All rights reserved.

Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Jennings, Esther H.; Sergui, John S.

2013-01-01

A large-scale network that supports a large number of users can have an aggregate data rate of hundreds of Mbps at any time. High-fidelity simulation of a large-scale network might be too complicated and memory-intensive for typical commercial-off-the-shelf (COTS) tools. Unlike a large commercial wide-area-network (WAN) that shares diverse network resources among diverse users and has a complex topology that requires routing mechanism and flow control, the ground communication links of a space network operate under the assumption of a guaranteed dedicated bandwidth allocation between specific sparse endpoints in a star-like topology. This work solved the network design problem of estimating the bandwidths of a ground network architecture option that offer different service classes to meet the latency requirements of different user data types. In this work, a top-down analysis and simulation approach was created to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. These techniques were used to estimate the WAN bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network. A new analytical approach, called the "leveling scheme," was developed to model the store-and-forward mechanism of the network data flow. The term "leveling" refers to the spreading of data across a longer time horizon without violating the corresponding latency requirement of the data type. Two versions of the leveling scheme were developed: 1. A straightforward version that simply spreads the data of each data type across the time horizon and doesn't take into account the interactions among data types within a pass, or between data types across overlapping passes at a network node, and is inherently sub-optimal. 2. Two-state Markov leveling scheme that takes into account the second order behavior of the store-and-forward mechanism, and the interactions among data types within a pass. The novelty of this approach lies in the modeling of the store-and-forward mechanism of each network node. The term store-and-forward refers to the data traffic regulation technique in which data is sent to an intermediate network node where they are temporarily stored and sent at a later time to the destination node or to another intermediate node. Store-and-forward can be applied to both space-based networks that have intermittent connectivity, and ground-based networks with deterministic connectivity. For groundbased networks, the store-and-forward mechanism is used to regulate the network data flow and link resource utilization such that the user data types can be delivered to their destination nodes without violating their respective latency requirements.

Dysfunctional Neural Network of Spatial Working Memory Contributes to Developmental Dyscalculia

ERIC Educational Resources Information Center

Rotzer, S.; Loenneker, T.; Kucian, K.; Martin, E.; Klaver, P.; von Aster, M.

2009-01-01

The underlying neural mechanisms of developmental dyscalculia (DD) are still far from being clearly understood. Even the behavioral processes that generate or influence this heterogeneous disorder are a matter of controversy. To date, the few studies examining functional brain activation in children with DD mainly focus on number and counting…

Animal, but Not Human, Faces Engage the Distributed Face Network in Adolescents with Autism

ERIC Educational Resources Information Center

Whyte, Elisabeth M.; Behrmann, Marlene; Minshew, Nancy J.; Garcia, Natalie V.; Scherf, K. Suzanne

2016-01-01

Multiple hypotheses have been offered to explain the impaired face-processing behavior and the accompanying underlying disruptions in neural circuitry among individuals with autism. We explored the specificity of atypical face-processing activation and potential alterations to fusiform gyrus (FG) morphology as potential underlying mechanisms.…

Impact of Social Networking Sites on Children in Military Families.

PubMed

McGuire, Austen B; Steele, Ric G

2016-09-01

Youth in military families experience a relatively unique set of stressors that can put them at risk for numerous psychological and behavior problems. Thus, there is a need to identify potential mechanisms by which children can gain resiliency against these stressors. One potential mechanism that has yet to be empirically studied with military youth is social networking sites (SNSs). SNSs have gained significant popularity among society, especially youth. Given the significance of these communication tools in youths' lives, it is important to analyze how SNS use may affect military youth and their ability to cope with common military life stressors. The current review examines the potential positive and negative consequences associated with SNS use in coping with three common stressors of youth in military families: parent deployment, frequent relocation, and having a family member with a psychological or physical disability. By drawing from SNS and military literature, we predict that SNS use can be a positive tool for helping children in military families to cope with stressors. However, certain SNS behaviors can potentially result in more negative outcomes. Recommendations for future research are also discussed.

Metamorphosis in the Porosity of Recycled Concretes Through the Use of a Recycled Polyethylene Terephthalate (PET) Additive. Correlations between the Porous Network and Concrete Properties

PubMed Central

Mendivil-Escalante, José Miguel; Gómez-Soberón, José Manuel; Almaral-Sánchez, Jorge Luis; Cabrera-Covarrubias, Francisca Guadalupe

2017-01-01

In the field of construction, sustainable building materials are currently undergoing a process of technological development. This study aims to contribute to understanding the behavior of the fundamental properties of concretes prepared with recycled coarse aggregates that incorporate a polyethylene terephthalate (PET)-based additive in their matrix (produced by synthesis and glycolysis of recycled PET bottles) in an attempt to reduce their high porosity. Techniques to measure the gas adsorption, water porosity, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to evaluate the effect of the additive on the physical, mechanical and microstructural properties of these concretes. Porosity reductions of up to 30.60% are achieved with the addition of 1%, 3%, 4%, 5%, 7% and 9% of the additive, defining a new state in the behavioral model of the additive (the overdosage point) in the concrete matrix; in addition, the porous network of these concretes and their correlation with other physical and mechanical properties are also explained. PMID:28772540

Metamorphosis in the Porosity of Recycled Concretes Through the Use of a Recycled Polyethylene Terephthalate (PET) Additive. Correlations between the Porous Network and Concrete Properties.

PubMed

Mendivil-Escalante, José Miguel; Gómez-Soberón, José Manuel; Almaral-Sánchez, Jorge Luis; Cabrera-Covarrubias, Francisca Guadalupe

2017-02-14

In the field of construction, sustainable building materials are currently undergoing a process of technological development. This study aims to contribute to understanding the behavior of the fundamental properties of concretes prepared with recycled coarse aggregates that incorporate a polyethylene terephthalate (PET)-based additive in their matrix (produced by synthesis and glycolysis of recycled PET bottles) in an attempt to reduce their high porosity. Techniques to measure the gas adsorption, water porosity, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) were used to evaluate the effect of the additive on the physical, mechanical and microstructural properties of these concretes. Porosity reductions of up to 30.60% are achieved with the addition of 1%, 3%, 4%, 5%, 7% and 9% of the additive, defining a new state in the behavioral model of the additive (the overdosage point) in the concrete matrix; in addition, the porous network of these concretes and their correlation with other physical and mechanical properties are also explained.

Structural evolution of Colloidal Gels under Flow

NASA Astrophysics Data System (ADS)

Boromand, Arman; Maia, Joao; Jamali, Safa

Colloidal suspensions are ubiquitous in different industrial applications ranging from cosmetic and food industries to soft robotics and aerospace. Owing to the fact that mechanical properties of colloidal gels are controlled by its microstructure and network topology, we trace the particles in the networks formed under different attraction potentials and try to find a universal behavior in yielding of colloidal gels. Many authors have implemented different simulation techniques such as molecular dynamics (MD) and Brownian dynamics (BD) to capture better picture during phase separation and yielding mechanism in colloidal system with short-ranged attractive force. However, BD neglects multi-body hydrodynamic interactions (HI) which are believed to be responsible for the second yielding of colloidal gels. We envision using dissipative particle dynamics (DPD) with modified depletion potential and hydrodynamic interactions, as a coarse-grain model, can provide a robust simulation package to address the gel formation process and yielding in short ranged-attractive colloidal systems. The behavior of colloidal gels with different attraction potentials under flow is examined and structural fingerprints of yielding in these systems will be discussed.

Towards an Automated Development Methodology for Dependable Systems with Application to Sensor Networks

NASA Technical Reports Server (NTRS)

Hinchey, Michael G.; Rash, James L.; Rouff, Christopher A.

2005-01-01

A general-purpose method to mechanically transform system requirements into a probably equivalent model has yet to appeal: Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including sensor networks and autonomous systems. Currently available tools and methods that start with a formal model of a system and mechanically produce a probably equivalent implementation are valuable but not su8cient. The "gap" unfilled by such tools and methods is that their. formal models cannot be proven to be equivalent to the system requirements as originated by the customel: For the classes of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a probably equivalent formal model that can be used as the basis for code generation and other transformations.

Running hot and cold: behavioral strategies, neural circuits, and the molecular machinery for thermotaxis in C. elegans and Drosophila

PubMed Central

Garrity, Paul A.; Goodman, Miriam B.; Samuel, Aravinthan D.; Sengupta, Piali

2010-01-01

Like other ectotherms, the roundworm Caenorhabditis elegans and the fruit fly Drosophila melanogaster rely on behavioral strategies to stabilize their body temperature. Both animals use specialized sensory neurons to detect small changes in temperature, and the activity of these thermosensors governs the neural circuits that control migration and accumulation at preferred temperatures. Despite these similarities, the underlying molecular, neuronal, and computational mechanisms responsible for thermotaxis are distinct in these organisms. Here, we discuss the role of thermosensation in the development and survival of C. elegans and Drosophila, and review the behavioral strategies, neuronal circuits, and molecular networks responsible for thermotaxis behavior. PMID:21041406

Understanding the mechanisms leading to failure in metallic nanowire-based transparent heaters, and solution for stability enhancement

NASA Astrophysics Data System (ADS)

Lagrange, M.; Sannicolo, T.; Muñoz-Rojas, D.; Guillo Lohan, B.; Khan, A.; Anikin, M.; Jiménez, C.; Bruckert, F.; Bréchet, Y.; Bellet, D.

2017-02-01

Silver nanowire (AgNW) networks are emerging as one of the most promising alternatives to indium tin oxide (ITO) for transparent electrodes in flexible electronic devices. They can be used in a variety of optoelectronic applications such as solar cells, touch panels and organic light-emitting diodes. Recently they have also proven to be very efficient when used as transparent heaters (THs). In addition to the study of AgNW networks acting as THs in regular use, i.e. at low voltage and moderate temperature, their stability and physical behavior at higher voltages and for longer durations should be studied in view of their integration into real devices. The properties of AgNW networks deposited by spray coating on glass or flexible transparent substrates are thoroughly studied via in situ measurements. The AgNW networks’ behavior at different voltages for different durations and under different atmospheric conditions, both in air and under vacuum, has been examined. At low voltage, a reversible electrical response is observed while irreversibility and even failure are observed at higher voltages. In order to gain a deeper insight into the behavior of AgNW networks used as THs, simple but realistic physical models are proposed and are found to be in fair agreement with the experimental data. Finally, as the stability of AgNW networks is a key issue, we demonstrate that coating AgNW networks with a very thin layer of TiO2 using atomic layer deposition (ALD) improves the material’s resistance against electrical and thermal instabilities without altering optical transmittance. We show that the critical annealing temperature associated to network breakdown increases from 270 °C for the as-deposited AgNW networks to 420 °C for AgNW networks coated with TiO2. Similarly, the electrical failure which occurs at 7 V for the as-deposited networks increases to 13 V for TiO2-coated networks. TiO2 is also proved to stabilize AgNW networks during long duration operation and at high voltage. Temperature higher than 235 °C was achieved at 7 V without failure.

Effects of a spaceflight analog environment on brain connectivity and behavior.

PubMed

Cassady, Kaitlin; Koppelmans, Vincent; Reuter-Lorenz, Patricia; De Dios, Yiri; Gadd, Nichole; Wood, Scott; Castenada, Roy Riascos; Kofman, Igor; Bloomberg, Jacob; Mulavara, Ajitkumar; Seidler, Rachael

2016-11-01

Sensorimotor functioning is adaptively altered following long-duration spaceflight. The question of whether microgravity affects other central nervous system functions such as brain network organization and its relationship with behavior is largely unknown, but of importance to the health and performance of astronauts both during and post-flight. In the present study, we investigate the effects of prolonged exposure to an established spaceflight analog on resting state brain functional connectivity and its association with behavioral changes in 17 male participants. These bed rest participants remained in bed with their heads tilted down six degrees below their feet for 70 consecutive days. Resting state functional magnetic resonance imaging (rs-fMRI) and behavioral data were obtained at seven time points averaging around: 12 and 8days prior to bed rest; 7, 50, and 70days during bed rest; and 8 and 12days after bed rest. To assess potential confounding effects due to scanning interval or task practice, we also acquired rs-fMRI and behavioral measurements from 14 control participants at four time points. 70days of head-down tilt (HDT) bed rest resulted in significant changes in the functional connectivity of motor, somatosensory, and vestibular areas of the brain. Moreover, several of these network alterations were significantly associated with changes in sensorimotor and spatial working memory performance, which suggests that neuroplasticity mechanisms may facilitate adaptation to the microgravity analog environment. The findings from this study provide novel insights into the underlying neural mechanisms and operational risks of spaceflight analog-related changes in sensorimotor performance. Copyright © 2016 Elsevier Inc. All rights reserved.

A Non-Intrusive Cyber Physical Social Sensing Solution to People Behavior Tracking: Mechanism, Prototype, and Field Experiments.

PubMed

Jia, Yunjian; Zhou, Zhenyu; Chen, Fei; Duan, Peng; Guo, Zhen; Mumtaz, Shahid

2017-01-13

Tracking people's behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless fidelity (Wi-Fi)-based tracking method. To show the feasibility, we target tracking people's access behaviors in Wi-Fi networks, which has drawn a lot of interest from the academy and industry recently. Existing methods used for acquiring access traces either provide very limited visibility into media access control (MAC)-level transmission dynamics or sometimes are inflexible and costly. In this article, we present a passive CPSS system operating in a non-intrusive, flexible, and simplified manner to overcome above limitations. We have implemented the prototype on the off-the-shelf personal computer, and performed real-world deployment experiments. The experimental results show that the method is feasible, and people's access behaviors can be correctly tracked within a one-second delay.

A Non-Intrusive Cyber Physical Social Sensing Solution to People Behavior Tracking: Mechanism, Prototype, and Field Experiments

PubMed Central

Jia, Yunjian; Zhou, Zhenyu; Chen, Fei; Duan, Peng; Guo, Zhen; Mumtaz, Shahid

2017-01-01

Tracking people’s behaviors is a main category of cyber physical social sensing (CPSS)-related people-centric applications. Most tracking methods utilize camera networks or sensors built into mobile devices such as global positioning system (GPS) and Bluetooth. In this article, we propose a non-intrusive wireless fidelity (Wi-Fi)-based tracking method. To show the feasibility, we target tracking people’s access behaviors in Wi-Fi networks, which has drawn a lot of interest from the academy and industry recently. Existing methods used for acquiring access traces either provide very limited visibility into media access control (MAC)-level transmission dynamics or sometimes are inflexible and costly. In this article, we present a passive CPSS system operating in a non-intrusive, flexible, and simplified manner to overcome above limitations. We have implemented the prototype on the off-the-shelf personal computer, and performed real-world deployment experiments. The experimental results show that the method is feasible, and people’s access behaviors can be correctly tracked within a one-second delay. PMID:28098772

Neural mechanisms of behavioral change in young adults with high-functioning autism receiving virtual reality social cognition training: A pilot study.

PubMed

Yang, Y J Daniel; Allen, Tandra; Abdullahi, Sebiha M; Pelphrey, Kevin A; Volkmar, Fred R; Chapman, Sandra B

2018-05-01

Measuring treatment efficacy in individuals with Autism Spectrum Disorder (ASD) relies primarily on behaviors, with limited evidence as to the neural mechanisms underlying these behavioral gains. This pilot study addresses this void by investigating neural and behavioral changes in a Phase I trial in young adults with high-functioning ASD who received an evidence-based behavioral intervention, Virtual Reality-Social Cognition Training over 5 weeks for a total of 10 hr. The participants were tested pre- and post-training with a validated biological/social versus scrambled/nonsocial motion neuroimaging task, previously shown to activate regions within the social brain networks. Three significant brain-behavior changes were identified. First, the right posterior superior temporal sulcus, a hub for socio-cognitive processing, showed increased brain activation to social versus nonsocial stimuli in individuals with greater gains on a theory-of-mind measure. Second, the left inferior frontal gyrus, a region for socio-emotional processing, tracked individual gains in emotion recognition with decreased activation to social versus nonsocial stimuli. Finally, the left superior parietal lobule, a region for visual attention, showed significantly decreased activation to nonsocial versus social stimuli across all participants, where heightened attention to nonsocial contingencies has been considered a disabling aspect of ASD. This study provides, albeit preliminary, some of the first evidence of the harnessable neuroplasticity in adults with ASD through an age-appropriate intervention in brain regions tightly linked to social abilities. This pilot trial motivates future efforts to develop and test social interventions to improve behaviors and supporting brain networks in adults with ASD. Autism Res 2018, 11: 713-725. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc. This study addresses how the behavioral changes after treatment for ASD reflect underlying brain changes. Before and after receiving VR-SCT, young adults with high-functioning ASD passively viewed biological motion stimuli in a MRI scanner, tapping changes in the social brain network. The results reveal neuroplasticity in this age population, extending the window of opportunity for interventions to impact social competency in adults with ASD. © 2018 The Authors Autism Research published by International Society for Autism Research and Wiley Periodicals, Inc.

Heterogeneous mechanics of the mouse pulmonary arterial network.

PubMed

Lee, Pilhwa; Carlson, Brian E; Chesler, Naomi; Olufsen, Mette S; Qureshi, M Umar; Smith, Nicolas P; Sochi, Taha; Beard, Daniel A

2016-10-01

Individualized modeling and simulation of blood flow mechanics find applications in both animal research and patient care. Individual animal or patient models for blood vessel mechanics are based on combining measured vascular geometry with a fluid structure model coupling formulations describing dynamics of the fluid and mechanics of the wall. For example, one-dimensional fluid flow modeling requires a constitutive law relating vessel cross-sectional deformation to pressure in the lumen. To investigate means of identifying appropriate constitutive relationships, an automated segmentation algorithm was applied to micro-computerized tomography images from a mouse lung obtained at four different static pressures to identify the static pressure-radius relationship for four generations of vessels in the pulmonary arterial network. A shape-fitting function was parameterized for each vessel in the network to characterize the nonlinear and heterogeneous nature of vessel distensibility in the pulmonary arteries. These data on morphometric and mechanical properties were used to simulate pressure and flow velocity propagation in the network using one-dimensional representations of fluid and vessel wall mechanics. Moreover, wave intensity analysis was used to study effects of wall mechanics on generation and propagation of pressure wave reflections. Simulations were conducted to investigate the role of linear versus nonlinear formulations of wall elasticity and homogeneous versus heterogeneous treatments of vessel wall properties. Accounting for heterogeneity, by parameterizing the pressure/distention equation of state individually for each vessel segment, was found to have little effect on the predicted pressure profiles and wave propagation compared to a homogeneous parameterization based on average behavior. However, substantially different results were obtained using a linear elastic thin-shell model than were obtained using a nonlinear model that has a more physiologically realistic pressure versus radius relationship.

The Role of Gender and Friends’ Gender on Peer Socialization of Adolescent Drinking: A Prospective Multilevel Social Network Analysis

PubMed Central

Steinley, Douglas; Slutske, Wendy S.

2014-01-01

Although socializing effects of friends’ drinking on adolescent drinking behavior have been firmly established in previous literature, study results on the importance of gender, as well as the specific role that gender may play in peer socialization, are very mixed. Given the increasing importance of gender in friendships (particularly opposite-sex friendships) during adolescence, it is necessary to better understand the nuanced roles that gender can play in peer socialization effects on alcohol use. In addition, previous studies focusing on the interplay between individual gender and friends’ gender have been largely dyadic; less is known about potential gendered effects of broader social networks. The current study sought to further investigate potential effects of gender on friends’ influence on adolescent drinking behavior with particular emphasis on the number of same-sex and opposite-sex friends within one’s friendship network, as well as closeness to these friends. Using Waves I and II of the saturated sample of the National Longitudinal Study of Adolescent Health (Add Health), adolescent friendship networks were used to calculate the mean drinking behaviors of adolescent friends. Multi-level models estimated the effects of individual drinking behaviors, friend drinking behaviors, and school-level drinking behaviors on adolescent drinking 1 year later, as well as moderating effects of gender composition of friendship groups and male and female friend closeness on the relationship between friends’ drinking behaviors and adolescent drinking behavior. Results documented that gender composition of friendship groups did not influence the effect of friends’ drinking on individual drinking 1 year later. However, closeness to friends did influence this relationship. As closeness to male friends decreased, the influence of their drinking behavior increased, for both boys and girls. A similar effect was found for female friends, but only for boys. Female friend closeness did not affect the relationship between peer alcohol socialization and girls’ alcohol use. The findings indicate that the role of gender on alcohol socialization may be more complex than previously thought, particularly when examining the potential role that alcohol use may play as a mechanism for social bonding within opposite-sex friendships and same-sex male friendships. PMID:24170437

The role of gender and friends' gender on peer socialization of adolescent drinking: a prospective multilevel social network analysis.

PubMed

Deutsch, Arielle R; Steinley, Douglas; Slutske, Wendy S

2014-09-01

Although socializing effects of friends' drinking on adolescent drinking behavior have been firmly established in previous literature, study results on the importance of gender, as well as the specific role that gender may play in peer socialization, are very mixed. Given the increasing importance of gender in friendships (particularly opposite-sex friendships) during adolescence, it is necessary to better understand the nuanced roles that gender can play in peer socialization effects on alcohol use. In addition, previous studies focusing on the interplay between individual gender and friends' gender have been largely dyadic; less is known about potential gendered effects of broader social networks. The current study sought to further investigate potential effects of gender on friends' influence on adolescent drinking behavior with particular emphasis on the number of same-sex and opposite-sex friends within one's friendship network, as well as closeness to these friends. Using Waves I and II of the saturated sample of the National Longitudinal Study of Adolescent Health (Add Health), adolescent friendship networks were used to calculate the mean drinking behaviors of adolescent friends. Multi-level models estimated the effects of individual drinking behaviors, friend drinking behaviors, and school-level drinking behaviors on adolescent drinking 1 year later, as well as moderating effects of gender composition of friendship groups and male and female friend closeness on the relationship between friends' drinking behaviors and adolescent drinking behavior. Results documented that gender composition of friendship groups did not influence the effect of friends' drinking on individual drinking 1 year later. However, closeness to friends did influence this relationship. As closeness to male friends decreased, the influence of their drinking behavior increased, for both boys and girls. A similar effect was found for female friends, but only for boys. Female friend closeness did not affect the relationship between peer alcohol socialization and girls' alcohol use. The findings indicate that the role of gender on alcohol socialization may be more complex than previously thought, particularly when examining the potential role that alcohol use may play as a mechanism for social bonding within opposite-sex friendships and same-sex male friendships.

The evolutionary advantage of limited network knowledge.

PubMed

Larson, Jennifer M

2016-06-07

Groups of individuals have social networks that structure interactions within the groups; evolutionary theory increasingly uses this fact to explain the emergence of cooperation (Eshel and Cavalli-Sforza, 1982; Boyd and Richerson, 1988, 1989; Ohtsuki et al., 2006; Nowak et al., 2010; Van Veelen et al., 2012). This approach has resulted in a number of important insights for the evolution of cooperation in the biological and social sciences, but omits a key function of social networks that has persisted throughout recent evolutionary history (Apicella et al., 2012): their role in transmitting gossip about behavior within a group. Accounting for this well-established role of social networks among rational agents in a setting of indirect reciprocity not only shows a new mechanism by which the structure of networks is fitness-relevant, but also reveals that knowledge of social networks can be fitness-relevant as well. When groups enforce cooperation by sanctioning peers whom gossip reveals to have deviated, individuals in certain peripheral network positions are tempting targets of uncooperative behavior because gossip they share about misbehavior spreads slowly through the network. The ability to identify these individuals creates incentives to behave uncooperatively. Consequently, groups comprised of individuals who knew precise information about their social networks would be at a fitness disadvantage relative to groups of individuals with a coarser knowledge of their networks. Empirical work has consistently shown that modern humans know little about the structure of their own social networks and perform poorly when tasked with learning new ones. This robust empirical regularity may be the product of natural selection in an environment of strong selective pressure at the group level. Imprecise views of networks make enforcing cooperation easier. Copyright © 2016 Elsevier Ltd. All rights reserved.

Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters.

PubMed

Katsumi, Yuta; Dolcos, Sanda

2017-01-01

Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants' ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds.

Neural Correlates of Racial Ingroup Bias in Observing Computer-Animated Social Encounters

PubMed Central

Katsumi, Yuta; Dolcos, Sanda

2018-01-01

Despite evidence for the role of group membership in the neural correlates of social cognition, the mechanisms associated with processing non-verbal behaviors displayed by racially ingroup vs. outgroup members remain unclear. Here, 20 Caucasian participants underwent fMRI recording while observing social encounters with ingroup and outgroup characters displaying dynamic and static non-verbal behaviors. Dynamic behaviors included approach and avoidance behaviors, preceded or not by a handshake; both dynamic and static behaviors were followed by participants’ ratings. Behaviorally, participants showed bias toward their ingroup members, demonstrated by faster/slower reaction times for evaluating ingroup static/approach behaviors, respectively. At the neural level, despite overall similar responses in the action observation network to ingroup and outgroup encounters, the medial prefrontal cortex showed dissociable activation, possibly reflecting spontaneous processing of ingroup static behaviors and positive evaluations of ingroup approach behaviors. The anterior cingulate and superior frontal cortices also showed sensitivity to race, reflected in coordinated and reduced activation for observing ingroup static behaviors. Finally, the posterior superior temporal sulcus showed uniquely increased activity to observing ingroup handshakes. These findings shed light on the mechanisms of racial ingroup bias in observing social encounters, and have implications for understanding factors related to successful interactions with individuals from diverse backgrounds. PMID:29354042

Human Behavior Modeling in Network Science

DTIC Science & Technology

2010-03-01

in Network Science bringing three distinct research areas together, communication networks, information networks, and social /cognitive networks. The...researchers. A critical part of the social /cognitive network effort is the modeling of human behavior. The modeling efforts range from organizational...behavior to social cognitive trust to explore and refine the theoretical and applied network relationships between and among the human

Social network correlates of risky sexual behavior among adolescents in Bahir Dar and Mecha Districts, North West Ethiopia: an institution-based study.

PubMed

Asrese, Kerebih; Mekonnen, Alemtsehay

2018-04-11

Behaviors established during adolescence such as risky sexual behaviors have negative effects on future health and well-being. Extant literature indicated that individual attributes such as peer pressure and substance use have impacts on healthy development of young peoples' sexual behavior. The patterns of relationships (social network structure) and the social network content (members' norm regarding sexual practice) established by adolescents' network on adolescents' risky sexual behaviors are not well investigated. This cross-sectional study assessed the roles of social networks on sexual behavior of high school adolescents in Bahir Dar and Mecha district, North West Ethiopia. Data were collected from 806 high school adolescents using a pretested anonymously self administered questionnaire. Hierarchical logistic regression model was used for analysis. The results indicated that more than 13% had risky sexual behavior. Taking social networks into account improved the explanation of risky sexual behavior over individual attributes. Adolescents embedded within increasing sexual practice approving norm (AOR 1.61; 95%CI: 1.04 - 2.50), increasing network tie strength (AOR 1.12; 95% CI: 1.06 - 1.19), and homogeneous networks (AOR 1.58; 95% CI: .98 - 2.55) were more likely to had risky sexual behavior. Engaging within increasing number of sexuality discussion networks was found protective of risky sexual behavior (AOR .84; 95% CI: .72 - .97). Social networks better predict adolescent's risky sexual behavior than individual attributes. The findings indicated the circumstances or contexts that social networks exert risks or protective effects on adolescents' sexual behavior. Programs designed to reduce school adolescents' sexual risk behavior should consider their patterns of social relationships.

Probing embryonic tissue mechanics with laser hole drilling

NASA Astrophysics Data System (ADS)

Ma, Xiaoyan; Lynch, Holley E.; Scully, Peter C.; Hutson, M. Shane

2009-09-01

We use laser hole drilling to assess the mechanics of an embryonic epithelium during development—in vivo and with subcellular resolution. We ablate a subcellular cylindrical hole clean through the epithelium and track the subsequent recoil of adjacent cells (on ms time scales). We investigate dorsal closure in the fruit fly with emphasis on apical constriction of amnioserosa cells. The mechanical behavior of this epithelium falls between that of a continuous sheet and a 2D cellular foam (a network of tensile interfaces). Tensile stress is carried both by cell-cell interfaces and by the cells' apical actin networks. Our results show that stress is slightly concentrated along interfaces (1.6-fold), but only in early closure. Furthermore, closure is marked by a decrease in the recoil power-law exponent, implying a transition to a more solid-like tissue. We use the site and stage dependence of the recoil kinetics to constrain how the cellular mechanics change during closure. We apply these results to test extant computational models.

Molecular mechanics of silk nanostructures under varied mechanical loading.

PubMed

Bratzel, Graham; Buehler, Markus J

2012-06-01

Spider dragline silk is a self-assembling tunable protein composite fiber that rivals many engineering fibers in tensile strength, extensibility, and toughness, making it one of the most versatile biocompatible materials and most inviting for synthetic mimicry. While experimental studies have shown that the peptide sequence and molecular structure of silk have a direct influence on the stiffness, toughness, and failure strength of silk, few molecular-level analyses of the nanostructure of silk assemblies, in particular, under variations of genetic sequences have been reported. In this study, atomistic-level structures of wildtype as well as modified MaSp1 protein from the Nephila clavipes spider dragline silk sequences, obtained using an in silico approach based on replica exchange molecular dynamics and explicit water molecular dynamics, are subjected to simulated nanomechanical testing using different force-control loading conditions including stretch, pull-out, and peel. The authors have explored the effects of the poly-alanine length of the N. clavipes MaSp1 peptide sequence and identify differences in nanomechanical loading conditions on the behavior of a unit cell of 15 strands with 840-990 total residues used to represent a cross-linking β-sheet crystal node in the network within a fibril of the dragline silk thread. The specific loading condition used, representing concepts derived from the protein network connectivity at larger scales, have a significant effect on the mechanical behavior. Our analysis incorporates stretching, pull-out, and peel testing to connect biochemical features to mechanical behavior. The method used in this study could find broad applications in de novo design of silk-like tunable materials for an array of applications. Copyright © 2011 Wiley Periodicals, Inc.

Regulation Mechanism of Salt Ions for Superlubricity of Hydrophilic Polymer Cross-Linked Networks on Ti6Al4V.

PubMed

Zhang, Caixia; Liu, Yuhong; Liu, Zhifeng; Zhang, Hongyu; Cheng, Qiang; Yang, Congbin

2017-03-07

Poly(vinylphosphonic acid) (PVPA) cross-linked networks on Ti 6 Al 4 V show superlubricity behavior when sliding against polytetrafluoroethylene in water-based lubricants. The superlubricity can occur but only with the existence of salt ions in the polymer cross-linked networks. This is different from the phenomenon in most polymer brushes. An investigation into the mechanism revealed that cations and anions in the lubricants worked together to yield the superlubricity even under harsh conditions. It is proposed that the preferential interactions of cations with PVPA molecules rather than water molecules are the main reason for the superlubricity in water-based lubricants. The interaction of anions with water molecules regulates the properties of the tribological interfaces, which influences the magnitude of the friction coefficient. Owing to the novel cross-linked networks and the interactions between cations and polymer molecules, their superlubricity can be maintained even at a high salt ion concentration of 5 M. These excellent properties make PVPA-modified Ti 6 Al 4 V a potential candidate for application in artificial implants.

Engagement of large-scale networks is related to individual differences in inhibitory control

PubMed Central

Congdon, Eliza; Mumford, Jeanette A.; Cohen, Jessica R.; Galvan, Adriana; Aron, Adam R.; Xue, Gui; Miller, Eric; Poldrack, Russell A.

2010-01-01

Understanding which brain regions regulate the execution, and suppression, of goal-directed behavior has implications for a number of areas of research. In particular, understanding which brain regions engaged during tasks requiring the execution and inhibition of a motor response provides insight into the mechanisms underlying individual differences in response inhibition ability. However, neuroimaging studies examing the relation between activation and stopping have been inconsistent regarding the direction of the relationship, and also regarding the anatomical location of regions that correlate with behavior. These limitations likely arise from the relatively low power of vox-elwise correlations with small sample sizes. Here, we pooled data over five separate fMRI studies of the Stop-signal task in order to obtain a sufficiently large sample size to robustly detect brain/behavior correlations. In addition, rather than performing mass univariate correlation analysis across all voxels, we increased statistical power by reducing the dimensionality of the data set using independent components analysis and then examined correlations between behavior and the resulting component scores. We found that components reflecting activity in regions thought to be involved in stopping were associated with better stopping ability, while activity in a default-mode network was associated with poorer stopping ability across individuals. These results clearly show a relationship between individual differences in stopping ability in specific activated networks, including regions known to be critical for the behavior. The results also highlight the usefulness of using dimensionality reduction to increase the power to detect brain/behavior correlations in individual differences research. PMID:20600962

From single fiber to macro-level mechanics: A structural finite-element model for elastomeric fibrous biomaterials.

PubMed

D'Amore, Antonio; Amoroso, Nicholas; Gottardi, Riccardo; Hobson, Christopher; Carruthers, Christopher; Watkins, Simon; Wagner, William R; Sacks, Michael S

2014-11-01

In the present work, we demonstrate that the mesoscopic in-plane mechanical behavior of membrane elastomeric scaffolds can be simulated by replication of actual quantified fibrous geometries. Elastomeric electrospun polyurethane (ES-PEUU) scaffolds, with and without particulate inclusions, were utilized. Simulations were developed from experimentally-derived fiber network geometries, based on a range of scaffold isotropic and anisotropic behaviors. These were chosen to evaluate the effects on macro-mechanics based on measurable geometric parameters such as fiber intersections, connectivity, orientation, and diameter. Simulations were conducted with only the fiber material model parameters adjusted to match the macro-level mechanical test data. Fiber model validation was performed at the microscopic level by individual fiber mechanical tests using AFM. Results demonstrated very good agreement to the experimental data, and revealed the formation of extended preferential fiber orientations spanning the entire model space. We speculate that these emergent structures may be responsible for the tissue-like macroscale behaviors observed in electrospun scaffolds. To conclude, the modeling approach has implications for (1) gaining insight on the intricate relationship between fabrication variables, structure, and mechanics to manufacture more functional devices/materials, (2) elucidating the effects of cell or particulate inclusions on global construct mechanics, and (3) fabricating better performing tissue surrogates that could recapitulate native tissue mechanics. Copyright © 2014 Elsevier Ltd. All rights reserved.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot.

PubMed

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world.

Synaptic plasticity in a recurrent neural network for versatile and adaptive behaviors of a walking robot

PubMed Central

Grinke, Eduard; Tetzlaff, Christian; Wörgötter, Florentin; Manoonpong, Poramate

2015-01-01

Walking animals, like insects, with little neural computing can effectively perform complex behaviors. For example, they can walk around their environment, escape from corners/deadlocks, and avoid or climb over obstacles. While performing all these behaviors, they can also adapt their movements to deal with an unknown situation. As a consequence, they successfully navigate through their complex environment. The versatile and adaptive abilities are the result of an integration of several ingredients embedded in their sensorimotor loop. Biological studies reveal that the ingredients include neural dynamics, plasticity, sensory feedback, and biomechanics. Generating such versatile and adaptive behaviors for a many degrees-of-freedom (DOFs) walking robot is a challenging task. Thus, in this study, we present a bio-inspired approach to solve this task. Specifically, the approach combines neural mechanisms with plasticity, exteroceptive sensory feedback, and biomechanics. The neural mechanisms consist of adaptive neural sensory processing and modular neural locomotion control. The sensory processing is based on a small recurrent neural network consisting of two fully connected neurons. Online correlation-based learning with synaptic scaling is applied to adequately change the connections of the network. By doing so, we can effectively exploit neural dynamics (i.e., hysteresis effects and single attractors) in the network to generate different turning angles with short-term memory for a walking robot. The turning information is transmitted as descending steering signals to the neural locomotion control which translates the signals into motor actions. As a result, the robot can walk around and adapt its turning angle for avoiding obstacles in different situations. The adaptation also enables the robot to effectively escape from sharp corners or deadlocks. Using backbone joint control embedded in the the locomotion control allows the robot to climb over small obstacles. Consequently, it can successfully explore and navigate in complex environments. We firstly tested our approach on a physical simulation environment and then applied it to our real biomechanical walking robot AMOSII with 19 DOFs to adaptively avoid obstacles and navigate in the real world. PMID:26528176

Goal-Directed Decision Making with Spiking Neurons.

PubMed

Friedrich, Johannes; Lengyel, Máté

2016-02-03

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. Copyright © 2016 the authors 0270-6474/16/361529-18$15.00/0.

Goal-Directed Decision Making with Spiking Neurons

PubMed Central

Lengyel, Máté

2016-01-01

Behavioral and neuroscientific data on reward-based decision making point to a fundamental distinction between habitual and goal-directed action selection. The formation of habits, which requires simple updating of cached values, has been studied in great detail, and the reward prediction error theory of dopamine function has enjoyed prominent success in accounting for its neural bases. In contrast, the neural circuit mechanisms of goal-directed decision making, requiring extended iterative computations to estimate values online, are still unknown. Here we present a spiking neural network that provably solves the difficult online value estimation problem underlying goal-directed decision making in a near-optimal way and reproduces behavioral as well as neurophysiological experimental data on tasks ranging from simple binary choice to sequential decision making. Our model uses local plasticity rules to learn the synaptic weights of a simple neural network to achieve optimal performance and solves one-step decision-making tasks, commonly considered in neuroeconomics, as well as more challenging sequential decision-making tasks within 1 s. These decision times, and their parametric dependence on task parameters, as well as the final choice probabilities match behavioral data, whereas the evolution of neural activities in the network closely mimics neural responses recorded in frontal cortices during the execution of such tasks. Our theory provides a principled framework to understand the neural underpinning of goal-directed decision making and makes novel predictions for sequential decision-making tasks with multiple rewards. SIGNIFICANCE STATEMENT Goal-directed actions requiring prospective planning pervade decision making, but their circuit-level mechanisms remain elusive. We show how a model circuit of biologically realistic spiking neurons can solve this computationally challenging problem in a novel way. The synaptic weights of our network can be learned using local plasticity rules such that its dynamics devise a near-optimal plan of action. By systematically comparing our model results to experimental data, we show that it reproduces behavioral decision times and choice probabilities as well as neural responses in a rich set of tasks. Our results thus offer the first biologically realistic account for complex goal-directed decision making at a computational, algorithmic, and implementational level. PMID:26843636

A model for integrating elementary neural functions into delayed-response behavior.

PubMed

Gisiger, Thomas; Kerszberg, Michel

2006-04-01

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

Altered intrinsic hippocmapus declarative memory network and its association with impulsivity in abstinent heroin dependent subjects.

PubMed

Zhai, Tian-Ye; Shao, Yong-Cong; Xie, Chun-Ming; Ye, En-Mao; Zou, Feng; Fu, Li-Ping; Li, Wen-Jun; Chen, Gang; Chen, Guang-Yu; Zhang, Zheng-Guo; Li, Shi-Jiang; Yang, Zheng

2014-10-01

Converging evidence suggests that addiction can be considered a disease of aberrant learning and memory with impulsive decision-making. In the past decades, numerous studies have demonstrated that drug addiction is involved in multiple memory systems such as classical conditioned drug memory, instrumental learning memory and the habitual learning memory. However, most of these studies have focused on the contributions of non-declarative memory, and declarative memory has largely been neglected in the research of addiction. Based on a recent finding that hippocampus, as a core functioning region of declarative memory, was proved biased the decision-making process based on past experiences by spreading associated reward values throughout memory. Our present study focused on the hippocampus. By utilizing seed-based network analysis on the resting-state functional MRI datasets with the seed hippocampus we tested how the intrinsic hippocampal memory network altered toward drug addiction, and examined how the functional connectivity strength within the altered hippocampal network correlated with behavioral index 'impulsivity'. Our results demonstrated that HD group showed enhanced coherence between hippocampus which represents declarative memory system and non-declarative reward-guided learning memory system, and also showed attenuated intrinsic functional link between hippocampus and top-down control system, compared to the CN group. This alteration was furthered found to have behavioral significance over the behavioral index 'impulsivity' measured with Barratt Impulsiveness Scale (BIS). These results provide insights into the mechanism of declarative memory underlying the impulsive behavior in drug addiction. Copyright © 2014 Elsevier B.V. All rights reserved.

Epigenetic Principles and Mechanisms Underlying Nervous System Functions in Health and Disease

PubMed Central

Mehler, Mark F.

2009-01-01

Epigenetics and epigenomic medicine encompass a new science of brain and behavior that are already providing unique insights into the mechanisms underlying brain development, evolution, neuronal and network plasticity and homeostasis, senescence, the etiology of diverse neurological diseases and neural regenerative processes. Epigenetic mechanisms include DNA methylation, histone modifications, nucleosome repositioning, higher-order chromatin remodeling, non-coding RNAs, and RNA and DNA editing. RNA is centrally involved in directing these processes, implying that the transcriptional state of the cell is the primary determinant of epigenetic memory. This transcriptional state can be modified by internal and external cues affecting gene expression and post-transcriptional processing, but also by RNA and DNA editing through activity-dependent intracellular transport and modulation of RNAs and RNA regulatory supercomplexes, and through trans-neuronal and systemic trafficking of functional RNA subclasses. These integrated processes promote dynamic reorganization of nuclear architecture and the genomic landscape to modulate functional gene and neural networks with complex temporal and spatial trajectories. Epigenetics represents the long sought after molecular interface mediating gene-environmental interactions during critical periods throughout the lifecycle. The discipline of environmental epigenomics has begun to identify combinatorial profiles of environmental stressors modulating the latency, initiation and progression of specific neurological disorders, and more selective disease biomarkers and graded molecular responses to emerging therapeutic interventions. Pharmacoepigenomic therapies will promote accelerated recovery of impaired and seemingly irrevocably lost cognitive, behavioral, sensorimotor functions through epigenetic reprogramming of endogenous regional neural stem cell fate decisions, targeted tissue remodeling and restoration of neural network integrity, plasticity and connectivity. PMID:18940229

Large-scale functional networks connect differently for processing words and symbol strings.

PubMed

Liljeström, Mia; Vartiainen, Johanna; Kujala, Jan; Salmelin, Riitta

2018-01-01

Reconfigurations of synchronized large-scale networks are thought to be central neural mechanisms that support cognition and behavior in the human brain. Magnetoencephalography (MEG) recordings together with recent advances in network analysis now allow for sub-second snapshots of such networks. In the present study, we compared frequency-resolved functional connectivity patterns underlying reading of single words and visual recognition of symbol strings. Word reading emphasized coherence in a left-lateralized network with nodes in classical perisylvian language regions, whereas symbol processing recruited a bilateral network, including connections between frontal and parietal regions previously associated with spatial attention and visual working memory. Our results illustrate the flexible nature of functional networks, whereby processing of different form categories, written words vs. symbol strings, leads to the formation of large-scale functional networks that operate at distinct oscillatory frequencies and incorporate task-relevant regions. These results suggest that category-specific processing should be viewed not so much as a local process but as a distributed neural process implemented in signature networks. For words, increased coherence was detected particularly in the alpha (8-13 Hz) and high gamma (60-90 Hz) frequency bands, whereas increased coherence for symbol strings was observed in the high beta (21-29 Hz) and low gamma (30-45 Hz) frequency range. These findings attest to the role of coherence in specific frequency bands as a general mechanism for integrating stimulus-dependent information across brain regions.

Global connectivity of prefrontal cortex predicts cognitive control and intelligence

PubMed Central

Cole, Michael W.; Yarkoni, Tal; Repovs, Grega; Anticevic, Alan; Braver, Todd S.

2012-01-01

Control of thought and behavior is fundamental to human intelligence. Evidence suggests a fronto-parietal brain network implements such cognitive control across diverse contexts. We identify a mechanism – global connectivity – by which components of this network might coordinate control of other networks. A lateral prefrontal cortex (LPFC) region’s activity was found to predict performance in a high control demand working memory task, and also to exhibit high global connectivity. Critically, global connectivity in this LPFC region, involving connections both within and outside the fronto-parietal network, showed a highly selective relationship with individual differences in fluid intelligence. These findings suggest LPFC is a global hub with a brain-wide influence that facilitates the ability to implement control processes central to human intelligence. PMID:22745498

Quantitative petri net model of gene regulated metabolic networks in the cell.

PubMed

Chen, Ming; Hofestädt, Ralf

2011-01-01

A method to exploit hybrid Petri nets (HPN) for quantitatively modeling and simulating gene regulated metabolic networks is demonstrated. A global kinetic modeling strategy and Petri net modeling algorithm are applied to perform the bioprocess functioning and model analysis. With the model, the interrelations between pathway analysis and metabolic control mechanism are outlined. Diagrammatical results of the dynamics of metabolites are simulated and observed by implementing a HPN tool, Visual Object Net ++. An explanation of the observed behavior of the urea cycle is proposed to indicate possibilities for metabolic engineering and medical care. Finally, the perspective of Petri nets on modeling and simulation of metabolic networks is discussed.

Development of social skills in children: neural and behavioral evidence for the elaboration of cognitive models

PubMed Central

Soto-Icaza, Patricia; Aboitiz, Francisco; Billeke, Pablo

2015-01-01

Social skills refer to a wide group of abilities that allow us to interact and communicate with others. Children learn how to solve social situations by predicting and understanding other's behaviors. The way in which humans learn to interact successfully with others encompasses a complex interaction between neural, behavioral, and environmental elements. These have a role in the accomplishment of positive developmental outcomes, including peer acceptance, academic achievement, and mental health. All these social abilities depend on widespread brain networks that are recently being studied by neuroscience. In this paper, we will first review the studies on this topic, aiming to clarify the behavioral and neural mechanisms related to the acquisition of social skills during infancy and their appearance in time. Second, we will briefly describe how developmental diseases like Autism Spectrum Disorders (ASD) can inform about the neurobiological mechanisms of social skills. We finally sketch a general framework for the elaboration of cognitive models in order to facilitate the comprehension of human social development. PMID:26483621

Development of social skills in children: neural and behavioral evidence for the elaboration of cognitive models.

PubMed

Soto-Icaza, Patricia; Aboitiz, Francisco; Billeke, Pablo

2015-01-01

Social skills refer to a wide group of abilities that allow us to interact and communicate with others. Children learn how to solve social situations by predicting and understanding other's behaviors. The way in which humans learn to interact successfully with others encompasses a complex interaction between neural, behavioral, and environmental elements. These have a role in the accomplishment of positive developmental outcomes, including peer acceptance, academic achievement, and mental health. All these social abilities depend on widespread brain networks that are recently being studied by neuroscience. In this paper, we will first review the studies on this topic, aiming to clarify the behavioral and neural mechanisms related to the acquisition of social skills during infancy and their appearance in time. Second, we will briefly describe how developmental diseases like Autism Spectrum Disorders (ASD) can inform about the neurobiological mechanisms of social skills. We finally sketch a general framework for the elaboration of cognitive models in order to facilitate the comprehension of human social development.

Reinforcement Learning Explains Conditional Cooperation and Its Moody Cousin.

PubMed

Ezaki, Takahiro; Horita, Yutaka; Takezawa, Masanori; Masuda, Naoki

2016-07-01

Direct reciprocity, or repeated interaction, is a main mechanism to sustain cooperation under social dilemmas involving two individuals. For larger groups and networks, which are probably more relevant to understanding and engineering our society, experiments employing repeated multiplayer social dilemma games have suggested that humans often show conditional cooperation behavior and its moody variant. Mechanisms underlying these behaviors largely remain unclear. Here we provide a proximate account for this behavior by showing that individuals adopting a type of reinforcement learning, called aspiration learning, phenomenologically behave as conditional cooperator. By definition, individuals are satisfied if and only if the obtained payoff is larger than a fixed aspiration level. They reinforce actions that have resulted in satisfactory outcomes and anti-reinforce those yielding unsatisfactory outcomes. The results obtained in the present study are general in that they explain extant experimental results obtained for both so-called moody and non-moody conditional cooperation, prisoner's dilemma and public goods games, and well-mixed groups and networks. Different from the previous theory, individuals are assumed to have no access to information about what other individuals are doing such that they cannot explicitly use conditional cooperation rules. In this sense, myopic aspiration learning in which the unconditional propensity of cooperation is modulated in every discrete time step explains conditional behavior of humans. Aspiration learners showing (moody) conditional cooperation obeyed a noisy GRIM-like strategy. This is different from the Pavlov, a reinforcement learning strategy promoting mutual cooperation in two-player situations.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates

PubMed Central

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-01-01

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks. DOI: http://dx.doi.org/10.7554/eLife.15719.001 PMID:27525488

Drivers' social-work relationships as antecedents of unsafe driving: A social network perspective.

PubMed

Arizon Peretz, Renana; Luria, Gil

2017-09-01

In order to reduce road accidents rates, studies around the globe have attempted to shed light on the antecedents for unsafe road behaviors. The aim of the current research is to contribute to this literature by offering a new organizational antecedent of driver's unsafe behavior: The driver's relationships with his or her peers, as reflected in three types of social networks: negative relationships network, friendship networks and advice networks (safety consulting). We hypothesized that a driver's position in negative relationship networks, friendship networks, and advice networks will predict unsafe driving. Additionally, we hypothesized the existence of mutual influences among the driver's positions in these various networks, and suggested that the driver's positions interact to predict unsafe driving behaviors. The research included 83 professional drivers from four different organizations. Driving behavior data were gathered via the IVDR (In-Vehicle Data Recorder) system, installed in every truck to measure and record the driver's behavior. The findings indicated that the drivers' position in the team networks predicts safe driving behavior: Centrality in negative relationship networks is positively related to unsafe driving, and centrality in friendship networks is negatively related to unsafe driving, while centrality in advice networks is not related to unsafe driving. Furthermore, we found an interaction effect between negative network centrality and centrality in friendship networks. The relation between negative networks and unsafe behavior is weaker when high levels of friendship network centrality exist. The implications will be presented in the Discussion section. Copyright © 2017 Elsevier Ltd. All rights reserved.

The potential for accurately measuring behavioral and economic dimensions of consumption, prices, and markets for illegal drugs.

PubMed

Johnson, Bruce D; Golub, Andrew

2007-09-01

There are numerous analytic and methodological limitations to current measures of drug market activity. This paper explores the structure of markets and individual user behavior to provide an integrated understanding of behavioral and economic (and market) aspects of illegal drug use with an aim toward developing improved procedures for measurement. This involves understanding the social processes that structure illegal distribution networks and drug users' interactions with them. These networks are where and how social behaviors, prices, and markets for illegal drugs intersect. Our focus is upon getting an up close measurement of these activities. Building better measures of consumption behaviors necessitates building better rapport with subjects than typically achieved with one-time surveys in order to overcome withholding and underreporting and to get a comprehensive understanding of the processes involved. This can be achieved through repeated interviews and observations of behaviors. This paper also describes analytic advances that could be adopted to direct this inquiry including behavioral templates, and insights into the economic valuation of labor inputs and cash expenditures for various illegal drugs. Additionally, the paper makes recommendations to funding organizations for developing the mechanisms that would support behavioral scientists to weigh specimens and to collect small samples for laboratory analysis-by providing protection from the potential for arrest. The primary focus is upon U.S. markets. The implications for other countries are discussed.

The Potential for Accurately Measuring Behavioral and Economic Dimensions of Consumption, Prices, and Markets for Illegal Drugs

PubMed Central

Johnson, Bruce D.; Golub, Andrew

2007-01-01

There are numerous analytic and methodological limitations to current measures of drug market activity. This paper explores the structure of markets and individual user behavior to provide an integrated understanding of behavioral and economic (and market) aspects of illegal drug use with an aim toward developing improved procedures for measurement. This involves understanding the social processes that structure illegal distribution networks and drug users’ interactions with them. These networks are where and how social behaviors, prices, and markets for illegal drugs intersect. Our focus is upon getting an up close measurement of these activities. Building better measures of consumption behaviors necessitates building better rapport with subjects than typically achieved with one-time surveys in order to overcome withholding and underreporting and to get a comprehensive understanding of the processes involved. This can be achieved through repeated interviews and observations of behaviors. This paper also describes analytic advances that could be adopted to direct this inquiry including behavioral templates, and insights into the economic valuation of labor inputs and cash expenditures for various illegal drugs. Additionally, the paper makes recommendations to funding organizations for developing the mechanisms that would support behavioral scientists to weigh specimens and to collect small samples for laboratory analysis—by providing protection from the potential for arrest. The primary focus is upon U.S. markets. The implications for other countries are discussed. PMID:16978801

Contagion of Cooperation in Static and Fluid Social Networks.

PubMed

Jordan, Jillian J; Rand, David G; Arbesman, Samuel; Fowler, James H; Christakis, Nicholas A

2013-01-01

Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects' desire to attract new cooperative partners: even if many of one's current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.

Intranasal oxytocin modulates neural functional connectivity during human social interaction.

PubMed

Rilling, James K; Chen, Xiangchuan; Chen, Xu; Haroon, Ebrahim

2018-02-10

Oxytocin (OT) modulates social behavior in primates and many other vertebrate species. Studies in non-primate animals have demonstrated that, in addition to influencing activity within individual brain areas, OT influences functional connectivity across networks of areas involved in social behavior. Previously, we used fMRI to image brain function in human subjects during a dyadic social interaction task following administration of either intranasal oxytocin (INOT) or placebo, and analyzed the data with a standard general linear model. Here, we conduct an extensive re-analysis of these data to explore how OT modulates functional connectivity across a neural network that animal studies implicate in social behavior. OT induced widespread increases in functional connectivity in response to positive social interactions among men and widespread decreases in functional connectivity in response to negative social interactions among women. Nucleus basalis of Meynert, an important regulator of selective attention and motivation with a particularly high density of OT receptors, had the largest number of OT-modulated connections. Regions known to receive mesolimbic dopamine projections such as the nucleus accumbens and lateral septum were also hubs for OT effects on functional connectivity. Our results suggest that the neural mechanism by which OT influences primate social cognition may include changes in patterns of activity across neural networks that regulate social behavior in other animals. © 2018 Wiley Periodicals, Inc.

Thermodynamics and mechanics of photochemcially reacting polymers

NASA Astrophysics Data System (ADS)

Long, Rong; Qi, H. Jerry; Dunn, Martin L.

2013-11-01

We develop a thermodynamics and mechanics theory for polymers that when irradiated with light, undergo photochemical reactions that alter their macromolecular structure, e.g., by bond breaking and/or reformation, and in turn affect their mechanical and physical behavior. This emerging class of highly-engineered active materials shows great promise for myriad applications and is a subset of a broader class of polymers with covalent bonds that can be dynamically tuned with various environmental stimuli. We formulate a general thermodynamic and kinetic framework to model the complex photochemical-thermal-mechanical coupling in these materials. Our theory considers the behavior of a polymer that is subjected to the combination of mechanical and thermal loading while simultaneously irradiated by light with multiple frequency components and directions. We introduce an approach to model the photochemical reactions that can change the network topology, resulting chemical species transport, heat conduction and finite deformation. We describe the interaction of the material with light via a radiometric description and show how it can be linked to a full electromagnetic treatment when appropriate and if desired. Our approach is sufficiently general to permit the modeling of various materials that operate via different photochemical reaction mechanisms. After formulating the general theory, we specialize it to a polymer that when irradiated with light undergoes a series of photochemical reactions that cause chain scission and reformation which continuously rearrange the polymer network into a stress-free configuration. Based on the operant physical mechanisms we develop a constitutive model using a polymer chain decomposition and evolution approach to track the molecular structure changes during simultaneous irradiation and mechanical loading. In the special case of isothermal conditions with monochromatic and unidirectional irradiation, we recover a previous model based on intuitive ad-hoc assumptions and thus put it on strong thermodynamic footing. Finally we use our model to simulate the behavior of a polymer that is biaxially stretched and then irradiated with light from one side. We simulate the process and emphasize the spontaneous bending that occurs due to inhomogeneous photoinduced stress relaxation. From our theory, we obtain an analytical expression of a characteristic time for photo-induced stress relaxation in terms of the dominating system parameters.

Neural mechanisms of movement planning: motor cortex and beyond.

PubMed

Svoboda, Karel; Li, Nuo

2018-04-01

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes. Copyright © 2017. Published by Elsevier Ltd.

Working Memory and Parent-Rated Components of Attention in Middle Childhood: A Behavioral Genetic Study

PubMed Central

Deater-Deckard, Kirby; Cutting, Laurie; Thompson, Lee A.; Petrill, Stephen A.

2012-01-01

The purpose of the current study was to investigate potential genetic and environmental correlations between working memory and three behavioral aspects of the attention network (i.e., executive, alerting, and orienting) using a twin design. Data were from 90 monozygotic (39% male) and 112 same-sex dizygotic (41% male) twins. Individual differences in working memory performance (digit span) and parent-rated measures of executive, alerting, and orienting attention included modest to moderate genetic variance, modest shared environmental variance, and modest to moderate nonshared environmental variance. As hypothesized, working memory performance was correlated with executive and alerting attention, but not orienting attention. The correlation between working memory, executive attention, and alerting attention was completely accounted for by overlapping genetic covariance, suggesting a common genetic mechanism or mechanisms underlying the links between working memory and certain parent-rated indicators of attentive behavior. PMID:21948215

Implication of the Slow-5 Oscillations in the Disruption of the Default-Mode Network in Healthy Aging and Stroke

PubMed Central

Nair, Veena A.; Mossahebi, Pouria; Young, Brittany M.; Chacon, Marcus; Jensen, Matthew; Birn, Rasmus M.; Meyerand, Mary E.; Prabhakaran, Vivek

2016-01-01

Abstract The processes of normal aging and aging-related pathologies subject the brain to an active re-organization of its brain networks. Among these, the default-mode network (DMN) is consistently implicated with a demonstrated reduction in functional connectivity within the network. However, no clear stipulation on the underlying mechanisms of the de-synchronization has yet been provided. In this study, we examined the spectral distribution of the intrinsic low-frequency oscillations (LFOs) of the DMN sub-networks in populations of young normals, older subjects, and acute and subacute ischemic stroke patients. The DMN sub-networks were derived using a mid-order group independent component analysis with 117 eyes-closed resting-state functional magnetic resonance imaging (rs-fMRI) sessions from volunteers in those population groups, isolating three robust components of the DMN among other resting-state networks. The posterior component of the DMN presented noticeable differences. Measures of amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) of the network component demonstrated a decrease in resting-state cortical oscillation power in the elderly (normal and patient), specifically in the slow-5 (0.01–0.027 Hz) range of oscillations. Furthermore, the contribution of the slow-5 oscillations during the resting state was diminished for a greater influence of the slow-4 (0.027–0.073 Hz) oscillations in the subacute stroke group, not only suggesting a vulnerability of the slow-5 oscillations to disruption but also indicating a change in the distribution of the oscillations within the resting-state frequencies. The reduction of network slow-5 fALFF in the posterior DMN component was found to present a potential association with behavioral measures, suggesting a brain–behavior relationship to those oscillations, with this change in behavior potentially resulting from an altered network integrity induced by a weakening of the slow-5 oscillations during the resting state. The repeated identification of those frequencies in the disruption of DMN stresses a critical role of the slow-5 oscillations in network disruption, and it accentuates the importance of managing those oscillations in the health of the DMN. PMID:27130180

Punishment diminishes the benefits of network reciprocity in social dilemma experiments

PubMed Central

Li, Xuelong; Wang, Zhen; Li, Huijia; Shi, Lei; Podobnik, Boris; Havlin, Shlomo; Boccaletti, Stefano

2018-01-01

Network reciprocity has been widely advertised in theoretical studies as one of the basic cooperation-promoting mechanisms, but experimental evidence favoring this type of reciprocity was published only recently. When organized in an unchanging network of social contacts, human subjects cooperate provided the following strict condition is satisfied: The benefit of cooperation must outweigh the total cost of cooperating with all neighbors. In an attempt to relax this condition, we perform social dilemma experiments wherein network reciprocity is aided with another theoretically hypothesized cooperation-promoting mechanism—costly punishment. The results reveal how networks promote and stabilize cooperation. This stabilizing effect is stronger in a smaller-size neighborhood, as expected from theory and experiments. Contrary to expectations, punishment diminishes the benefits of network reciprocity by lowering assortment, payoff per round, and award for cooperative behavior. This diminishing effect is stronger in a larger-size neighborhood. An immediate implication is that the psychological effects of enduring punishment override the rational response anticipated in quantitative models of cooperation in networks. PMID:29259113

A memristive plasticity model of voltage-based STDP suitable for recurrent bidirectional neural networks in the hippocampus.

PubMed

Diederich, Nick; Bartsch, Thorsten; Kohlstedt, Hermann; Ziegler, Martin

2018-06-19

Memristive systems have gained considerable attention in the field of neuromorphic engineering, because they allow the emulation of synaptic functionality in solid state nano-physical systems. In this study, we show that memristive behavior provides a broad working framework for the phenomenological modelling of cellular synaptic mechanisms. In particular, we seek to understand how close a memristive system can account for the biological realism. The basic characteristics of memristive systems, i.e. voltage and memory behavior, are used to derive a voltage-based plasticity rule. We show that this model is suitable to account for a variety of electrophysiology plasticity data. Furthermore, we incorporate the plasticity model into an all-to-all connecting network scheme. Motivated by the auto-associative CA3 network of the hippocampus, we show that the implemented network allows the discrimination and processing of mnemonic pattern information, i.e. the formation of functional bidirectional connections resulting in the formation of local receptive fields. Since the presented plasticity model can be applied to real memristive devices as well, the presented theoretical framework can support both, the design of appropriate memristive devices for neuromorphic computing and the development of complex neuromorphic networks, which account for the specific advantage of memristive devices.

Mechanisms Underlying Adaptation of Respiratory Network Activity to Modulatory Stimuli in the Mouse Embryo.

PubMed

Chevalier, Marc; De Sa, Rafaël; Cardoit, Laura; Thoby-Brisson, Muriel

2016-01-01

Breathing is a rhythmic behavior that requires organized contractions of respiratory effector muscles. This behavior must adapt to constantly changing conditions in order to ensure homeostasis, proper body oxygenation, and CO2/pH regulation. Respiratory rhythmogenesis is controlled by neural networks located in the brainstem. One area considered to be essential for generating the inspiratory phase of the respiratory rhythm is the preBötzinger complex (preBötC). Rhythmogenesis emerges from this network through the interplay between the activation of intrinsic cellular properties (pacemaker properties) and intercellular synaptic connections. Respiratory activity continuously changes under the impact of numerous modulatory substances depending on organismal needs and environmental conditions. The preBötC network has been shown to become active during the last third of gestation. But only little is known regarding the modulation of inspiratory rhythmicity at embryonic stages and even less on a possible role of pacemaker neurons in this functional flexibility during the prenatal period. By combining electrophysiology and calcium imaging performed on embryonic brainstem slice preparations, we provide evidence showing that embryonic inspiratory pacemaker neurons are already intrinsically sensitive to neuromodulation and external conditions (i.e., temperature) affecting respiratory network activity, suggesting a potential role of pacemaker neurons in mediating rhythm adaptation to modulatory stimuli in the embryo.

Mechanisms Underlying Adaptation of Respiratory Network Activity to Modulatory Stimuli in the Mouse Embryo

PubMed Central

Chevalier, Marc; De Sa, Rafaël; Cardoit, Laura; Thoby-Brisson, Muriel

2016-01-01

Breathing is a rhythmic behavior that requires organized contractions of respiratory effector muscles. This behavior must adapt to constantly changing conditions in order to ensure homeostasis, proper body oxygenation, and CO2/pH regulation. Respiratory rhythmogenesis is controlled by neural networks located in the brainstem. One area considered to be essential for generating the inspiratory phase of the respiratory rhythm is the preBötzinger complex (preBötC). Rhythmogenesis emerges from this network through the interplay between the activation of intrinsic cellular properties (pacemaker properties) and intercellular synaptic connections. Respiratory activity continuously changes under the impact of numerous modulatory substances depending on organismal needs and environmental conditions. The preBötC network has been shown to become active during the last third of gestation. But only little is known regarding the modulation of inspiratory rhythmicity at embryonic stages and even less on a possible role of pacemaker neurons in this functional flexibility during the prenatal period. By combining electrophysiology and calcium imaging performed on embryonic brainstem slice preparations, we provide evidence showing that embryonic inspiratory pacemaker neurons are already intrinsically sensitive to neuromodulation and external conditions (i.e., temperature) affecting respiratory network activity, suggesting a potential role of pacemaker neurons in mediating rhythm adaptation to modulatory stimuli in the embryo. PMID:27239348

Exploring the Feasibility of Reputation Models for Improving P2P Routing under Churn

NASA Astrophysics Data System (ADS)

Sànchez-Artigas, Marc; García-López, Pedro; Herrera, Blas

Reputation mechanisms help peer-to-peer (P2P) networks to detect and avoid unreliable or uncooperative peers. Recently, it has been discussed that routing protocols can be improved by conditioning routing decisions to the past behavior of forwarding peers. However, churn — the continuous process of node arrival and departure — may severely hinder the applicability of rating mechanisms. In particular, short lifetimes mean that reputations are often generated from a small number of transactions.

The salience of social referents: a field experiment on collective norms and harassment behavior in a school social network.

PubMed

Paluck, Elizabeth Levy; Shepherd, Hana

2012-12-01

Persistent, widespread harassment in schools can be understood as a product of collective school norms that deem harassment, and behavior allowing harassment to escalate, as typical and even desirable. Thus, one approach to reducing harassment is to change students' perceptions of these collective norms. Theory suggests that the public behavior of highly connected and chronically salient actors in a group, called social referents, may provide influential cues for individuals' perception of collective norms. Using repeated, complete social network surveys of a public high school, we demonstrate that changing the public behavior of a randomly assigned subset of student social referents changes their peers' perceptions of school collective norms and their harassment behavior. Social referents exert their influence over peers' perceptions of collective norms through the mechanism of everyday social interaction, particularly interaction that is frequent and personally motivated, in contrast to interaction shaped by institutional channels like shared classes. These findings clarify the development of collective social norms: They depend on certain patterns of and motivations for social interactions within groups across time, and are not static but constantly reshaped and reproduced through these interactions. Understanding this process creates opportunities for changing collective norms and behavior. (PsycINFO Database Record (c) 2012 APA, all rights reserved).

Spinal sensory circuits in motion.

PubMed

Böhm, Urs Lucas; Wyart, Claire

2016-12-01

The role of sensory feedback in shaping locomotion has been long debated. Recent advances in genetics and behavior analysis revealed the importance of proprioceptive pathways in spinal circuits. The mechanisms underlying peripheral mechanosensation enabled to unravel the networks that feedback to spinal circuits in order to modulate locomotion. Sensory inputs to the vertebrate spinal cord were long thought to originate from the periphery. Recent studies challenge this view: GABAergic sensory neurons located within the spinal cord have been shown to relay mechanical and chemical information from the cerebrospinal fluid to motor circuits. Innovative approaches combining genetics, quantitative analysis of behavior and optogenetics now allow probing the contribution of these sensory feedback pathways to locomotion and recovery following spinal cord injury. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mdm2 mediates FMRP- and Gp1 mGluR-dependent protein translation and neural network activity.

PubMed

Liu, Dai-Chi; Seimetz, Joseph; Lee, Kwan Young; Kalsotra, Auinash; Chung, Hee Jung; Lu, Hua; Tsai, Nien-Pei

2017-10-15

Activating Group 1 (Gp1) metabotropic glutamate receptors (mGluRs), including mGluR1 and mGluR5, elicits translation-dependent neural plasticity mechanisms that are crucial to animal behavior and circuit development. Dysregulated Gp1 mGluR signaling has been observed in numerous neurological and psychiatric disorders. However, the molecular pathways underlying Gp1 mGluR-dependent plasticity mechanisms are complex and have been elusive. In this study, we identified a novel mechanism through which Gp1 mGluR mediates protein translation and neural plasticity. Using a multi-electrode array (MEA) recording system, we showed that activating Gp1 mGluR elevates neural network activity, as demonstrated by increased spontaneous spike frequency and burst activity. Importantly, we validated that elevating neural network activity requires protein translation and is dependent on fragile X mental retardation protein (FMRP), the protein that is deficient in the most common inherited form of mental retardation and autism, fragile X syndrome (FXS). In an effort to determine the mechanism by which FMRP mediates protein translation and neural network activity, we demonstrated that a ubiquitin E3 ligase, murine double minute-2 (Mdm2), is required for Gp1 mGluR-induced translation and neural network activity. Our data showed that Mdm2 acts as a translation suppressor, and FMRP is required for its ubiquitination and down-regulation upon Gp1 mGluR activation. These data revealed a novel mechanism by which Gp1 mGluR and FMRP mediate protein translation and neural network activity, potentially through de-repressing Mdm2. Our results also introduce an alternative way for understanding altered protein translation and brain circuit excitability associated with Gp1 mGluR in neurological diseases such as FXS. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The ETS-5 transcription factor regulates activity states in Caenorhabditis elegans by controlling satiety

PubMed Central

Juozaityte, Vaida; Pladevall-Morera, David; Podolska, Agnieszka; Nørgaard, Steffen; Pocock, Roger

2017-01-01

Animal behavior is shaped through interplay among genes, the environment, and previous experience. As in mammals, satiety signals induce quiescence in Caenorhabditis elegans. Here we report that the C. elegans transcription factor ETS-5, an ortholog of mammalian FEV/Pet1, controls satiety-induced quiescence. Nutritional status has a major influence on C. elegans behavior. When foraging, food availability controls behavioral state switching between active (roaming) and sedentary (dwelling) states; however, when provided with high-quality food, C. elegans become sated and enter quiescence. We show that ETS-5 acts to promote roaming and inhibit quiescence by setting the internal “satiety quotient” through fat regulation. Acting from the ASG and BAG sensory neurons, we show that ETS-5 functions in a complex network with serotonergic and neuropeptide signaling pathways to control food-regulated behavioral state switching. Taken together, our results identify a neuronal mechanism for controlling intestinal fat stores and organismal behavioral states in C. elegans, and establish a paradigm for the elucidation of obesity-relevant mechanisms. PMID:28193866

A gut (microbiome) feeling about the brain.

PubMed

Sherwin, Eoin; Rea, Kieran; Dinan, Timothy G; Cryan, John F

2016-03-01

There is an increasing realization that the microorganisms which reside within our gut form part of a complex multidirectional communication network with the brain known as the microbiome-gut-brain axis. In this review, we focus on recent findings which support a role for this axis in modulating neurodevelopment and behavior. A growing body of research is uncovering that under homeostatic conditions and in response to internal and external stressors, the bacterial commensals of our gut can signal to the brain through a variety of mechanisms to influence processes such neurotransmission, neurogenesis, microglia activation, and modulate behavior. Moreover, the mechanisms underlying the ability of stress to modulate the microbiota and also for microbiota to change the set point for stress sensitivity are being unraveled. Dysregulation of the gut microbiota composition has been identified in a number of psychiatric disorders, including depression. This has led to the concept of bacteria that have a beneficial effect upon behavior and mood (psychobiotics) being proposed for potential therapeutic interventions. Understanding the mechanisms by which the bacterial commensals of our gut are involved in brain function may lead to the development of novel microbiome-based therapies for these mood and behavioral disorders.

Social Network Factors and Addictive Behaviors among College Students

PubMed Central

Rinker, Dipali Venkataraman; Krieger, Heather; Neighbors, Clayton

2016-01-01

Purpose of the review To provide an overview of studies within the past five years examining the impact of social network factors on addictive behaviors among college students, to discuss gaps, limitations, and controversies in the field, and to summarize with a discussion of future directions and implications for interventions. Recent findings A review of 13 studies indicated that greater network exposure, centrality, reciprocated ties, and more tightly interconnected networks were associated with greater alcohol use and other addictive behaviors among college students. Summary Greater research is needed that expands beyond alcohol use to other addictive behaviors among college students. Additionally, more studies are needed that longitudinally study the impact of changes in social networks on addictive behaviors and vice versa, as well as studies examining sociocentric (whole) networks. Social network approaches offer innovative perspectives in understanding social influences on addictive behaviors and novel intervention strategies for potentially reducing addictive behaviors among college students. PMID:28580226

Predicting language diversity with complex networks

PubMed Central

Gubiec, Tomasz

2018-01-01

We analyze the model of social interactions with coevolution of the topology and states of the nodes. This model can be interpreted as a model of language change. We propose different rewiring mechanisms and perform numerical simulations for each. Obtained results are compared with the empirical data gathered from two online databases and anthropological study of Solomon Islands. We study the behavior of the number of languages for different system sizes and we find that only local rewiring, i.e. triadic closure, is capable of reproducing results for the empirical data in a qualitative manner. Furthermore, we cancel the contradiction between previous models and the Solomon Islands case. Our results demonstrate the importance of the topology of the network, and the rewiring mechanism in the process of language change. PMID:29702699

Applying gene regulatory network logic to the evolution of social behavior.

PubMed

Baran, Nicole M; McGrath, Patrick T; Streelman, J Todd

2017-06-06

Animal behavior is ultimately the product of gene regulatory networks (GRNs) for brain development and neural networks for brain function. The GRN approach has advanced the fields of genomics and development, and we identify organizational similarities between networks of genes that build the brain and networks of neurons that encode brain function. In this perspective, we engage the analogy between developmental networks and neural networks, exploring the advantages of using GRN logic to study behavior. Applying the GRN approach to the brain and behavior provides a quantitative and manipulative framework for discovery. We illustrate features of this framework using the example of social behavior and the neural circuitry of aggression.

Removal of Default-State Associated Inhibition During Repetition Priming Improves Response Articulation

PubMed Central

Dacks, Andrew M.; Siniscalchi, Michael J.; Weiss, Klaudiusz R.

2012-01-01

Behavior is a product of both the stimuli encountered and the current internal state. At the level of the nervous system, the internal state alters the biophysical properties of, and connections between, neurons establishing a “network state”. To establish a network state, the nervous system must be altered from an initial default/resting state, but what remains unclear is the extent to which this process represents induction from a passive default state or the removal of suppression by an active default state. We use repetition priming (a history-dependent improvement of behavioral responses to repeatedly encountered stimuli) to determine the cellular mechanisms underlying the transition from the default to the primed network state. We demonstrate that both removal of active suppression and induction of neuron excitability changes each contribute separately to the production of a primed state. The feeding system of Aplysia californica displays repetition priming via an increase in the activity of the radula closure neuron B8, which results in increased bite strength with each motor program. We found that during priming, B8 received progressively less inhibitory input from the multi-functional neurons B4/5. Additionally, priming enhanced the excitability of B8, but the rate at which B8 activity increased as a result of these changes was regulated by the progressive removal of inhibitory input. Thus, the establishment of the network state involves the induction of processes from a rested state, yet the consequences of these processes are conditional upon critical gating mechanisms actively enforced by the default state. PMID:23223294

Progressive gender differences of structural brain networks in healthy adults: a longitudinal, diffusion tensor imaging study.

PubMed

Sun, Yu; Lee, Renick; Chen, Yu; Collinson, Simon; Thakor, Nitish; Bezerianos, Anastasios; Sim, Kang

2015-01-01

Sexual dimorphism in the brain maturation during childhood and adolescence has been repeatedly documented, which may underlie the differences in behaviors and cognitive performance. However, our understanding of how gender modulates the development of structural connectome in healthy adults is still not entirely clear. Here we utilized graph theoretical analysis of longitudinal diffusion tensor imaging data over a five-year period to investigate the progressive gender differences of brain network topology. The brain networks of both genders showed prominent economical "small-world" architecture (high local clustering and short paths between nodes). Additional analysis revealed a more economical "small-world" architecture in females as well as a greater global efficiency in males regardless of scan time point. At the regional level, both increased and decreased efficiency were found across the cerebral cortex for both males and females, indicating a compensation mechanism of cortical network reorganization over time. Furthermore, we found that weighted clustering coefficient exhibited significant gender-time interactions, implying different development trends between males and females. Moreover, several specific brain regions (e.g., insula, superior temporal gyrus, cuneus, putamen, and parahippocampal gyrus) exhibited different development trajectories between males and females. Our findings further prove the presence of sexual dimorphism in brain structures that may underlie gender differences in behavioral and cognitive functioning. The sex-specific progress trajectories in brain connectome revealed in this work provide an important foundation to delineate the gender related pathophysiological mechanisms in various neuropsychiatric disorders, which may potentially guide the development of sex-specific treatments for these devastating brain disorders.

Species traits and network structure predict the success and impacts of pollinator invasions.

PubMed

Valdovinos, Fernanda S; Berlow, Eric L; Moisset de Espanés, Pablo; Ramos-Jiliberto, Rodrigo; Vázquez, Diego P; Martinez, Neo D

2018-05-31

Species invasions constitute a major and poorly understood threat to plant-pollinator systems. General theory predicting which factors drive species invasion success and subsequent effects on native ecosystems is particularly lacking. We address this problem using a consumer-resource model of adaptive behavior and population dynamics to evaluate the invasion success of alien pollinators into plant-pollinator networks and their impact on native species. We introduce pollinator species with different foraging traits into network models with different levels of species richness, connectance, and nestedness. Among 31 factors tested, including network and alien properties, we find that aliens with high foraging efficiency are the most successful invaders. Networks exhibiting high alien-native diet overlap, fraction of alien-visited plant species, most-generalist plant connectivity, and number of specialist pollinator species are the most impacted by invaders. Our results mimic several disparate observations conducted in the field and potentially elucidate the mechanisms responsible for their variability.

Religiosity, Heavy Alcohol Use, and Vicarious Learning Networks among Adolescents in the United States

ERIC Educational Resources Information Center

Gryczynski, Jan; Ward, Brian W.

2012-01-01

Previous research has found that religiosity may protect against risky alcohol and drug use behaviors among adolescents, but the social mechanics underpinning the relationship are not well understood. This study examined the relationship between religiosity, heavy drinking, and social norms among U.S. adolescents aged 12 to 17 years, using the…

Such Stuff as Habits Are Made on: A Reply to Cooper and Shallice (2006)

ERIC Educational Resources Information Center

Botvinick, Matthew M.; Plaut, David C.

2006-01-01

The representations and mechanisms guiding everyday routine sequential action remain incompletely understood. In recent work, the authors proposed a computational model of routine sequential behavior that took the form of a recurrent neural network (M. Botvinick & D. C. Plaut, 2004). Subsequently, R. P. Cooper and T. Shallice (2006) put forth a…

Counterintuitive Behavior in Mechanical Networks

ERIC Educational Resources Information Center

Peters, Sarah; Vondracek, Mark

2012-01-01

Almost all introductory physics classes will, at some point, include springs and elastic forces. When studying such topics, it is interesting to consider the spring system shown in Fig. 1. In this system, two identical springs are arranged with the top of one spring anchored to the ceiling and the bottom of the second spring attached to a hanging…

Cascading failures and the emergence of cooperation in evolutionary-game based models of social and economical networks.

PubMed

Wang, Wen-Xu; Lai, Ying-Cheng; Armbruster, Dieter

2011-09-01

We study catastrophic behaviors in large networked systems in the paradigm of evolutionary games by incorporating a realistic "death" or "bankruptcy" mechanism. We find that a cascading bankruptcy process can arise when defection strategies exist and individuals are vulnerable to deficit. Strikingly, we observe that, after the catastrophic cascading process terminates, cooperators are the sole survivors, regardless of the game types and of the connection patterns among individuals as determined by the topology of the underlying network. It is necessary that individuals cooperate with each other to survive the catastrophic failures. Cooperation thus becomes the optimal strategy and absolutely outperforms defection in the game evolution with respect to the "death" mechanism. Our results can be useful for understanding large-scale catastrophe in real-world systems and in particular, they may yield insights into significant social and economical phenomena such as large-scale failures of financial institutions and corporations during an economic recession.