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Sample records for network cortical simulation

  1. Communication Structure of Cortical Networks

    PubMed Central

    da Fontoura Costa, Luciano; Batista, João Luiz B.; Ascoli, Giorgio A.

    2011-01-01

    Large-scale cortical networks exhibit characteristic topological properties that shape communication between brain regions and global cortical dynamics. Analysis of complex networks allows the description of connectedness, distance, clustering, and centrality that reveal different aspects of how the network's nodes communicate. Here, we focus on a novel analysis of complex walks in a series of mammalian cortical networks that model potential dynamics of information flow between individual brain regions. We introduce two new measures called absorption and driftness. Absorption is the average length of random walks between any two nodes, and takes into account all paths that may diffuse activity throughout the network. Driftness is the ratio between absorption and the corresponding shortest path length. For a given node of the network, we also define four related measurements, namely in- and out-absorption as well as in- and out-driftness, as the averages of the corresponding measures from all nodes to that node, and from that node to all nodes, respectively. We find that the cat thalamo-cortical system incorporates features of two classic network topologies, Erdös–Rényi graphs with respect to in-absorption and in-driftness, and configuration models with respect to out-absorption and out-driftness. Moreover, taken together these four measures separate the network nodes based on broad functional roles (visual, auditory, somatomotor, and frontolimbic). PMID:21427794

  2. Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics.

    PubMed

    Chao, Zenas C; Bakkum, Douglas J; Potter, Steve M

    2007-09-01

    Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we compared five established statistical methods to one of our own design, called center of activity trajectory (CAT). CAT, which depicts dynamics of the location-weighted average of spatiotemporal patterns of action potentials across the physical space of the neuronal circuitry, was the most sensitive statistic for detecting tetanus-induced plasticity in both simulated and living networks. By reducing the dimensionality of multi-unit data while still including spatial information, CAT allows efficient real-time computation of spatiotemporal activity patterns. Thus, CAT will be useful for studies in vivo or in vitro in which the locations of recording sites on multi-electrode probes are important. PMID:17873432

  3. Region-specific network plasticity in simulated and living cortical networks: comparison of the center of activity trajectory (CAT) with other statistics

    NASA Astrophysics Data System (ADS)

    Chao, Zenas C.; Bakkum, Douglas J.; Potter, Steve M.

    2007-09-01

    Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we compared five established statistical methods to one of our own design, called center of activity trajectory (CAT). CAT, which depicts dynamics of the location-weighted average of spatiotemporal patterns of action potentials across the physical space of the neuronal circuitry, was the most sensitive statistic for detecting tetanus-induced plasticity in both simulated and living networks. By reducing the dimensionality of multi-unit data while still including spatial information, CAT allows efficient real-time computation of spatiotemporal activity patterns. Thus, CAT will be useful for studies in vivo or in vitro in which the locations of recording sites on multi-electrode probes are important.

  4. Cortical Control of Affective Networks

    PubMed Central

    Kumar, Sunil; Black, Sherilynn J.; Hultman, Rainbo; Szabo, Steven T.; DeMaio, Kristine D.; Du, Jeanette; Katz, Brittany M.; Feng, Guoping; Covington, Herbert E.; Dzirasa, Kafui

    2013-01-01

    Transcranial magnetic stimulation and deep brain stimulation have emerged as therapeutic modalities for treatment refractory depression; however, little remains known regarding the circuitry that mediates the therapeutic effect of these approaches. Here we show that direct optogenetic stimulation of prefrontal cortex (PFC) descending projection neurons in mice engineered to express Chr2 in layer V pyramidal neurons (Thy1–Chr2 mice) models an antidepressant-like effect in mice subjected to a forced-swim test. Furthermore, we show that this PFC stimulation induces a long-lasting suppression of anxiety-like behavior (but not conditioned social avoidance) in socially stressed Thy1–Chr2 mice: an effect that is observed >10 d after the last stimulation. Finally, we use optogenetic stimulation and multicircuit recording techniques concurrently in Thy1–Chr2 mice to demonstrate that activation of cortical projection neurons entrains neural oscillatory activity and drives synchrony across limbic brain areas that regulate affect. Importantly, these neural oscillatory changes directly correlate with the temporally precise activation and suppression of limbic unit activity. Together, our findings show that the direct activation of cortical projection systems is sufficient to modulate activity across networks underlying affective regulation. They also suggest that optogenetic stimulation of cortical projection systems may serve as a viable therapeutic strategy for treating affective disorders. PMID:23325249

  5. Linking cortical network synchrony and excitability

    PubMed Central

    Meisel, Christian

    2016-01-01

    ABSTRACT Theoretical approaches based on dynamical systems theory can provide useful frameworks to guide experiments and analysis techniques when investigating cortical network activity. The notion of phase transitions between qualitatively different kinds of network dynamics has been such a framework inspiring novel approaches to neurophysiological data analysis over the recent years. One particular intriguing hypothesis has been that cortical networks reside in the vicinity of a phase transition. Although the final verdict on this hypothesis is still out, trying to understand cortex dynamics from this viewpoint has recently led to interesting insights on cortical network function with relevance for clinical practice. PMID:27065159

  6. Cortical attractor network dynamics with diluted connectivity.

    PubMed

    Rolls, Edmund T; Webb, Tristan J

    2012-01-24

    The connectivity of the cerebral cortex is diluted, with the probability of excitatory connections between even nearby pyramidal cells rarely more than 0.1, and in the hippocampus 0.04. To investigate the extent to which this diluted connectivity affects the dynamics of attractor networks in the cerebral cortex, we simulated an integrate-and-fire attractor network taking decisions between competing inputs with diluted connectivity of 0.25 or 0.1, and with the same number of synaptic connections per neuron for the recurrent collateral synapses within an attractor population as for full connectivity. The results indicated that there was less spiking-related noise with the diluted connectivity in that the stability of the network when in the spontaneous state of firing increased, and the accuracy of the correct decisions increased. The decision times were a little slower with diluted than with complete connectivity. Given that the capacity of the network is set by the number of recurrent collateral synaptic connections per neuron, on which there is a biological limit, the findings indicate that the stability of cortical networks, and the accuracy of their correct decisions or memory recall operations, can be increased by utilizing diluted connectivity and correspondingly increasing the number of neurons in the network, with little impact on the speed of processing of the cortex. Thus diluted connectivity can decrease cortical spiking-related noise. In addition, we show that the Fano factor for the trial-to-trial variability of the neuronal firing decreases from the spontaneous firing state value when the attractor network makes a decision. This article is part of a Special Issue entitled "Neural Coding". PMID:21875702

  7. Network Simulation

    SciTech Connect

    Fujimoto, Richard; Perumalla, Kalyan S; Riley, George F.

    2006-01-01

    A detailed introduction to the design, implementation and use of network simulation tools is presented. The requirements and issues faced in the design of simulators for wired and wireless networks are discussed. Abstractions such as packet- and fluid-level network models are covered. Several existing simulations are given as examples, with details and rationales regarding design decisions presented. Issues regarding performance and scalability are discussed in detail, describing how one can utilize distributed simulation methods to increase the scale and performance of a simulation environment. Finally, a case study of two simulation tools is presented that have been developed using distributed simulation techniques. This text is essential to any student, researcher or network architect desiring a detailed understanding of how network simulation tools are designed, implemented, and used.

  8. Multiscale Modeling of Cortical Neural Networks

    NASA Astrophysics Data System (ADS)

    Torben-Nielsen, Benjamin; Stiefel, Klaus M.

    2009-09-01

    In this study, we describe efforts at modeling the electrophysiological dynamics of cortical networks in a multi-scale manner. Specifically, we describe the implementation of a network model composed of simple single-compartmental neuron models, in which a single complex multi-compartmental model of a pyramidal neuron is embedded. The network is capable of generating Δ (2 Hz, observed during deep sleep states) and γ (40 Hz, observed during wakefulness) oscillations, which are then imposed onto the multi-compartmental model, thus providing realistic, dynamic boundary conditions. We furthermore discuss the challenges and chances involved in multi-scale modeling of neural function.

  9. Parcellating Cortical Functional Networks in Individuals

    PubMed Central

    Wang, Danhong; Buckner, Randy L.; Fox, Michael D.; Holt, Daphne J.; Holmes, Avram J.; Stoecklein, Sophia; Langs, Georg; Pan, Ruiqi; Qian, Tianyi; Li, Kuncheng; Baker, Justin T.; Stufflebeam, Steven M.; Wang, Kai; Wang, Xiaomin; Hong, Bo; Liu, Hesheng

    2015-01-01

    The capacity to identify the unique functional architecture of an individual’s brain is a critical step towards personalized medicine and understanding the neural basis of variations in human cognition and behavior. Here, we developed a novel cortical parcellation approach to accurately map functional organization at the individual level using resting-state fMRI. A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting great potential for use in clinical applications. PMID:26551545

  10. Functional Calcium Imaging in Developing Cortical Networks

    PubMed Central

    Dawitz, Julia; Kroon, Tim; Hjorth, J.J. Johannes; Meredith, Rhiannon M.

    2011-01-01

    A hallmark pattern of activity in developing nervous systems is spontaneous, synchronized network activity. Synchronized activity has been observed in intact spinal cord, brainstem, retina, cortex and dissociated neuronal culture preparations. During periods of spontaneous activity, neurons depolarize to fire single or bursts of action potentials, activating many ion channels. Depolarization activates voltage-gated calcium channels on dendrites and spines that mediate calcium influx. Highly synchronized electrical activity has been measured from local neuronal networks using field electrodes. This technique enables high temporal sampling rates but lower spatial resolution due to integrated read-out of multiple neurons at one electrode. Single cell resolution of neuronal activity is possible using patch-clamp electrophysiology on single neurons to measure firing activity. However, the ability to measure from a network is limited to the number of neurons patched simultaneously, and typically is only one or two neurons. The use of calcium-dependent fluorescent indicator dyes has enabled the measurement of synchronized activity across a network of cells. This technique gives both high spatial resolution and sufficient temporal sampling to record spontaneous activity of the developing network. A key feature of newly-forming cortical and hippocampal networks during pre- and early postnatal development is spontaneous, synchronized neuronal activity (Katz & Shatz, 1996; Khaziphov & Luhmann, 2006). This correlated network activity is believed to be essential for the generation of functional circuits in the developing nervous system (Spitzer, 2006). In both primate and rodent brain, early electrical and calcium network waves are observed pre- and postnatally in vivo and in vitro (Adelsberger et al., 2005; Garaschuk et al., 2000; Lamblin et al., 1999). These early activity patterns, which are known to control several developmental processes including neuronal differentiation

  11. Cortical Resonance Frequencies Emerge from Network Size and Connectivity

    PubMed Central

    Lea-Carnall, Caroline A.; Montemurro, Marcelo A.; Trujillo-Barreto, Nelson J.; Parkes, Laura M.; El-Deredy, Wael

    2016-01-01

    Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks. PMID:26914905

  12. The ontogeny of the cortical language network.

    PubMed

    Skeide, Michael A; Friederici, Angela D

    2016-05-01

    Language-processing functions follow heterogeneous developmental trajectories. The human embryo can already distinguish vowels in utero, but grammatical complexity is usually not fully mastered until at least 7 years of age. Examining the current literature, we propose that the ontogeny of the cortical language network can be roughly subdivided into two main developmental stages. In the first stage extending over the first 3 years of life, the infant rapidly acquires bottom-up processing capacities, which are primarily implemented bilaterally in the temporal cortices. In the second stage continuing into adolescence, top-down processes emerge gradually with the increasing functional selectivity and structural connectivity of the left inferior frontal cortex. PMID:27040907

  13. Serotonin modulation of cortical neurons and networks

    PubMed Central

    Celada, Pau; Puig, M. Victoria; Artigas, Francesc

    2013-01-01

    The serotonergic pathways originating in the dorsal and median raphe nuclei (DR and MnR, respectively) are critically involved in cortical function. Serotonin (5-HT), acting on postsynaptic and presynaptic receptors, is involved in cognition, mood, impulse control and motor functions by (1) modulating the activity of different neuronal types, and (2) varying the release of other neurotransmitters, such as glutamate, GABA, acetylcholine and dopamine. Also, 5-HT seems to play an important role in cortical development. Of all cortical regions, the frontal lobe is the area most enriched in serotonergic axons and 5-HT receptors. 5-HT and selective receptor agonists modulate the excitability of cortical neurons and their discharge rate through the activation of several receptor subtypes, of which the 5-HT1A, 5-HT1B, 5-HT2A, and 5-HT3 subtypes play a major role. Little is known, however, on the role of other excitatory receptors moderately expressed in cortical areas, such as 5-HT2C, 5-HT4, 5-HT6, and 5-HT7. In vitro and in vivo studies suggest that 5-HT1A and 5-HT2A receptors are key players and exert opposite effects on the activity of pyramidal neurons in the medial prefrontal cortex (mPFC). The activation of 5-HT1A receptors in mPFC hyperpolarizes pyramidal neurons whereas that of 5-HT2A receptors results in neuronal depolarization, reduction of the afterhyperpolarization and increase of excitatory postsynaptic currents (EPSCs) and of discharge rate. 5-HT can also stimulate excitatory (5-HT2A and 5-HT3) and inhibitory (5-HT1A) receptors in GABA interneurons to modulate synaptic GABA inputs onto pyramidal neurons. Likewise, the pharmacological manipulation of various 5-HT receptors alters oscillatory activity in PFC, suggesting that 5-HT is also involved in the control of cortical network activity. A better understanding of the actions of 5-HT in PFC may help to develop treatments for mood and cognitive disorders associated with an abnormal function of the frontal lobe

  14. Anatomical imbalance between cortical networks in autism

    PubMed Central

    Watanabe, Takamitsu; Rees, Geraint

    2016-01-01

    Influential psychological models of autism spectrum disorder (ASD) have proposed that this prevalent developmental disorder results from impairment of global (integrative) information processing and overload of local (sensory) information. However, little neuroanatomical evidence consistent with this account has been reported. Here, we examined relative grey matter volumes (rGMVs) between three cortical networks, how they changed with age, and their relationship with core symptomatology. Using public neuroimaging data of high-functioning ASD males and age-/sex-/IQ-matched controls, we first identified age-associated atypical increases in rGMVs of the regions of two sensory systems (auditory and visual networks), and an age-related aberrant decrease in rGMV of a task-control system (fronto-parietal network, FPN) in ASD children. While the enlarged rGMV of the auditory network in ASD adults was associated with the severity of autistic socio-communicational core symptom, that of the visual network was instead correlated with the severity of restricted and repetitive behaviours in ASD. Notably, the atypically decreased rGMV of FPN predicted both of the two core symptoms. These findings suggest that disproportionate undergrowth of a task-control system (FPN) may be a common anatomical basis for the two ASD core symptoms, and relative overgrowth of the two different sensory systems selectively compounds the distinct symptoms. PMID:27484308

  15. Anatomical imbalance between cortical networks in autism.

    PubMed

    Watanabe, Takamitsu; Rees, Geraint

    2016-01-01

    Influential psychological models of autism spectrum disorder (ASD) have proposed that this prevalent developmental disorder results from impairment of global (integrative) information processing and overload of local (sensory) information. However, little neuroanatomical evidence consistent with this account has been reported. Here, we examined relative grey matter volumes (rGMVs) between three cortical networks, how they changed with age, and their relationship with core symptomatology. Using public neuroimaging data of high-functioning ASD males and age-/sex-/IQ-matched controls, we first identified age-associated atypical increases in rGMVs of the regions of two sensory systems (auditory and visual networks), and an age-related aberrant decrease in rGMV of a task-control system (fronto-parietal network, FPN) in ASD children. While the enlarged rGMV of the auditory network in ASD adults was associated with the severity of autistic socio-communicational core symptom, that of the visual network was instead correlated with the severity of restricted and repetitive behaviours in ASD. Notably, the atypically decreased rGMV of FPN predicted both of the two core symptoms. These findings suggest that disproportionate undergrowth of a task-control system (FPN) may be a common anatomical basis for the two ASD core symptoms, and relative overgrowth of the two different sensory systems selectively compounds the distinct symptoms. PMID:27484308

  16. Cortical Network for Reading Linear Words in an Alphasyllabary

    ERIC Educational Resources Information Center

    Das, Tanusree; Bapi, Raju S.; Padakannaya, Prakash; Singh, Nandini C.

    2011-01-01

    Functional imaging studies have established cortical networks for reading alphabetic, syllabic and logographic scripts. There is little information about the different cortical areas that participate in reading an alphasyllabary. We use functional brain imaging to study the reading network for Devanagari, an alphasyllabary. Similar to syllabic…

  17. A simulated actuator driven by motor cortical signals.

    PubMed

    Lukashin, A V; Amirikian, B R; Georgopoulos, A P

    1996-11-01

    One problem in motor control concerns the mechanism whereby the central nervous system translates the motor cortical command encoded in cell activity into a coordinated contraction of limb muscles to generate a desired motor output. This problem is closely related to the design of adaptive systems that transform neuronal signals chronically recorded from the motor cortex into the physiologically appropriate motor output of multijoint prosthetic limbs. In this study we demonstrated how this transformation can be carried out by an artificial neural network using as command signals the actual impulse activity obtained from recordings in the motor cortex of monkeys during the performance of a task that required the exertion of force in different directions. The network receives experimentally measured brain signals and recodes them into motor actions of a simulated actuator that mimics the primate arm. The actuator responds to the motor cortical commands with surprising fidelity, generating forces in close quantitative agreement with those exerted by trained monkeys, in both the temporal and spatial domains. Moreover, we show that the time-varying motor output may be controlled by the impulse activity of as few as 15 motor cortical cells. These results outline a potentially implementable computation scheme that utilizes raw neuronal signals to drive artificial mechanical systems. PMID:8981430

  18. The Convallis rule for unsupervised learning in cortical networks.

    PubMed

    Yger, Pierre; Harris, Kenneth D

    2013-10-01

    The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the "Convallis rule", mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex. PMID:24204224

  19. The Convallis Rule for Unsupervised Learning in Cortical Networks

    PubMed Central

    Yger, Pierre; Harris, Kenneth D.

    2013-01-01

    The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex. PMID:24204224

  20. Altered cortical anatomical networks in temporal lobe epilepsy

    NASA Astrophysics Data System (ADS)

    Lv, Bin; He, Huiguang; Lu, Jingjing; Li, Wenjing; Dai, Dai; Li, Meng; Jin, Zhengyu

    2011-03-01

    Temporal lobe epilepsy (TLE) is one of the most common epilepsy syndromes with focal seizures generated in the left or right temporal lobes. With the magnetic resonance imaging (MRI), many evidences have demonstrated that the abnormalities in hippocampal volume and the distributed atrophies in cortical cortex. However, few studies have investigated if TLE patients have the alternation in the structural networks. In the present study, we used the cortical thickness to establish the morphological connectivity networks, and investigated the network properties using the graph theoretical methods. We found that all the morphological networks exhibited the small-world efficiency in left TLE, right TLE and normal groups. And the betweenness centrality analysis revealed that there were statistical inter-group differences in the right uncus region. Since the right uncus located at the right temporal lobe, these preliminary evidences may suggest that there are topological alternations of the cortical anatomical networks in TLE, especially for the right TLE.

  1. Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model

    PubMed Central

    Murray, John D.; McCormick, David A.

    2016-01-01

    Abstract Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input–output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation. PMID:27595137

  2. Simulating Cortical Feedback Modulation as Changes in Excitation and Inhibition in a Cortical Circuit Model.

    PubMed

    Zagha, Edward; Murray, John D; McCormick, David A

    2016-01-01

    Cortical feedback pathways are hypothesized to distribute context-dependent signals during flexible behavior. Recent experimental work has attempted to understand the mechanisms by which cortical feedback inputs modulate their target regions. Within the mouse whisker sensorimotor system, cortical feedback stimulation modulates spontaneous activity and sensory responsiveness, leading to enhanced sensory representations. However, the cellular mechanisms underlying these effects are currently unknown. In this study we use a simplified neural circuit model, which includes two recurrent excitatory populations and global inhibition, to simulate cortical modulation. First, we demonstrate how changes in the strengths of excitation and inhibition alter the input-output processing responses of our model. Second, we compare these responses with experimental findings from cortical feedback stimulation. Our analyses predict that enhanced inhibition underlies the changes in spontaneous and sensory evoked activity observed experimentally. More generally, these analyses provide a framework for relating cellular and synaptic properties to emergent circuit function and dynamic modulation. PMID:27595137

  3. Graph analysis of cortical networks reveals complex anatomical communication substrate

    NASA Astrophysics Data System (ADS)

    Zamora-López, Gorka; Zhou, Changsong; Kurths, Jürgen

    2009-03-01

    Sensory information entering the nervous system follows independent paths of processing such that specific features are individually detected. However, sensory perception, awareness, and cognition emerge from the combination of information. Here we have analyzed the corticocortical network of the cat, looking for the anatomical substrate which permits the simultaneous segregation and integration of information in the brain. We find that cortical communications are mainly governed by three topological factors of the underlying network: (i) a large density of connections, (ii) segregation of cortical areas into clusters, and (iii) the presence of highly connected hubs aiding the multisensory processing and integration. Statistical analysis of the shortest paths reveals that, while information is highly accessible to all cortical areas, the complexity of cortical information processing may arise from the rich and intricate alternative paths in which areas can influence each other.

  4. An Efficient Simulation Environment for Modeling Large-Scale Cortical Processing

    PubMed Central

    Richert, Micah; Nageswaran, Jayram Moorkanikara; Dutt, Nikil; Krichmar, Jeffrey L.

    2011-01-01

    We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. It uses a standard network construction interface. The simulator allows for execution on either GPUs or CPUs. The simulator, which is written in C/C++, allows for both fine grain and coarse grain specificity of a host of parameters. We demonstrate the ease of use and computational efficiency of this model by implementing a large-scale model of cortical areas V1, V4, and area MT. The complete model, which has 138,240 neurons and approximately 30 million synapses, runs in real-time on an off-the-shelf GPU. The simulator source code, as well as the source code for the cortical model examples is publicly available. PMID:22007166

  5. Cortical network architecture for context processing in primate brain

    PubMed Central

    Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka

    2015-01-01

    Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticography, we measured large-scale neuronal activity from monkeys observing videos of agents interacting in situations with different contexts. We extracted five context-related network structures including a bottom-up network during encoding and, seconds later, cue-dependent retrieval of the same network with the opposite top-down connectivity. These findings show that context is represented in the cortical network as distributed communication structures with dynamic information flows. This study provides a general methodology for recording and analyzing cortical network neuronal communication during cognition. DOI: http://dx.doi.org/10.7554/eLife.06121.001 PMID:26416139

  6. Tagging cortical networks in emotion: a topographical analysis

    PubMed Central

    Keil, Andreas; Costa, Vincent; Smith, J. Carson; Sabatinelli, Dean; McGinnis, E. Menton; Bradley, Margaret M.; Lang, Peter J.

    2013-01-01

    Viewing emotional pictures is associated with heightened perception and attention, indexed by a relative increase in visual cortical activity. Visual cortical modulation by emotion is hypothesized to reflect re-entrant connectivity originating in higher-order cortical and/or limbic structures. The present study used dense-array electroencephalography and individual brain anatomy to investigate functional coupling between the visual cortex and other cortical areas during affective picture viewing. Participants viewed pleasant, neutral, and unpleasant pictures that flickered at a rate of 10 Hz to evoke steady-state visual evoked potentials (ssVEPs) in the EEG. The spectral power of ssVEPs was quantified using Fourier transform, and cortical sources were estimated using beamformer spatial filters based on individual structural magnetic resonance images. In addition to lower-tier visual cortex, a network of occipito-temporal and parietal (bilateral precuneus, inferior parietal lobules) structures showed enhanced ssVEP power when participants viewed emotional (either pleasant or unpleasant), compared to neutral pictures. Functional coupling during emotional processing was enhanced between the bilateral occipital poles and a network of temporal (left middle/inferior temporal gyrus), parietal (bilateral parietal lobules), and frontal (left middle/inferior frontal gyrus) structures. These results converge with findings from hemodynamic analyses of emotional picture viewing and suggest that viewing emotionally engaging stimuli is associated with the formation of functional links between visual cortex and the cortical regions underlying attention modulation and preparation for action. PMID:21954087

  7. Damselfly Network Simulator

    Energy Science and Technology Software Center (ESTSC)

    2014-04-01

    Damselfly is a model-based parallel network simulator. It can simulate communication patterns of High Performance Computing applications on different network topologies. It outputs steady-state network traffic for a communication pattern, which can help in studying network congestion and its impact on performance.

  8. Dynamic functional tuning of nonlinear cortical networks

    NASA Astrophysics Data System (ADS)

    Stetter, Martin

    2006-03-01

    The mammalian neocortex is a highly complex and nonlinear dynamic system. One of its most prominent features is an omnipresent spontaneous neuronal activity. Here the possible functional role of this global background for cognitive flexibility is studied in a prototypic mean-field model area. It is demonstrated that the level of global background current efficiently controls the stimulus-response threshold and the stability and properties of short-term memory states. Moreover, it can dynamically gate arbitrary cortical subnetworks, when applied to parts of the area as a weak bias signal. These results suggest a central functional role of the level of background activation: the dynamic functional tuning of neocortical circuits.

  9. Mapping human brain networks with cortico-cortical evoked potentials

    PubMed Central

    Keller, Corey J.; Honey, Christopher J.; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D.

    2014-01-01

    The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

  10. Mapping human brain networks with cortico-cortical evoked potentials.

    PubMed

    Keller, Corey J; Honey, Christopher J; Mégevand, Pierre; Entz, Laszlo; Ulbert, Istvan; Mehta, Ashesh D

    2014-10-01

    The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex. PMID:25180306

  11. Somatostatin-expressing neurons in cortical networks.

    PubMed

    Urban-Ciecko, Joanna; Barth, Alison L

    2016-07-01

    Somatostatin-expressing GABAergic neurons constitute a major class of inhibitory neurons in the mammalian cortex and are characterized by dense wiring into the local network and high basal firing activity that persists in the absence of synaptic input. This firing provides both GABA type A receptor (GABAAR)- and GABABR-mediated inhibition that operates at fast and slow timescales. The activity of somatostatin-expressing neurons is regulated by brain state, during learning and in rewarded behaviour. Here, we review recent advances in our understanding of how this class of cells can control network activity, with specific reference to how this is constrained by their anatomical and electrophysiological properties. PMID:27225074

  12. Neural Synchrony in Cortical Networks: History, Concept and Current Status

    PubMed Central

    Uhlhaas, Peter J.; Pipa, Gordon; Lima, Bruss; Melloni, Lucia; Neuenschwander, Sergio; Nikolić, Danko; Singer, Wolf

    2009-01-01

    Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies. PMID:19668703

  13. CAISSON: Interconnect Network Simulator

    NASA Technical Reports Server (NTRS)

    Springer, Paul L.

    2006-01-01

    Cray response to HPCS initiative. Model future petaflop computer interconnect. Parallel discrete event simulation techniques for large scale network simulation. Built on WarpIV engine. Run on laptop and Altix 3000. Can be sized up to 1000 simulated nodes per host node. Good parallel scaling characteristics. Flexible: multiple injectors, arbitration strategies, queue iterators, network topologies.

  14. Malformations of cortical development and aberrant cortical networks: epileptogenesis and functional organization.

    PubMed

    Guerrini, Renzo; Barba, Carmen

    2010-12-01

    Malformations of cortical development are a major cause of drug-resistant epilepsy. Focal cortical dysplasia, heterotopia, and polymicrogyria are often manifested as discrete areas of abnormal neuronal migration and improper development of the cerebral cortex. Some of the patients harboring these malformations have obvious neurologic impairment, but others show unexpected deficits that are detectable only by screening. The role of surgical treatment of epilepsy due to localized malformations of cortical development is now established. However, its technical application can be challenging in that localization of function based on anatomic landmarks may not be reliable. Intracranial recordings have shown a high propensity for complex epileptogenic networks that may include remote cortical and subcortical regions. The MRI visible area of cortical abnormality should therefore be regarded as just an indicator of the epileptogenic zone rather than its tangible substrate. Completeness of resection, after delineation of the ictal onset zone, a key factor for successful epilepsy surgery, may be particularly difficult, and invasive EEG monitoring is necessary in most patients. Neural plasticity issues are of primary importance to surgical planning as the possibility of removing eloquent cortex permits more complete procedures with potentially higher rates of success. However, the functional consequences of malformative lesions are still poorly understood; conservation of function in the dysplastic cortex, its atypical representation, and relocation outside the malformed area are all possible. Surgical planning for associated epilepsy should therefore be based on individual assessments of structural imaging and of the major functions relevant to the area in question in the individual patient. PMID:21076336

  15. Cortical Hubs Form a Module for Multisensory Integration on Top of the Hierarchy of Cortical Networks

    PubMed Central

    Zamora-López, Gorka; Zhou, Changsong; Kurths, Jürgen

    2009-01-01

    Sensory stimuli entering the nervous system follow particular paths of processing, typically separated (segregated) from the paths of other modal information. However, sensory perception, awareness and cognition emerge from the combination of information (integration). The corticocortical networks of cats and macaque monkeys display three prominent characteristics: (i) modular organisation (facilitating the segregation), (ii) abundant alternative processing paths and (iii) the presence of highly connected hubs. Here, we study in detail the organisation and potential function of the cortical hubs by graph analysis and information theoretical methods. We find that the cortical hubs form a spatially delocalised, but topologically central module with the capacity to integrate multisensory information in a collaborative manner. With this, we resolve the underlying anatomical substrate that supports the simultaneous capacity of the cortex to segregate and to integrate multisensory information. PMID:20428515

  16. Cortical network models of impulse firing in the resting and active states predict cortical energetics.

    PubMed

    Bennett, Maxwell R; Farnell, Les; Gibson, William G; Lagopoulos, Jim

    2015-03-31

    Measurements of the cortical metabolic rate of glucose oxidation [CMR(glc(ox))] have provided a number of interesting and, in some cases, surprising observations. One is the decline in CMR(glc(ox)) during anesthesia and non-rapid eye movement (NREM) sleep, and another, the inverse relationship between the resting-state CMR(glc(ox)) and the transient following input from the thalamus. The recent establishment of a quantitative relationship between synaptic and action potential activity on the one hand and CMR(glc(ox)) on the other allows neural network models of such activity to probe for possible mechanistic explanations of these phenomena. We have carried out such investigations using cortical models consisting of networks of modules with excitatory and inhibitory neurons, each receiving excitatory inputs from outside the network in addition to intermodular connections. Modules may be taken as regions of cortical interest, the inputs from outside the network as arising from the thalamus, and the intermodular connections as long associational fibers. The model shows that the impulse frequency of different modules can differ from each other by less than 10%, consistent with the relatively uniform CMR(glc(ox)) observed across different regions of cortex. The model also shows that, if correlations of the average impulse rate between different modules decreases, there is a concomitant decrease in the average impulse rate in the modules, consistent with the observed drop in CMR(glc(ox)) in NREM sleep and under anesthesia. The model also explains why a transient thalamic input to sensory cortex gives rise to responses with amplitudes inversely dependent on the resting-state frequency, and therefore resting-state CMR(glc(ox)). PMID:25775588

  17. Anatomical connectivity and the resting state activity of large cortical networks

    PubMed Central

    Pinotsis, D.A.; Hansen, E.; Friston, K.J.; Jirsa, V.K.

    2013-01-01

    This paper uses mathematical modelling and simulations to explore the dynamics that emerge in large scale cortical networks, with a particular focus on the topological properties of the structural connectivity and its relationship to functional connectivity. We exploit realistic anatomical connectivity matrices (from diffusion spectrum imaging) and investigate their capacity to generate various types of resting state activity. In particular, we study emergent patterns of activity for realistic connectivity configurations together with approximations formulated in terms of neural mass or field models. We find that homogenous connectivity matrices, of the sort of assumed in certain neural field models give rise to damped spatially periodic modes, while more localised modes reflect heterogeneous coupling topologies. When simulating resting state fluctuations under realistic connectivity, we find no evidence for a spectrum of spatially periodic patterns, even when grouping together cortical nodes into communities, using graph theory. We conclude that neural field models with translationally invariant connectivity may be best applied at the mesoscopic scale and that more general models of cortical networks that embed local neural fields, may provide appropriate models of macroscopic cortical dynamics over the whole brain. PMID:23085498

  18. Self-organized criticality in cortical assemblies occurs in concurrent scale-free and small-world networks

    PubMed Central

    Massobrio, Paolo; Pasquale, Valentina; Martinoia, Sergio

    2015-01-01

    The spontaneous activity of cortical networks is characterized by the emergence of different dynamic states. Although several attempts were accomplished to understand the origin of these dynamics, the underlying factors continue to be elusive. In this work, we specifically investigated the interplay between network topology and spontaneous dynamics within the framework of self-organized criticality (SOC). The obtained results support the hypothesis that the emergence of critical states occurs in specific complex network topologies. By combining multi-electrode recordings of spontaneous activity of in vitro cortical assemblies with theoretical models, we demonstrate that different ‘connectivity rules’ drive the network towards different dynamic states. In particular, scale-free architectures with different degree of small-worldness account better for the variability observed in experimental data, giving rise to different dynamic states. Moreover, in relationship with the balance between excitation and inhibition and percentage of inhibitory hubs, the simulated cortical networks fall in a critical regime. PMID:26030608

  19. Spatiotemporal memory is an intrinsic property of networks of dissociated cortical neurons.

    PubMed

    Ju, Han; Dranias, Mark R; Banumurthy, Gokulakrishna; VanDongen, Antonius M J

    2015-03-01

    The ability to process complex spatiotemporal information is a fundamental process underlying the behavior of all higher organisms. However, how the brain processes information in the temporal domain remains incompletely understood. We have explored the spatiotemporal information-processing capability of networks formed from dissociated rat E18 cortical neurons growing in culture. By combining optogenetics with microelectrode array recording, we show that these randomly organized cortical microcircuits are able to process complex spatiotemporal information, allowing the identification of a large number of temporal sequences and classification of musical styles. These experiments uncovered spatiotemporal memory processes lasting several seconds. Neural network simulations indicated that both short-term synaptic plasticity and recurrent connections are required for the emergence of this capability. Interestingly, NMDA receptor function is not a requisite for these short-term spatiotemporal memory processes. Indeed, blocking the NMDA receptor with the antagonist APV significantly improved the temporal processing ability of the networks, by reducing spontaneously occurring network bursts. These highly synchronized events have disastrous effects on spatiotemporal information processing, by transiently erasing short-term memory. These results show that the ability to process and integrate complex spatiotemporal information is an intrinsic property of generic cortical networks that does not require specifically designed circuits. PMID:25740531

  20. Neural network model of cortical EEG response to olfactory stimuli

    NASA Astrophysics Data System (ADS)

    Dunbar, George L.; Van Toller, Steve

    1995-04-01

    We describe three experiments attempting to model differences in cortical EEG following stimulation with different odors. The data used in these experiments was obtained in previous studies, described briefly here. Subjects sit in an environmentally stabilized low odor cubicle. Twenty-eight electrodes are placed on the scalp and connect the subject to a neurosciences brain imager, which digitizes cortical EEG response. In a given trial, a specific odor is introduced, and the response recorded. In the first experiment, alpha wave data from a subset of ten electrodes and a single subject was used. In the original experiment, the subject was presented with a number of odors and the resulting brain electrical activity was resolved into 16 time slices (5 preceding presentation, 4 during presentation and 7 following presentation). Only data from frames 6, 7 and 8 (during presentation) was used here. A model was constructed to discriminate morning from afternoon responses. The network used measurements from 10 electrodes as input, and backpropagation was used for training. During training, the network was presented with responses to just one odor. Generalization was demonstrated for five other odors. The weights in the network have been analyzed and indicate a role for a specific group of electrode sites in this discrimination. The second experiment involved constructing a network to discriminate cortical EEG responses to two odors. In the original experiment from which we drew our data, fourteen subjects were presented with each odor once. Data from only the frame at first presentation of the odor were used here. Data from three subjects (chosen pseudo-randomly) was selected for use in the generalization phase and dropped from the training set. Output targets were constructed that took account of subjective ratings of `pleasantness.' A feed-forward network with twenty-eight input units was trained using data from the eleven remaining subjects, using conjugate gradient

  1. The up and down states of cortical networks

    NASA Astrophysics Data System (ADS)

    Ghorbani, Maryam; Levine, Alex J.; Mehta, Mayank; Bruinsma, Robijn

    2011-03-01

    The cortical networks show a collective activity of alternating active and silent states known as up and down states during slow wave sleep or anesthesia. The mechanism of this spontaneous activity as well as the anesthesia or sleep are still not clear. Here, using a mean field approach, we present a simple model to study the spontaneous activity of a homogenous cortical network of excitatory and inhibitory neurons that are recurrently connected. A key new ingredient in this model is that the activity-dependant synaptic depression is considered only for the excitatory neurons. We find depending on the strength of the synaptic depression and synaptic efficacies, the phase space contains strange attractors or stable fixed points at active or quiescent regimes. At the strange attractor phase, we can have oscillations similar to up and down states with flat and noisy up states. Moreover, we show that by increasing the synaptic efficacy corresponding to the connections between the excitatory neurons, the characteristics of the up and down states change in agreement with the changes that we observe in the intracellular recordings of the membrane potential from the entorhinal cortex by varying the depth of anesthesia. Thus, we propose that by measuring the value of this synaptic efficacy, one can quantify the depth of anesthesia which is clinically very important. These findings provide a simple, analytical understanding of the spontaneous cortical dynamics.

  2. Actin kinetics shapes cortical network structure and mechanics

    PubMed Central

    Fritzsche, Marco; Erlenkämper, Christoph; Moeendarbary, Emad; Charras, Guillaume; Kruse, Karsten

    2016-01-01

    The actin cortex of animal cells is the main determinant of cellular mechanics. The continuous turnover of cortical actin filaments enables cells to quickly respond to stimuli. Recent work has shown that most of the cortical actin is generated by only two actin nucleators, the Arp2/3 complex and the formin Diaph1. However, our understanding of their interplay, their kinetics, and the length distribution of the filaments that they nucleate within living cells is poor. Such knowledge is necessary for a thorough comprehension of cellular processes and cell mechanics from basic polymer physics principles. We determined cortical assembly rates in living cells by using single-molecule fluorescence imaging in combination with stochastic simulations. We find that formin-nucleated filaments are, on average, 10 times longer than Arp2/3-nucleated filaments. Although formin-generated filaments represent less than 10% of all actin filaments, mechanical measurements indicate that they are important determinants of cortical elasticity. Tuning the activity of actin nucleators to alter filament length distribution may thus be a mechanism allowing cells to adjust their macroscopic mechanical properties to their physiological needs. PMID:27152338

  3. Functional Cortical Network in Alpha Band Correlates with Social Bargaining

    PubMed Central

    Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

    2014-01-01

    Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

  4. Rich club neurons dominate Information Transfer in local cortical networks

    NASA Astrophysics Data System (ADS)

    Nigam, Sunny; Shimono, Masanori; Sporns, Olaf; Beggs, John

    2015-03-01

    The performance of complex networks depends on how they route their traffic. It is unknown how information is transferred in local cortical networks of hundreds of closely-spaced neurons. To address this, it is necessary to record simultaneously from hundreds of neurons at a spacing that matches typical axonal connection distances, and at a temporal resolution that matches synaptic delays. We used a 512 electrode array (60 μm spacing) to record spontaneous activity at 20 kHz, simultaneously from up to 700 neurons in slice cultures of mouse somatosensory cortex for 1 hr at a time. We used transfer entropy to quantify directed information transfer (IT) between pairs of neurons. We found an approximately lognormal distribution of firing rates as reported in in-vivo. Pairwise information transfer strengths also were nearly lognormally distributed, similar to synaptic strengths. 20% of the neurons accounted for 70% of the total IT coming into, and going out of the network and were defined as rich nodes. These rich nodes were more densely and strongly connected to each other expected by chance, forming a rich club. This highly uneven distribution of IT has implications for the efficiency and robustness of local cortical networks, and gives clues to the plastic processes that shape them. JSPS.

  5. Interaction of cortical networks mediating object motion detection by moving observers.

    PubMed

    Calabro, F J; Vaina, L M

    2012-08-01

    The task of parceling perceived visual motion into self- and object motion components is critical to safe and accurate visually guided navigation. In this paper, we used functional magnetic resonance imaging to determine the cortical areas functionally active in this task and the pattern connectivity among them to investigate the cortical regions of interest and networks that allow subjects to detect object motion separately from induced self-motion. Subjects were presented with nine textured objects during simulated forward self-motion and were asked to identify the target object, which had an additional, independent motion component toward or away from the observer. Cortical activation was distributed among occipital, intra-parietal and fronto-parietal areas. We performed a network analysis of connectivity data derived from partial correlation and multivariate Granger causality analyses among functionally active areas. This revealed four coarsely separated network clusters: bilateral V1 and V2; visually responsive occipito-temporal areas, including bilateral LO, V3A, KO (V3B) and hMT; bilateral VIP, DIPSM and right precuneus; and a cluster of higher, primarily left hemispheric regions, including the central sulcus, post-, pre- and sub-central sulci, pre-central gyrus, and FEF. We suggest that the visually responsive networks are involved in forming the representation of the visual stimulus, while the higher, left hemisphere cluster is involved in mediating the interpretation of the stimulus for action. Our main focus was on the relationships of activations during our task among the visually responsive areas. To determine the properties of the mechanism corresponding to the visual processing networks, we compared subjects' psychophysical performance to a model of object motion detection based solely on relative motion among objects and found that it was inconsistent with observer performance. Our results support the use of scene context (e.g., eccentricity, depth

  6. A thalamo-cortical neural mass model for the simulation of brain rhythms during sleep.

    PubMed

    Cona, F; Lacanna, M; Ursino, M

    2014-08-01

    Cortico-thalamic interactions are known to play a pivotal role in many brain phenomena, including sleep, attention, memory consolidation and rhythm generation. Hence, simple mathematical models that can simulate the dialogue between the cortex and the thalamus, at a mesoscopic level, have a great cognitive value. In the present work we describe a neural mass model of a cortico-thalamic module, based on neurophysiological mechanisms. The model includes two thalamic populations (a thalamo-cortical relay cell population, TCR, and its related thalamic reticular nucleus, TRN), and a cortical column consisting of four connected populations (pyramidal neurons, excitatory interneurons, inhibitory interneurons with slow and fast kinetics). Moreover, thalamic neurons exhibit two firing modes: bursting and tonic. Finally, cortical synapses among pyramidal neurons incorporate a disfacilitation mechanism following prolonged activity. Simulations show that the model is able to mimic the different patterns of rhythmic activity in cortical and thalamic neurons (beta and alpha waves, spindles, delta waves, K-complexes, slow sleep waves) and their progressive changes from wakefulness to deep sleep, by just acting on modulatory inputs. Moreover, simulations performed by providing short sensory inputs to the TCR show that brain rhythms during sleep preserve the cortex from external perturbations, still allowing a high cortical activity necessary to drive synaptic plasticity and memory consolidation. In perspective, the present model may be used within larger cortico-thalamic networks, to gain a deeper understanding of mechanisms beneath synaptic changes during sleep, to investigate the specific role of brain rhythms, and to explore cortical synchronization achieved via thalamic influences. PMID:24402459

  7. Cortical networks of procedural learning: evidence from cerebellar damage.

    PubMed

    Torriero, Sara; Oliveri, Massimiliano; Koch, Giacomo; Lo Gerfo, Emanuele; Salerno, Silvia; Petrosini, Laura; Caltagirone, Carlo

    2007-03-25

    The lateral cerebellum plays a critical role in procedural learning that goes beyond the strict motor control functions attributed to it. Patients with cerebellar damage show marked impairment in the acquisition of procedures, as revealed by their performance on the serial reaction time task (SRTT). Here we present the case of a patient affected by ischemic damage involving the left cerebellum who showed a selective deficit in procedural learning while performing the SRTT with the left hand. The deficit recovered when the cortical excitability of an extensive network involving both cerebellar hemispheres and the dorsolateral prefrontal cortex (DLPFC) was decreased by low-frequency repetitive transcranial magnetic stimulation (rTMS). Although inhibition of the right DLPFC or a control fronto-parietal region did not modify the patient's performance, inhibition of the right (unaffected) cerebellum and the left DLPFC markedly improved task performance. These findings could be explained by the modulation of a set of inhibitory and excitatory connections between the lateral cerebellum and the contralateral prefrontal area induced by rTMS. The presence of left cerebellar damage is likely associated with a reduced excitatory drive from sub-cortical left cerebellar nuclei towards the right DLPFC, causing reduced excitability of the right DLPFC and, conversely, unbalanced activation of the left DLPFC. Inhibition of the left DLPFC would reduce the unbalancing of cortical activation, thus explaining the observed selective recovery of procedural memory. PMID:17166525

  8. Prototyping distributed simulation networks

    NASA Technical Reports Server (NTRS)

    Doubleday, Dennis L.

    1990-01-01

    Durra is a declarative language designed to support application-level programming. The use of Durra is illustrated to describe a simple distributed application: a simulation of a collection of networked vehicle simulators. It is shown how the language is used to describe the application, its components and structure, and how the runtime executive provides for the execution of the application.

  9. Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality

    PubMed Central

    Shew, Woodrow L.; Yang, Hongdian; Petermann, Thomas; Roy, Rajarshi

    2009-01-01

    Spontaneous neuronal activity is a ubiquitous feature of cortex. Its spatiotemporal organization reflects past input and modulates future network output. Here we study whether a particular type of spontaneous activity is generated by a network that is optimized for input processing. Neuronal avalanches are a type of spontaneous activity observed in superficial cortical layers in vitro and in vivo with statistical properties expected from a network operating at “criticality.” Theory predicts that criticality and, therefore, neuronal avalanches are optimal for input processing, but until now, this has not been tested in experiments. Here, we use cortex slice cultures grown on planar microelectrode arrays to demonstrate that cortical networks that generate neuronal avalanches benefit from a maximized dynamic range, i.e., the ability to respond to the greatest range of stimuli. By changing the ratio of excitation and inhibition in the cultures, we derive a network tuning curve for stimulus processing as a function of distance from criticality in agreement with predictions from our simulations. Our findings suggest that in the cortex, (1) balanced excitation and inhibition establishes criticality, which maximizes the range of inputs that can be processed, and (2) spontaneous activity and input processing are unified in the context of critical phenomena. PMID:20007483

  10. Modeling of cortical signals using echo state networks

    NASA Astrophysics Data System (ADS)

    Zhou, Hanying; Wang, Yongji; Huang, Jiangshuai

    2009-10-01

    Diverse modeling frameworks have been utilized with the ultimate goal of translating brain cortical signals into prediction of visible behavior. The inputs to these models are usually multidimensional neural recordings collected from relevant regions of a monkey's brain while the outputs are the associated behavior which is typically the 2-D or 3-D hand position of a primate. Here our task is to set up a proper model in order to figure out the move trajectories by input the neural signals which are simultaneously collected in the experiment. In this paper, we propose to use Echo State Networks (ESN) to map the neural firing activities into hand positions. ESN is a newly developed recurrent neural network(RNN) model. Besides its dynamic property and short term memory just as other recurrent neural networks have, it has a special echo state property which endows it with the ability to model nonlinear dynamic systems powerfully. What distinguished it from transitional recurrent neural networks most significantly is its special learning method. In this paper we train this net with a refined version of its typical training method and get a better model.

  11. Slow oscillations during sleep coordinate interregional communication in cortical networks.

    PubMed

    Cox, Roy; van Driel, Joram; de Boer, Marieke; Talamini, Lucia M

    2014-12-10

    Large-amplitude sleep slow oscillations group faster neuronal oscillations and are of functional relevance for memory performance. However, relatively little is known about the impact of slow oscillations on functionally coupled networks. Here, we provide a comprehensive view on how human slow oscillatory dynamics influence various measures of brain processing. We demonstrate that slow oscillations coordinate interregional cortical communication, as assessed by phase synchrony in the sleep spindle frequency range and cross-frequency coupling between spindle and beta activity. Furthermore, we show that the organizing role of slow oscillations is restricted to circumscribed topographical areas. These findings add importantly to our basic understanding of the orchestrating role of slow oscillations. In addition, they are of considerable relevance for accounts of sleep-dependent memory reprocessing and consolidation. PMID:25505340

  12. Dynamics of myosin II organization into cortical contractile networks and fibers

    NASA Astrophysics Data System (ADS)

    Nie, Wei; Wei, Ming-Tzo; Ou-Yang, Daniel; Jedlicka, Sabrina; Vavylonis, Dimitrios

    2014-03-01

    The morphology of adhered cells critically depends on the formation of a contractile meshwork of parallel and cross-linked stress fibers along the contacting surface. The motor activity and mini-filament assembly of non-muscle myosin II is an important component of cell-level cytoskeletal remodeling during mechanosensing. To monitor the dynamics of myosin II, we used confocal microscopy to image cultured HeLa cells that stably express myosin regulatory light chain tagged with GFP (MRLC-GFP). MRLC-GFP was monitored in time-lapse movies at steady state and during the response of cells to varying concentrations of blebbistatin which disrupts actomyosin stress fibers. Using image correlation spectroscopy analysis, we quantified the kinetics of disassembly and reassembly of actomyosin networks and compared them to studies by other groups. This analysis suggested that the following processes contribute to the assembly of cortical actomyosin into fibers: random myosin mini-filament assembly and disassembly along the cortex; myosin mini-filament aligning and contraction; stabilization of cortical myosin upon increasing contractile tension. We developed simple numerical simulations that include those processes. The results of simulations of cells at steady state and in response to blebbistatin capture some of the main features observed in the experiments. This study provides a framework to help interpret how different cortical myosin remodeling kinetics may contribute to different cell shape and rigidity depending on substrate stiffness.

  13. Can the activities of the large scale cortical network be expressed by neural energy? A brief review.

    PubMed

    Wang, Rubin; Zhu, Yating

    2016-02-01

    This paper mainly discusses and summarize that the changes of biological energy in the brain can be expressed by the biophysical energy we constructed. Different from the electrochemical energy, the biophysical energy proposed in the paper not only can be used to simulate the activity of neurons but also be used to simulate the neural activity of large scale cortical networks, so that the scientific nature of the neural energy coding was discussed. PMID:26834857

  14. Coarse-grained reduction and analysis of a network model of cortical response: I. Drifting grating stimuli.

    PubMed

    Shelley, Michael; McLaughlin, David

    2002-01-01

    We present a reduction of a large-scale network model of visual cortex developed by McLaughlin, Shapley, Shelley, and Wielaard. The reduction is from many integrate-and-fire neurons to a spatially coarse-grained system for firing rates of neuronal subpopulations. It accounts explicitly for spatially varying architecture, ordered cortical maps (such as orientation preference) that vary regularly across the cortical layer, and disordered cortical maps (such as spatial phase preference or stochastic input conductances) that may vary widely from cortical neuron to cortical neuron. The result of the reduction is a set of nonlinear spatiotemporal integral equations for "phase-averaged" firing rates of neuronal subpopulations across the model cortex, derived asymptotically from the full model without the addition of any extra phenomological constants. This reduced system is used to study the response of the model to drifting grating stimuli-where it is shown to be useful for numerical investigations that reproduce, at far less computational cost, the salient features of the point-neuron network and for analytical investigations that unveil cortical mechanisms behind the responses observed in the simulations of the large-scale computational model. For example, the reduced equations clearly show (1) phase averaging as the source of the time-invariance of cortico-cortical conductances, (2) the mechanisms in the model for higher firing rates and better orientation selectivity of simple cells which are near pinwheel centers, (3) the effects of the length-scales of cortico-cortical coupling, and (4) the role of noise in improving the contrast invariance of orientation selectivity. PMID:12053156

  15. Feedback stabilizes propagation of synchronous spiking in cortical neural networks.

    PubMed

    Moldakarimov, Samat; Bazhenov, Maxim; Sejnowski, Terrence J

    2015-02-24

    Precisely timed action potentials related to stimuli and behavior have been observed in the cerebral cortex. However, information carried by the precise spike timing has to propagate through many cortical areas, and noise could disrupt millisecond precision during the transmission. Previous studies have demonstrated that only strong stimuli that evoke a large number of spikes with small dispersion of spike times can propagate through multilayer networks without degrading the temporal precision. Here we show that feedback projections can increase the number of spikes in spike volleys without degrading their temporal precision. Feedback also increased the range of spike volleys that can propagate through multilayer networks. Our work suggests that feedback projections could be responsible for the reliable propagation of information encoded in spike times through cortex, and thus could serve as an attentional mechanism to regulate the flow of information in the cortex. Feedback projections may also participate in generating spike synchronization that is engaged in cognitive behaviors by the same mechanisms described here for spike propagation. PMID:25675531

  16. Oscillations in large-scale cortical networks: map-based model.

    PubMed

    Rulkov, N F; Timofeev, I; Bazhenov, M

    2004-01-01

    We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). We developed a set of map-based models that replicate spiking activity of cortical fast spiking, regular spiking and intrinsically bursting neurons. Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. Our study suggests that map-based models can be widely used for large-scale simulations and that such models are especially useful for tasks where the modeling of specific firing patterns of different cell classes is important. PMID:15306740

  17. Anesthesia differentially modulates spontaneous network dynamics by cortical area and layer.

    PubMed

    Sellers, Kristin K; Bennett, Davis V; Hutt, Axel; Fröhlich, Flavio

    2013-12-01

    Anesthesia is widely used in medicine and research to achieve altered states of consciousness and cognition. Whereas changes to macroscopic cortical activity patterns by anesthesia measured at the spatial resolution of electroencephalography have been widely studied, modulation of mesoscopic and microscopic network dynamics by anesthesia remain poorly understood. To address this gap in knowledge, we recorded spontaneous mesoscopic (local field potential) and microscopic (multiunit activity) network dynamics in primary visual cortex (V1) and prefrontal cortex (PFC) of awake and isoflurane anesthetized ferrets (Mustela putoris furo). This approach allowed for examination of activity as a function of cortical area, cortical layer, and anesthetic depth with much higher spatial and temporal resolution than in previous studies. We hypothesized that a primary sensory area and an association cortical area would exhibit different patterns of network modulation by anesthesia due to their different functional roles. Indeed, we found effects specific to cortical area and cortical layer. V1 exhibited minimal changes in rhythmic structure with anesthesia but differential modulation of input layer IV. In contrast, anesthesia profoundly altered spectral power in PFC, with more uniform modulation across cortical layers. Our results demonstrate that anesthesia modulates spontaneous cortical activity in an area- and layer-specific manner. These finding provide the basis for 1) refining anesthesia monitoring algorithms, 2) reevaluating the large number of systems neuroscience studies performed in anesthetized animals, and 3) increasing our understanding of differential dynamics across cortical layers and areas. PMID:24047911

  18. Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults

    PubMed Central

    Shaw, Emily E.; Schultz, Aaron P.; Sperling, Reisa A.

    2015-01-01

    Abstract Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65–90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging. PMID:25827242

  19. Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults.

    PubMed

    Shaw, Emily E; Schultz, Aaron P; Sperling, Reisa A; Hedden, Trey

    2015-10-01

    Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65-90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging. PMID:25827242

  20. Network bursting dynamics in excitatory cortical neuron cultures results from the combination of different adaptive mechanisms.

    PubMed

    Masquelier, Timothée; Deco, Gustavo

    2013-01-01

    In the brain, synchronization among cells of an assembly is a common phenomenon, and thought to be functionally relevant. Here we used an in vitro experimental model of cell assemblies, cortical cultures, combined with numerical simulations of a spiking neural network (SNN) to investigate how and why spontaneous synchronization occurs. In order to deal with excitation only, we pharmacologically blocked GABAAergic transmission using bicuculline. Synchronous events in cortical cultures tend to involve almost every cell and to display relatively constant durations. We have thus named these "network spikes" (NS). The inter-NS-intervals (INSIs) proved to be a more interesting phenomenon. In most cortical cultures NSs typically come in series or bursts ("bursts of NSs", BNS), with short (~1 s) INSIs and separated by long silent intervals (tens of s), which leads to bimodal INSI distributions. This suggests that a facilitating mechanism is at work, presumably short-term synaptic facilitation, as well as two fatigue mechanisms: one with a short timescale, presumably short-term synaptic depression, and another one with a longer timescale, presumably cellular adaptation. We thus incorporated these three mechanisms into the SNN, which, indeed, produced realistic BNSs. Next, we systematically varied the recurrent excitation for various adaptation timescales. Strong excitability led to frequent, quasi-periodic BNSs (CV~0), and weak excitability led to rare BNSs, approaching a Poisson process (CV~1). Experimental cultures appear to operate within an intermediate weakly-synchronized regime (CV~0.5), with an adaptation timescale in the 2-8 s range, and well described by a Poisson-with-refractory-period model. Taken together, our results demonstrate that the INSI statistics are indeed informative: they allowed us to infer the mechanisms at work, and many parameters that we cannot access experimentally. PMID:24146781

  1. Temporal microstructure of cortical networks (TMCN) underlying task-related differences.

    PubMed

    Banerjee, Arpan; Pillai, Ajay S; Sperling, Justin R; Smith, Jason F; Horwitz, Barry

    2012-09-01

    Neuro-electromagnetic recording techniques (EEG, MEG, iEEG) provide high temporal resolution data to study the dynamics of neurocognitive networks: large scale neural assemblies involved in task-specific information processing. How does a neurocognitive network reorganize spatiotemporally on the order of a few milliseconds to process specific aspects of the task? At what times do networks segregate for task processing, and at what time scales does integration of information occur via changes in functional connectivity? Here, we propose a data analysis framework-Temporal microstructure of cortical networks (TMCN)-that answers these questions for EEG/MEG recordings in the signal space. Method validation is established on simulated MEG data from a delayed-match to-sample (DMS) task. We then provide an example application on MEG recordings during a paired associate task (modified from the simpler DMS paradigm) designed to study modality specific long term memory recall. Our analysis identified the times at which network segregation occurs for processing the memory recall of an auditory object paired to a visual stimulus (visual-auditory) in comparison to an analogous visual-visual pair. Across all subjects, onset times for first network divergence appeared within a range of 0.08-0.47 s after initial visual stimulus onset. This indicates that visual-visual and visual auditory memory recollection involves equivalent network components without any additional recruitment during an initial period of the sensory processing stage which is then followed by recruitment of additional network components for modality specific memory recollection. Therefore, we propose TMCN as a viable computational tool for extracting network timing in various cognitive tasks. PMID:22728151

  2. Neural network models for spatial data mining, map production, and cortical direction selectivity

    NASA Astrophysics Data System (ADS)

    Parsons, Olga

    A family of ARTMAP neural networks for incremental supervised learning has been developed over the last decade. The Sensor Exploitation Group of MIT Lincoln Laboratory (LL) has incorporated an early version of this network as the recognition engine of a hierarchical system for fusion and data mining of multiple registered geospatial images. The LL system has been successfully fielded, but it is limited to target vs. non-target identifications and does not produce whole maps. This dissertation expands the capabilities of the LL system so that it learns to identify arbitrarily many target classes at once and can thus produce a whole map. This new spatial data mining system is designed particularly to cope with the highly skewed class distributions of typical mapping problems. Specification of a consistent procedure and a benchmark testbed has permitted the evaluation of candidate recognition networks as well as pre- and post-processing and feature extraction options. The resulting default ARTMAP network and mapping methodology set a standard for a variety of related mapping problems and application domains. The second part of the dissertation investigates the development of cortical direction selectivity. The possible role of visual experience and oculomotor behavior in the maturation of cells in the primary visual cortex is studied. The responses of neurons in the thalamus and cortex of the cat are modeled when natural scenes are scanned by several types of eye movements. Inspired by the Hebbian-like synaptic plasticity, which is based upon correlations between cell activations, the second-order statistical structure of thalamo-cortical activity is examined. In the simulations, patterns of neural activity that lead to a correct refinement of cell responses are observed during visual fixation, when small ocular movements occur, but are not observed in the presence of large saccades. Simulations also replicate experiments in which kittens are reared under stroboscopic

  3. Amygdala lesions do not compromise the cortical network for false-belief reasoning.

    PubMed

    Spunt, Robert P; Elison, Jed T; Dufour, Nicholas; Hurlemann, René; Saxe, Rebecca; Adolphs, Ralph

    2015-04-14

    The amygdala plays an integral role in human social cognition and behavior, with clear links to emotion recognition, trust judgments, anthropomorphization, and psychiatric disorders ranging from social phobia to autism. A central feature of human social cognition is a theory-of-mind (ToM) that enables the representation other people's mental states as distinct from one's own. Numerous neuroimaging studies of the best studied use of ToM--false-belief reasoning--suggest that it relies on a specific cortical network; moreover, the amygdala is structurally and functionally connected with many components of this cortical network. It remains unknown whether the cortical implementation of any form of ToM depends on amygdala function. Here we investigated this question directly by conducting functional MRI on two patients with rare bilateral amygdala lesions while they performed a neuroimaging protocol standardized for measuring cortical activity associated with false-belief reasoning. We compared patient responses with those of two healthy comparison groups that included 480 adults. Based on both univariate and multivariate comparisons, neither patient showed any evidence of atypical cortical activity or any evidence of atypical behavioral performance; moreover, this pattern of typical cortical and behavioral response was replicated for both patients in a follow-up session. These findings argue that the amygdala is not necessary for the cortical implementation of ToM in adulthood and suggest a reevaluation of the role of the amygdala and its cortical interactions in human social cognition. PMID:25825732

  4. Cortical Surface-Based Construction of Individual Structural Network with Application to Early Brain Development Study

    PubMed Central

    Meng, Yu; Li, Gang; Lin, Weili; Gilmore, John H.; Shen, Dinggang

    2016-01-01

    Analysis of anatomical covariance for cortex morphology in individual subjects plays an important role in the study of human brains. However, the approaches for constructing individual structural networks have not been well developed yet. Existing methods based on patch-wise image intensity similarity suffer from several major drawbacks, i.e., 1) violation of cortical topological properties, 2) sensitivity to intensity heterogeneity, and 3) influence by patch size heterogeneity. To overcome these limitations, this paper presents a novel cortical surface-based method for constructing individual structural networks. Specifically, our method first maps the cortical surfaces onto a standard spherical surface atlas and then uniformly samples vertices on the spherical surface as the nodes of the networks. The similarity between any two nodes is computed based on the biologically-meaningful cortical attributes (e.g., cortical thickness) in the spherical neighborhood of their sampled vertices. The connection between any two nodes is established only if the similarity is larger than a user-specified threshold. Through leveraging spherical cortical surface patches, our method generates biologically-meaningful individual networks that are comparable across ages and subjects. The proposed method has been applied to construct cortical-thickness networks for 73 healthy infants, with each infant having two MRI scans at 0 and 1 year of age. The constructed networks during the two ages were compared using various network metrics, such as degree, clustering coefficient, shortest path length, small world property, global efficiency, and local efficiency. Experimental results demonstrate that our method can effectively construct individual structural networks and reveal meaningful patterns in early brain development. PMID:27169140

  5. Evoked potentials in large-scale cortical networks elicited by TMS of the visual cortex

    PubMed Central

    Grossman, Emily D.; Srinivasan, Ramesh

    2011-01-01

    Single pulses of transcranial magnetic stimulation (TMS) result in distal and long-lasting oscillations, a finding directly challenging the virtual lesion hypothesis. Previous research supporting this finding has primarily come from stimulation of the motor cortex. We have used single-pulse TMS with simultaneous EEG to target seven brain regions, six of which belong to the visual system [left and right primary visual area V1, motion-sensitive human middle temporal cortex, and a ventral temporal region], as determined with functional MRI-guided neuronavigation, and a vertex “control” site to measure the network effects of the TMS pulse. We found the TMS-evoked potential (TMS-EP) over visual cortex consists mostly of site-dependent theta- and alphaband oscillations. These site-dependent oscillations extended beyond the stimulation site to functionally connected cortical regions and correspond to time windows where the EEG responses maximally diverge (40, 200, and 385 ms). Correlations revealed two site-independent oscillations ∼350 ms after the TMS pulse: a theta-band oscillation carried by the frontal cortex, and an alpha-band oscillation over parietal and frontal cortical regions. A manipulation of stimulation intensity at one stimulation site (right hemisphere V1-V3) revealed sensitivity to the stimulation intensity at different regions of cortex, evidence of intensity tuning in regions distal to the site of stimulation. Together these results suggest that a TMS pulse applied to the visual cortex has a complex effect on brain function, engaging multiple brain networks functionally connected to the visual system with both invariant and site-specific spatiotemporal dynamics. With this characterization of TMS, we propose an alternative to the virtual lesion hypothesis. Rather than a technique that simulates lesions, we propose TMS generates natural brain signals and engages functional networks. PMID:21715670

  6. Three-dimensional simulations of ultrasonic axial transmission velocity measurement on cortical bone models

    NASA Astrophysics Data System (ADS)

    Bossy, Emmanuel; Talmant, Maryline; Laugier, Pascal

    2004-05-01

    The ultrasonic axial transmission technique, used to assess cortical shells of long bones, is investigated using numerical simulations based on a three-dimensional (3D) finite difference code. We focus our interest on the effects of 3D cortical bone geometry (curvature, cortical thickness), anisotropy, and microporosity on speed of sound (SOS) measurements for different frequencies in the MHz range. We first show that SOS values measured on tubular cortical shells are identical to those measured on cortical plates of equal thickness. Anisotropy of cortical bone is then shown to have a major impact on SOS measurement as a function of cortical thickness. The range of SOS values measured on anisotropic bone is half the range found when bone is considered isotropic. Dependence of thickness occurs for cortical shell thinner than 0.5×λbone in anisotropic bone (λbone: wavelength in bone), whereas it occurs for cortical shell thinner than λbone when anisotropy is neglected. Sensitivity of SOS along the bone axis to intracortical microporosity is shown to be approximately -20 m s-1 per percent of porosity. Using homogenized porous bone, we finally show that the cortical depth that contributes to lateral wave SOS measurement is approximately 1-1.5 mm for frequencies ranging from 500 kHz to 2 MHz under classical in vivo measurement conditions.

  7. Simulating synchronization in neuronal networks

    NASA Astrophysics Data System (ADS)

    Fink, Christian G.

    2016-06-01

    We discuss several techniques used in simulating neuronal networks by exploring how a network's connectivity structure affects its propensity for synchronous spiking. Network connectivity is generated using the Watts-Strogatz small-world algorithm, and two key measures of network structure are described. These measures quantify structural characteristics that influence collective neuronal spiking, which is simulated using the leaky integrate-and-fire model. Simulations show that adding a small number of random connections to an otherwise lattice-like connectivity structure leads to a dramatic increase in neuronal synchronization.

  8. The maturation of cortical sleep rhythms and networks over early development

    PubMed Central

    Chu, CJ; Leahy, J; Pathmanathan, J; Kramer, MA; Cash, SS

    2014-01-01

    Objective Although neuronal activity drives all aspects of cortical development, how human brain rhythms spontaneously mature remains an active area of research. We sought to systematically evaluate the emergence of human brain rhythms and functional cortical networks over early development. Methods We examined cortical rhythms and coupling patterns from birth through adolescence in a large cohort of healthy children (n=384) using scalp electroencephalogram (EEG) in the sleep state. Results We found that the emergence of brain rhythms follows a stereotyped sequence over early development. In general, higher frequencies increase in prominence with striking regional specificity throughout development. The coordination of these rhythmic activities across brain regions follows a general pattern of maturation in which broadly distributed networks of low-frequency oscillations increase in density while networks of high frequency oscillations become sparser and more highly clustered. Conclusion Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. Significance This work expands our knowledge of normal cortical development. The stereotyped neurophysiological processes observed at the level of rhythms and networks may provide a scaffolding to support critical periods of cognitive growth. Furthermore, these conserved patterns could provide a sensitive biomarker for cortical health across development. PMID:24418219

  9. Modeling and Simulations in Time Domain of a Stimulation Set-up for Cortical Applications

    PubMed Central

    Schweigmann, Michael; Kirchhoff, Frank; Koch, Klaus P.

    2016-01-01

    Electrical stimulation is used for example to treat neuronal disorders and depression with deep brain stimulation or transcranial electrical stimulation. Depending on the application, different electrodes are used and thus different electrical characteristics exist, which have to be handled by the stimulator. Without a measuring device the user would have to rely on the stimulator being able to deliver the needed stimulation signal. Therefore, the objective of this paper is to present a method to increase the level of confidence with characterization and modelling of the electrical behavior by using the example of one channel of our stimulation device for experimental use. In several simulation studies with an electrode model with values in a typical range for cortical applications the influence of the load onto the stimulator and the possibility to pre-estimate measuring signals in complex networks are shown. PMID:27478564

  10. Modeling and Simulations in Time Domain of a Stimulation Set-up for Cortical Applications.

    PubMed

    Schweigmann, Michael; Kirchhoff, Frank; Koch, Klaus P

    2016-06-13

    Electrical stimulation is used for example to treat neuronal disorders and depression with deep brain stimulation or transcranial electrical stimulation. Depending on the application, different electrodes are used and thus different electrical characteristics exist, which have to be handled by the stimulator. Without a measuring device the user would have to rely on the stimulator being able to deliver the needed stimulation signal. Therefore, the objective of this paper is to present a method to increase the level of confidence with characterization and modelling of the electrical behavior by using the example of one channel of our stimulation device for experimental use. In several simulation studies with an electrode model with values in a typical range for cortical applications the influence of the load onto the stimulator and the possibility to pre-estimate measuring signals in complex networks are shown. PMID:27478564

  11. Distributed simulation of network protocols

    NASA Technical Reports Server (NTRS)

    Paterra, Frank; Overstreet, C. Michael; Maly, Kurt J.

    1990-01-01

    Simulations of high speed network protocols are very CPU intensive operations requiring very long run times. Very high speed network protocols (Gigabit/sec rates) require longer simulation runs in order to reach a steady state, while at the same time requiring additional CPU processing for each unit of time because of the data rates for the traffic being simulated. As protocol development proceeds and simulations provide insights into any problems associated with the protocol, the simulation model often must be changed to generate additional or finer statistical performance information. Iterating on this process is very time consuming due to the required run times for the simulation models. The results of the efforts to distribute a high speed ring network protocol, Carrier Sensed Multiple Access/Ring Network (CSMA/RN), are presented.

  12. Subthalamic stimulation modulates cortical motor network activity and synchronization in Parkinson's disease.

    PubMed

    Weiss, Daniel; Klotz, Rosa; Govindan, Rathinaswamy B; Scholten, Marlieke; Naros, Georgios; Ramos-Murguialday, Ander; Bunjes, Friedemann; Meisner, Christoph; Plewnia, Christian; Krüger, Rejko; Gharabaghi, Alireza

    2015-03-01

    Dynamic modulations of large-scale network activity and synchronization are inherent to a broad spectrum of cognitive processes and are disturbed in neuropsychiatric conditions including Parkinson's disease. Here, we set out to address the motor network activity and synchronization in Parkinson's disease and its modulation with subthalamic stimulation. To this end, 20 patients with idiopathic Parkinson's disease with subthalamic nucleus stimulation were analysed on externally cued right hand finger movements with 1.5-s interstimulus interval. Simultaneous recordings were obtained from electromyography on antagonistic muscles (right flexor digitorum and extensor digitorum) together with 64-channel electroencephalography. Time-frequency event-related spectral perturbations were assessed to determine cortical and muscular activity. Next, cross-spectra in the time-frequency domain were analysed to explore the cortico-cortical synchronization. The time-frequency modulations enabled us to select a time-frequency range relevant for motor processing. On these time-frequency windows, we developed an extension of the phase synchronization index to quantify the global cortico-cortical synchronization and to obtain topographic differentiations of distinct electrode sites with respect to their contributions to the global phase synchronization index. The spectral measures were used to predict clinical and reaction time outcome using regression analysis. We found that movement-related desynchronization of cortical activity in the upper alpha and beta range was significantly facilitated with 'stimulation on' compared to 'stimulation off' on electrodes over the bilateral parietal, sensorimotor, premotor, supplementary-motor, and prefrontal areas, including the bilateral inferior prefrontal areas. These spectral modulations enabled us to predict both clinical and reaction time improvement from subthalamic stimulation. With 'stimulation on', interhemispheric cortico-cortical

  13. Controlling instabilities in manipulation requires specific cortical-striatal-cerebellar networks

    PubMed Central

    Lau, Chad; Wang, Yang; Venkadesan, Madhusudhan; Valero-Cuevas, Francisco J.

    2011-01-01

    Dexterous manipulation requires both strength, the ability to produce fingertip forces of a specific magnitude, and dexterity, the ability to dynamically regulate the magnitude and direction of fingertip force vectors and finger motions. Although cortical activity in fronto-parietal networks has been established for stable grip and pinch forces, the cortical regulation in the dexterous control of unstable objects remains unknown. We used functional magnetic resonance imaging (fMRI) to interrogate cortical networks engaged in the control of four objects with increasing instabilities but requiring constant strength. In addition to expected activity in fronto-parietal networks we find that dexterous manipulation of increasingly unstable objects is associated with a linear increase in the amplitude of the BOLD signal in the basal ganglia (P = 0.007 and P = 0.023 for 2 compression tasks). A computational regression (connectivity) model identified independent subsets of cortical networks whose connection strengths were mutable and associated with object instability (P < 0.001). Our results suggest that in the presence of object instability, the basal ganglia may modulate the activity of premotor areas and subsequent motor output. This work, therefore, provides new evidence for the selectable cortical representation and execution of dynamic multifinger manipulation for grasp stability. PMID:21228301

  14. Alterations in cortical network oscillations and parvalbumin neurons in schizophrenia.

    PubMed

    Gonzalez-Burgos, Guillermo; Cho, Raymond Y; Lewis, David A

    2015-06-15

    Cognitive deficits are a core clinical feature of schizophrenia but respond poorly to available medications. Thus, understanding the neural basis of these deficits is crucial for the development of new therapeutic interventions. The types of cognitive processes affected in schizophrenia are thought to depend on the precisely timed transmission of information in cortical regions via synchronous oscillations at gamma band frequency. Here, we review 1) data from clinical studies suggesting that induction of frontal cortex gamma oscillations during tasks that engage cognitive or complex perceptual functions is attenuated in schizophrenia; 2) findings from basic neuroscience studies highlighting the features of parvalbumin-positive interneurons that are critical for gamma oscillation production; and 3) results from recent postmortem human brain studies providing additional molecular bases for parvalbumin-positive interneuron alterations in prefrontal cortical circuitry in schizophrenia. PMID:25863358

  15. Alterations in Cortical Network Oscillations and Parvalbumin Neurons in Schizophrenia

    PubMed Central

    Gonzalez-Burgos, Guillermo; Cho, Raymond Y; Lewis, David A

    2015-01-01

    Cognitive deficits are a core clinical feature of schizophrenia but respond poorly to available medications. Thus, understanding the neural basis of these deficits is crucial for the development of new therapeutic interventions. The types of cognitive processes affected in schizophrenia are thought to depend on the precisely timed transmission of information in cortical regions via synchronous oscillations at gamma band frequency. Here, we review 1) data from clinical studies suggesting that induction of frontal cortex gamma oscillations during tasks that engage cognitive or complex perceptual functions is attenuated in schizophrenia, 2) findings from basic neuroscience studies highlighting the features of parvalbumin-positive (PV) interneurons that are critical for gamma oscillation production and 3) results from recent postmortem human brain studies providing additional molecular bases for PV interneuron alterations in prefrontal cortical circuitry in schizophrenia. PMID:25863358

  16. Large-scale cortical network properties predict future sound-to-word learning success.

    PubMed

    Sheppard, John Patrick; Wang, Ji-Ping; Wong, Patrick C M

    2012-05-01

    The human brain possesses a remarkable capacity to interpret and recall novel sounds as spoken language. These linguistic abilities arise from complex processing spanning a widely distributed cortical network and are characterized by marked individual variation. Recently, graph theoretical analysis has facilitated the exploration of how such aspects of large-scale brain functional organization may underlie cognitive performance. Brain functional networks are known to possess small-world topologies characterized by efficient global and local information transfer, but whether these properties relate to language learning abilities remains unknown. Here we applied graph theory to construct large-scale cortical functional networks from cerebral hemodynamic (fMRI) responses acquired during an auditory pitch discrimination task and found that such network properties were associated with participants' future success in learning words of an artificial spoken language. Successful learners possessed networks with reduced local efficiency but increased global efficiency relative to less successful learners and had a more cost-efficient network organization. Regionally, successful and less successful learners exhibited differences in these network properties spanning bilateral prefrontal, parietal, and right temporal cortex, overlapping a core network of auditory language areas. These results suggest that efficient cortical network organization is associated with sound-to-word learning abilities among healthy, younger adults. PMID:22360625

  17. Traumatic Brain Injury Increases Cortical Glutamate Network Activity by Compromising GABAergic Control

    PubMed Central

    Cantu, David; Walker, Kendall; Andresen, Lauren; Taylor-Weiner, Amaro; Hampton, David; Tesco, Giuseppina; Dulla, Chris G.

    2015-01-01

    Traumatic brain injury (TBI) is a major risk factor for developing pharmaco-resistant epilepsy. Although disruptions in brain circuitry are associated with TBI, the precise mechanisms by which brain injury leads to epileptiform network activity is unknown. Using controlled cortical impact (CCI) as a model of TBI, we examined how cortical excitability and glutamatergic signaling was altered following injury. We optically mapped cortical glutamate signaling using FRET-based glutamate biosensors, while simultaneously recording cortical field potentials in acute brain slices 2–4 weeks following CCI. Cortical electrical stimulation evoked polyphasic, epileptiform field potentials and disrupted the input–output relationship in deep layers of CCI-injured cortex. High-speed glutamate biosensor imaging showed that glutamate signaling was significantly increased in the injured cortex. Elevated glutamate responses correlated with epileptiform activity, were highest directly adjacent to the injury, and spread via deep cortical layers. Immunoreactivity for markers of GABAergic interneurons were significantly decreased throughout CCI cortex. Lastly, spontaneous inhibitory postsynaptic current frequency decreased and spontaneous excitatory postsynaptic current increased after CCI injury. Our results suggest that specific cortical neuronal microcircuits may initiate and facilitate the spread of epileptiform activity following TBI. Increased glutamatergic signaling due to loss of GABAergic control may provide a mechanism by which TBI can give rise to post-traumatic epilepsy. PMID:24610117

  18. Topological Properties of Large-Scale Cortical Networks Based on Multiple Morphological Features in Amnestic Mild Cognitive Impairment

    PubMed Central

    Li, Qiongling; Li, Xinwei; Wang, Xuetong; Li, Yuxia; Li, Kuncheng; Yu, Yang; Yin, Changhao; Li, Shuyu; Han, Ying

    2016-01-01

    Previous studies have demonstrated that amnestic mild cognitive impairment (aMCI) has disrupted properties of large-scale cortical networks based on cortical thickness and gray matter volume. However, it is largely unknown whether the topological properties of cortical networks based on geometric measures (i.e., sulcal depth, curvature, and metric distortion) change in aMCI patients compared with normal controls because these geometric features of cerebral cortex may be related to its intrinsic connectivity. Here, we compare properties in cortical networks constructed by six different morphological features in 36 aMCI participants and 36 normal controls. Six cortical features (3 volumetric and 3 geometric features) were extracted for each participant, and brain abnormities in aMCI were identified by cortical network based on graph theory method. All the cortical networks showed small-world properties. Regions showing significant differences mainly located in the medial temporal lobe and supramarginal and right inferior parietal lobe. In addition, we also found that the cortical networks constructed by cortical thickness and sulcal depth showed significant differences between the two groups. Our results indicated that geometric measure (i.e., sulcal depth) can be used to construct network to discriminate individuals with aMCI from controls besides volumetric measures. PMID:27057360

  19. Topological Properties of Large-Scale Cortical Networks Based on Multiple Morphological Features in Amnestic Mild Cognitive Impairment.

    PubMed

    Li, Qiongling; Li, Xinwei; Wang, Xuetong; Li, Yuxia; Li, Kuncheng; Yu, Yang; Yin, Changhao; Li, Shuyu; Han, Ying

    2016-01-01

    Previous studies have demonstrated that amnestic mild cognitive impairment (aMCI) has disrupted properties of large-scale cortical networks based on cortical thickness and gray matter volume. However, it is largely unknown whether the topological properties of cortical networks based on geometric measures (i.e., sulcal depth, curvature, and metric distortion) change in aMCI patients compared with normal controls because these geometric features of cerebral cortex may be related to its intrinsic connectivity. Here, we compare properties in cortical networks constructed by six different morphological features in 36 aMCI participants and 36 normal controls. Six cortical features (3 volumetric and 3 geometric features) were extracted for each participant, and brain abnormities in aMCI were identified by cortical network based on graph theory method. All the cortical networks showed small-world properties. Regions showing significant differences mainly located in the medial temporal lobe and supramarginal and right inferior parietal lobe. In addition, we also found that the cortical networks constructed by cortical thickness and sulcal depth showed significant differences between the two groups. Our results indicated that geometric measure (i.e., sulcal depth) can be used to construct network to discriminate individuals with aMCI from controls besides volumetric measures. PMID:27057360

  20. Development of coherent neuronal activity patterns in mammalian cortical networks: common principles and local hetereogeneity.

    PubMed

    Egorov, Alexei V; Draguhn, Andreas

    2013-01-01

    Many mammals are born in a very immature state and develop their rich repertoire of behavioral and cognitive functions postnatally. This development goes in parallel with changes in the anatomical and functional organization of cortical structures which are involved in most complex activities. The emerging spatiotemporal activity patterns in multi-neuronal cortical networks may indeed form a direct neuronal correlate of systemic functions like perception, sensorimotor integration, decision making or memory formation. During recent years, several studies--mostly in rodents--have shed light on the ontogenesis of such highly organized patterns of network activity. While each local network has its own peculiar properties, some general rules can be derived. We therefore review and compare data from the developing hippocampus, neocortex and--as an intermediate region--entorhinal cortex. All cortices seem to follow a characteristic sequence starting with uncorrelated activity in uncoupled single neurons where transient activity seems to have mostly trophic effects. In rodents, before and shortly after birth, cortical networks develop weakly coordinated multineuronal discharges which have been termed synchronous plateau assemblies (SPAs). While these patterns rely mostly on electrical coupling by gap junctions, the subsequent increase in number and maturation of chemical synapses leads to the generation of large-scale coherent discharges. These patterns have been termed giant depolarizing potentials (GDPs) for predominantly GABA-induced events or early network oscillations (ENOs) for mostly glutamatergic bursts, respectively. During the third to fourth postnatal week, cortical areas reach their final activity patterns with distinct network oscillations and highly specific neuronal discharge sequences which support adult behavior. While some of the mechanisms underlying maturation of network activity have been elucidated much work remains to be done in order to fully

  1. Decreased centrality of cortical volume covariance networks in autism spectrum disorders.

    PubMed

    Balardin, Joana Bisol; Comfort, William Edgar; Daly, Eileen; Murphy, Clodagh; Andrews, Derek; Murphy, Declan G M; Ecker, Christine; Sato, João Ricardo

    2015-10-01

    Autism spectrum disorders (ASD) are a group of neurodevelopmental conditions characterized by atypical structural and functional brain connectivity. Complex network analysis has been mainly used to describe altered network-level organization for functional systems and white matter tracts in ASD. However, atypical functional and structural connectivity are likely to be also linked to abnormal development of the correlated structure of cortical gray matter. Such covariations of gray matter are particularly well suited to the investigation of the complex cortical pathology of ASD, which is not confined to isolated brain regions but instead acts at the systems level. In this study, we examined network centrality properties of gray matter networks in adults with ASD (n = 84) and neurotypical controls (n = 84) using graph theoretical analysis. We derived a structural covariance network for each group using interregional correlation matrices of cortical volumes extracted from a surface-based parcellation scheme containing 68 cortical regions. Differences between groups in closeness network centrality measures were evaluated using permutation testing. We identified several brain regions in the medial frontal, parietal and temporo-occipital cortices with reductions in closeness centrality in ASD compared to controls. We also found an association between an increased number of autistic traits and reduced centrality of visual nodes in neurotypicals. Our study shows that ASD are accompanied by atypical organization of structural covariance networks by means of a decreased centrality of regions relevant for social and sensorimotor processing. These findings provide further evidence for the altered network-level connectivity model of ASD. PMID:26343606

  2. Altered resting-state functional connectivity in cortical networks in psychopathy.

    PubMed

    Philippi, Carissa L; Pujara, Maia S; Motzkin, Julian C; Newman, Joseph; Kiehl, Kent A; Koenigs, Michael

    2015-04-15

    Psychopathy is a personality disorder characterized by callous antisocial behavior and criminal recidivism. Here we examine whether psychopathy is associated with alterations in functional connectivity in three large-scale cortical networks. Using fMRI in 142 adult male prison inmates, we computed resting-state functional connectivity using seeds from the default mode network, frontoparietal network, and cingulo-opercular network. To determine the specificity of our findings to these cortical networks, we also calculated functional connectivity using seeds from two comparison primary sensory networks: visual and auditory networks. Regression analyses related network connectivity to overall psychopathy scores and to subscores for the "factors" and "facets" of psychopathy: Factor 1, interpersonal/affective traits; Factor 2, lifestyle/antisocial traits; Facet 1, interpersonal; Facet 2, affective; Facet 3, lifestyle; Facet 4, antisocial. Overall psychopathy severity was associated with reduced functional connectivity between lateral parietal cortex and dorsal anterior cingulate cortex. The two factor scores exhibited contrasting relationships with functional connectivity: Factor 1 scores were associated with reduced functional connectivity in the three cortical networks, whereas Factor 2 scores were associated with heightened connectivity in the same networks. This dissociation was evident particularly in the functional connectivity between anterior insula and dorsal anterior cingulate cortex. The facet scores also demonstrated distinct patterns of connectivity. We found no associations between psychopathy scores and functional connectivity within visual or auditory networks. These findings provide novel evidence on the neural correlates of psychopathy and suggest that connectivity between cortical association hubs, such as the dorsal anterior cingulate cortex, may be a neurobiological marker of the disorder. PMID:25878280

  3. Altered Resting-State Functional Connectivity in Cortical Networks in Psychopathy

    PubMed Central

    Pujara, Maia S.; Motzkin, Julian C.; Newman, Joseph; Kiehl, Kent A.

    2015-01-01

    Psychopathy is a personality disorder characterized by callous antisocial behavior and criminal recidivism. Here we examine whether psychopathy is associated with alterations in functional connectivity in three large-scale cortical networks. Using fMRI in 142 adult male prison inmates, we computed resting-state functional connectivity using seeds from the default mode network, frontoparietal network, and cingulo-opercular network. To determine the specificity of our findings to these cortical networks, we also calculated functional connectivity using seeds from two comparison primary sensory networks: visual and auditory networks. Regression analyses related network connectivity to overall psychopathy scores and to subscores for the “factors” and “facets” of psychopathy: Factor 1, interpersonal/affective traits; Factor 2, lifestyle/antisocial traits; Facet 1, interpersonal; Facet 2, affective; Facet 3, lifestyle; Facet 4, antisocial. Overall psychopathy severity was associated with reduced functional connectivity between lateral parietal cortex and dorsal anterior cingulate cortex. The two factor scores exhibited contrasting relationships with functional connectivity: Factor 1 scores were associated with reduced functional connectivity in the three cortical networks, whereas Factor 2 scores were associated with heightened connectivity in the same networks. This dissociation was evident particularly in the functional connectivity between anterior insula and dorsal anterior cingulate cortex. The facet scores also demonstrated distinct patterns of connectivity. We found no associations between psychopathy scores and functional connectivity within visual or auditory networks. These findings provide novel evidence on the neural correlates of psychopathy and suggest that connectivity between cortical association hubs, such as the dorsal anterior cingulate cortex, may be a neurobiological marker of the disorder. PMID:25878280

  4. A mechanism for ultra-slow oscillations in the cortical default network.

    PubMed

    Steyn-Ross, Moira L; Steyn-Ross, D A; Sleigh, J W; Wilson, M T

    2011-02-01

    When the brain is in its noncognitive "idling" state, functional MRI measurements reveal the activation of default cortical networks whose activity is suppressed during cognitive processing. This default or background mode is characterized by ultra-slow BOLD oscillations (∼0.05 Hz), signaling extremely slow cycling in cortical metabolic demand across distinct cortical regions. Here we describe a model of the cortex which predicts that slow cycling of cortical activity can arise naturally as a result of nonlinear interactions between temporal (Hopf) and spatial (Turing) instabilities. The Hopf instability is triggered by delays in the inhibitory postsynaptic response, while the Turing instability is precipitated by increases in the strength of the gap-junction coupling between interneurons. We comment on possible implications for slow dendritic computation and information processing. PMID:20821063

  5. Building a Large-Scale Computational Model of a Cortical Neuronal Network

    NASA Astrophysics Data System (ADS)

    Zemanová, Lucia; Zhou, Changsong; Kurths, Jürgen

    We introduce the general framework of the large-scale neuronal model used in the 5th Helmholtz Summer School — Complex Brain Networks. The main aim is to build a universal large-scale model of a cortical neuronal network, structured as a network of networks, which is flexible enough to implement different kinds of topology and neuronal models and which exhibits behavior in various dynamical regimes. First, we describe important biological aspects of brain topology and use them in the construction of a large-scale cortical network. Second, the general dynamical model is presented together with explanations of the major dynamical properties of neurons. Finally, we discuss the implementation of the model into parallel code and its possible modifications and improvements.

  6. Computational Study of Subdural Cortical Stimulation: Effects of Simulating Anisotropic Conductivity on Activation of Cortical Neurons

    PubMed Central

    Seo, Hyeon; Kim, Donghyeon; Jun, Sung Chan

    2015-01-01

    Subdural cortical stimulation (SuCS) is an appealing method in the treatment of neurological disorders, and computational modeling studies of SuCS have been applied to determine the optimal design for electrotherapy. To achieve a better understanding of computational modeling on the stimulation effects of SuCS, the influence of anisotropic white matter conductivity on the activation of cortical neurons was investigated in a realistic head model. In this paper, we constructed pyramidal neuronal models (layers 3 and 5) that showed primary excitation of the corticospinal tract, and an anatomically realistic head model reflecting complex brain geometry. The anisotropic information was acquired from diffusion tensor magnetic resonance imaging (DT-MRI) and then applied to the white matter at various ratios of anisotropic conductivity. First, we compared the isotropic and anisotropic models; compared to the isotropic model, the anisotropic model showed that neurons were activated in the deeper bank during cathodal stimulation and in the wider crown during anodal stimulation. Second, several popular anisotropic principles were adapted to investigate the effects of variations in anisotropic information. We observed that excitation thresholds varied with anisotropic principles, especially with anodal stimulation. Overall, incorporating anisotropic conductivity into the anatomically realistic head model is critical for accurate estimation of neuronal responses; however, caution should be used in the selection of anisotropic information. PMID:26057524

  7. Mapping Sensorimotor Cortex Using Slow Cortical Potential Resting-State Networks While Awake and Under Anesthesia

    PubMed Central

    Breshears, Jonathan D.; Gaona, Charles M.; Roland, Jarod L.; Sharma, Mohit; Bundy, David T.; Shimony, Joshua S.; Rashid, Samiya; Eisenman, Lawrence N.; Hogan, R. Edward; Snyder, Abraham Z.; Leuthardt, Eric C.

    2015-01-01

    Background The emerging insight into resting-state cortical networks has been important in understanding the fundamental architecture of brain organization. These networks, which were originally identified with functional MRI, are also seen in the correlation topography of the infraslow rhythms of local field potentials. Because of the fundamental nature of these networks and their independence from task-related activations, we posit that in addition to their neuroscientific relevance, these slow cortical potential (SCP) networks could also play an important role in clinical brain mapping. Objective We hypothesized that these networks would be useful in identifying eloquent cortex, such as sensorimotor cortex, in patients both awake and under anesthesia. Methods This study included eight subjects undergoing surgical treatment for intractable epilepsy. SCPs were recorded from the cortical surface while awake and under propofol anesthesia. To test brain-mapping utility, slow cortical potential networks were identified using data-driven (seed-independent) and anatomy-driven (seed-based) approaches. Using electrocortical stimulation as the gold standard for comparison, the sensitivity and specificity of these networks for identifying sensorimotor cortex was calculated. Results Networks identified with a data-driven approach in patients under anesthesia and awake were 90% and 93% sensitive, and 58% and 55% specific for sensorimotor cortex, respectively. Networks identified with systematic seed selection in patients under anesthesia and awake were 78% and 83% sensitive, and 67% and 60% specific, respectively. Conclusion Resting-state networks may be useful for tailoring stimulation mapping and could provide a means of identifying eloquent regions in patients while under anesthesia. PMID:22517255

  8. Molecular Correlates of Cortical Network Modulation by Long-Term Sensory Experience in the Adult Rat Barrel Cortex

    ERIC Educational Resources Information Center

    Vallès, Astrid; Granic, Ivica; De Weerd, Peter; Martens, Gerard J. M.

    2014-01-01

    Modulation of cortical network connectivity is crucial for an adaptive response to experience. In the rat barrel cortex, long-term sensory stimulation induces cortical network modifications and neuronal response changes of which the molecular basis is unknown. Here, we show that long-term somatosensory stimulation by enriched environment…

  9. Parallel Cortical Networks Formed by Modular Organization of Primary Motor Cortex Outputs.

    PubMed

    Hamadjida, Adjia; Dea, Melvin; Deffeyes, Joan; Quessy, Stephan; Dancause, Numa

    2016-07-11

    In primates, the refinement of motor behaviors, in particular hand use, is associated with the establishment of more direct projections from primary motor cortex (M1) onto cervical motoneurons [1, 2] and the appearance of additional premotor and sensory cortical areas [3]. All of these areas have reciprocal connections with M1 [4-7]. Thus, during the evolution of the sensorimotor network, the number of interlocutors with which M1 interacts has tremendously increased. It is not clear how these additional interconnections are organized in relation to one another within the hand representation of M1. This is important because the organization of connections between M1 and phylogenetically newer and specialized cortical areas is likely to be key to the increased repertoire of hand movements in primates. In cebus monkeys, we used injections of retrograde tracers into the hand representation of different cortical areas of the sensorimotor network (ventral and dorsal premotor areas [PMv and PMd], supplementary motor area [SMA], and posterior parietal cortex [area 5]), and we analyzed the pattern of labeled neurons within the hand representation of M1. Instead of being uniformly dispersed across M1, neurons sending projections to each distant cortical area were largely segregated in different subregions of M1. These data support the view that primates split the cortical real estate of M1 into modules, each preferentially interconnected with a particular cortical area within the sensorimotor network. This modular organization could sustain parallel processing of interactions with multiple specialized cortical areas to increase the behavioral repertoire of the hand. PMID:27322001

  10. Frontotemporal dementia and language networks: cortical thickness reduction is driven by dyslexia susceptibility genes

    PubMed Central

    Paternicó, Donata; Manes, Marta; Premi, Enrico; Cosseddu, Maura; Gazzina, Stefano; Alberici, Antonella; Archetti, Silvana; Bonomi, Elisa; Cotelli, Maria Sofia; Cotelli, Maria; Turla, Marinella; Micheli, Anna; Gasparotti, Roberto; Padovani, Alessandro; Borroni, Barbara

    2016-01-01

    Variations within genes associated with dyslexia result in a language network vulnerability, and in patients with Frontotemporal Dementia (FTD), language disturbances represent a disease core feature. Here we explored whether variations within three related-dyslexia genes, namely KIAA0319, DCDC2, and CNTNAP, might affect cortical thickness measures in FTD patients. 112 FTD patients underwent clinical and neuropsychological examination, genetic analyses and brain Magnetic Resonance Imaging (MRI). KIAA0319 rs17243157 G/A, DCDC2 rs793842 A/G and CNTNAP2 rs17236239 A/G genetic variations were assessed. Cortical thickness was analysed by Freesurfer. Patients carrying KIAA0319 A*(AG or AA) carriers showed greater cortical thickness atrophy in the left fusiform and inferior temporal gyri, compared to KIAA0319 GG (p ≤ 0.001). Patients carrying CNTNAP2 G*(GA or GG) showed reduced cortical thickness in the left insula thenCNTNAP2 AA carriers (p≤0.001). When patients with both at-risk polymorphisms were considered (KIAA0319 A* and CNTNAP2 G*), greater and addictive cortical thickness atrophy of the left insula and the inferior temporal gyrus was demonstrated (p ≤ 0.001). No significant effect of DCDC2 was found. In FTD, variations of KIAA0319 and CNTNAP2 genes were related to cortical thickness abnormalities in those brain areas involved in language abilities. These findings shed light on genetic predisposition in defining phenotypic variability in FTD. PMID:27484312

  11. Frontotemporal dementia and language networks: cortical thickness reduction is driven by dyslexia susceptibility genes.

    PubMed

    Paternicó, Donata; Manes, Marta; Premi, Enrico; Cosseddu, Maura; Gazzina, Stefano; Alberici, Antonella; Archetti, Silvana; Bonomi, Elisa; Cotelli, Maria Sofia; Cotelli, Maria; Turla, Marinella; Micheli, Anna; Gasparotti, Roberto; Padovani, Alessandro; Borroni, Barbara

    2016-01-01

    Variations within genes associated with dyslexia result in a language network vulnerability, and in patients with Frontotemporal Dementia (FTD), language disturbances represent a disease core feature. Here we explored whether variations within three related-dyslexia genes, namely KIAA0319, DCDC2, and CNTNAP, might affect cortical thickness measures in FTD patients. 112 FTD patients underwent clinical and neuropsychological examination, genetic analyses and brain Magnetic Resonance Imaging (MRI). KIAA0319 rs17243157 G/A, DCDC2 rs793842 A/G and CNTNAP2 rs17236239 A/G genetic variations were assessed. Cortical thickness was analysed by Freesurfer. Patients carrying KIAA0319 A*(AG or AA) carriers showed greater cortical thickness atrophy in the left fusiform and inferior temporal gyri, compared to KIAA0319 GG (p ≤ 0.001). Patients carrying CNTNAP2 G*(GA or GG) showed reduced cortical thickness in the left insula thenCNTNAP2 AA carriers (p≤0.001). When patients with both at-risk polymorphisms were considered (KIAA0319 A* and CNTNAP2 G*), greater and addictive cortical thickness atrophy of the left insula and the inferior temporal gyrus was demonstrated (p ≤ 0.001). No significant effect of DCDC2 was found. In FTD, variations of KIAA0319 and CNTNAP2 genes were related to cortical thickness abnormalities in those brain areas involved in language abilities. These findings shed light on genetic predisposition in defining phenotypic variability in FTD. PMID:27484312

  12. Inter-synaptic learning of combination rules in a cortical network model

    PubMed Central

    Lavigne, Frédéric; Avnaïm, Francis; Dumercy, Laurent

    2014-01-01

    Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network. PMID:25221529

  13. Visual cortical mechanisms of perceptual grouping: interacting layers, networks, columns, and maps.

    PubMed

    Ross, W D; Grossberg, S; Mingolla, E

    2000-07-01

    The visual cortex has a laminar organization whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood. A neural model proposes how the laminar circuits of V1 and V2 generate perceptual groupings that maintain sensitivity to the contrasts and spatial organization of scenic cues. The model can decisively choose which groupings cohere and survive, even while balanced excitatory and inhibitory interactions preserve contrast-sensitive measures of local boundary likelihood or strength. In the model, excitatory inputs from lateral geniculate nucleus (LGN) activate layers 4 and 6 of V1. Layer 6 activates an on-center off-surround network of inputs to layer 4. Together these layer 4 inputs preserve analog sensitivity to LGN input contrasts. Layer 4 cells excite pyramidal cells in layer 2/3, which activate monosynaptic long-range horizontal excitatory connections between layer 2/3 pyramidal cells, and short-range disynaptic inhibitory connections mediated by smooth stellate cells. These interactions support inward perceptual grouping between two or more boundary inducers, but not outward grouping from a single inducer. These boundary signals feed back to layer 4 via the layer 6-to-4 on-center off-surround network. This folded feedback joins cells in different layers into functional columns while selecting winning groupings. Layer 6 in V1 also sends top-down signals to LGN using an on-center off-surround network, which suppresses LGN cells that do not receive feedback, while selecting, enhancing, and synchronizing activity of those that do. The model is used to simulate psychophysical and neurophysiological data about perceptual grouping, including various Gestalt grouping laws. PMID:10987511

  14. 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

  15. Functional networks in parallel with cortical development associate with executive functions in children.

    PubMed

    Zhong, Jidan; Rifkin-Graboi, Anne; Ta, Anh Tuan; Yap, Kar Lai; Chuang, Kai-Hsiang; Meaney, Michael J; Qiu, Anqi

    2014-07-01

    Children begin performing similarly to adults on tasks requiring executive functions in late childhood, a transition that is probably due to neuroanatomical fine-tuning processes, including myelination and synaptic pruning. In parallel to such structural changes in neuroanatomical organization, development of functional organization may also be associated with cognitive behaviors in children. We examined 6- to 10-year-old children's cortical thickness, functional organization, and cognitive performance. We used structural magnetic resonance imaging (MRI) to identify areas with cortical thinning, resting-state fMRI to identify functional organization in parallel to cortical development, and working memory/response inhibition tasks to assess executive functioning. We found that neuroanatomical changes in the form of cortical thinning spread over bilateral frontal, parietal, and occipital regions. These regions were engaged in 3 functional networks: sensorimotor and auditory, executive control, and default mode network. Furthermore, we found that working memory and response inhibition only associated with regional functional connectivity, but not topological organization (i.e., local and global efficiency of information transfer) of these functional networks. Interestingly, functional connections associated with "bottom-up" as opposed to "top-down" processing were more clearly related to children's performance on working memory and response inhibition, implying an important role for brain systems involved in late childhood. PMID:23448875

  16. Cortical morphometry in frontoparietal and default mode networks in math-gifted adolescents.

    PubMed

    Navas-Sánchez, Francisco J; Carmona, Susana; Alemán-Gómez, Yasser; Sánchez-González, Javier; Guzmán-de-Villoria, Juan; Franco, Carolina; Robles, Olalla; Arango, Celso; Desco, Manuel

    2016-05-01

    Math-gifted subjects are characterized by above-age performance in intelligence tests, exceptional creativity, and high task commitment. Neuroimaging studies reveal enhanced functional brain organization and white matter microstructure in the frontoparietal executive network of math-gifted individuals. However, the cortical morphometry of these subjects remains largely unknown. The main goal of this study was to compare the cortical morphometry of math-gifted adolescents with that of an age- and IQ-matched control group. We used surface-based methods to perform a vertex-wise analysis of cortical thickness and surface area. Our results show that math-gifted adolescents present a thinner cortex and a larger surface area in key regions of the frontoparietal and default mode networks, which are involved in executive processing and creative thinking, respectively. The combination of reduced cortical thickness and larger surface area suggests above-age neural maturation of these networks in math-gifted individuals. Hum Brain Mapp 37:1893-1902, 2016. © 2016 Wiley Periodicals, Inc. PMID:26917433

  17. Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks.

    PubMed

    Sanabria-Diaz, Gretel; Melie-García, Lester; Iturria-Medina, Yasser; Alemán-Gómez, Yasser; Hernández-González, Gertrudis; Valdés-Urrutia, Lourdes; Galán, Lídice; Valdés-Sosa, Pedro

    2010-05-01

    Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks. PMID:20083210

  18. Neural network models of cortical functions based on the computational properties of the cerebral cortex.

    PubMed

    Guigon, E; Grandguillaume, P; Otto, I; Boutkhil, L; Burnod, Y

    1994-01-01

    We describe a biologically plausible modelling framework based on the architectural and processing characteristics of the cerebral cortex. Its key feature is a multicellular processing unit (cortical column) reflecting the modular nature of cortical organization and function. In this framework, we describe a neural network model organization and function. In this framework, we describe a neural network model of the neuronal circuits of the cerebral cortex that learn different functions associated with different parts of the cortex: 1) visual integration for invariant pattern recognition, performed by a cooperation between temporal and parietal areas; 2) visual-to-motor transformation for 3D arm reaching movements, performed by parietal and motor areas; and 3) temporal integration and storage of sensorimotor programs, performed by networks linking the prefrontal cortex to associative sensory and motor areas. The architecture of the network is inspired from the features of the architecture of cortical pathways involved in these functions. We propose two rules which describe neural processing and plasticity in the network. The first rule (adaptive tuning if gating) is an analog of operant conditioning and permits to learn to anticipate an action. The second rule (adaptive timing) is based on a bistable state of activity and permits to learn temporally separate events forming a behavioral sequence. PMID:7787829

  19. Parallel Network Simulations with NEURON

    PubMed Central

    Migliore, M.; Cannia, C.; Lytton, W.W; Markram, Henry; Hines, M. L.

    2009-01-01

    The NEURON simulation environment has been extended to support parallel network simulations. Each processor integrates the equations for its subnet over an interval equal to the minimum (interprocessor) presynaptic spike generation to postsynaptic spike delivery connection delay. The performance of three published network models with very different spike patterns exhibits superlinear speedup on Beowulf clusters and demonstrates that spike communication overhead is often less than the benefit of an increased fraction of the entire problem fitting into high speed cache. On the EPFL IBM Blue Gene, almost linear speedup was obtained up to 100 processors. Increasing one model from 500 to 40,000 realistic cells exhibited almost linear speedup on 2000 processors, with an integration time of 9.8 seconds and communication time of 1.3 seconds. The potential for speed-ups of several orders of magnitude makes practical the running of large network simulations that could otherwise not be explored. PMID:16732488

  20. Beyond Scale-Free Small-World Networks: Cortical Columns for Quick Brains

    NASA Astrophysics Data System (ADS)

    Stoop, Ralph; Saase, Victor; Wagner, Clemens; Stoop, Britta; Stoop, Ruedi

    2013-03-01

    We study to what extent cortical columns with their particular wiring boost neural computation. Upon a vast survey of columnar networks performing various real-world cognitive tasks, we detect no signs of enhancement. It is on a mesoscopic—intercolumnar—scale that the existence of columns, largely irrespective of their inner organization, enhances the speed of information transfer and minimizes the total wiring length required to bind distributed columnar computations towards spatiotemporally coherent results. We suggest that brain efficiency may be related to a doubly fractal connectivity law, resulting in networks with efficiency properties beyond those by scale-free networks.

  1. In Vivo Mapping of Cortical Columnar Networks in the Monkey with Focal Electrical and Optical Stimulation

    PubMed Central

    Roe, Anna Wang; Chernov, Mykyta M.; Friedman, Robert M.; Chen, Gang

    2015-01-01

    There are currently largescale efforts to understand the brain as a connection machine. However, there has been little emphasis on understanding connection patterns between functionally specific cortical columns. Here, we review development and application of focal electrical and optical stimulation methods combined with optical imaging and fMRI mapping in the non-human primate. These new approaches, when applied systematically on a large scale, will elucidate functionally specific intra-areal and inter-areal network connection patterns. Such functionally specific network data can provide accurate views of brain network topology. PMID:26635539

  2. The causal inference of cortical neural networks during music improvisations.

    PubMed

    Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft

    2014-01-01

    We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and "let-go" mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and "let-go" mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions. PMID:25489852

  3. The Causal Inference of Cortical Neural Networks during Music Improvisations

    PubMed Central

    Wan, Xiaogeng; Crüts, Björn; Jensen, Henrik Jeldtoft

    2014-01-01

    We present an EEG study of two music improvisation experiments. Professional musicians with high level of improvisation skills were asked to perform music either according to notes (composed music) or in improvisation. Each piece of music was performed in two different modes: strict mode and “let-go” mode. Synchronized EEG data was measured from both musicians and listeners. We used one of the most reliable causality measures: conditional Mutual Information from Mixed Embedding (MIME), to analyze directed correlations between different EEG channels, which was combined with network theory to construct both intra-brain and cross-brain networks. Differences were identified in intra-brain neural networks between composed music and improvisation and between strict mode and “let-go” mode. Particular brain regions such as frontal, parietal and temporal regions were found to play a key role in differentiating the brain activities between different playing conditions. By comparing the level of degree centralities in intra-brain neural networks, we found a difference between the response of musicians and the listeners when comparing the different playing conditions. PMID:25489852

  4. Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features.

    PubMed

    Li, Yang; Wang, Yaping; Wu, Guorong; Shi, Feng; Zhou, Luping; Lin, Weili; Shen, Dinggang

    2012-02-01

    Neuroimage measures from magnetic resonance (MR) imaging, such as cortical thickness, have been playing an increasingly important role in searching for biomarkers of Alzheimer's disease (AD). Recent studies show that, AD, mild cognitive impairment (MCI) and normal control (NC) can be distinguished with relatively high accuracy using the baseline cortical thickness. With the increasing availability of large longitudinal datasets, it also becomes possible to study the longitudinal changes of cortical thickness and their correlation with the development of pathology in AD. In this study, the longitudinal cortical thickness changes of 152 subjects from 4 clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) are measured by our recently developed 4 D (spatial+temporal) thickness measuring algorithm. It is found that the 4 clinical groups demonstrate very similar spatial distribution of grey matter (GM) loss on cortex. To fully utilize the longitudinal information and better discriminate the subjects from 4 groups, especially between Stable-MCI and Progressive-MCI, 3 different categories of features are extracted for each subject, i.e., (1) static cortical thickness measures computed from the baseline and endline, (2) cortex thinning dynamics, such as the thinning speed (mm/year) and the thinning ratio (endline/baseline), and (3) network features computed from the brain network constructed based on the correlation between the longitudinal thickness changes of different regions of interest (ROIs). By combining the complementary information provided by features from the 3 categories, 2 classifiers are trained to diagnose AD and to predict the conversion to AD in MCI subjects, respectively. In the leave-one-out cross-validation, the proposed method can distinguish AD patients from NC at an accuracy of 96.1%, and can detect 81.7% (AUC = 0.875) of the MCI converters 6 months ahead of their conversions to AD

  5. Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model.

    PubMed

    Compte, A; Brunel, N; Goldman-Rakic, P S; Wang, X J

    2000-09-01

    Single-neuron recordings from behaving primates have established a link between working memory processes and information-specific neuronal persistent activity in the prefrontal cortex. Using a network model endowed with a columnar architecture and based on the physiological properties of cortical neurons and synapses, we have examined the synaptic mechanisms of selective persistent activity underlying spatial working memory in the prefrontal cortex. Our model reproduces the phenomenology of the oculomotor delayed-response experiment of Funahashi et al. (S. Funahashi, C.J. Bruce and P.S. Goldman-Rakic, Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. J Neurophysiol 61:331-349, 1989). To observe stable spontaneous and persistent activity, we find that recurrent synaptic excitation should be primarily mediated by NMDA receptors, and that overall recurrent synaptic interactions should be dominated by inhibition. Isodirectional tuning of adjacent pyramidal cells and interneurons can be accounted for by a structured pyramid-to-interneuron connectivity. Robust memory storage against random drift of the tuned persistent activity and against distractors (intervening stimuli during the delay period) may be enhanced by neuromodulation of recurrent synapses. Experimentally testable predictions concerning the neural basis of working memory are discussed. PMID:10982751

  6. Pore network microarchitecture influences human cortical bone elasticity during growth and aging.

    PubMed

    Bala, Yohann; Lefèvre, Emmanuelle; Roux, Jean-Paul; Baron, Cécile; Lasaygues, Philippe; Pithioux, Martine; Kaftandjian, Valérie; Follet, Hélène

    2016-10-01

    Cortical porosity is a major determinant of bone strength. Haversian and Volkmann׳s canals are׳seen' as pores in 2D cross-section but fashion a dynamic network of interconnected channels in 3D, a quantifiable footprint of intracortical remodeling. Given the changes in bone remodeling across life, we hypothesized that the 3D microarchitecture of the cortical pore network influences its stiffness during growth and ageing. Cubes of cortical bone of 2 mm side-length were harvested in the distal 1/3 of the fibula in 13 growing children (mean age±SD: 13±4 yrs) and 16 adults (age: 75±13 yrs). The cubes were imaged using desktop micro-CT (8.14µm isotropic voxel size). Pores were segmented as a solid to assess pore volume fraction, number, diameter, separation, connectivity and structure model index. Elastic coefficients were derived from measurements of ultrasonic bulk compression and shear wave velocities and apparent mass density. The pore volume fraction did not significantly differ between children and adults but originates from different microarchitectural patterns. Compared to children, adults had 42% (p=0.033) higher pore number that were more connected (Connective Density: +205%, p=0.001) with a 18% (p=0.007) lower pore separation. After accounting for the contribution of pore volume fraction, axial elasticity in traction-compression mode was significantly correlated with better connectivity in growing children and with pore separation among adults. The changes in intracortical remodeling across life alter the distribution, size and connectedness of the channels from which cortical void fraction originates. These alterations in pore network microarchitecture participate in changes in compressive and shear mechanical behavior, partly in a porosity-independent manner. The assessment of pore volume fraction (i.e., porosity) provides only a limited understanding of the role of cortical void volume fraction in its mechanical properties. PMID:27389322

  7. Cortical thickness and oscillatory phase resetting: a proposed mechanism of salience network dysfunction in schizophrenia.

    PubMed

    Palaniyappan, L; Doege, K; Mallikarjun, P; Liddle, E; Francis-Liddle, P

    2012-01-01

    Schizophrenia is characterised by both electrophysiological abnormalities and consistent changes in the structure of cortical grey matter. But the relationship between these two observations is largely unknown. Structural changes reported in schizophrenia include reduced grey matter volume, thickness and surface area in several cortical regions, but most frequently in the insula and anterior cingulate cortex. These two regions together constitute an intrinsic brain circuit known as the "Salience Network", which has a key role in stimulus processing. During stimulus processing tasks, evoked activity is noted using electroencephalography (EEG). Phase resetting of ongoing oscillations contributes to this evoked activity. Neuronal oscillations play a crucial role in cerebral recruitment during cognitive tasks, and influencing the oscillatory phase can modulate cortical excitability and the transition between various cognitive states. At a network level, such a transition or switch is thought to be enabled by the Salience Network. In this study, we investigated the relationship between the cortical thickness in the Salience Network (measured using MRI) and the degree of phase resetting observed during an oddball task (measured using EEG) in 18 medicated male patients in a clinically stable phase of schizophrenia and 20 age and gender matched healthy controls. We obtained a measure of partial phase resetting after a stimulus is presented, and a second measure representing mean evoked activity, using the methods proposed by Martinez-Montes. Using MRI analysis, we have firstly shown that there is a significant loss of cortical thickness of regions that constitute the Salience Network in patients with schizophrenia. EEG analysis revealed that in healthy controls, the expected relationship between phase resetting and evoked electrical activity is observed, but in patients with schizophrenia the theta phase resetting is a weak predictor of the activity evoked by attending to

  8. Visual cortical areas of the mouse: comparison of parcellation and network structure with primates

    PubMed Central

    Laramée, Marie-Eve; Boire, Denis

    2015-01-01

    Brains have evolved to optimize sensory processing. In primates, complex cognitive tasks must be executed and evolution led to the development of large brains with many cortical areas. Rodents do not accomplish cognitive tasks of the same level of complexity as primates and remain with small brains both in relative and absolute terms. But is a small brain necessarily a simple brain? In this review, several aspects of the visual cortical networks have been compared between rodents and primates. The visual system has been used as a model to evaluate the level of complexity of the cortical circuits at the anatomical and functional levels. The evolutionary constraints are first presented in order to appreciate the rules for the development of the brain and its underlying circuits. The organization of sensory pathways, with their parallel and cross-modal circuits, is also examined. Other features of brain networks, often considered as imposing constraints on the development of underlying circuitry, are also discussed and their effect on the complexity of the mouse and primate brain are inspected. In this review, we discuss the common features of cortical circuits in mice and primates and see how these can be useful in understanding visual processing in these animals. PMID:25620914

  9. Cultured networks of excitatory projection neurons and inhibitory interneurons for studying human cortical neurotoxicity.

    PubMed

    Xu, Jin-Chong; Fan, Jing; Wang, Xueqing; Eacker, Stephen M; Kam, Tae-In; Chen, Li; Yin, Xiling; Zhu, Juehua; Chi, Zhikai; Jiang, Haisong; Chen, Rong; Dawson, Ted M; Dawson, Valina L

    2016-04-01

    Translating neuroprotective treatments from discovery in cell and animal models to the clinic has proven challenging. To reduce the gap between basic studies of neurotoxicity and neuroprotection and clinically relevant therapies, we developed a human cortical neuron culture system from human embryonic stem cells or human inducible pluripotent stem cells that generated both excitatory and inhibitory neuronal networks resembling the composition of the human cortex. This methodology used timed administration of retinoic acid to FOXG1(+)neural precursor cells leading to differentiation of neuronal populations representative of the six cortical layers with both excitatory and inhibitory neuronal networks that were functional and homeostatically stable. In human cortical neuronal cultures, excitotoxicity or ischemia due to oxygen and glucose deprivation led to cell death that was dependent onN-methyl-d-aspartate (NMDA) receptors, nitric oxide (NO), and poly(ADP-ribose) polymerase (PARP) (a cell death pathway called parthanatos that is distinct from apoptosis, necroptosis, and other forms of cell death). Neuronal cell death was attenuated by PARP inhibitors that are currently in clinical trials for cancer treatment. This culture system provides a new platform for the study of human cortical neurotoxicity and suggests that PARP inhibitors may be useful for ameliorating excitotoxic and ischemic cell death in human neurons. PMID:27053772

  10. Subthalamic stimulation modulates cortical motor network activity and synchronization in Parkinson’s disease

    PubMed Central

    Klotz, Rosa; Govindan, Rathinaswamy B.; Scholten, Marlieke; Naros, Georgios; Ramos-Murguialday, Ander; Bunjes, Friedemann; Meisner, Christoph; Plewnia, Christian; Krüger, Rejko

    2015-01-01

    Dynamic modulations of large-scale network activity and synchronization are inherent to a broad spectrum of cognitive processes and are disturbed in neuropsychiatric conditions including Parkinson’s disease. Here, we set out to address the motor network activity and synchronization in Parkinson’s disease and its modulation with subthalamic stimulation. To this end, 20 patients with idiopathic Parkinson’s disease with subthalamic nucleus stimulation were analysed on externally cued right hand finger movements with 1.5-s interstimulus interval. Simultaneous recordings were obtained from electromyography on antagonistic muscles (right flexor digitorum and extensor digitorum) together with 64-channel electroencephalography. Time-frequency event-related spectral perturbations were assessed to determine cortical and muscular activity. Next, cross-spectra in the time-frequency domain were analysed to explore the cortico-cortical synchronization. The time-frequency modulations enabled us to select a time-frequency range relevant for motor processing. On these time-frequency windows, we developed an extension of the phase synchronization index to quantify the global cortico-cortical synchronization and to obtain topographic differentiations of distinct electrode sites with respect to their contributions to the global phase synchronization index. The spectral measures were used to predict clinical and reaction time outcome using regression analysis. We found that movement-related desynchronization of cortical activity in the upper alpha and beta range was significantly facilitated with ‘stimulation on’ compared to ‘stimulation off’ on electrodes over the bilateral parietal, sensorimotor, premotor, supplementary-motor, and prefrontal areas, including the bilateral inferior prefrontal areas. These spectral modulations enabled us to predict both clinical and reaction time improvement from subthalamic stimulation. With ‘stimulation on’, interhemispheric cortico-cortical

  11. Reactivation of visual-evoked activity in human cortical networks.

    PubMed

    Chelaru, Mircea I; Hansen, Bryan J; Tandon, Nitin; Conner, Chris R; Szukalski, Susann; Slater, Jeremy D; Kalamangalam, Giridhar P; Dragoi, Valentin

    2016-06-01

    In the absence of sensory input, neuronal networks are far from being silent. Whether spontaneous changes in ongoing activity reflect previous sensory experience or stochastic fluctuations in brain activity is not well understood. Here we demonstrate reactivation of stimulus-evoked activity that is distributed across large areas in the human brain. We performed simultaneous electrocorticography recordings from occipital, parietal, temporal, and frontal areas in awake humans in the presence and absence of sensory stimulation. We found that, in the absence of visual input, repeated exposure to brief natural movies induces robust stimulus-specific reactivation at individual recording sites. The reactivation sites were characterized by greater global connectivity compared with those sites that did not exhibit reactivation. Our results indicate a surprising degree of short-term plasticity across multiple networks in the human brain as a result of repeated exposure to unattended information. PMID:26984423

  12. A Multisensory Cortical Network for Understanding Speech in Noise

    PubMed Central

    Bishop, Christopher W.; Miller, Lee M.

    2010-01-01

    In noisy environments, listeners tend to hear a speaker’s voice yet struggle to understand what is said. The most effective way to improve intelligibility in such conditions is to watch the speaker’s mouth movements. Here we identify the neural networks that distinguish understanding from merely hearing speech, and determine how the brain applies visual information to improve intelligibility. Using functional magnetic resonance imaging, we show that understanding speech-in-noise is supported by a network of brain areas including the left superior parietal lobule, the motor/premotor cortex, and the left anterior superior temporal sulcus (STS), a likely apex of the acoustic processing hierarchy. Multisensory integration likely improves comprehension through improved communication between the left temporal–occipital boundary, the left medial-temporal lobe, and the left STS. This demonstrates how the brain uses information from multiple modalities to improve speech comprehension in naturalistic, acoustically adverse conditions. PMID:18823249

  13. Modeling network correlations in cortical tissue from juvenile human epileptics

    NASA Astrophysics Data System (ADS)

    Hobbs, Jonathan Paul

    Models of neural tissue can make predictions about a real neural network, but these predictions rely on the data to determine parameters. Hence, the model is only as good as the data. I collected in vitro data removed from juvenile humans with refractory epilepsy, and found human-specific spatial and temporal dynamics that are not found in rats. I will first describe the general characteristics of the human data in comparison with rat data, and my attempts to model these differences with three popular models of neural networks: branching, pair-wise maximum entropy, and a forest fire model. I will describe three key discoveries from this exploration: first, spatial dynamics are more easily satisfied than temporal in both the rat and human tissue, second temporal correlations are not captured by the branching or the maximum entropy model, and thirdly, strong temporal correlations can be accounted for with the addition of a parameter in the forest fire model. Finally I will suggest new questions that this research has revealed about human tissue, and models of neural networks.

  14. Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks.

    PubMed

    Wang, Tao; Wang, Kangcheng; Qu, Hang; Zhou, Jingjing; Li, Qi; Deng, Zhou; Du, Xue; Lv, Fajin; Ren, Gaoping; Guo, Jing; Qiu, Jiang; Xie, Peng

    2016-01-01

    Major depressive disorder is associated with abnormal anatomical and functional connectivity, yet alterations in whole cortical thickness topology remain unknown. Here, we examined cortical thickness in medication-free adult depression patients (n = 76) and matched healthy controls (n = 116). Inter-regional correlation was performed to construct brain networks. By applying graph theory analysis, global (i.e., small-worldness) and regional (centrality) topology was compared between major depressive disorder patients and healthy controls. We found that in depression patients, topological organization of the cortical thickness network shifted towards randomness, and lower small-worldness was driven by a decreased clustering coefficient. Consistently, altered nodal centrality was identified in the isthmus of the cingulate cortex, insula, supra-marginal gyrus, middle temporal gyrus and inferior parietal gyrus, all of which are components within the default mode, salience and central executive networks. Disrupted nodes anchored in the default mode and executive networks were associated with depression severity. The brain systems involved sustain core symptoms in depression and implicate a structural basis for depression. Our results highlight the possibility that developmental and genetic factors are crucial to understand the neuropathology of depression. PMID:27302485

  15. Disorganized cortical thickness covariance network in major depressive disorder implicated by aberrant hubs in large-scale networks

    PubMed Central

    Wang, Tao; Wang, Kangcheng; Qu, Hang; Zhou, Jingjing; Li, Qi; Deng, Zhou; Du, Xue; Lv, Fajin; Ren, Gaoping; Guo, Jing; Qiu, Jiang; Xie, Peng

    2016-01-01

    Major depressive disorder is associated with abnormal anatomical and functional connectivity, yet alterations in whole cortical thickness topology remain unknown. Here, we examined cortical thickness in medication-free adult depression patients (n = 76) and matched healthy controls (n = 116). Inter-regional correlation was performed to construct brain networks. By applying graph theory analysis, global (i.e., small-worldness) and regional (centrality) topology was compared between major depressive disorder patients and healthy controls. We found that in depression patients, topological organization of the cortical thickness network shifted towards randomness, and lower small-worldness was driven by a decreased clustering coefficient. Consistently, altered nodal centrality was identified in the isthmus of the cingulate cortex, insula, supra-marginal gyrus, middle temporal gyrus and inferior parietal gyrus, all of which are components within the default mode, salience and central executive networks. Disrupted nodes anchored in the default mode and executive networks were associated with depression severity. The brain systems involved sustain core symptoms in depression and implicate a structural basis for depression. Our results highlight the possibility that developmental and genetic factors are crucial to understand the neuropathology of depression. PMID:27302485

  16. Covert neurofeedback without awareness shapes cortical network spontaneous connectivity.

    PubMed

    Ramot, Michal; Grossman, Shany; Friedman, Doron; Malach, Rafael

    2016-04-26

    Recent advances in blood oxygen level-dependent-functional MRI (BOLD-fMRI)-based neurofeedback reveal that participants can modulate neuronal properties. However, it is unknown whether such training effects can be introduced in the absence of participants' awareness that they are being trained. Here, we show unconscious neurofeedback training, which consequently produced changes in functional connectivity, introduced in participants who received positive and negative rewards that were covertly coupled to activity in two category-selective visual cortex regions. The results indicate that brain networks can be modified even in the complete absence of intention and awareness of the learning situation, raising intriguing possibilities for clinical interventions. PMID:27071084

  17. The plasticity of the mirror system: how reward learning modulates cortical motor simulation of others.

    PubMed

    Trilla Gros, Irene; Panasiti, Maria Serena; Chakrabarti, Bhismadev

    2015-04-01

    Cortical motor simulation supports the understanding of others' actions and intentions. This mechanism is thought to rely on the mirror neuron system (MNS), a brain network that is active both during action execution and observation. Indirect evidence suggests that (alpha/beta) mu suppression, an electroencephalographic (EEG) index of MNS activity, is modulated by reward. In this study we aimed to test the plasticity of the MNS by directly investigating the link between (alpha/beta) mu suppression and reward. 40 individuals from a general population sample took part in an evaluative conditioning experiment, where different neutral faces were associated with high or low reward values. In the test phase, EEG was recorded while participants viewed videoclips of happy expressions made by the conditioned faces. Alpha/beta mu suppression (identified using event-related desynchronisation of specific independent components) in response to rewarding faces was found to be greater than for non-rewarding faces. This result provides a mechanistic insight into the plasticity of the MNS and, more generally, into the role of reward in modulating physiological responses linked to empathy. PMID:25744871

  18. The plasticity of the mirror system: How reward learning modulates cortical motor simulation of others

    PubMed Central

    Trilla Gros, Irene; Panasiti, Maria Serena; Chakrabarti, Bhismadev

    2015-01-01

    Cortical motor simulation supports the understanding of others' actions and intentions. This mechanism is thought to rely on the mirror neuron system (MNS), a brain network that is active both during action execution and observation. Indirect evidence suggests that (alpha/beta) mu suppression, an electroencephalographic (EEG) index of MNS activity, is modulated by reward. In this study we aimed to test the plasticity of the MNS by directly investigating the link between (alpha/beta) mu suppression and reward. 40 individuals from a general population sample took part in an evaluative conditioning experiment, where different neutral faces were associated with high or low reward values. In the test phase, EEG was recorded while participants viewed videoclips of happy expressions made by the conditioned faces. Alpha/beta mu suppression (identified using event-related desynchronisation of specific independent components) in response to rewarding faces was found to be greater than for non-rewarding faces. This result provides a mechanistic insight into the plasticity of the MNS and, more generally, into the role of reward in modulating physiological responses linked to empathy. PMID:25744871

  19. Altered Modular Organization of Structural Cortical Networks in Children with Autism

    PubMed Central

    Shi, Feng; Wang, Li; Peng, Ziwen; Wee, Chong-Yaw; Shen, Dinggang

    2013-01-01

    Autism is a complex developmental disability that characterized by deficits in social interaction, language skills, repetitive stereotyped behaviors and restricted interests. Although great heterogeneity exists, previous findings suggest that autism has atypical brain connectivity patterns and disrupted small-world network properties. However, the organizational alterations in the autistic brain network are still poorly understood. We explored possible organizational alterations of 49 autistic children and 51 typically developing controls, by investigating their brain network metrics that are constructed upon cortical thickness correlations. Three modules were identified in controls, including cortical regions associated with brain functions of executive strategic, spatial/auditory/visual, and self-reference/episodic memory. There are also three modules found in autistic children with similar patterns. Compared with controls, autism demonstrates significantly reduced gross network modularity, and a larger number of inter-module connections. However, the autistic brain network demonstrates increased intra- and inter-module connectivity in brain regions including middle frontal gyrus, inferior parietal gyrus, and cingulate, suggesting one underlying compensatory mechanism associated with brain functions of self-reference and episodic memory. Results also show that there is increased correlation strength between regions inside frontal lobe, as well as impaired correlation strength between frontotemporal and frontoparietal regions. This alteration of correlation strength may contribute to the organization alteration of network structures in autistic brains. PMID:23675456

  20. Storing structured sparse memories in a multi-modular cortical network model.

    PubMed

    Dubreuil, Alexis M; Brunel, Nicolas

    2016-04-01

    We study the memory performance of a class of modular attractor neural networks, where modules are potentially fully-connected networks connected to each other via diluted long-range connections. On this anatomical architecture we store memory patterns of activity using a Willshaw-type learning rule. P patterns are split in categories, such that patterns of the same category activate the same set of modules. We first compute the maximal storage capacity of these networks. We then investigate their error-correction properties through an exhaustive exploration of parameter space, and identify regions where the networks behave as an associative memory device. The crucial parameters that control the retrieval abilities of the network are (1) the ratio between the number of synaptic contacts of long- and short-range origins (2) the number of categories in which a module is activated and (3) the amount of local inhibition. We discuss the relationship between our model and networks of cortical patches that have been observed in different cortical areas. PMID:26852335

  1. Mathematically gifted adolescents mobilize enhanced workspace configuration of theta cortical network during deductive reasoning.

    PubMed

    Zhang, L; Gan, J Q; Wang, H

    2015-03-19

    Previous studies have established the importance of the fronto-parietal brain network in the information processing of reasoning. At the level of cortical source analysis, this eletroencepalogram (EEG) study investigates the functional reorganization of the theta-band (4-8Hz) neurocognitive network of mathematically gifted adolescents during deductive reasoning. Depending on the dense increase of long-range phase synchronizations in the reasoning process, math-gifted adolescents show more significant adaptive reorganization and enhanced "workspace" configuration in the theta network as compared with average-ability control subjects. The salient areas are mainly located in the anterior cortical vertices of the fronto-parietal network. Further correlation analyses have shown that the enhanced workspace configuration with respect to the global topological metrics of the theta network in math-gifted subjects is correlated with the intensive frontal midline theta (fm theta) response that is related to strong neural effort for cognitive events. These results suggest that by investing more cognitive resources math-gifted adolescents temporally mobilize an enhanced task-related global neuronal workspace, which is manifested as a highly integrated fronto-parietal information processing network during the reasoning process. PMID:25595993

  2. Reversible large-scale modification of cortical networks during neuroprosthetic control.

    PubMed

    Ganguly, Karunesh; Dimitrov, Dragan F; Wallis, Jonathan D; Carmena, Jose M

    2011-05-01

    Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control. PMID:21499255

  3. Cortical Network Models of Firing Rates in the Resting and Active States Predict BOLD Responses

    PubMed Central

    Bennett, Maxwell R.; Farnell, Les; Gibson, William G.; Lagopoulos, Jim

    2015-01-01

    Measurements of blood oxygenation level dependent (BOLD) signals have produced some surprising observations. One is that their amplitude is proportional to the entire activity in a region of interest and not just the fluctuations in this activity. Another is that during sleep and anesthesia the average BOLD correlations between regions of interest decline as the activity declines. Mechanistic explanations of these phenomena are described here using a cortical network model consisting of modules with excitatory and inhibitory neurons, taken as regions of cortical interest, each receiving excitatory inputs from outside the network, taken as subcortical driving inputs in addition to extrinsic (intermodular) connections, such as provided by associational fibers. The model shows that the standard deviation of the firing rate is proportional to the mean frequency of the firing when the extrinsic connections are decreased, so that the mean BOLD signal is proportional to both as is observed experimentally. The model also shows that if these extrinsic connections are decreased or the frequency of firing reaching the network from the subcortical driving inputs is decreased, or both decline, there is a decrease in the mean firing rate in the modules accompanied by decreases in the mean BOLD correlations between the modules, consistent with the observed changes during NREM sleep and under anesthesia. Finally, the model explains why a transient increase in the BOLD signal in a cortical area, due to a transient subcortical input, gives rises to responses throughout the cortex as observed, with these responses mediated by the extrinsic (intermodular) connections. PMID:26659399

  4. Instantaneous stimulus paradigm: cortical network and dynamics of figure-ground organization

    NASA Astrophysics Data System (ADS)

    Likova, Lora T.; Tyler, Christopher W.

    2007-02-01

    To reveal the cortical network underlying figure/ground perception and to understand its neural dynamics, we developed a novel paradigm that creates distinct and prolonged percepts of spatial structures by instantaneous refreshes in random dot fields. Three different forms of spatial configuration were generated by: (i) updating the whole stimulus field, (ii) updating the ground region only (negative-figure), and (iii) updating the figure and ground regions in brief temporal asynchrony. FMRI responses were measured throughout the brain. As expected, activation by the homogenous whole-field update was focused onto the posterior part of the brain, but distinct networks extending beyond the occipital lobe into the parietal and frontal cortex were activated by the figure/ground and by the negativefigure configurations. The instantaneous stimulus paradigm generated a wide variety of BOLD waveforms and corresponding neural response estimates throughout the network. Such expressly different responses evoked by differential stimulation of the identical cortical regions assure that the differences could be securely attributed to the neural dynamics, not to spatial variations in the HRF. The activation pattern for figure/ground implies a widely distributed neural architecture, distinct from the control conditions. Even where activations are partially overlapping, an integrated analysis of the BOLD response properties will enable the functional specificity of the cortical areas to be distinguished.

  5. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

    PubMed Central

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A.; Przekwas, Andrzej; Francis, Joseph T.; Lytton, William W.

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  6. Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm.

    PubMed

    Dura-Bernal, Salvador; Zhou, Xianlian; Neymotin, Samuel A; Przekwas, Andrzej; Francis, Joseph T; Lytton, William W

    2015-01-01

    Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of

  7. Prefrontal Cortical Opioids and Dysregulated Motivation: A Network Hypothesis.

    PubMed

    Baldo, Brian A

    2016-06-01

    Loss of inhibitory control over appetitively motivated behavior occurs in multiple psychiatric disorders, including drug abuse, behavioral addictions, and eating disorders with binge features. In this opinion article, novel actions of μ-opioid peptides in the prefrontal cortex (PFC) that could contribute to inhibitory control deficits will be discussed. Evidence has accrued to suggest that excessive intra-PFC μ-opioid receptor (μ-OR) signaling alters the PFC response to excitatory drive, resulting in supernormal and incoherent recruitment of multiple PFC output pathways. Affected pathways include functionally opposed PFC→hypothalamus 'appetitive driver' and PFC→striatum 'appetitive limiter' projections. This network perturbation engenders disorganized, impulsive appetitive responses. Evidence supporting this hypothesis from human imaging and animal studies will be discussed, and combinatorial drug treatments targeting μ-ORs and specific PFC subcortical targets will be explored. PMID:27233653

  8. Regulating Cortical Oscillations in an Inhibition-Stabilized Network.

    PubMed

    Jadi, Monika P; Sejnowski, Terrence J

    2014-04-21

    Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system. PMID:24966414

  9. Induction of Calcium Influx in Cortical Neural Networks by Nanomagnetic Forces.

    PubMed

    Tay, Andy; Kunze, Anja; Murray, Coleman; Di Carlo, Dino

    2016-02-23

    Nanomagnetic force stimulation with ferromagnetic nanoparticles was found to trigger calcium influx in cortical neural networks without observable cytotoxicity. Stimulated neural networks showed an average of 20% increment in calcium fluorescence signals and a heightened frequency in calcium spiking. These effects were also confined spatially to areas with engineered high magnetic field gradients. Furthermore, blockage of N-type calcium channels inhibited the stimulatory effects of the nanomagnetic forces, suggesting the role of mechano-sensitive ion channels in mediating calcium influx. PMID:26805612

  10. The mechanisms of generation and propagation of synchronized bursting in developing networks of cortical neurons.

    PubMed

    Maeda, E; Robinson, H P; Kawana, A

    1995-10-01

    The characteristics and mechanisms of synchronized firing in developing networks of cultured cortical neurons were studied using multisite recording through planar electrode arrays (PEAs). With maturation of the network (from 3 to 40 d after plating), the frequency and propagation velocity of bursts increased markedly (approximately from 0.01 to 0.5 Hz and from 5 to 100 mm/sec, respectively), and the sensitivity to extracellular magnesium concentration (0-10 mM) decreased. The source of spontaneous bursts, estimated from the relative delay of onset of activity between electrodes, varied randomly with each burst. Physical separation of synchronously bursting networks into several parts using an ultraviolet laser, divided synchronous bursting into different frequencies and phases in each part. Focal stimulation through the PEA was effective at multiple sites in eliciting bursts, which propagated over the network from the site of stimulation. Stimulated bursts exhibited both an absolute refractory period and a relative refractory period, in which partially propagating bursts could be elicited. Periodic electrical stimulation (at 1 to 30 sec intervals) produced slower propagation velocities and smaller numbers of spikes per burst at shorter stimulation intervals. These results suggest that the generation and propagation of spontaneous synchronous bursts in cultured cortical neurons is governed by the level of spontaneous presynaptic firing, by the degree of connectivity of the network, and by a distributed balance between excitation and recovery processes. PMID:7472441

  11. Increased cortical-limbic anatomical network connectivity in major depression revealed by diffusion tensor imaging.

    PubMed

    Fang, Peng; Zeng, Ling-Li; Shen, Hui; Wang, Lubin; Li, Baojuan; Liu, Li; Hu, Dewen

    2012-01-01

    Magnetic resonance imaging studies have reported significant functional and structural differences between depressed patients and controls. Little attention has been given, however, to the abnormalities in anatomical connectivity in depressed patients. In the present study, we aim to investigate the alterations in connectivity of whole-brain anatomical networks in those suffering from major depression by using machine learning approaches. Brain anatomical networks were extracted from diffusion magnetic resonance images obtained from both 22 first-episode, treatment-naive adults with major depressive disorder and 26 matched healthy controls. Using machine learning approaches, we differentiated depressed patients from healthy controls based on their whole-brain anatomical connectivity patterns and identified the most discriminating features that represent between-group differences. Classification results showed that 91.7% (patients=86.4%, controls=96.2%; permutation test, p<0.0001) of subjects were correctly classified via leave-one-out cross-validation. Moreover, the strengths of all the most discriminating connections were increased in depressed patients relative to the controls, and these connections were primarily located within the cortical-limbic network, especially the frontal-limbic network. These results not only provide initial steps toward the development of neurobiological diagnostic markers for major depressive disorder, but also suggest that abnormal cortical-limbic anatomical networks may contribute to the anatomical basis of emotional dysregulation and cognitive impairments associated with this disease. PMID:23049910

  12. The Age of Cortical Neural Networks Affects Their Interactions with Magnetic Nanoparticles.

    PubMed

    Tay, Andy; Kunze, Anja; Jun, Dukwoo; Hoek, Eric; Di Carlo, Dino

    2016-07-01

    Despite increasing use of nanotechnology in neuroscience, the characterization of interactions between magnetic nanoparticles (MNPs) and primary cortical neural networks remains underdeveloped. In particular, how the age of primary neural networks affects MNP uptake and endocytosis is critical when considering MNP-based therapies for age-related diseases. Here, primary cortical neural networks are cultured up to 4 weeks and with CCL11/eotaxin, an age-inducing chemokine, to create aged neural networks. As the neural networks are aged, their association with membrane-bound starch-coated ferromagnetic nanoparticles (fMNPs) increases while their endocytic mechanisms are impaired, resulting in reduced internalization of chitosan-coated fMNPs. The age of the neurons also negates the neuroprotective effects of chitosan coatings on fMNPs, attributing to decreased intracellular trafficking and increased colocalization of MNPs with lysosomes. These findings demonstrate the importance of age and developmental stage of primary neural cells when developing in vitro models for fMNP therapeutics targeting age-related diseases. PMID:27228954

  13. Bayesian network classifiers for categorizing cortical GABAergic interneurons.

    PubMed

    Mihaljević, Bojan; Benavides-Piccione, Ruth; Bielza, Concha; DeFelipe, Javier; Larrañaga, Pedro

    2015-04-01

    An accepted classification of GABAergic interneurons of the cerebral cortex is a major goal in neuroscience. A recently proposed taxonomy based on patterns of axonal arborization promises to be a pragmatic method for achieving this goal. It involves characterizing interneurons according to five axonal arborization features, called F1-F5, and classifying them into a set of predefined types, most of which are established in the literature. Unfortunately, there is little consensus among expert neuroscientists regarding the morphological definitions of some of the proposed types. While supervised classifiers were able to categorize the interneurons in accordance with experts' assignments, their accuracy was limited because they were trained with disputed labels. Thus, here we automatically classify interneuron subsets with different label reliability thresholds (i.e., such that every cell's label is backed by at least a certain (threshold) number of experts). We quantify the cells with parameters of axonal and dendritic morphologies and, in order to predict the type, also with axonal features F1-F4 provided by the experts. Using Bayesian network classifiers, we accurately characterize and classify the interneurons and identify useful predictor variables. In particular, we discriminate among reliable examples of common basket, horse-tail, large basket, and Martinotti cells with up to 89.52% accuracy, and single out the number of branches at 180 μm from the soma, the convex hull 2D area, and the axonal features F1-F4 as especially useful predictors for distinguishing among these types. These results open up new possibilities for an objective and pragmatic classification of interneurons. PMID:25420745

  14. Simulations of a Microearthquake Network

    NASA Astrophysics Data System (ADS)

    Valtonen, O.; Uski, M.; Korja, A.; Tiira, T.; Kortström, J.

    2012-04-01

    Sites of vulnerable facilities, such as power plants, are required to be evaluated and monitored for possible earthquakes. Seismic networks having a recording capability for microearthquakes are well suited for acquiring more detailed information on local seismicity. When a dense, local seismic network is set up, numerous microearthquakes are expected to be recorded within a relatively short time period. Thus seismotectonic interpretation and seismic hazard evaluation of the area can be improved with the accurately locatable earthquakes recorded by the microearthquake network. This study gives an example of simulations of a local microearthquake network centred around a future power plant -site. The site area is characterised by low intraplate seismicity, with earthquake magnitudes rarely exceeding 4.0. The network is required to fulfil the preconditions of azimuthal coverage better than 180° and automatic event location capability down to ML~0 within the study area. Automatic event detection capability is simulated based on a relationship derived between event magnitude and maximum observation distance. The azimuthal coverage and the threshold magnitude are then computed for different station configurations and the results are presented as contour maps. An optimal configuration of ten seismograph stations is proposed for further on-site research. The threshold magnitude within the study area and the annual number of earthquakes detected by the network are estimated. Also the automatic earthquake location accuracy for horizontal coordinates and depth is approximated. Location accuracy can be further improved by the application of local velocity models and relative location schemes. Modifications to the optimal configuration are expected in the deployment phase, because the area is surrounded by industrial noise sources.

  15. Simulator of Space Communication Networks

    NASA Technical Reports Server (NTRS)

    Clare, Loren; Jennings, Esther; Gao, Jay; Segui, John; Kwong, Winston

    2005-01-01

    Multimission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) is a suite of software tools that simulates the behaviors of communication networks to be used in space exploration, and predict the performance of established and emerging space communication protocols and services. MACHETE consists of four general software systems: (1) a system for kinematic modeling of planetary and spacecraft motions; (2) a system for characterizing the engineering impact on the bandwidth and reliability of deep-space and in-situ communication links; (3) a system for generating traffic loads and modeling of protocol behaviors and state machines; and (4) a system of user-interface for performance metric visualizations. The kinematic-modeling system makes it possible to characterize space link connectivity effects, including occultations and signal losses arising from dynamic slant-range changes and antenna radiation patterns. The link-engineering system also accounts for antenna radiation patterns and other phenomena, including modulations, data rates, coding, noise, and multipath fading. The protocol system utilizes information from the kinematic-modeling and link-engineering systems to simulate operational scenarios of space missions and evaluate overall network performance. In addition, a Communications Effect Server (CES) interface for MACHETE has been developed to facilitate hybrid simulation of space communication networks with actual flight/ground software/hardware embedded in the overall system.

  16. Multiplex Networks of Cortical and Hippocampal Neurons Revealed at Different Timescales

    PubMed Central

    Timme, Nicholas; Ito, Shinya; Myroshnychenko, Maxym; Yeh, Fang-Chin; Hiolski, Emma; Hottowy, Pawel; Beggs, John M.

    2014-01-01

    Recent studies have emphasized the importance of multiplex networks – interdependent networks with shared nodes and different types of connections – in systems primarily outside of neuroscience. Though the multiplex properties of networks are frequently not considered, most networks are actually multiplex networks and the multiplex specific features of networks can greatly affect network behavior (e.g. fault tolerance). Thus, the study of networks of neurons could potentially be greatly enhanced using a multiplex perspective. Given the wide range of temporally dependent rhythms and phenomena present in neural systems, we chose to examine multiplex networks of individual neurons with time scale dependent connections. To study these networks, we used transfer entropy – an information theoretic quantity that can be used to measure linear and nonlinear interactions – to systematically measure the connectivity between individual neurons at different time scales in cortical and hippocampal slice cultures. We recorded the spiking activity of almost 12,000 neurons across 60 tissue samples using a 512-electrode array with 60 micrometer inter-electrode spacing and 50 microsecond temporal resolution. To the best of our knowledge, this preparation and recording method represents a superior combination of number of recorded neurons and temporal and spatial recording resolutions to any currently available in vivo system. We found that highly connected neurons (“hubs”) were localized to certain time scales, which, we hypothesize, increases the fault tolerance of the network. Conversely, a large proportion of non-hub neurons were not localized to certain time scales. In addition, we found that long and short time scale connectivity was uncorrelated. Finally, we found that long time scale networks were significantly less modular and more disassortative than short time scale networks in both tissue types. As far as we are aware, this analysis represents the first

  17. A GPU-accelerated cortical neural network model for visually guided robot navigation.

    PubMed

    Beyeler, Michael; Oros, Nicolas; Dutt, Nikil; Krichmar, Jeffrey L

    2015-12-01

    Humans and other terrestrial animals use vision to traverse novel cluttered environments with apparent ease. On one hand, although much is known about the behavioral dynamics of steering in humans, it remains unclear how relevant perceptual variables might be represented in the brain. On the other hand, although a wealth of data exists about the neural circuitry that is concerned with the perception of self-motion variables such as the current direction of travel, little research has been devoted to investigating how this neural circuitry may relate to active steering control. Here we present a cortical neural network model for visually guided navigation that has been embodied on a physical robot exploring a real-world environment. The model includes a rate based motion energy model for area V1, and a spiking neural network model for cortical area MT. The model generates a cortical representation of optic flow, determines the position of objects based on motion discontinuities, and combines these signals with the representation of a goal location to produce motor commands that successfully steer the robot around obstacles toward the goal. The model produces robot trajectories that closely match human behavioral data. This study demonstrates how neural signals in a model of cortical area MT might provide sufficient motion information to steer a physical robot on human-like paths around obstacles in a real-world environment, and exemplifies the importance of embodiment, as behavior is deeply coupled not only with the underlying model of brain function, but also with the anatomical constraints of the physical body it controls. PMID:26494281

  18. Different categories of living and non-living sound-sources activate distinct cortical networks

    PubMed Central

    Engel, Lauren R.; Frum, Chris; Puce, Aina; Walker, Nathan A.; Lewis, James W.

    2009-01-01

    With regard to hearing perception, it remains unclear as to whether, or the extent to which, different conceptual categories of real-world sounds and related categorical knowledge are differentially represented in the brain. Semantic knowledge representations are reported to include the major divisions of living versus non-living things, plus more specific categories including animals, tools, biological motion, faces, and places—categories typically defined by their characteristic visual features. Here, we used functional magnetic resonance imaging (fMRI) to identify brain regions showing preferential activity to four categories of action sounds, which included non-vocal human and animal actions (living), plus mechanical and environmental sound-producing actions (non-living). The results showed a striking antero-posterior division in cortical representations for sounds produced by living versus non-living sources. Additionally, there were several significant differences by category, depending on whether the task was category-specific (e.g. human or not) versus non-specific (detect end-of-sound). In general, (1) human-produced sounds yielded robust activation in the bilateral posterior superior temporal sulci independent of task. Task demands modulated activation of left-lateralized fronto-parietal regions, bilateral insular cortices, and subcortical regions previously implicated in observation-execution matching, consistent with “embodied” and mirror-neuron network representations subserving recognition. (2) Animal action sounds preferentially activated the bilateral posterior insulae. (3) Mechanical sounds activated the anterior superior temporal gyri and parahippocampal cortices. (4) Environmental sounds preferentially activated dorsal occipital and medial parietal cortices. Overall, this multi-level dissociation of networks for preferentially representing distinct sound-source categories provides novel support for grounded cognition models that may

  19. Aberrant Topological Patterns of Structural Cortical Networks in Psychogenic Erectile Dysfunction

    PubMed Central

    Zhao, Lu; Guan, Min; Zhu, Xiaobo; Karama, Sherif; Khundrakpam, Budhachandra; Wang, Meiyun; Dong, Minghao; Qin, Wei; Tian, Jie; Evans, Alan C.; Shi, Dapeng

    2015-01-01

    Male sexual arousal (SA) has been known as a multidimensional experience involving closely interrelated and coordinated neurobehavioral components that rely on widespread brain regions. Recent functional neuroimaging studies have shown relation between abnormal/altered dynamics in these circuits and male sexual dysfunction. However, alterations in the topological1 organization of structural brain networks in male sexual dysfunction are still unclear. Here, we used graph theory2 to investigate the topological properties of large-scale structural brain networks, which were constructed using inter-regional correlations of cortical thickness between 78 cortical regions in 40 patients with psychogenic erectile dysfunction (pED) and 39 normal controls. Compared with normal controls, pED patients exhibited a less optimal global topological organization with reduced global and increased local efficiencies. Our results suggest disrupted neural integration among distant brain regions in pED patients, consistent with previous reports of impaired white matter structure and abnormal functional integrity in pED. Additionally, disrupted global network topology in pED was observed to be primarily relevant to altered subnetwork and nodal properties within the networks mediating the cognitive, motivational and inhibitory processes of male SA, possibly indicating disrupted integration of these networks in the whole brain networks and might account for pED patients' abnormal cognitive, motivational and inhibitory processes for male SA. In total, our findings provide evidence for disrupted integrity in large-scale brain networks underlying the neurobehavioral processes of male SA in pED and provide new insights into the understanding of the pathophysiological mechanisms of pED. PMID:26733849

  20. The Impact of Cortical Lesions on Thalamo-Cortical Network Dynamics after Acute Ischaemic Stroke: A Combined Experimental and Theoretical Study.

    PubMed

    van Wijngaarden, Joeri B G; Zucca, Riccardo; Finnigan, Simon; Verschure, Paul F M J

    2016-08-01

    The neocortex and thalamus provide a core substrate for perception, cognition, and action, and are interconnected through different direct and indirect pathways that maintain specific dynamics associated with functional states including wakefulness and sleep. It has been shown that a lack of excitation, or enhanced subcortical inhibition, can disrupt this system and drive thalamic nuclei into an attractor state of low-frequency bursting and further entrainment of thalamo-cortical circuits, also called thalamo-cortical dysrhythmia (TCD). The question remains however whether similar TCD-like phenomena can arise with a cortical origin. For instance, in stroke, a cortical lesion could disrupt thalamo-cortical interactions through an attenuation of the excitatory drive onto the thalamus, creating an imbalance between excitation and inhibition that can lead to a state of TCD. Here we tested this hypothesis by comparing the resting-state EEG recordings of acute ischaemic stroke patients (N = 21) with those of healthy, age-matched control-subjects (N = 17). We observed that these patients displayed the hallmarks of TCD: a characteristic downward shift of dominant α-peaks in the EEG power spectra, together with increased power over the lower frequencies (δ and θ-range). Contrary to general observations in TCD, the patients also displayed a broad reduction in β-band activity. In order to explain the genesis of this stroke-induced TCD, we developed a biologically constrained model of a general thalamo-cortical module, allowing us to identify the specific cellular and network mechanisms involved. Our model showed that a lesion in the cortical component leads to sustained cell membrane hyperpolarization in the corresponding thalamic relay neurons, that in turn leads to the de-inactivation of voltage-gated T-type Ca2+-channels, switching neurons from tonic spiking to a pathological bursting regime. This thalamic bursting synchronises activity on a population level through

  1. The Impact of Cortical Lesions on Thalamo-Cortical Network Dynamics after Acute Ischaemic Stroke: A Combined Experimental and Theoretical Study

    PubMed Central

    van Wijngaarden, Joeri B. G.; Finnigan, Simon

    2016-01-01

    The neocortex and thalamus provide a core substrate for perception, cognition, and action, and are interconnected through different direct and indirect pathways that maintain specific dynamics associated with functional states including wakefulness and sleep. It has been shown that a lack of excitation, or enhanced subcortical inhibition, can disrupt this system and drive thalamic nuclei into an attractor state of low-frequency bursting and further entrainment of thalamo-cortical circuits, also called thalamo-cortical dysrhythmia (TCD). The question remains however whether similar TCD-like phenomena can arise with a cortical origin. For instance, in stroke, a cortical lesion could disrupt thalamo-cortical interactions through an attenuation of the excitatory drive onto the thalamus, creating an imbalance between excitation and inhibition that can lead to a state of TCD. Here we tested this hypothesis by comparing the resting-state EEG recordings of acute ischaemic stroke patients (N = 21) with those of healthy, age-matched control-subjects (N = 17). We observed that these patients displayed the hallmarks of TCD: a characteristic downward shift of dominant α-peaks in the EEG power spectra, together with increased power over the lower frequencies (δ and θ-range). Contrary to general observations in TCD, the patients also displayed a broad reduction in β-band activity. In order to explain the genesis of this stroke-induced TCD, we developed a biologically constrained model of a general thalamo-cortical module, allowing us to identify the specific cellular and network mechanisms involved. Our model showed that a lesion in the cortical component leads to sustained cell membrane hyperpolarization in the corresponding thalamic relay neurons, that in turn leads to the de-inactivation of voltage-gated T-type Ca2+-channels, switching neurons from tonic spiking to a pathological bursting regime. This thalamic bursting synchronises activity on a population level through

  2. Multiscale approach including microfibril scale to assess elastic constants of cortical bone based on neural network computation and homogenization method.

    PubMed

    Barkaoui, Abdelwahed; Chamekh, Abdessalem; Merzouki, Tarek; Hambli, Ridha; Mkaddem, Ali

    2014-03-01

    The complexity and heterogeneity of bone tissue require a multiscale modeling to understand its mechanical behavior and its remodeling mechanisms. In this paper, a novel multiscale hierarchical approach including microfibril scale based on hybrid neural network (NN) computation and homogenization equations was developed to link nanoscopic and macroscopic scales to estimate the elastic properties of human cortical bone. The multiscale model is divided into three main phases: (i) in step 0, the elastic constants of collagen-water and mineral-water composites are calculated by averaging the upper and lower Hill bounds; (ii) in step 1, the elastic properties of the collagen microfibril are computed using a trained NN simulation. Finite element calculation is performed at nanoscopic levels to provide a database to train an in-house NN program; and (iii) in steps 2-10 from fibril to continuum cortical bone tissue, homogenization equations are used to perform the computation at the higher scales. The NN outputs (elastic properties of the microfibril) are used as inputs for the homogenization computation to determine the properties of mineralized collagen fibril. The mechanical and geometrical properties of bone constituents (mineral, collagen, and cross-links) as well as the porosity were taken in consideration. This paper aims to predict analytically the effective elastic constants of cortical bone by modeling its elastic response at these different scales, ranging from the nanostructural to mesostructural levels. Our findings of the lowest scale's output were well integrated with the other higher levels and serve as inputs for the next higher scale modeling. Good agreement was obtained between our predicted results and literature data. PMID:24123969

  3. Differences in human cortical gene expression match the temporal properties of large-scale functional networks.

    PubMed

    Cioli, Claudia; Abdi, Hervé; Beaton, Derek; Burnod, Yves; Mesmoudi, Salma

    2014-01-01

    We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring--Visual-Sensorimotor-Auditory (VSA)--comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring--Parieto-Temporo-Frontal (PTF)--comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found--with correspondence analysis--that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins--coded by genes that most differentiate the rings--were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium channels SCNA3

  4. Differences in Human Cortical Gene Expression Match the Temporal Properties of Large-Scale Functional Networks

    PubMed Central

    Cioli, Claudia; Abdi, Hervé; Beaton, Derek; Burnod, Yves; Mesmoudi, Salma

    2014-01-01

    We explore the relationships between the cortex functional organization and genetic expression (as provided by the Allen Human Brain Atlas). Previous work suggests that functional cortical networks (resting state and task based) are organized as two large networks (differentiated by their preferred information processing mode) shaped like two rings. The first ring–Visual-Sensorimotor-Auditory (VSA)–comprises visual, auditory, somatosensory, and motor cortices that process real time world interactions. The second ring–Parieto-Temporo-Frontal (PTF)–comprises parietal, temporal, and frontal regions with networks dedicated to cognitive functions, emotions, biological needs, and internally driven rhythms. We found–with correspondence analysis–that the patterns of expression of the 938 genes most differentially expressed across the cortex organized the cortex into two sets of regions that match the two rings. We confirmed this result using discriminant correspondence analysis by showing that the genetic profiles of cortical regions can reliably predict to what ring these regions belong. We found that several of the proteins–coded by genes that most differentiate the rings–were involved in neuronal information processing such as ionic channels and neurotransmitter release. The systematic study of families of genes revealed specific proteins within families preferentially expressed in each ring. The results showed strong congruence between the preferential expression of subsets of genes, temporal properties of the proteins they code, and the preferred processing modes of the rings. Ionic channels and release-related proteins more expressed in the VSA ring favor temporal precision of fast evoked neural transmission (Sodium channels SCNA1, SCNB1 potassium channel KCNA1, calcium channel CACNA2D2, Synaptotagmin SYT2, Complexin CPLX1, Synaptobrevin VAMP1). Conversely, genes expressed in the PTF ring favor slower, sustained, or rhythmic activation (Sodium

  5. Brief wide-field photostimuli evoke and modulate oscillatory reverberating activity in cortical networks

    PubMed Central

    Pulizzi, Rocco; Musumeci, Gabriele; Van den Haute, Chris; Van De Vijver, Sebastiaan; Baekelandt, Veerle; Giugliano, Michele

    2016-01-01

    Cell assemblies manipulation by optogenetics is pivotal to advance neuroscience and neuroengineering. In in vivo applications, photostimulation often broadly addresses a population of cells simultaneously, leading to feed-forward and to reverberating responses in recurrent microcircuits. The former arise from direct activation of targets downstream, and are straightforward to interpret. The latter are consequence of feedback connectivity and may reflect a variety of time-scales and complex dynamical properties. We investigated wide-field photostimulation in cortical networks in vitro, employing substrate-integrated microelectrode arrays and long-term cultured neuronal networks. We characterized the effect of brief light pulses, while restricting the expression of channelrhodopsin to principal neurons. We evoked robust reverberating responses, oscillating in the physiological gamma frequency range, and found that such a frequency could be reliably manipulated varying the light pulse duration, not its intensity. By pharmacology, mathematical modelling, and intracellular recordings, we conclude that gamma oscillations likely emerge as in vivo from the excitatory-inhibitory interplay and that, unexpectedly, the light stimuli transiently facilitate excitatory synaptic transmission. Of relevance for in vitro models of (dys)functional cortical microcircuitry and in vivo manipulations of cell assemblies, we give for the first time evidence of network-level consequences of the alteration of synaptic physiology by optogenetics. PMID:27099182

  6. Primary motor and sensory cortical areas communicate via spatiotemporally coordinated networks at multiple frequencies.

    PubMed

    Arce-McShane, Fritzie I; Ross, Callum F; Takahashi, Kazutaka; Sessle, Barry J; Hatsopoulos, Nicholas G

    2016-05-01

    Skilled movements rely on sensory information to shape optimal motor responses, for which the sensory and motor cortical areas are critical. How these areas interact to mediate sensorimotor integration is largely unknown. Here, we measure intercortical coherence between the orofacial motor (MIo) and somatosensory (SIo) areas of cortex as monkeys learn to generate tongue-protrusive force. We report that coherence between MIo and SIo is reciprocal and that neuroplastic changes in coherence gradually emerge over a few days. These functional networks of coherent spiking and local field potentials exhibit frequency-specific spatiotemporal properties. During force generation, theta coherence (2-6 Hz) is prominent and exhibited by numerous paired signals; before or after force generation, coherence is evident in alpha (6-13 Hz), beta (15-30 Hz), and gamma (30-50 Hz) bands, but the functional networks are smaller and weaker. Unlike coherence in the higher frequency bands, the distribution of the phase at peak theta coherence is bimodal with peaks near 0° and ±180°, suggesting that communication between somatosensory and motor areas is coordinated temporally by the phase of theta coherence. Time-sensitive sensorimotor integration and plasticity may rely on coherence of local and large-scale functional networks for cortical processes to operate at multiple temporal and spatial scales. PMID:27091982

  7. Cell Assembly Signatures Defined by Short-Term Synaptic Plasticity in Cortical Networks.

    PubMed

    Carrillo-Reid, Luis; Lopez-Huerta, Violeta G; Garcia-Munoz, Marianela; Theiss, Stephan; Arbuthnott, Gordon W

    2015-11-01

    The cell assembly (CA) hypothesis has been used as a conceptual framework to explain how groups of neurons form memories. CAs are defined as neuronal pools with synchronous, recurrent and sequential activity patterns. However, neuronal interactions and synaptic properties that define CAs signatures have been difficult to examine because identities and locations of assembly members are usually unknown. In order to study synaptic properties that define CAs, we used optical and electrophysiological approaches to record activity of identified neurons in mouse cortical cultures. Population analysis and graph theory techniques allowed us to find sequential patterns that represent repetitive transitions between network states. Whole cell pair recordings of neurons participating in repeated sequences demonstrated that synchrony is exhibited by groups of neurons with strong synaptic connectivity (concomitant firing) showing short-term synaptic depression (STD), whereas alternation (sequential firing) is seen in groups of neurons with weaker synaptic connections showing short-term synaptic facilitation (STF). Decreasing synaptic weights of a network promoted the generation of sequential activity patterns, whereas increasing synaptic weights restricted state transitions. Thus in simple cortical networks of real neurons, basic signatures of CAs, the properties that underlie perception and memory in Hebb's original description, are already present. PMID:26173906

  8. Brief wide-field photostimuli evoke and modulate oscillatory reverberating activity in cortical networks.

    PubMed

    Pulizzi, Rocco; Musumeci, Gabriele; Van den Haute, Chris; Van De Vijver, Sebastiaan; Baekelandt, Veerle; Giugliano, Michele

    2016-01-01

    Cell assemblies manipulation by optogenetics is pivotal to advance neuroscience and neuroengineering. In in vivo applications, photostimulation often broadly addresses a population of cells simultaneously, leading to feed-forward and to reverberating responses in recurrent microcircuits. The former arise from direct activation of targets downstream, and are straightforward to interpret. The latter are consequence of feedback connectivity and may reflect a variety of time-scales and complex dynamical properties. We investigated wide-field photostimulation in cortical networks in vitro, employing substrate-integrated microelectrode arrays and long-term cultured neuronal networks. We characterized the effect of brief light pulses, while restricting the expression of channelrhodopsin to principal neurons. We evoked robust reverberating responses, oscillating in the physiological gamma frequency range, and found that such a frequency could be reliably manipulated varying the light pulse duration, not its intensity. By pharmacology, mathematical modelling, and intracellular recordings, we conclude that gamma oscillations likely emerge as in vivo from the excitatory-inhibitory interplay and that, unexpectedly, the light stimuli transiently facilitate excitatory synaptic transmission. Of relevance for in vitro models of (dys)functional cortical microcircuitry and in vivo manipulations of cell assemblies, we give for the first time evidence of network-level consequences of the alteration of synaptic physiology by optogenetics. PMID:27099182

  9. Circular representation of human cortical networks for subject and population-level connectomic visualization

    PubMed Central

    Irimia, Andrei; Chambers, Micah C.; Torgerson, Carinna M.; Van, John D.

    2012-01-01

    Cortical network architecture has predominantly been investigated visually using graph theory representations. In the context of human connectomics, such representations are not however always satisfactory because canonical methods for vertex–edge relationship representation do not always offer optimal insight regarding functional and structural neural connectivity. This article introduces an innovative framework for the depiction of human connectomics by employing a circular visualization method which is highly suitable to the exploration of central nervous system architecture. This type of representation, which we name a ‘connectogram’, has the capability of classifying neuroconnectivity relationships intuitively and elegantly. A multimodal protocol for MRI/DTI neuroimaging data acquisition is here combined with automatic image segmentation to (1) extract cortical and non-cortical anatomical structures, (2) calculate associated volumetrics and morphometrics, and (3) determine patient-specific connectivity profiles to generate subject-level and population-level connectograms. The scalability of our approach is demonstrated for a population of 50 adults. Two essential advantages of the connectogram are (1) the enormous potential for mapping and analyzing the human connectome, and (2) the unconstrained ability to expand and extend this analysis framework to the investigation of clinical populations and animal models. PMID:22305988

  10. Trace Replay and Network Simulation Tool

    Energy Science and Technology Software Center (ESTSC)

    2015-03-23

    TraceR is a trace reply tool built upon the ROSS-based CODES simulation framework. TraceR can be used for predicting network performances and understanding network behavior by simulating messaging in High Performance Computing applications on interconnection networks.

  11. Reinforce Networking Theory with OPNET Simulation

    ERIC Educational Resources Information Center

    Guo, Jinhua; Xiang, Weidong; Wang, Shengquan

    2007-01-01

    As networking systems have become more complex and expensive, hands-on experiments based on networking simulation have become essential for teaching the key computer networking topics to students. The simulation approach is the most cost effective and highly useful because it provides a virtual environment for an assortment of desirable features…

  12. Cortical electrical stimulation alters erythrocyte perfusion pattern in the cerebral capillary network of the rat.

    PubMed

    Schulte, M L; Wood, J D; Hudetz, A G

    2003-02-14

    The effect of direct cortical electrical stimulation on the pattern of erythrocyte perfusion in the capillary network of the rat cerebral cortex was studied by fluorescence intravital video-microscopy. The movement of fluorescently labeled red blood cells (FRBCs) in individual capillaries 50-70 microm subsurface in the dorsal somatosensory cortex was visualized using a closed cranial window. Cortical stimulation electrodes were placed on opposite sides of the window. FRBC velocity (mm/s) and supply rate (cells/s) were measured in 51 capillaries from six rats before and during electrical stimulation of increasing intensities (15-s trains of 3-Hz, 3-ms, 0.5-5.0-mA, square pulses). FRBC velocity, supply rate, and the instantaneous capillary erythrocyte content (lineal cell density, LCD, cells/mm) increased with the stimulation current and reached maxima of 110, 160 and 33% above control, respectively. Capillaries with low resting velocity showed a greater response than those with high resting velocity. The fraction of capillaries in which FRBC velocity increased was not constant, but increased with the stimulation current, as did the magnitude of the velocity change in these capillaries. A few capillaries showed a negative FRBC velocity response at stimulations <4 mA. These results suggest that a robust rise in the fraction of responding (engaged) capillaries and a smaller rise in the capillary LCD contribute to neuronal activation-induced cortical hyperemia. Thus, capillary engagement and erythrocyte recruitment appear to represent important components of the cortical functional hyperemic response. These results provide insight into some of the specific hemodynamic changes associated with functional hyperemia occurring at the capillary level. PMID:12560113

  13. Activity-driven relaxation of the cortical actomyosin II network synchronizes Munc18-1-dependent neurosecretory vesicle docking.

    PubMed

    Papadopulos, Andreas; Gomez, Guillermo A; Martin, Sally; Jackson, Jade; Gormal, Rachel S; Keating, Damien J; Yap, Alpha S; Meunier, Frederic A

    2015-01-01

    In neurosecretory cells, secretory vesicles (SVs) undergo Ca(2+)-dependent fusion with the plasma membrane to release neurotransmitters. How SVs cross the dense mesh of the cortical actin network to reach the plasma membrane remains unclear. Here we reveal that, in bovine chromaffin cells, SVs embedded in the cortical actin network undergo a highly synchronized transition towards the plasma membrane and Munc18-1-dependent docking in response to secretagogues. This movement coincides with a translocation of the cortical actin network in the same direction. Both effects are abolished by the knockdown or the pharmacological inhibition of myosin II, suggesting changes in actomyosin-generated forces across the cell cortex. Indeed, we report a reduction in cortical actin network tension elicited on secretagogue stimulation that is sensitive to myosin II inhibition. We reveal that the cortical actin network acts as a 'casting net' that undergoes activity-dependent relaxation, thereby driving tethered SVs towards the plasma membrane where they undergo Munc18-1-dependent docking. PMID:25708831

  14. The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions

    PubMed Central

    Taylor, P.; Hobbs, J. N.; Burroni, J.; Siegelmann, H. T.

    2015-01-01

    Though widely hypothesized, limited evidence exists that human brain functions organize in global gradients of abstraction starting from sensory cortical inputs. Hierarchical representation is accepted in computational networks, and tentatively in visual neuroscience, yet no direct holistic demonstrations exist in vivo. Our methods developed network models enriched with tiered directionality, by including input locations, a critical feature for localizing representation in networks generally. Grouped primary sensory cortices defined network inputs, displaying global connectivity to fused inputs. Depth-oriented networks guided analyses of fMRI databases (~17,000 experiments;~1/4 of fMRI literature). Formally, we tested whether network depth predicted localization of abstract versus concrete behaviors over the whole set of studied brain regions. For our results, new cortical graph metrics, termed network-depth, ranked all databased cognitive function activations by network-depth. Thus, we objectively sorted stratified landscapes of cognition, starting from grouped sensory inputs in parallel, progressing deeper into cortex. This exposed escalating amalgamation of function or abstraction with increasing network-depth, globally. Nearly 500 new participants confirmed our results. In conclusion, data-driven analyses defined a hierarchically ordered connectome, revealing a related continuum of cognitive function. Progressive functional abstraction over network depth may be a fundamental feature of brains, and is observed in artificial networks. PMID:26669858

  15. The global landscape of cognition: hierarchical aggregation as an organizational principle of human cortical networks and functions.

    PubMed

    Taylor, P; Hobbs, J N; Burroni, J; Siegelmann, H T

    2015-01-01

    Though widely hypothesized, limited evidence exists that human brain functions organize in global gradients of abstraction starting from sensory cortical inputs. Hierarchical representation is accepted in computational networks, and tentatively in visual neuroscience, yet no direct holistic demonstrations exist in vivo. Our methods developed network models enriched with tiered directionality, by including input locations, a critical feature for localizing representation in networks generally. Grouped primary sensory cortices defined network inputs, displaying global connectivity to fused inputs. Depth-oriented networks guided analyses of fMRI databases (~17,000 experiments;~1/4 of fMRI literature). Formally, we tested whether network depth predicted localization of abstract versus concrete behaviors over the whole set of studied brain regions. For our results, new cortical graph metrics, termed network-depth, ranked all databased cognitive function activations by network-depth. Thus, we objectively sorted stratified landscapes of cognition, starting from grouped sensory inputs in parallel, progressing deeper into cortex. This exposed escalating amalgamation of function or abstraction with increasing network-depth, globally. Nearly 500 new participants confirmed our results. In conclusion, data-driven analyses defined a hierarchically ordered connectome, revealing a related continuum of cognitive function. Progressive functional abstraction over network depth may be a fundamental feature of brains, and is observed in artificial networks. PMID:26669858

  16. Functional connectivity and dynamics of cortical-thalamic networks co-cultured in a dual compartment device

    NASA Astrophysics Data System (ADS)

    Kanagasabapathi, Thirukumaran T.; Massobrio, Paolo; Barone, Rocco Andrea; Tedesco, Mariateresa; Martinoia, Sergio; Wadman, Wytse J.; Decré, Michel M. J.

    2012-06-01

    Co-cultures containing dissociated cortical and thalamic cells may provide a unique model for understanding the pathophysiology in the respective neuronal sub-circuitry. In addition, developing an in vitro dissociated co-culture model offers the possibility of studying the system without influence from other neuronal sub-populations. Here we demonstrate a dual compartment system coupled to microelectrode arrays (MEAs) for co-culturing and recording spontaneous activities from neuronal sub-populations. Propagation of electrical activities between cortical and thalamic regions and their interdependence in connectivity is verified by means of a cross-correlation algorithm. We found that burst events originate in the cortical region and drive the entire cortical-thalamic network bursting behavior while mutually weak thalamic connections play a relevant role in sustaining longer burst events in cortical cells. To support these experimental findings, a neuronal network model was developed and used to investigate the interplay between network dynamics and connectivity in the cortical-thalamic system.

  17. Transfer entropy in magnetoencephalographic data: quantifying information flow in cortical and cerebellar networks.

    PubMed

    Wibral, Michael; Rahm, Benjamin; Rieder, Maria; Lindner, Michael; Vicente, Raul; Kaiser, Jochen

    2011-03-01

    The analysis of cortical and subcortical networks requires the identification of their nodes, and of the topology and dynamics of their interactions. Exploratory tools for the identification of nodes are available, e.g. magnetoencephalography (MEG) in combination with beamformer source analysis. Competing network topologies and interaction models can be investigated using dynamic causal modelling. However, we lack a method for the exploratory investigation of network topologies to choose from the very large number of possible network graphs. Ideally, this method should not require a pre-specified model of the interaction. Transfer entropy--an information theoretic implementation of Wiener-type causality--is a method for the investigation of causal interactions (or information flow) that is independent of a pre-specified interaction model. We analysed MEG data from an auditory short-term memory experiment to assess whether the reconfiguration of networks implied in this task can be detected using transfer entropy. Transfer entropy analysis of MEG source-level signals detected changes in the network between the different task types. These changes prominently involved the left temporal pole and cerebellum--structures that have previously been implied in auditory short-term or working memory. Thus, the analysis of information flow with transfer entropy at the source-level may be used to derive hypotheses for further model-based testing. PMID:21115029

  18. Development of Distributed Generic Simulator (GenSim) through Invention of Simulated Network (simNetwork)

    NASA Astrophysics Data System (ADS)

    Koo, Cheol-Hea; Lee, Hoon-Hee; Cheon, Yee-Jin

    2011-09-01

    A simulated network protocol provides the capability of distributed simulation to a generic simulator. Through this, full coverage of management of data and service handling among separated simulators is achieved. The distributed simulation environment is much more conducive to handling simulation load balancing and hazard treatment than a standalone computer. According to the simulated network protocol, one simulator takes on the role of server and the other simulators take on the role of client, and client is controlled by server. The purpose of the simulated network protocol is to seamlessly connect multiple simulator instances into a single simulation environment. This paper presents the development of a simulated network (simNetwork) that provides the capability of distributed simulation to a generic simulator (GenSim), which is a software simulator of satellites that has been developed by the Korea Aerospace Research Institute since 2010, to use as a flight software! validation bench for future satellite development.

  19. Simulating Cortical Development as a Self Constructing Process: A Novel Multi-Scale Approach Combining Molecular and Physical Aspects

    PubMed Central

    Zubler, Frederic; Hauri, Andreas; Pfister, Sabina; Bauer, Roman; Anderson, John C.; Whatley, Adrian M.; Douglas, Rodney J.

    2013-01-01

    Current models of embryological development focus on intracellular processes such as gene expression and protein networks, rather than on the complex relationship between subcellular processes and the collective cellular organization these processes support. We have explored this collective behavior in the context of neocortical development, by modeling the expansion of a small number of progenitor cells into a laminated cortex with layer and cell type specific projections. The developmental process is steered by a formal language analogous to genomic instructions, and takes place in a physically realistic three-dimensional environment. A common genome inserted into individual cells control their individual behaviors, and thereby gives rise to collective developmental sequences in a biologically plausible manner. The simulation begins with a single progenitor cell containing the artificial genome. This progenitor then gives rise through a lineage of offspring to distinct populations of neuronal precursors that migrate to form the cortical laminae. The precursors differentiate by extending dendrites and axons, which reproduce the experimentally determined branching patterns of a number of different neuronal cell types observed in the cat visual cortex. This result is the first comprehensive demonstration of the principles of self-construction whereby the cortical architecture develops. In addition, our model makes several testable predictions concerning cell migration and branching mechanisms. PMID:23966845

  20. Statistical Analysis of Tract-Tracing Experiments Demonstrates a Dense, Complex Cortical Network in the Mouse.

    PubMed

    Ypma, Rolf J F; Bullmore, Edward T

    2016-09-01

    Anatomical tract tracing methods are the gold standard for estimating the weight of axonal connectivity between a pair of pre-defined brain regions. Large studies, comprising hundreds of experiments, have become feasible by automated methods. However, this comes at the cost of positive-mean noise making it difficult to detect weak connections, which are of particular interest as recent high resolution tract-tracing studies of the macaque have identified many more weak connections, adding up to greater connection density of cortical networks, than previously recognized. We propose a statistical framework that estimates connectivity weights and credibility intervals from multiple tract-tracing experiments. We model the observed signal as a log-normal distribution generated by a combination of tracer fluorescence and positive-mean noise, also accounting for injections into multiple regions. Using anterograde viral tract-tracing data provided by the Allen Institute for Brain Sciences, we estimate the connection density of the mouse intra-hemispheric cortical network to be 73% (95% credibility interval (CI): 71%, 75%); higher than previous estimates (40%). Inter-hemispheric density was estimated to be 59% (95% CI: 54%, 62%). The weakest estimable connections (about 6 orders of magnitude weaker than the strongest connections) are likely to represent only one or a few axons. These extremely weak connections are topologically more random and longer distance than the strongest connections, which are topologically more clustered and shorter distance (spatially clustered). Weak links do not substantially contribute to the global topology of a weighted brain graph, but incrementally increased topological integration of a binary graph. The topology of weak anatomical connections in the mouse brain, rigorously estimable down to the biological limit of a single axon between cortical areas in these data, suggests that they might confer functional advantages for integrative

  1. Frequency-selective control of cortical and subcortical networks by central thalamus

    PubMed Central

    Liu, Jia; Lee, Hyun Joo; Weitz, Andrew J; Fang, Zhongnan; Lin, Peter; Choy, ManKin; Fisher, Robert; Pinskiy, Vadim; Tolpygo, Alexander; Mitra, Partha; Schiff, Nicholas; Lee, Jin Hyung

    2015-01-01

    Central thalamus plays a critical role in forebrain arousal and organized behavior. However, network-level mechanisms that link its activity to brain state remain enigmatic. Here, we combined optogenetics, fMRI, electrophysiology, and video-EEG monitoring to characterize the central thalamus-driven global brain networks responsible for switching brain state. 40 and 100 Hz stimulations of central thalamus caused widespread activation of forebrain, including frontal cortex, sensorimotor cortex, and striatum, and transitioned the brain to a state of arousal in asleep rats. In contrast, 10 Hz stimulation evoked significantly less activation of forebrain, inhibition of sensory cortex, and behavioral arrest. To investigate possible mechanisms underlying the frequency-dependent cortical inhibition, we performed recordings in zona incerta, where 10, but not 40, Hz stimulation evoked spindle-like oscillations. Importantly, suppressing incertal activity during 10 Hz central thalamus stimulation reduced the evoked cortical inhibition. These findings identify key brain-wide dynamics underlying central thalamus arousal regulation. DOI: http://dx.doi.org/10.7554/eLife.09215.001 PMID:26652162

  2. Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.

    PubMed

    Hage, Ilige S; Hamade, Ramsey F

    2016-05-01

    In cortical bone, solid (lamellar and interstitial) matrix occupies space left over by porous microfeatures such as Haversian canals, lacunae, and canaliculi-containing clusters. In this work, pulse-coupled neural networks (PCNN) were used to automatically distinguish the microfeatures present in histology slides of cortical bone. The networks' parameters were optimized using particle swarm optimization (PSO). When forming the fitness functions for the PSO, we considered the microfeatures' geometric attributes-namely, their size (based on measures of elliptical perimeter or area), shape (based on measures of compactness or the ratio of minor axis length to major axis length), and a two-way combination of these two geometric attributes. This hybrid PCNN-PSO method was further enhanced for pulse evaluation by combination with yet another method, adaptive threshold (AT), where the PCNN algorithm is repeated until the best threshold is found corresponding to the maximum variance between two segmented regions. Together, this framework of using PCNN-PSO-AT constitutes, we believe, a novel framework in biomedical imaging. Using this framework and extracting microfeatures from only one training image, we successfully extracted microfeatures from other test images. The high fidelity of all resultant segments was established using quantitative metrics such as precision, specificity, and Dice indices. PMID:26104115

  3. Dysfunction of a Cortical Midline Network During Emotional Appraisals in Schizophrenia

    PubMed Central

    Holt, Daphne. J.; Lakshmanan, Balaji; Freudenreich, Oliver; Goff, Donald C.; Rauch, Scott L.; Kuperberg, Gina R.

    2011-01-01

    A cardinal feature of schizophrenia is the poor comprehension, or misinterpretation, of the emotional meaning of social interactions and events, which can sometimes take the form of a persecutory delusion. It has been shown that the comprehension of the emotional meaning of the social world involves a midline paralimbic cortical network. However, the function of this network during emotional appraisals in patients with schizophrenia is not well understood. In this study, hemodynamic responses were measured in 14 patients with schizophrenia and 18 healthy subjects during the evaluation of descriptions of social situations with negative, positive, and neutral affective valence. The healthy and schizophrenia groups displayed opposite patterns of responses to emotional and neutral social situations within the medial prefrontal and posterior cingulate cortices—healthy participants showed greater activity to the emotional compared to the neutral situations, while patients exhibited greater responses to the neutral compared to the emotional situations. Moreover, the magnitude of the response within bilateral cingulate gyri to the neutral social stimuli predicted delusion severity in the patients with schizophrenia. These findings suggest that impaired functioning of cortical midline structures in schizophrenia may underlie faulty interpretations of social events, contributing to delusion formation. PMID:19605517

  4. Histological analysis of the alterations on cortical bone channels network after radiotherapy: A rabbit study.

    PubMed

    Rabelo, Gustavo Davi; Beletti, Marcelo Emílio; Dechichi, Paula

    2010-10-01

    The aim of this study was to evaluate the effects of radiotherapy in cortical bone channels network. Fourteen rabbits were divided in two groups and test group received single dose of 15 Gy cobalt-60 radiation in tibia, bilaterally. The animals were sacrificed and a segment of tibia was removed and histologically processed. Histological images were taken and had their bone channels segmented and called regions of interest (ROI). Images were analyzed through developed algorithms using the SCILAB mathematical environment, getting percentage of bone matrix, ROI areas, ROI perimeters, their standard deviations and Lacunarity. The osteocytes and empty lacunae were also counted. Data were evaluated using Kolmogorov-Smirnov, Mann Whitney, and Student's t test (P < 0.05). Significant differences in bone matrix percentage, area and perimeters of the channels, their respective standard deviations and lacunarity were found between groups. In conclusion, the radiotherapy causes reduction of bone matrix and modifies the morphology of bone channels network. PMID:20169617

  5. Human brain networks in physiological aging: a graph theoretical analysis of cortical connectivity from EEG data.

    PubMed

    Vecchio, Fabrizio; Miraglia, Francesca; Bramanti, Placido; Rossini, Paolo Maria

    2014-01-01

    Modern analysis of electroencephalographic (EEG) rhythms provides information on dynamic brain connectivity. To test the hypothesis that aging processes modulate the brain connectivity network, EEG recording was conducted on 113 healthy volunteers. They were divided into three groups in accordance with their ages: 36 Young (15-45 years), 46 Adult (50-70 years), and 31 Elderly (>70 years). To evaluate the stability of the investigated parameters, a subgroup of 10 subjects underwent a second EEG recording two weeks later. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by eLORETA on cortical sources. EEG frequency bands of interest were: delta (2-4 Hz), theta (4-8 Hz), alpha1 (8-10.5 Hz), alpha2 (10.5-13 Hz), beta1 (13-20 Hz), beta2 (20-30 Hz), and gamma (30-40 Hz). The spectral connectivity analysis of cortical sources showed that the normalized Characteristic Path Length (λ) presented the pattern Young > Adult>Elderly in the higher alpha band. Elderly also showed a greater increase in delta and theta bands than Young. The correlation between age and λ showed that higher ages corresponded to higher λ in delta and theta and lower in the alpha2 band; this pattern reflects the age-related modulation of higher (alpha) and decreased (delta) connectivity. The Normalized Clustering coefficient (γ) and small-world network modeling (σ) showed non-significant age-modulation. Evidence from the present study suggests that graph theory can aid in the analysis of connectivity patterns estimated from EEG and can facilitate the study of the physiological and pathological brain aging features of functional connectivity networks. PMID:24820018

  6. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates

    PubMed Central

    Cotterill, Ellese; Hall, Diana; Wallace, Kathleen; Mundy, William R.; Eglen, Stephen J.; Shafer, Timothy J.

    2016-01-01

    We examined neural network ontogeny using microelectrode array (MEA) recordings made in multiwell MEA (mwMEA) plates over the first 12 days in vitro (DIV). In primary cortical cultures, action potential spiking activity developed rapidly between DIV 5 and 12. Spiking was sporadic and unorganized at early DIV, and became progressively more organized with time, with bursting parameters, synchrony, and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity; principal components analysis using these features demonstrated segregation of data by age at both the well and plate levels. Using random forest classifiers and support vector machines, we demonstrated that four features (coefficient of variation [CV] of within-burst interspike interval, CV of interburst interval, network spike rate, and burst rate) could predict the age of each well recording with >65% accuracy. When restricting the classification to a binary decision, accuracy improved to as high as 95%. Further, we present a novel resampling approach to determine the number of wells needed for comparing different treatments. Overall, these results demonstrate that network development on mwMEA plates is similar to development in single-well MEAs. The increased throughput of mwMEAs will facilitate screening drugs, chemicals, or disease states for effects on neurodevelopment. PMID:27028607

  7. Cryopreserved rat cortical cells develop functional neuronal networks on microelectrode arrays.

    PubMed

    Otto, Frauke; Görtz, Philipp; Fleischer, Wiebke; Siebler, Mario

    2003-09-30

    Neurons growing on microelectrode arrays (MEAs) are promising tools to investigate principal neuronal network mechanisms and network responses to pharmaceutical substances. However, broad application of these tools, e.g. in pharmaceutical substance screening, requires neuronal cells that provide stable activity on MEAs. Cryopreserved cortical neurons (CCx) from embryonic rats were cultured on MEAs and their immunocytochemical and electrophysiological properties were compared with acutely dissociated neurons (Cx). Both cell types formed neuritic networks and expressed the neuron-specific markers microtubule associated protein 2, synaptophysin, neurofilament and gamma-aminobutyric acid (GABA). Spontaneous spike activity (SSA) was recorded after 9 up to 74 days in vitro (DIV) in CCx and from 5 to 30 DIV in Cx, respectively. Cx and CCx exhibited synchronized burst activity with similar spiking characteristics. Tetrodotoxin (TTX) abolished the SSA of both cell types reversibly. In CCx SSA-inhibition occurred with an IC50 of 1.1 nM for TTX, 161 microM for magnesium, 18 microM for D,L-2-amino-5-phosphonovaleric acid (APV) and 1 microM for GABA. CCx cells were easy to handle and developed long living, stable and active neuronal networks on MEAs with similar characteristics as Cx. Thus, these neurochips seem to be suitable for studying neuronal network properties and screening in pharmaceutical research. PMID:12948560

  8. A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks

    PubMed Central

    Sweeney, Yann; Hellgren Kotaleski, Jeanette; Hennig, Matthias H.

    2015-01-01

    Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes. PMID:26158556

  9. Characterization of Early Cortical Neural Network Development in Multiwell Microelectrode Array Plates.

    PubMed

    Cotterill, Ellese; Hall, Diana; Wallace, Kathleen; Mundy, William R; Eglen, Stephen J; Shafer, Timothy J

    2016-06-01

    We examined neural network ontogeny using microelectrode array (MEA) recordings made in multiwell MEA (mwMEA) plates over the first 12 days in vitro (DIV). In primary cortical cultures, action potential spiking activity developed rapidly between DIV 5 and 12. Spiking was sporadic and unorganized at early DIV, and became progressively more organized with time, with bursting parameters, synchrony, and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity; principal components analysis using these features demonstrated segregation of data by age at both the well and plate levels. Using random forest classifiers and support vector machines, we demonstrated that four features (coefficient of variation [CV] of within-burst interspike interval, CV of interburst interval, network spike rate, and burst rate) could predict the age of each well recording with >65% accuracy. When restricting the classification to a binary decision, accuracy improved to as high as 95%. Further, we present a novel resampling approach to determine the number of wells needed for comparing different treatments. Overall, these results demonstrate that network development on mwMEA plates is similar to development in single-well MEAs. The increased throughput of mwMEAs will facilitate screening drugs, chemicals, or disease states for effects on neurodevelopment. PMID:27028607

  10. Estimating computer communication network performance using network simulations

    SciTech Connect

    Garcia, A.B.

    1985-01-01

    A generalized queuing model simulation of store-and-forward computer communication networks is developed and implemented using Simulation Language for Alternative Modeling (SLAM). A baseline simulation model is validated by comparison with published analytic models. The baseline model is expanded to include an ACK/NAK data link protocol, four-level message precedence, finite queues, and a response traffic scenario. Network performance, as indicated by average message delay and message throughput, is estimated using the simulation model.

  11. Increased Functional Connectivity Between Subcortical and Cortical Resting-State Networks in Autism Spectrum Disorder

    PubMed Central

    Cerliani, Leonardo; Mennes, Maarten; Thomas, Rajat M.; Di Martino, Adriana; Thioux, Marc; Keysers, Christian

    2016-01-01

    Importance Individuals with autism spectrum disorder (ASD) exhibit severe difficulties in social interaction, motor coordination, behavioral flexibility, and atypical sensory processing, with considerable interindividual variability. This heterogeneous set of symptoms recently led to investigating the presence of abnormalities in the interaction across large-scale brain networks. To date, studies have focused either on constrained sets of brain regions or whole-brain analysis, rather than focusing on the interaction between brain networks. Objectives To compare the intrinsic functional connectivity between brain networks in a large sample of individuals with ASD and typically developing control subjects and to estimate to what extent group differences would predict autistic traits and reflect different developmental trajectories. Design, Setting, and Participants We studied 166 male individuals (mean age, 17.6 years; age range, 7-50 years) diagnosed as having DSM-IV-TR autism or Asperger syndrome and 193 typical developing male individuals (mean age, 16.9 years; age range, 6.5-39.4 years) using resting-state functional magnetic resonance imaging (MRI). Participants were matched for age, IQ, head motion, and eye status (open or closed) in the MRI scanner. We analyzed data from the Autism Brain Imaging Data Exchange (ABIDE), an aggregated MRI data set from 17 centers, made public in August 2012. Main Outcomes and Measures We estimated correlations between time courses of brain networks extracted using a data-driven method (independent component analysis). Subsequently, we associated estimates of interaction strength between networks with age and autistic traits indexed by the Social Responsiveness Scale. Results Relative to typically developing control participants, individuals with ASD showed increased functional connectivity between primary sensory networks and subcortical networks (thalamus and basal ganglia) (all t ≥ 3.13, P < .001 corrected). The strength of

  12. Meeting the Memory Challenges of Brain-Scale Network Simulation

    PubMed Central

    Kunkel, Susanne; Potjans, Tobias C.; Eppler, Jochen M.; Plesser, Hans Ekkehard; Morrison, Abigail; Diesmann, Markus

    2012-01-01

    The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 105 neurons with up to 109 synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and less

  13. Meeting the memory challenges of brain-scale network simulation.

    PubMed

    Kunkel, Susanne; Potjans, Tobias C; Eppler, Jochen M; Plesser, Hans Ekkehard; Morrison, Abigail; Diesmann, Markus

    2011-01-01

    The development of high-performance simulation software is crucial for studying the brain connectome. Using connectome data to generate neurocomputational models requires software capable of coping with models on a variety of scales: from the microscale, investigating plasticity, and dynamics of circuits in local networks, to the macroscale, investigating the interactions between distinct brain regions. Prior to any serious dynamical investigation, the first task of network simulations is to check the consistency of data integrated in the connectome and constrain ranges for yet unknown parameters. Thanks to distributed computing techniques, it is possible today to routinely simulate local cortical networks of around 10(5) neurons with up to 10(9) synapses on clusters and multi-processor shared-memory machines. However, brain-scale networks are orders of magnitude larger than such local networks, in terms of numbers of neurons and synapses as well as in terms of computational load. Such networks have been investigated in individual studies, but the underlying simulation technologies have neither been described in sufficient detail to be reproducible nor made publicly available. Here, we discover that as the network model sizes approach the regime of meso- and macroscale simulations, memory consumption on individual compute nodes becomes a critical bottleneck. This is especially relevant on modern supercomputers such as the Blue Gene/P architecture where the available working memory per CPU core is rather limited. We develop a simple linear model to analyze the memory consumption of the constituent components of neuronal simulators as a function of network size and the number of cores used. This approach has multiple benefits. The model enables identification of key contributing components to memory saturation and prediction of the effects of potential improvements to code before any implementation takes place. As a consequence, development cycles can be shorter and

  14. Framework for Network Co-Simulation

    SciTech Connect

    Daily, Jeff; Ciraci, PNNL Selim; Fuller, PNNL Jason; Marinovici, PNNL Laurentiu; Fisher, PNNL Andrew; Lo, PNNL Chaomei; Hauer, PNNL Matthew

    2014-01-09

    The Framework for Network Co-Simulation (FNCS) uses a federated approach to integrate simulations which may have differing time scales. Special consideration is given to integration with a communication network simulation such that inter-simulation messages may be optionally routed through and delayed by such a simulation. In addition, FNCS uses novel time synchronization algorithms to accelerate co-simulation including the application of speculative multithreading. FNCS accomplishes all of these improvements with minimal end user intervention. Simulations can be integrated using FNCS while maintaining their original model input files simply by linking with the FNCS library and making appropriate calls into the FNCS API.

  15. Oscillatory entrainment of the motor cortical network during motor imagery is modulated by the feedback modality.

    PubMed

    Vukelić, Mathias; Gharabaghi, Alireza

    2015-05-01

    Neurofeedback of self-regulated brain activity in circumscribed cortical regions is used as a novel strategy to facilitate functional restoration following stroke. Basic knowledge about its impact on motor system oscillations and functional connectivity is however scarce. Specifically, a direct comparison between different feedback modalities and their neural signatures is missing. We assessed a neurofeedback training intervention of modulating β-activity in circumscribed sensorimotor regions by kinesthetic motor imagery (MI). Right-handed healthy participants received two different feedback modalities contingent to their MI-associated brain activity in a cross-over design: (I) visual feedback with a brain-computer interface (BCI) and (II) proprioceptive feedback with a brain-robot interface (BRI) orthosis attached to the right hand. High-density electroencephalography was used to examine the reactivity of the cortical motor system during the training session of each task by studying both local oscillatory power entrainment and distributed functional connectivity. Both feedback modalities activated a distributed functional connectivity network of coherent oscillations. A significantly higher skill and lower variability of self-controlled sensorimotor β-band modulation could, however, be achieved in the BRI condition. This gain in controlling regional motor oscillations was accompanied by functional coupling of remote β-band and θ-band activity in bilateral fronto-central regions and left parieto-occipital regions, respectively. The functional coupling of coherent θ-band oscillations correlated moreover with the skill of regional β-modulation thus revealing a motor learning related network. Our findings indicate that proprioceptive feedback is more suitable than visual feedback to entrain the motor network architecture during the interplay between motor imagery and feedback processing thus resulting in better volitional control of regional brain activity. PMID

  16. Posterior Cingulate, Precuneal & Retrosplenial Cortices: Cytology & Components of the Neural Network Correlates of Consciousness*

    PubMed Central

    Vogt, Brent A.; Laureys, Steven

    2008-01-01

    Neuronal aggregates involved in conscious awareness are not evenly distributed throughout the CNS but are comprised of key components referred to as the neural network correlates of consciousness (NNCC). A critical node in this network is the retrosplenial, posterior cingulate, and precuneal cortices (RSC/PCC/PrCC). The cytological and neurochemical composition of this region is reviewed in relation to the Brodmann map. This region has the highest level of brain glucose metabolism and cytochrome c oxidase activity. Monkey studies suggest that the anterior thalamic projection likely drives RSC and PCC metabolism and that the midbrain projection to the anteroventral thalamic nucleus is a key coupling site between the brainstem system for arousal and cortical systems for cognitive processing and awareness. The pivotal role of RSC/PCC/PrCC in consciousness is demonstrated with posterior cingulate epilepsy cases, midcingulate lesions that de-afferent this region and are associated with unilateral sensory neglect, observations from stroke and vegetative state patients, alterations in blood flow during sleep, and the actions of anesthetics. Since this region is critically involved in self reflection, it is not surprising that it is similarly a site for the NNCC. Interestingly, information processing during complex cognitive tasks and during aversive sensations such as pain induces efforts to terminate self reflection and result in decreased processing in PCC/PrCC. Finally, anatomical relations between the neural correlates of mind and NNCC in the cingulate gyrus do not appear to overlap and suggests that mental function and conscious awareness may be mediated by two neural networks. PMID:16186025

  17. Inferring network properties of cortical neurons with synaptic coupling and parameter dispersion.

    PubMed

    Roy, Dipanjan; Jirsa, Viktor

    2013-01-01

    Computational models at different space-time scales allow us to understand the fundamental mechanisms that govern neural processes and relate uniquely these processes to neuroscience data. In this work, we propose a novel neurocomputational unit (a mesoscopic model which tell us about the interaction between local cortical nodes in a large scale neural mass model) of bursters that qualitatively captures the complex dynamics exhibited by a full network of parabolic bursting neurons. We observe that the temporal dynamics and fluctuation of mean synaptic action term exhibits a high degree of correlation with the spike/burst activity of our population. With heterogeneity in the applied drive and mean synaptic coupling derived from fast excitatory synapse approximations we observe long term behavior in our population dynamics such as partial oscillations, incoherence, and synchrony. In order to understand the origin of multistability at the population level as a function of mean synaptic coupling and heterogeneity in the firing rate threshold we employ a simple generative model for parabolic bursting recently proposed by Ghosh et al. (2009). Further, we use here a mean coupling formulated for fast spiking neurons for our analysis of generic model. Stability analysis of this mean field network allow us to identify all the relevant network states found in the detailed biophysical model. We derive here analytically several boundary solutions, a result which holds for any number of spikes per burst. These findings illustrate the role of oscillations occurring at slow time scales (bursts) on the global behavior of the network. PMID:23533147

  18. Network algorithmics and the emergence of information integration in cortical models

    NASA Astrophysics Data System (ADS)

    Nathan, Andre; Barbosa, Valmir C.

    2011-07-01

    An information-theoretic framework known as integrated information theory (IIT) has been introduced recently for the study of the emergence of consciousness in the brain [D. Balduzzi and G. Tononi, PLoS Comput. Biol.1553-734X10.1371/journal.pcbi.1000091 4, e1000091 (2008)]. IIT purports that this phenomenon is to be equated with the generation of information by the brain surpassing the information that the brain’s constituents already generate independently of one another. IIT is not fully plausible in its modeling assumptions nor is it testable due to severe combinatorial growth embedded in its key definitions. Here we introduce an alternative to IIT which, while inspired in similar information-theoretic principles, seeks to address some of IIT’s shortcomings to some extent. Our alternative framework uses the same network-algorithmic cortical model we introduced earlier [A. Nathan and V. C. Barbosa, Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.81.021916 81, 021916 (2010)] and, to allow for somewhat improved testability relative to IIT, adopts the well-known notions of information gain and total correlation applied to a set of variables representing the reachability of neurons by messages in the model’s dynamics. We argue that these two quantities relate to each other in such a way that can be used to quantify the system’s efficiency in generating information beyond that which does not depend on integration. We give computational results on our cortical model and on variants thereof that are either structurally random in the sense of an Erdős-Rényi random directed graph or structurally deterministic. We have found that our cortical model stands out with respect to the others in the sense that many of its instances are capable of integrating information more efficiently than most of those others’ instances.

  19. Theta-burst Transcranial Magnetic Stimulation Alters the Functional Topography of the Cortical Motor Network

    PubMed Central

    NOH, Nor Azila; FUGGETTA, Giorgio; MANGANOTTI, Paolo

    2015-01-01

    Background: Transcranial magnetic stimulation (TMS) is a non-invasive tool that is able to modulate the electrical activity of the brain depending upon its protocol of stimulation. Theta burst stimulation (TBS) is a high-frequency TMS protocol that is able to induce prolonged plasticity changes in the brain. The induction of plasticity-like effects by TBS is useful in both experimental and therapeutic settings; however, the underlying neural mechanisms of this modulation remain unclear. The aim of this study was to investigate the effects of continuous TBS (cTBS) on the intrahemispheric and interhemispheric functional connectivity of the resting and active brain. Methods: A total of 26 healthy humans were randomly divided into two groups that received either real cTBS or sham (control) over the left primary motor cortex. Surface electroencephalogram (EEG) was used to quantify the changes of neural oscillations after cTBS at rest and after a choice reaction time test. The cTBS-induced EEG oscillations were computed using spectral analysis of event-related coherence (ERCoh) of theta (4–7.5 Hz), low alpha (8–9.5 Hz), high alpha (10–12.5 Hz), low beta (13–19.5 Hz), and high beta (20–30 Hz) brain rhythms. Results: We observed a global decrease in functional connectivity of the brain in the cTBS group when compared to sham in the low beta brain rhythm at rest and high beta brain rhythm during the active state. In particular, EEG spectral analysis revealed that high-frequency beta, a cortically generated brain rhythm, was the most sensitive band that was modulated by cTBS. Conclusion: Overall, our findings suggest that cTBS, a TMS protocol that mimics the mechanism of long-term depression of synaptic plasticity, modulates motor network oscillations primarily at the cortical level and might interfere with cortical information coding. PMID:27006636

  20. Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans

    PubMed Central

    Haagensen, Brian N.; Christensen, Mark S.; Madsen, Kristoffer H.; Rowe, James B.; Løkkegaard, Annemette; Siebner, Hartwig R.

    2015-01-01

    Dopaminergic signalling in the striatum contributes to reinforcement of actions and motivational enhancement of motor vigour. Parkinson's disease leads to progressive dopaminergic denervation of the striatum, impairing the function of cortico-basal ganglia networks. While levodopa therapy alleviates basal ganglia dysfunction in Parkinson's disease, it often elicits involuntary movements, referred to as levodopa-induced peak-of-dose dyskinesias. Here, we used a novel pharmacodynamic neuroimaging approach to identify the changes in cortico-basal ganglia connectivity that herald the emergence of levodopa-induced dyskinesias. Twenty-six patients with Parkinson's disease (age range: 51–84 years; 11 females) received a single dose of levodopa and then performed a task in which they had to produce or suppress a movement in response to visual cues. Task-related activity was continuously mapped with functional magnetic resonance imaging. Dynamic causal modelling was applied to assess levodopa-induced modulation of effective connectivity between the pre-supplementary motor area, primary motor cortex and putamen when patients suppressed a motor response. Bayesian model selection revealed that patients who later developed levodopa-induced dyskinesias, but not patients without dyskinesias, showed a linear increase in connectivity between the putamen and primary motor cortex after levodopa intake during movement suppression. Individual dyskinesia severity was predicted by levodopa-induced modulation of striato-cortical feedback connections from putamen to the pre-supplementary motor area (Pcorrected = 0.020) and primary motor cortex (Pcorrected = 0.044), but not feed-forward connections from the cortex to the putamen. Our results identify for the first time, aberrant dopaminergic modulation of striatal-cortical connectivity as a neural signature of levodopa-induced dyskinesias in humans. We argue that excessive striato-cortical connectivity in response to levodopa produces an

  1. Abnormal dopaminergic modulation of striato-cortical networks underlies levodopa-induced dyskinesias in humans.

    PubMed

    Herz, Damian M; Haagensen, Brian N; Christensen, Mark S; Madsen, Kristoffer H; Rowe, James B; Løkkegaard, Annemette; Siebner, Hartwig R

    2015-06-01

    Dopaminergic signalling in the striatum contributes to reinforcement of actions and motivational enhancement of motor vigour. Parkinson's disease leads to progressive dopaminergic denervation of the striatum, impairing the function of cortico-basal ganglia networks. While levodopa therapy alleviates basal ganglia dysfunction in Parkinson's disease, it often elicits involuntary movements, referred to as levodopa-induced peak-of-dose dyskinesias. Here, we used a novel pharmacodynamic neuroimaging approach to identify the changes in cortico-basal ganglia connectivity that herald the emergence of levodopa-induced dyskinesias. Twenty-six patients with Parkinson's disease (age range: 51-84 years; 11 females) received a single dose of levodopa and then performed a task in which they had to produce or suppress a movement in response to visual cues. Task-related activity was continuously mapped with functional magnetic resonance imaging. Dynamic causal modelling was applied to assess levodopa-induced modulation of effective connectivity between the pre-supplementary motor area, primary motor cortex and putamen when patients suppressed a motor response. Bayesian model selection revealed that patients who later developed levodopa-induced dyskinesias, but not patients without dyskinesias, showed a linear increase in connectivity between the putamen and primary motor cortex after levodopa intake during movement suppression. Individual dyskinesia severity was predicted by levodopa-induced modulation of striato-cortical feedback connections from putamen to the pre-supplementary motor area (Pcorrected = 0.020) and primary motor cortex (Pcorrected = 0.044), but not feed-forward connections from the cortex to the putamen. Our results identify for the first time, aberrant dopaminergic modulation of striatal-cortical connectivity as a neural signature of levodopa-induced dyskinesias in humans. We argue that excessive striato-cortical connectivity in response to levodopa produces an

  2. Program Helps Simulate Neural Networks

    NASA Technical Reports Server (NTRS)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  3. Effects of Increasing Neuromuscular Electrical Stimulation Current Intensity on Cortical Sensorimotor Network Activation: A Time Domain fNIRS Study

    PubMed Central

    Zucchelli, Lucia; Perrey, Stephane; Contini, Davide; Caffini, Matteo; Spinelli, Lorenzo; Kerr, Graham; Quaresima, Valentina; Ferrari, Marco; Torricelli, Alessandro

    2015-01-01

    Neuroimaging studies have shown neuromuscular electrical stimulation (NMES)-evoked movements activate regions of the cortical sensorimotor network, including the primary sensorimotor cortex (SMC), premotor cortex (PMC), supplementary motor area (SMA), and secondary somatosensory area (S2), as well as regions of the prefrontal cortex (PFC) known to be involved in pain processing. The aim of this study, on nine healthy subjects, was to compare the cortical network activation profile and pain ratings during NMES of the right forearm wrist extensor muscles at increasing current intensities up to and slightly over the individual maximal tolerated intensity (MTI), and with reference to voluntary (VOL) wrist extension movements. By exploiting the capability of the multi-channel time domain functional near-infrared spectroscopy technique to relate depth information to the photon time-of-flight, the cortical and superficial oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin concentrations were estimated. The O2Hb and HHb maps obtained using the General Linear Model (NIRS-SPM) analysis method, showed that the VOL and NMES-evoked movements significantly increased activation (i.e., increase in O2Hb and corresponding decrease in HHb) in the cortical layer of the contralateral sensorimotor network (SMC, PMC/SMA, and S2). However, the level and area of contralateral sensorimotor network (including PFC) activation was significantly greater for NMES than VOL. Furthermore, there was greater bilateral sensorimotor network activation with the high NMES current intensities which corresponded with increased pain ratings. In conclusion, our findings suggest that greater bilateral sensorimotor network activation profile with high NMES current intensities could be in part attributable to increased attentional/pain processing and to increased bilateral sensorimotor integration in these cortical regions. PMID:26158464

  4. Altered sensory processing and dendritic remodeling in hyperexcitable visual cortical networks.

    PubMed

    Vannini, Eleonora; Restani, Laura; Pietrasanta, Marta; Panarese, Alessandro; Mazzoni, Alberto; Rossetto, Ornella; Middei, Silvia; Micera, Silvestro; Caleo, Matteo

    2016-07-01

    Epilepsy is characterized by impaired circuit function and a propensity for spontaneous seizures, but how plastic rearrangements within the epileptic focus trigger cortical dysfunction and hyperexcitability is only partly understood. Here we have examined alterations in sensory processing and the underlying biochemical and neuroanatomical changes in tetanus neurotoxin (TeNT)-induced focal epilepsy in mouse visual cortex. We documented persistent epileptiform electrographic discharges and upregulation of GABAergic markers at the completion of TeNT effects. We also found a significant remodeling of the dendritic arbors of pyramidal neurons, with increased dendritic length and branching, and overall reduction in spine density but significant preservation of mushroom, mature spines. Functionally, spontaneous neuronal discharge was increased, visual responses were less reliable, and electrophysiological and behavioural visual acuity was consistently impaired in TeNT-injected mice. These data demonstrate robust, long-term remodeling of both inhibitory and excitatory circuitry associated with specific disturbances of network function in neocortical epilepsy. PMID:26163822

  5. Brain changes within the visuo-spatial attentional network in posterior cortical atrophy.

    PubMed

    Cerami, Chiara; Crespi, Chiara; Della Rosa, Pasquale Anthony; Dodich, Alessandra; Marcone, Alessandra; Magnani, Giuseppe; Coppi, Elisabetta; Falini, Andrea; Cappa, Stefano F; Perani, Daniela

    2015-01-01

    Posterior cortical atrophy (PCA) is characterized by basic visual and high order visual-spatial dysfunctions. In this study, we investigated long-distance deafferentation processes within the frontal-parietal-occipital network in ten PCA patients using a MRI-PET combined approach. Objective voxel-based [18F]FDG-PET imaging measured metabolic changes in single patients. Comprehensive investigation of diffusion tensor imaging (DTI) metrics and grey-matter density with voxel-based morphometry were obtained in a subgroup of 6 patients. Fractional anisotropy in the superior longitudinal fasciculus correlated with the PET metabolic changes within the inferior parietal and frontal eye field regions. [18F]FDG-PET analysis showed in each PCA case the typical bilateral hypometabolic pattern, involving posterior temporal, parietal, and occipital cortex, with additional hypometabolic foci in the frontal eye fields. Voxel-based morphometry showed right-sided atrophy in the parieto-occipital cortex, as well as a limited temporal involvement. DTI revealed extensive degeneration of the major anterior-posterior connecting fiber bundles and of commissural frontal lobe tracts. Microstructural measures in the superior longitudinal fasciculus were correlated with the PET metabolic changes within the inferior parietal and frontal eye field regions. Our results confirmed the predominant occipital-temporal and occipital-parietal degeneration in PCA patients. [18F]FDG-PET and DTI-MRI combined approaches revealed neurodegeneration effects well beyond the classical posterior cortical involvement, most likely as a consequence of deafferentation processes within the occipital-parietal-frontal network that could be at the basis of visuo-perceptual, visuo-spatial integration and attention deficits in PCA. PMID:25114070

  6. Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model

    PubMed Central

    Ito, Shinya; Hansen, Michael E.; Heiland, Randy; Lumsdaine, Andrew; Litke, Alan M.; Beggs, John M.

    2011-01-01

    Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons. PMID:22102894

  7. Extending transfer entropy improves identification of effective connectivity in a spiking cortical network model.

    PubMed

    Ito, Shinya; Hansen, Michael E; Heiland, Randy; Lumsdaine, Andrew; Litke, Alan M; Beggs, John M

    2011-01-01

    Transfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic, as synaptic delays between cortical neurons, for example, range from one to tens of milliseconds. In addition, neurons produce bursts of spikes spanning multiple time bins. To address these issues, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance, we applied these extensions of TE to a spiking cortical network model (Izhikevich, 2006) with known connectivity and a range of synaptic delays. For comparison, we also investigated single-delay TE, at a message length of one bin (D1TE), and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE, this dramatically improved to 73% of true connections. In addition, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE, when used on currently available desktop computers, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons. PMID:22102894

  8. Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

    PubMed Central

    Martinet, Louis-Emmanuel; Sheynikhovich, Denis; Benchenane, Karim; Arleo, Angelo

    2011-01-01

    The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates. PMID:21625569

  9. Pharmacological Rescue of Cortical Synaptic and Network Potentiation in a Mouse Model for Fragile X Syndrome

    PubMed Central

    Chen, Tao; Lu, Jing-Shan; Song, Qian; Liu, Ming-Gang; Koga, Kohei; Descalzi, Giannina; Li, Yun-Qing; Zhuo, Min

    2014-01-01

    Fragile X syndrome, caused by the mutation of the Fmr1 gene, is characterized by deficits of attention and learning ability. In the hippocampus of Fmr1 knockout mice (KO), long-term depression is enhanced whereas long-term potentiation (LTP) including late-phase LTP (L-LTP) is reduced or unaffected. Here we examined L-LTP in the anterior cingulate cortex (ACC) in Fmr1 KO mice by using a 64-electrode array recording system. In wild-type mice, theta-burst stimulation induced L-LTP that does not occur in all active electrodes/channels within the cingulate circuit and is typically detected in ∼75% of active channels. Furthermore, L-LTP recruited new responses from previous inactive channels. Both L-LTP and the recruitment of inactive responses were blocked in the ACC slices of Fmr1 KO mice. Bath application of metabotropic glutamate receptor 5 (mGluR5) antagonist or glycogen synthase kinase-3 (GSK3) inhibitors rescued the L-LTP and network recruitment. Our results demonstrate that loss of FMRP will greatly impair L-LTP and recruitment of cortical network in the ACC that can be rescued by pharmacological inhibition of mGluR5 or GSK3. This study is the first report of the network properties of L-LTP in the ACC, and provides basic mechanisms for future treatment of cortex-related cognitive defects in fragile X patients. PMID:24553731

  10. A cortical network underpinning the perceptual priority for rising intensity and auditory ``looming.''

    NASA Astrophysics Data System (ADS)

    Neuhoff, John G.; Bilecen, Deniz; Mustovic, Henrietta; Schachinger, Hartmut; Seifritz, Erich; Scheffler, Klaus; di Salle, Francesco

    2002-05-01

    Relative motion between a sound source and a listener creates a change in acoustic intensity that can be used to anticipate the source's approach. Humans have been shown to overestimate the intensity change of rising compared to falling intensity sounds and underestimate the time-to-contact of approaching sound sources. From an evolutionary perspective, this perceptual priority for looming sounds may represent an adaptive advantage that provides an increased margin of safety for responding to approaching auditory objects. Here, using functional magnetic resonance imaging, we show that the prioritization of rising contrasted with falling intensity sine-tones is grounded in a specific neural network. This network is predominantly composed of the superior temporal sulci, the middle temporal gyri, the right temporo-parietal junction, the motor and premotor cortices mainly on the right hemisphere, the left frontal operculum, and the left superior posterior cerebellar cortex. These regions are critical for the allocation of attention, the analysis of space, object recognition, and neurobehavioral preparation for action. Our results identify a widespread neural network underpinning the perceptual priority for looming sounds that can be used in translating sensory information into preparedness for adverse events and appropriate action. [Work supported by the Swiss and the American NSFs.

  11. Complementary Characteristics of Correlation Patterns in Morphometric Correlation Networks of Cortical Thickness, Surface Area, and Gray Matter Volume.

    PubMed

    Yang, Jin-Ju; Kwon, Hunki; Lee, Jong-Min

    2016-01-01

    Morphometric correlation networks of cortical thickness, surface area, and gray matter volume have statistically different structural topology. However, there is no report directly describing their correlation patterns in view of interregional covariance. Here, we examined the characteristics of the correlation patterns in three morphometric networks of cortical thickness, surface area, and gray matter volume using a Venn diagram concept across 314 normal subjects. We found that over 60% of all nonoverlapping correlation patterns emerged with divergent unique patterns, while there were 10% of all common edges in ipsilateral and homotopic regions among the three morphometric correlation networks. It was also found that the network parameters of the three networks were different. Our findings showed that correlation patterns of the network itself can provide complementary information when compared with network properties. We demonstrate that morphometric correlation networks of distinct structural phenotypes have different correlation patterns and different network properties. This finding implies that the topology of each morphometric correlation network may reflect different aspects of each morphometric descriptor. PMID:27226000

  12. Complementary Characteristics of Correlation Patterns in Morphometric Correlation Networks of Cortical Thickness, Surface Area, and Gray Matter Volume

    PubMed Central

    Yang, Jin-Ju; Kwon, Hunki; Lee, Jong-Min

    2016-01-01

    Morphometric correlation networks of cortical thickness, surface area, and gray matter volume have statistically different structural topology. However, there is no report directly describing their correlation patterns in view of interregional covariance. Here, we examined the characteristics of the correlation patterns in three morphometric networks of cortical thickness, surface area, and gray matter volume using a Venn diagram concept across 314 normal subjects. We found that over 60% of all nonoverlapping correlation patterns emerged with divergent unique patterns, while there were 10% of all common edges in ipsilateral and homotopic regions among the three morphometric correlation networks. It was also found that the network parameters of the three networks were different. Our findings showed that correlation patterns of the network itself can provide complementary information when compared with network properties. We demonstrate that morphometric correlation networks of distinct structural phenotypes have different correlation patterns and different network properties. This finding implies that the topology of each morphometric correlation network may reflect different aspects of each morphometric descriptor. PMID:27226000

  13. A time-frequency analysis of the dynamics of cortical networks of sleep spindles from MEG-EEG recordings

    PubMed Central

    Zerouali, Younes; Lina, Jean-Marc; Sekerovic, Zoran; Godbout, Jonathan; Dube, Jonathan; Jolicoeur, Pierre; Carrier, Julie

    2014-01-01

    Sleep spindles are a hallmark of NREM sleep. They result from a widespread thalamo-cortical loop and involve synchronous cortical networks that are still poorly understood. We investigated whether brain activity during spindles can be characterized by specific patterns of functional connectivity among cortical generators. For that purpose, we developed a wavelet-based approach aimed at imaging the synchronous oscillatory cortical networks from simultaneous MEG-EEG recordings. First, we detected spindles on the EEG and extracted the corresponding frequency-locked MEG activity under the form of an analytic ridge signal in the time-frequency plane (Zerouali et al., 2013). Secondly, we performed source reconstruction of the ridge signal within the Maximum Entropy on the Mean framework (Amblard et al., 2004), yielding a robust estimate of the cortical sources producing observed oscillations. Lastly, we quantified functional connectivity among cortical sources using phase-locking values. The main innovations of this methodology are (1) to reveal the dynamic behavior of functional networks resolved in the time-frequency plane and (2) to characterize functional connectivity among MEG sources through phase interactions. We showed, for the first time, that the switch from fast to slow oscillatory mode during sleep spindles is required for the emergence of specific patterns of connectivity. Moreover, we show that earlier synchrony during spindles was associated with mainly intra-hemispheric connectivity whereas later synchrony was associated with global long-range connectivity. We propose that our methodology can be a valuable tool for studying the connectivity underlying neural processes involving sleep spindles, such as memory, plasticity or aging. PMID:25389381

  14. Splitting strategy for simulating genetic regulatory networks.

    PubMed

    You, Xiong; Liu, Xueping; Musa, Ibrahim Hussein

    2014-01-01

    The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions. PMID:24624223

  15. Spiking in auditory cortex following thalamic stimulation is dominated by cortical network activity

    PubMed Central

    Krause, Bryan M.; Raz, Aeyal; Uhlrich, Daniel J.; Smith, Philip H.; Banks, Matthew I.

    2014-01-01

    The state of the sensory cortical network can have a profound impact on neural responses and perception. In rodent auditory cortex, sensory responses are reported to occur in the context of network events, similar to brief UP states, that produce “packets” of spikes and are associated with synchronized synaptic input (Bathellier et al., 2012; Hromadka et al., 2013; Luczak et al., 2013). However, traditional models based on data from visual and somatosensory cortex predict that ascending sensory thalamocortical (TC) pathways sequentially activate cells in layers 4 (L4), L2/3, and L5. The relationship between these two spatio-temporal activity patterns is unclear. Here, we used calcium imaging and electrophysiological recordings in murine auditory TC brain slices to investigate the laminar response pattern to stimulation of TC afferents. We show that although monosynaptically driven spiking in response to TC afferents occurs, the vast majority of spikes fired following TC stimulation occurs during brief UP states and outside the context of the L4>L2/3>L5 activation sequence. Specifically, monosynaptic subthreshold TC responses with similar latencies were observed throughout layers 2–6, presumably via synapses onto dendritic processes located in L3 and L4. However, monosynaptic spiking was rare, and occurred primarily in L4 and L5 non-pyramidal cells. By contrast, during brief, TC-induced UP states, spiking was dense and occurred primarily in pyramidal cells. These network events always involved infragranular layers, whereas involvement of supragranular layers was variable. During UP states, spike latencies were comparable between infragranular and supragranular cells. These data are consistent with a model in which activation of auditory cortex, especially supragranular layers, depends on internally generated network events that represent a non-linear amplification process, are initiated by infragranular cells and tightly regulated by feed-forward inhibitory

  16. Interaction of electrically evoked activity with intrinsic dynamics of cultured cortical networks with and without functional fast GABAergic synaptic transmission

    PubMed Central

    Baltz, Thomas; Voigt, Thomas

    2015-01-01

    The modulation of neuronal activity by means of electrical stimulation is a successful therapeutic approach for patients suffering from a variety of central nervous system disorders. Prototypic networks formed by cultured cortical neurons represent an important model system to gain general insights in the input–output relationships of neuronal tissue. These networks undergo a multitude of developmental changes during their maturation, such as the excitatory–inhibitory shift of the neurotransmitter GABA. Very few studies have addressed how the output properties to a given stimulus change with ongoing development. Here, we investigate input–output relationships of cultured cortical networks by probing cultures with and without functional GABAAergic synaptic transmission with a set of stimulation paradigms at various stages of maturation. On the cellular level, low stimulation rates (<15 Hz) led to reliable neuronal responses; higher rates were increasingly ineffective. Similarly, on the network level, lowest stimulation rates (<0.1 Hz) lead to maximal output rates at all ages, indicating a network wide refractory period after each stimulus. In cultures aged 3 weeks and older, a gradual recovery of the network excitability within tens of milliseconds was in contrast to an abrupt recovery after about 5 s in cultures with absent GABAAergic synaptic transmission. In these GABA deficient cultures evoked responses were prolonged and had multiple discharges. Furthermore, the network excitability changed periodically, with a very slow spontaneous change of the overall network activity in the minute range, which was not observed in cultures with absent GABAAergic synaptic transmission. The electrically evoked activity of cultured cortical networks, therefore, is governed by at least two potentially interacting mechanisms: A refractory period in the order of a few seconds and a very slow GABA dependent oscillation of the network excitability. PMID:26236196

  17. Interaction of electrically evoked activity with intrinsic dynamics of cultured cortical networks with and without functional fast GABAergic synaptic transmission.

    PubMed

    Baltz, Thomas; Voigt, Thomas

    2015-01-01

    The modulation of neuronal activity by means of electrical stimulation is a successful therapeutic approach for patients suffering from a variety of central nervous system disorders. Prototypic networks formed by cultured cortical neurons represent an important model system to gain general insights in the input-output relationships of neuronal tissue. These networks undergo a multitude of developmental changes during their maturation, such as the excitatory-inhibitory shift of the neurotransmitter GABA. Very few studies have addressed how the output properties to a given stimulus change with ongoing development. Here, we investigate input-output relationships of cultured cortical networks by probing cultures with and without functional GABAAergic synaptic transmission with a set of stimulation paradigms at various stages of maturation. On the cellular level, low stimulation rates (<15 Hz) led to reliable neuronal responses; higher rates were increasingly ineffective. Similarly, on the network level, lowest stimulation rates (<0.1 Hz) lead to maximal output rates at all ages, indicating a network wide refractory period after each stimulus. In cultures aged 3 weeks and older, a gradual recovery of the network excitability within tens of milliseconds was in contrast to an abrupt recovery after about 5 s in cultures with absent GABAAergic synaptic transmission. In these GABA deficient cultures evoked responses were prolonged and had multiple discharges. Furthermore, the network excitability changed periodically, with a very slow spontaneous change of the overall network activity in the minute range, which was not observed in cultures with absent GABAAergic synaptic transmission. The electrically evoked activity of cultured cortical networks, therefore, is governed by at least two potentially interacting mechanisms: A refractory period in the order of a few seconds and a very slow GABA dependent oscillation of the network excitability. PMID:26236196

  18. A cortical network that marks the moment when conscious representations are updated.

    PubMed

    Stöttinger, Elisabeth; Filipowicz, Alex; Valadao, Derick; Culham, Jody C; Goodale, Melvyn A; Anderson, Britt; Danckert, James

    2015-12-01

    In order to survive in a complex, noisy and constantly changing environment we need to categorize the world (e.g., Is this food edible or poisonous?) and we need to update our interpretations when things change. How does our brain update when object categories change from one to the next? We investigated the neural correlates associated with this updating process. We used event-related fMRI while people viewed a sequence of images that morphed from one object (e.g., a plane) to another (e.g., a shark). All participants were naïve as to the identity of the second object. The point at which participants 'saw' the second object was unpredictable and uncontaminated by any dramatic or salient change to the images themselves. The moment when subjective perceptual representations changed activated a circumscribed network including the anterior insula, medial and inferior frontal regions and inferior parietal cortex. In a setting where neither the timing nor nature of the visual transition was predictable, this restricted cortical network signals the time of updating a perceptual representation. The anterior insula and mid-frontal regions (including the ACC) were activated not only at the actual time when change was reported, but also immediately before, suggesting that these areas are also involved in processing alternative options after a mismatch has been detected. PMID:26529489

  19. Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks.

    PubMed

    Schwiedrzik, Caspar M; Ruff, Christian C; Lazar, Andreea; Leitner, Frauke C; Singer, Wolf; Melloni, Lucia

    2014-05-01

    Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain "decide" what to perceive. However, a critical aspect that has not been addressed is that previous experience can exert 2 opposing effects on perception: An attractive effect, sensitizing the brain to perceive the same again (hysteresis), or a repulsive effect, making it more likely to perceive something else (adaptation). We used functional magnetic resonance imaging and modeling to elucidate how the brain entertains these 2 opposing processes, and what determines the direction of such experience-dependent perceptual effects. We found that although affecting our perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while adaptation was confined to early visual areas. This areal and hierarchical segregation may explain how the brain maintains the balance between exploiting redundancies and staying sensitive to new information. We provide a Bayesian model that accounts for the coexistence of hysteresis and adaptation by separating their causes into 2 distinct terms: Hysteresis alters the prior, whereas adaptation changes the sensory evidence (the likelihood function). PMID:23236204

  20. 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

  1. Experimental and Computational Studies of Cortical Neural Network Properties Through Signal Processing

    NASA Astrophysics Data System (ADS)

    Clawson, Wesley Patrick

    Previous studies, both theoretical and experimental, of network level dynamics in the cerebral cortex show evidence for a statistical phenomenon called criticality; a phenomenon originally studied in the context of phase transitions in physical systems and that is associated with favorable information processing in the context of the brain. The focus of this thesis is to expand upon past results with new experimentation and modeling to show a relationship between criticality and the ability to detect and discriminate sensory input. A line of theoretical work predicts maximal sensory discrimination as a functional benefit of criticality, which can then be characterized using mutual information between sensory input, visual stimulus, and neural response,. The primary finding of our experiments in the visual cortex in turtles and neuronal network modeling confirms this theoretical prediction. We show that sensory discrimination is maximized when visual cortex operates near criticality. In addition to presenting this primary finding in detail, this thesis will also address our preliminary results on change-point-detection in experimentally measured cortical dynamics.

  2. Functional Connectivity in Frequency-Tagged Cortical Networks During Active Harm Avoidance

    PubMed Central

    Miskovic, Vladimir; Príncipe, José C.; Keil, Andreas

    2015-01-01

    Abstract Many behavioral and cognitive processes are grounded in widespread and dynamic communication between brain regions. Thus, the quantification of functional connectivity with high temporal resolution is highly desirable for capturing in vivo brain function. However, many of the commonly used measures of functional connectivity capture only linear signal dependence and are based entirely on relatively simple quantitative measures such as mean and variance. In this study, the authors used a recently developed algorithm, the generalized measure of association (GMA), to quantify dynamic changes in cortical connectivity using steady-state visual evoked potentials (ssVEPs) measured in the context of a conditioned behavioral avoidance task. GMA uses a nonparametric estimator of statistical dependence based on ranks that are efficient and capable of providing temporal precision roughly corresponding to the timing of cognitive acts (∼100–200 msec). Participants viewed simple gratings predicting the presence/absence of an aversive loud noise, co-occurring with peripheral cues indicating whether the loud noise could be avoided by means of a key press (active) or not (passive). For active compared with passive trials, heightened connectivity between visual and central areas was observed in time segments preceding and surrounding the avoidance cue. Viewing of the threat stimuli also led to greater initial connectivity between occipital and central regions, followed by heightened local coupling among visual regions surrounding the motor response. Local neural coupling within extended visual regions was sustained throughout major parts of the viewing epoch. These findings are discussed in a framework of flexible synchronization between cortical networks as a function of experience and active sensorimotor coupling. PMID:25557925

  3. Generation and control of cortical gamma: findings from simulation at two scales.

    PubMed

    Wright, J J

    2009-05-01

    A continuum model of electrocortical activity was applied separately at centimetric and macrocolumnar scales, permitting analysis of interaction between scales. State equations included effects of retrograde action potential propagation in dendritic trees, and kinetics of AMPA, GABA and NMDA receptors. Parameter values were provided from independent physiological and anatomical estimates. Realistic field potentials and pulse rates were obtained, including resonances in the alpha/theta and gamma ranges, 1/f(2) background activity, and autonomous gamma activity. Zero-lag synchrony and travelling waves occurred as complementary aspects of cortical transmission, and lead/lag relations between excitatory and inhibitory cell populations varied systematically around transition to autonomous gamma oscillation. Properties of the simulations can account for generation and control of gamma activity. All factors acting on excitatory/inhibitory balance controlled the onset and offset of gamma oscillation. Autonomous gamma was initiated by focal excitation of excitatory cells, and suppressed by laterally spreading trans-cortical excitation, which acted on both excitatory and inhibitory cell populations. Consequently, although spatially extensive non-specific reticular activation tended to suppress autonomous gamma, spatial variation of reticular activation could preferentially select fields of synchrony. PMID:19095406

  4. Spatiotemporal alterations of cortical network activity by selective loss of NOS-expressing interneurons.

    PubMed

    Shlosberg, Dan; Buskila, Yossi; Abu-Ghanem, Yasmin; Amitai, Yael

    2012-01-01

    Deciphering the role of GABAergic neurons in large neuronal networks such as the neocortex forms a particularly complex task as they comprise a highly diverse population. The neuronal isoform of the enzyme nitric oxide synthase (nNOS) is expressed in the neocortex by specific subsets of GABAergic neurons. These neurons can be identified in live brain slices by the nitric oxide (NO) fluorescent indicator diaminofluorescein-2 diacetate (DAF-2DA). However, this indicator was found to be highly toxic to the stained neurons. We used this feature to induce acute phototoxic damage to NO-producing neurons in cortical slices, and measured subsequent alterations in parameters of cellular and network activity. Neocortical slices were briefly incubated in DAF-2DA and then illuminated through the 4× objective. Histochemistry for NADPH-diaphorase (NADPH-d), a marker for nNOS activity, revealed elimination of staining in the illuminated areas following treatment. Whole cell recordings from several neuronal types before, during, and after illumination confirmed the selective damage to non-fast-spiking (FS) interneurons. Treated slices displayed mild disinhibition. The reversal potential of compound synaptic events on pyramidal neurons became more positive, and their decay time constant was elongated, substantiating the removal of an inhibitory conductance. The horizontal decay of local field potentials (LFPs) was significantly reduced at distances of 300-400 μm from the stimulation, but not when inhibition was non-selectively weakened with the GABA(A) blocker picrotoxin. Finally, whereas the depression of LFPs along short trains of 40 Hz stimuli was linearly reduced with distance or initial amplitude in control slices, this ordered relationship was disrupted in DAF-treated slices. These results reveal that NO-producing interneurons in the neocortex convey lateral inhibition to neighboring columns, and shape the spatiotemporal dynamics of the network's activity. PMID:22347168

  5. Framework for Network Co-Simulation

    Energy Science and Technology Software Center (ESTSC)

    2014-01-09

    The Framework for Network Co-Simulation (FNCS) uses a federated approach to integrate simulations which may have differing time scales. Special consideration is given to integration with a communication network simulation such that inter-simulation messages may be optionally routed through and delayed by such a simulation. In addition, FNCS uses novel time synchronization algorithms to accelerate co-simulation including the application of speculative multithreading. FNCS accomplishes all of these improvements with minimal end user intervention. Simulations canmore » be integrated using FNCS while maintaining their original model input files simply by linking with the FNCS library and making appropriate calls into the FNCS API.« less

  6. Computer simulation and mathematical models of the noncentrosomal plant cortical microtubule cytoskeleton.

    PubMed

    Eren, Ezgi Can; Gautam, Natarajan; Dixit, Ram

    2012-03-01

    There is rising interest in modeling the noncentrosomal cortical microtubule cytoskeleton of plant cells, particularly its organization into ordered arrays and the mechanisms that facilitate this organization. In this review, we discuss quantitative models of this highly complex and dynamic structure both at a cellular and molecular level. We report differences in methodologies and assumptions of different models as well as their controversial results. Our review provides insights for future studies to resolve these controversies, in addition to underlining the common results between various models. We also highlight the need to compare the results from simulation and mathematical models with quantitative data from biological experiments in order to test the validity of the models and to further improve them. It is our hope that this review will serve to provide guidelines for how to combine quantitative and experimental techniques to develop higher-level models of the plant cytoskeleton in the future. PMID:22266809

  7. Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates

    PubMed Central

    Magrou, Loïc; Gămănuț, Bianca; Van Essen, David C.; Burkhalter, Andreas; Knoblauch, Kenneth; Toroczkai, Zoltán; Kennedy, Henry

    2016-01-01

    Mammals show a wide range of brain sizes, reflecting adaptation to diverse habitats. Comparing interareal cortical networks across brains of different sizes and mammalian orders provides robust information on evolutionarily preserved features and species-specific processing modalities. However, these networks are spatially embedded, directed, and weighted, making comparisons challenging. Using tract tracing data from macaque and mouse, we show the existence of a general organizational principle based on an exponential distance rule (EDR) and cortical geometry, enabling network comparisons within the same model framework. These comparisons reveal the existence of network invariants between mouse and macaque, exemplified in graph motif profiles and connection similarity indices, but also significant differences, such as fractionally smaller and much weaker long-distance connections in the macaque than in mouse. The latter lends credence to the prediction that long-distance cortico-cortical connections could be very weak in the much-expanded human cortex, implying an increased susceptibility to disconnection syndromes such as Alzheimer disease and schizophrenia. Finally, our data from tracer experiments involving only gray matter connections in the primary visual areas of both species show that an EDR holds at local scales as well (within 1.5 mm), supporting the hypothesis that it is a universally valid property across all scales and, possibly, across the mammalian class. PMID:27441598

  8. Simulating Network Retrieval of Arithmetic Facts.

    ERIC Educational Resources Information Center

    Ashcraft, Mark H.

    This report describes a simulation of adults' retrieval of arithmetic facts from a network-based memory representation. The goals of the simulation project are to: demonstrate in specific form the nature of a spreading activation model of mental arithmetic; account for three important reaction time effects observed in laboratory investigations;…

  9. Buffer Management Simulation in ATM Networks

    NASA Technical Reports Server (NTRS)

    Yaprak, E.; Xiao, Y.; Chronopoulos, A.; Chow, E.; Anneberg, L.

    1998-01-01

    This paper presents a simulation of a new dynamic buffer allocation management scheme in ATM networks. To achieve this objective, an algorithm that detects congestion and updates the dynamic buffer allocation scheme was developed for the OPNET simulation package via the creation of a new ATM module.

  10. Electronic Neural-Network Simulator

    NASA Technical Reports Server (NTRS)

    Moopenn, Alex W.; Thakoor, Anilkumar P.; Lambe, John J.

    1988-01-01

    Experimental circuits faster than simulation programs run on digital computers. Serial shift register routes clock pulses C1 to neurons in sequence. Clock pulses C2 interrogate neurons. Neuron interconnection information stored in simulated synapses. Can be expanded to greater complexity.

  11. Polymer networks and gels: Simulation and theory

    NASA Astrophysics Data System (ADS)

    Kenkare, Nirupama Ramamurthy

    1998-12-01

    The purpose of this research is to understand the molecular origins of the dynamic and swelling properties of polymer networks and gels. Our approach has been to apply computer simulations techniques to off-lattice, near-perfect, trifunctional and tetrafunctional network models. The networks are constructed by endlinking freely-jointed, tangent-hard-sphere chains. Equilibrium discontinuous molecular dynamics techniques are employed to simulate the relaxation of large networks of chain lengths ranging from N = 20 to N = 150 at a packing fraction of 0.43. The simulation trajectories are used to calculate the radius of gyration and end-to-end distance of the network chains, the static structure factor of the crosslinks, the mean-squared displacement of the crosslinks and chain inner segments, the intermediate scattering function of the chains and the elastic modulus of the network. The structure and properties of the networks are shown to depend heavily on the manner in which the network is initially constructed. The dynamics of the network crosslinks and chain inner segments are similar to those of melt chains at short times and show evidence of spatial localization at long times. The results from the elastic moduli and long-time crosslink and chain displacement calculations indicate that entanglement constraints act in conjunction with crosslink constraints to reduce crosslink and chain mobility. The presence of entanglements appears to cause the magnitude of the elastic modulus to be larger than the affine/phantom model predictions. The pressure-volume behavior and the chain configurational properties of deformed networks are investigated over a range of packing fractions. The variation of network pressure with density is found to be similar to that of uncrosslinked chain systems of the same chain length, except at low densities where the network pressures become negative due to elastic effects. We derive a simple, mean-field network equation of state in which the

  12. Endogenous cholinergic tone modulates spontaneous network level neuronal activity in primary cortical cultures grown on multi-electrode arrays

    PubMed Central

    2013-01-01

    Background Cortical cultures grown long-term on multi-electrode arrays (MEAs) are frequently and extensively used as models of cortical networks in studies of neuronal firing activity, neuropharmacology, toxicology and mechanisms underlying synaptic plasticity. However, in contrast to the predominantly asynchronous neuronal firing activity exhibited by intact cortex, electrophysiological activity of mature cortical cultures is dominated by spontaneous epileptiform-like global burst events which hinders their effective use in network-level studies, particularly for neurally-controlled animat (‘artificial animal’) applications. Thus, the identification of culture features that can be exploited to produce neuronal activity more representative of that seen in vivo could increase the utility and relevance of studies that employ these preparations. Acetylcholine has a recognised neuromodulatory role affecting excitability, rhythmicity, plasticity and information flow in vivo although its endogenous production by cortical cultures and subsequent functional influence upon neuronal excitability remains unknown. Results Consequently, using MEA electrophysiological recording supported by immunohistochemical and RT-qPCR methods, we demonstrate for the first time, the presence of intrinsic cholinergic neurons and significant, endogenous cholinergic tone in cortical cultures with a characterisation of the muscarinic and nicotinic components that underlie modulation of spontaneous neuronal activity. We found that tonic muscarinic ACh receptor (mAChR) activation affects global excitability and burst event regularity in a culture age-dependent manner whilst, in contrast, tonic nicotinic ACh receptor (nAChR) activation can modulate burst duration and the proportion of spikes occurring within bursts in a spatio-temporal fashion. Conclusions We suggest that the presence of significant endogenous cholinergic tone in cortical cultures and the comparability of its modulatory effects

  13. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism

    PubMed Central

    Willsey, A. Jeremy; Sanders, Stephan J.; Li, Mingfeng; Dong, Shan; Tebbenkamp, Andrew T.; Muhle, Rebecca A.; Reilly, Steven K.; Lin, Leon; Fertuzinhos, Sofia; Miller, Jeremy A.; Murtha, Michael T.; Bichsel, Candace; Niu, Wei; Cotney, Justin; Ercan-Sencicek, A. Gulhan; Gockley, Jake; Gupta, Abha; Han, Wenqi; He, Xin; Hoffman, Ellen; Klei, Lambertus; Lei, Jing; Liu, Wenzhong; Liu, Li; Lu, Cong; Xu, Xuming; Zhu, Ying; Mane, Shrikant M.; Lein, Edward S.; Wei, Liping; Noonan, James P.; Roeder, Kathryn; Devlin, Bernie; Šestan, Nenad; State, Matthew W.

    2013-01-01

    SUMMARY Autism spectrum disorder (ASD) is a complex developmental syndrome of unknown etiology. Recent studies employing exome- and genome-wide sequencing have identified nine high-confidence ASD (hcASD) genes. Working from the hypothesis that ASD-associated mutations in these biologically pleiotropic genes will disrupt intersecting developmental processes to contribute to a common phenotype, we have attempted to identify time periods, brain regions, and cell types in which these genes converge. We have constructed coexpression networks based on the hcASD “seed” genes, leveraging a rich expression data set encompassing multiple human brain regions across human development and into adulthood. By assessing enrichment of an independent set of probable ASD (pASD) genes, derived from the same sequencing studies, we demonstrate a key point of convergence in midfetal layer 5/6 cortical projection neurons. This approach informs when, where, and in what cell types mutations in these specific genes may be productively studied to clarify ASD pathophysiology. PMID:24267886

  14. Neuronal networks and mediators of cortical neurovascular coupling responses in normal and altered brain states.

    PubMed

    Lecrux, C; Hamel, E

    2016-10-01

    Brain imaging techniques that use vascular signals to map changes in neuronal activity, such as blood oxygenation level-dependent functional magnetic resonance imaging, rely on the spatial and temporal coupling between changes in neurophysiology and haemodynamics, known as 'neurovascular coupling (NVC)'. Accordingly, NVC responses, mapped by changes in brain haemodynamics, have been validated for different stimuli under physiological conditions. In the cerebral cortex, the networks of excitatory pyramidal cells and inhibitory interneurons generating the changes in neural activity and the key mediators that signal to the vascular unit have been identified for some incoming afferent pathways. The neural circuits recruited by whisker glutamatergic-, basal forebrain cholinergic- or locus coeruleus noradrenergic pathway stimulation were found to be highly specific and discriminative, particularly when comparing the two modulatory systems to the sensory response. However, it is largely unknown whether or not NVC is still reliable when brain states are altered or in disease conditions. This lack of knowledge is surprising since brain imaging is broadly used in humans and, ultimately, in conditions that deviate from baseline brain function. Using the whisker-to-barrel pathway as a model of NVC, we can interrogate the reliability of NVC under enhanced cholinergic or noradrenergic modulation of cortical circuits that alters brain states.This article is part of the themed issue 'Interpreting BOLD: a dialogue between cognitive and cellular neuroscience'. PMID:27574304

  15. Highly energized inhibitory interneurons are a central element for information processing in cortical networks

    PubMed Central

    Kann, Oliver; Papageorgiou, Ismini E; Draguhn, Andreas

    2014-01-01

    Gamma oscillations (∼30 to 100 Hz) provide a fundamental mechanism of information processing during sensory perception, motor behavior, and memory formation by coordination of neuronal activity in networks of the hippocampus and neocortex. We review the cellular mechanisms of gamma oscillations about the underlying neuroenergetics, i.e., high oxygen consumption rate and exquisite sensitivity to metabolic stress during hypoxia or poisoning of mitochondrial oxidative phosphorylation. Gamma oscillations emerge from the precise synaptic interactions of excitatory pyramidal cells and inhibitory GABAergic interneurons. In particular, specialized interneurons such as parvalbumin-positive basket cells generate action potentials at high frequency (‘fast-spiking') and synchronize the activity of numerous pyramidal cells by rhythmic inhibition (‘clockwork'). As prerequisites, fast-spiking interneurons have unique electrophysiological properties and particularly high energy utilization, which is reflected in the ultrastructure by enrichment with mitochondria and cytochrome c oxidase, most likely needed for extensive membrane ion transport and γ-aminobutyric acid metabolism. This supports the hypothesis that highly energized fast-spiking interneurons are a central element for cortical information processing and may be critical for cognitive decline when energy supply becomes limited (‘interneuron energy hypothesis'). As a clinical perspective, we discuss the functional consequences of metabolic and oxidative stress in fast-spiking interneurons in aging, ischemia, Alzheimer's disease, and schizophrenia. PMID:24896567

  16. Age-related decline in functional connectivity of the vestibular cortical network.

    PubMed

    Cyran, Carolin Anna Maria; Boegle, Rainer; Stephan, Thomas; Dieterich, Marianne; Glasauer, Stefan

    2016-04-01

    In the elderly, major complaints include dizziness and an increasing number of falls, possibly related to an altered processing of vestibular sensory input. In this study, we therefore investigate age-related changes induced by processing of vestibular sensory stimulation. While previous functional imaging studies of healthy aging have investigated brain function during task performance or at rest, we used galvanic vestibular stimulation during functional MRI in a task-free sensory stimulation paradigm to study the effect of healthy aging on central vestibular processing, which might only become apparent during stimulation processing. Since aging may affect signatures of brain function beyond the BOLD-signal amplitude-such as functional connectivity or temporal signal variability-we employed independent component analysis and partial least squares analysis of temporal signal variability. We tested for age-associated changes unrelated to vestibular processing, using a motor paradigm, voxel-based morphometry and diffusion tensor imaging. This allows us to control for general age-related modifications, possibly originating from vascular, atrophic or structural connectivity changes. Age-correlated decreases of functional connectivity and increases of BOLD-signal variability were associated with multisensory vestibular networks. In contrast, no age-related functional connectivity changes were detected in somatosensory networks or during the motor paradigm. The functional connectivity decrease was not due to structural changes but to a decrease in response amplitude. In synopsis, our data suggest that both the age-dependent functional connectivity decrease and the variability increase may be due to deteriorating reciprocal cortico-cortical inhibition with age and related to multimodal vestibular integration of sensory inputs. PMID:25567421

  17. Information-geometric measure of 3-neuron firing patterns characterizes scale-dependence in cortical networks

    PubMed Central

    Ohiorhenuan, Ifije E.; Victor, Jonathan D.

    2010-01-01

    To understand the functional connectivity of neural networks, it is important to develop simple and incisive descriptors of multineuronal firing patterns. Analysis at the pairwise level has proven to be a powerful approach in the retina, but it may not suffice to understand complex cortical networks. Here we address the problem of describing interactions among triplets of neurons. We consider two approaches: an information-geometric measure (Amari, 2001), which we call the “strain,” and the Kullback-Leibler divergence. While both approaches can be used to assess whether firing patterns differ from those predicted by a pairwise maximum-entropy model, the strain provides additional information. Specifically, when the observed firing patterns differ from those predicted by a pairwise model, the strain indicates the nature of this difference – whether there is an excess or a deficit of synchrony – while the Kullback-Leibler divergence only indicates the magnitude of the difference. We show that the strain has technical advantages, including ease of calculation of confidence bounds and bias, and robustness to the kinds of spike-sorting errors associated with tetrode recordings. We demonstrate the biological importance of these points via an analysis of multineuronal firing patterns in primary visual cortex. There is a striking scale-dependent behavior of triplet firing patterns: deviations from the pairwise model are substantial when the neurons are within 300 microns of each other, and negligible when they are at a distance of > 600 microns. The strain identifies a consistent pattern to these interactions: when triplet interactions are present, the strain is nearly always negative, indicating that there is less synchrony than would be expected from the pairwise interactions alone. PMID:20635129

  18. Simulation of Ames Backbone Network

    NASA Technical Reports Server (NTRS)

    Shahnasser, Hamid

    1998-01-01

    The networking demands of Ames Research Center are dramatically increasing. More and more workstations are requested to run video and audio applications on the network. These applications require a much greater bandwidth than data applications. The existing ARCLAN 2000 network bandwidth is insufficient, due to the use of FDDI as its backbone, for accommodating video applications. Operating at a maximum of 100 Mbps, FDDI can handle only a few workstations running multimedia applications. The ideal solution is to replace the current ARCLAN 2000 FDDI backbone with an ATM backbone. ATM has the capability to handle the increasing traffic loads on the ARCLAN 2000 that results from these new applications. As it can be seen from Figure 1, ARCLAN 2000 have a total of 32 routers (5 being core routers) each connected to the FDDI backbone via a 100 Mbps link. This network serves 34 different locations by using 34 hubs that are connected to secondary routers. End users are connected to the secondary routers with 10 Mbps links.

  19. Snazer: the simulations and networks analyzer

    PubMed Central

    2010-01-01

    Background Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them. From a pure dynamical perspective, simulation represents a relevant way-out. Many simulator tools capitalized on the "noisy" behavior of some systems and used formal models to represent cellular activities as temporal trajectories. Statistical methods have been applied to a fairly large number of replicated trajectories in order to infer knowledge. A tool which both graphically manipulates reactive models and deals with sets of simulation time-course data by aggregation, interpretation and statistical analysis is missing and could add value to simulators. Results We designed and implemented Snazer, the simulations and networks analyzer. Its goal is to aid the processes of visualizing and manipulating reactive models, as well as to share and interpret time-course data produced by stochastic simulators or by any other means. Conclusions Snazer is a solid prototype that integrates biological network and simulation time-course data analysis techniques. PMID:20056001

  20. Micro-electrode array recordings reveal reductions in both excitation and inhibition in cultured cortical neuron networks lacking Shank3.

    PubMed

    Lu, C; Chen, Q; Zhou, T; Bozic, D; Fu, Z; Pan, J Q; Feng, G

    2016-02-01

    Numerous risk genes have recently been implicated in susceptibility to autism and schizophrenia. Translating such genetic findings into disease-relevant neurobiological mechanisms is challenging due to the lack of throughput assays that can be used to assess their functions on an appropriate scale. To address this issue, we explored the feasibility of using a micro-electrode array (MEA) as a potentially scalable assay to identify the electrical network phenotypes associated with risk genes. We first characterized local and global network firing in cortical neurons with MEAs, and then developed methods to analyze the alternation between the network active period (NAP) and the network inactive period (NIP), each of which lasts tens of seconds. We then evaluated the electric phenotypes of neurons derived from Shank3 knockout (KO) mice. Cortical neurons cultured on MEAs displayed a rich repertoire of spontaneous firing, and Shank3 deletion led to reduced firing activity. Enhancing excitation with CX546 rescued the deficit in the spike rate in the Shank3 KO network. In addition, the Shank3 KO network produced a shorter NIP, and this altered network firing pattern was normalized by clonazepam, a positive modulator of the GABAA receptor. MEA recordings revealed electric phenotypes that displayed altered excitation and inhibition in the network lacking Shank3. Thus, our study highlights MEAs as an experimental framework for measuring multiple robust neurobiological end points in dynamic networks and as an assay system that could be used to identify electric phenotypes in cultured neuronal networks and to analyze additional risk genes identified in psychiatric genetics. PMID:26598066

  1. Default network connectivity decodes brain states with simulated microgravity.

    PubMed

    Zeng, Ling-Li; Liao, Yang; Zhou, Zongtan; Shen, Hui; Liu, Yadong; Liu, Xufeng; Hu, Dewen

    2016-04-01

    With great progress of space navigation technology, it becomes possible to travel beyond Earth's gravity. So far, it remains unclear whether the human brain can function normally within an environment of microgravity and confinement. Particularly, it is a challenge to figure out some neuroimaging-based markers for rapid screening diagnosis of disrupted brain function in microgravity environment. In this study, a 7-day -6° head down tilt bed rest experiment was used to simulate the microgravity, and twenty healthy male participants underwent resting-state functional magnetic resonance imaging scans at baseline and after the simulated microgravity experiment. We used a multivariate pattern analysis approach to distinguish the brain states with simulated microgravity from normal gravity based on the functional connectivity within the default network, resulting in an accuracy of no less than 85 % via cross-validation. Moreover, most discriminative functional connections were mainly located between the limbic system and cortical areas and were enhanced after simulated microgravity, implying a self-adaption or compensatory enhancement to fulfill the need of complex demand in spatial navigation and motor control functions in microgravity environment. Overall, the findings suggest that the brain states in microgravity are likely different from those in normal gravity and that brain connectome could act as a biomarker to indicate the brain state in microgravity. PMID:27066149

  2. Studies on deformational behavior of miniaturized cortical bone specimens using finite element simulation

    NASA Astrophysics Data System (ADS)

    Sharma, N. K.; Sehgal, D. K.; Pandey, R. K.

    2014-10-01

    Miniature specimen test technique provides a way of obtaining mechanical properties of components or structures while consuming an amount of material that is very small relative to that required for full-size conventional specimen. This technique is very helpful especially in the case of bone mechanics as bone properties are heterogeneous and anisotropic in nature and it is difficult to obtain standard size of specimen for mechanical testing. In the present study an effort is made to simulate punch specimen setup using mechanical properties of the cortical femur bone material for miniature specimen while considering its nature to be transversely isotropic. The samples were taken in both longitudinal as well as transverse direction. The various load displacement curves and contour profiles obtained for different thicknesses of the miniature specimen using finite element simulation were compared with each other. The values of load at breakaway point were obtained for different cases of miniature specimen. It is anticipated that these values can be further used to evaluate yield strength of the bone material in different cases.

  3. Electrophysiological Potentials Reveal Cortical Mechanisms for Mental Imagery, Mental Simulation, and Grounded (Embodied) Cognition

    PubMed Central

    Schendan, Haline E.; Ganis, Giorgio

    2012-01-01

    Grounded cognition theory proposes that cognition, including meaning, is grounded in sensorimotor processing. The mechanism for grounding cognition is mental simulation, which is a type of mental imagery that re-enacts modal processing. To reveal top-down, cortical mechanisms for mental simulation of shape, event-related potentials were recorded to face and object pictures preceded by mental imagery. Mental imagery of the identical face or object picture (congruous condition) facilitated not only categorical perception (VPP/N170) but also later visual knowledge [N3(00) complex] and linguistic knowledge (N400) for faces more than objects, and strategic semantic analysis (late positive complex) between 200 and 700 ms. The later effects resembled semantic congruity effects with pictures. Mental imagery also facilitated category decisions, as a P3 peaked earlier for congruous than incongruous (other category) pictures, resembling the case when identical pictures repeat immediately. Thus mental imagery mimics semantic congruity and immediate repetition priming processes with pictures. Perception control results showed the opposite for faces and were in the same direction for objects: Perceptual repetition adapts (and so impairs) processing of perceived faces from categorical perception onward, but primes processing of objects during categorical perception, visual knowledge processes, and strategic semantic analysis. For both imagery and perception, differences between faces and objects support domain-specificity and indicate that cognition is grounded in modal processing. Altogether, this direct neural evidence reveals that top-down processes of mental imagery sustain an imagistic representation that mimics perception well enough to prime subsequent perception and cognition. Findings also suggest that automatic mental simulation of the visual shape of faces and objects operates between 200 and 400 ms, and strategic mental simulation operates between 400 and 700

  4. Neural network computer simulation of medical aerosols.

    PubMed

    Richardson, C J; Barlow, D J

    1996-06-01

    Preliminary investigations have been conducted to assess the potential for using artificial neural networks to simulate aerosol behaviour, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems. Details are presented of the general purpose software developed for these tasks; it implements a feed-forward back-propagation algorithm with weight decay and connection pruning, the user having complete run-time control of the network architecture and mode of training. A series of exploratory investigations is then reported in which different network structures and training strategies are assessed in terms of their ability to simulate known patterns of fluid flow in simple model systems. The first of these involves simulations of cellular automata-generated data for fluid flow through a partially obstructed two-dimensional pipe. The artificial neural networks are shown to be highly successful in simulating the behaviour of this simple linear system, but with important provisos relating to the information content of the training data and the criteria used to judge when the network is properly trained. A second set of investigations is then reported in which similar networks are used to simulate patterns of fluid flow through aerosol generation devices, using training data furnished through rigorous computational fluid dynamics modelling. These more complex three-dimensional systems are modelled with equal success. It is concluded that carefully tailored, well trained networks could provide valuable tools not just for predicting but also for analysing the spatial dynamics of pharmaceutical aerosols. PMID:8832491

  5. Simulating Operation of a Complex Sensor Network

    NASA Technical Reports Server (NTRS)

    Jennings, Esther; Clare, Loren; Woo, Simon

    2008-01-01

    Simulation Tool for ASCTA Microsensor Network Architecture (STAMiNA) ["ASCTA" denotes the Advanced Sensors Collaborative Technology Alliance.] is a computer program for evaluating conceptual sensor networks deployed over terrain to provide military situational awareness. This or a similar program is needed because of the complexity of interactions among such diverse phenomena as sensing and communication portions of a network, deployment of sensor nodes, effects of terrain, data-fusion algorithms, and threat characteristics. STAMiNA is built upon a commercial network-simulator engine, with extensions to include both sensing and communication models in a discrete-event simulation environment. Users can define (1) a mission environment, including terrain features; (2) objects to be sensed; (3) placements and modalities of sensors, abilities of sensors to sense objects of various types, and sensor false alarm rates; (4) trajectories of threatening objects; (5) means of dissemination and fusion of data; and (6) various network configurations. By use of STAMiNA, one can simulate detection of targets through sensing, dissemination of information by various wireless communication subsystems under various scenarios, and fusion of information, incorporating such metrics as target-detection probabilities, false-alarm rates, and communication loads, and capturing effects of terrain and threat.

  6. CRF Network Simulations for the South

    NASA Technical Reports Server (NTRS)

    Titov, Oleg; Behrend, Dirk; Shu, Fengchun; MacMillan, Dan; Fey, Alan

    2010-01-01

    In order to monitor and improve the CRF in both the Southern Hemisphere and along the ecliptic, we perform various simulations using station networks based mostly on the Australian AuScope network, New Zealand s Warkworth antenna, and several Chinese antennas. The effect of other stations such as HartRAO and Kokee Park to enhance the East-West baseline coverage is also considered. It is anticipated that the simulation results will help IVS to decide on the composition of the CRF sessions of the IVS to be run from 2011 onward.

  7. Distinct Spatiotemporal Activation Patterns of the Perirhinal-Entorhinal Network in Response to Cortical and Amygdala Input

    PubMed Central

    Willems, Janske G. P.; Wadman, Wytse J.; Cappaert, Natalie L. M.

    2016-01-01

    The perirhinal (PER) and entorhinal cortex (EC) receive input from the agranular insular cortex (AiP) and the subcortical lateral amygdala (LA) and the main output area is the hippocampus. Information transfer through the PER/EC network however, is not always guaranteed. It is hypothesized that this network actively regulates the (sub)cortical activity transfer to the hippocampal network and that the inhibitory system is involved in this function. This study determined the recruitment by the AiP and LA afferents in PER/EC network with the use of voltage sensitive dye (VSD) imaging in horizontal mouse brain slices. Electrical stimulation (500 μA) of the AiP induced activity that gradually propagated predominantly in the rostro-caudal direction: from the PER to the lateral EC (LEC). In the presence of 1 μM of the competitive γ-aminobutyric acid (GABAA) receptor antagonist bicuculline, AiP stimulation recruited the medial EC (MEC) as well. In contrast, LA stimulation (500 μA) only induced activity in the deep layers of the PER. In the presence of bicuculline, the initial population activity in the PER propagated further towards the superficial layers and the EC after a delay. The latency of evoked responses decreased with increasing stimulus intensities (50–500 μA) for both the AiP and LA stimuli. The stimulation threshold for evoking responses in the PER/EC network was higher for the LA than for the AiP. This study showed that the extent of the PER/EC network activation depends on release of inhibition. When GABAA dependent inhibition is reduced, both the AiP and the LA activate spatially overlapping regions, although in a distinct spatiotemporal fashion. It is therefore hypothesized that the inhibitory network regulates excitatory activity from both cortical and subcortical areas that has to be transmitted through the PER/EC network. PMID:27378860

  8. Stochastic simulation of karst conduit networks

    NASA Astrophysics Data System (ADS)

    Pardo-Igúzquiza, Eulogio; Dowd, Peter A.; Xu, Chaoshui; Durán-Valsero, Juan José

    2012-01-01

    Karst aquifers have very high spatial heterogeneity. Essentially, they comprise a system of pipes (i.e., the network of conduits) superimposed on rock porosity and on a network of stratigraphic surfaces and fractures. This heterogeneity strongly influences the hydraulic behavior of the karst and it must be reproduced in any realistic numerical model of the karst system that is used as input to flow and transport modeling. However, the directly observed karst conduits are only a small part of the complete karst conduit system and knowledge of the complete conduit geometry and topology remains spatially limited and uncertain. Thus, there is a special interest in the stochastic simulation of networks of conduits that can be combined with fracture and rock porosity models to provide a realistic numerical model of the karst system. Furthermore, the simulated model may be of interest per se and other uses could be envisaged. The purpose of this paper is to present an efficient method for conditional and non-conditional stochastic simulation of karst conduit networks. The method comprises two stages: generation of conduit geometry and generation of topology. The approach adopted is a combination of a resampling method for generating conduit geometries from templates and a modified diffusion-limited aggregation method for generating the network topology. The authors show that the 3D karst conduit networks generated by the proposed method are statistically similar to observed karst conduit networks or to a hypothesized network model. The statistical similarity is in the sense of reproducing the tortuosity index of conduits, the fractal dimension of the network, the direction rose of directions, the Z-histogram and Ripley's K-function of the bifurcation points (which differs from a random allocation of those bifurcation points). The proposed method (1) is very flexible, (2) incorporates any experimental data (conditioning information) and (3) can easily be modified when

  9. Simulating fish assemblages in riverine networks

    EPA Science Inventory

    We describe a modeling approach for simulating assemblages of fish in riverine landscapes. The approach allows a user to determine the grain and extent of river networks within which fish populations reproduce, move, and survive in response to both environmental drivers and assem...

  10. 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.

  11. LANES - LOCAL AREA NETWORK EXTENSIBLE SIMULATOR

    NASA Technical Reports Server (NTRS)

    Gibson, J.

    1994-01-01

    The Local Area Network Extensible Simulator (LANES) provides a method for simulating the performance of high speed local area network (LAN) technology. LANES was developed as a design and analysis tool for networking on board the Space Station. The load, network, link and physical layers of a layered network architecture are all modeled. LANES models to different lower-layer protocols, the Fiber Distributed Data Interface (FDDI) and the Star*Bus. The load and network layers are included in the model as a means of introducing upper-layer processing delays associated with message transmission; they do not model any particular protocols. FDDI is an American National Standard and an International Organization for Standardization (ISO) draft standard for a 100 megabit-per-second fiber-optic token ring. Specifications for the LANES model of FDDI are taken from the Draft Proposed American National Standard FDDI Token Ring Media Access Control (MAC), document number X3T9.5/83-16 Rev. 10, February 28, 1986. This is a mature document describing the FDDI media-access-control protocol. Star*Bus, also known as the Fiber Optic Demonstration System, is a protocol for a 100 megabit-per-second fiber-optic star-topology LAN. This protocol, along with a hardware prototype, was developed by Sperry Corporation under contract to NASA Goddard Space Flight Center as a candidate LAN protocol for the Space Station. LANES can be used to analyze performance of a networking system based on either FDDI or Star*Bus under a variety of loading conditions. Delays due to upper-layer processing can easily be nullified, allowing analysis of FDDI or Star*Bus as stand-alone protocols. LANES is a parameter-driven simulation; it provides considerable flexibility in specifying both protocol an run-time parameters. Code has been optimized for fast execution and detailed tracing facilities have been included. LANES was written in FORTRAN 77 for implementation on a DEC VAX under VMS 4.6. It consists of two

  12. Cortical imaging on a head template: a simulation study using a resistor mesh model (RMM).

    PubMed

    Chauveau, Nicolas; Franceries, Xavier; Aubry, Florent; Celsis, Pierre; Rigaud, Bernard

    2008-09-01

    The T1 head template model used in Statistical Parametric Mapping Version 2000 (SPM2), was segmented into five layers (scalp, skull, CSF, grey and white matter) and implemented in 2 mm voxels. We designed a resistor mesh model (RMM), based on the finite volume method (FVM) to simulate the electrical properties of this head model along the three axes for each voxel. Then, we introduced four dipoles of high eccentricity (about 0.8) in this RMM, separately and simultaneously, to compute the potentials for two sets of conductivities. We used the direct cortical imaging technique (CIT) to recover the simulated dipoles, using 60 or 107 electrodes and with or without addition of Gaussian white noise (GWN). The use of realistic conductivities gave better CIT results than standard conductivities, lowering the blurring effect on scalp potentials and displaying more accurate position areas when CIT was applied to single dipoles. Simultaneous dipoles were less accurately localized, but good qualitative and stable quantitative results were obtained up to 5% noise level for 107 electrodes and up to 10% noise level for 60 electrodes, showing that a compromise must be found to optimize both the number of electrodes and the noise level. With the RMM defined in 2 mm voxels, the standard 128-electrode cap and 5% noise appears to be the upper limit providing reliable source positions when direct CIT is used. The admittance matrix defining the RMM is easy to modify so as to adapt to different conductivities. The next step will be the adaptation of individual real head T2 images to the RMM template and the introduction of anisotropy using diffusion imaging (DI). PMID:18629625

  13. Simulating Autonomous Telecommunication Networks for Space Exploration

    NASA Technical Reports Server (NTRS)

    Segui, John S.; Jennings, Esther H.

    2008-01-01

    Currently, most interplanetary telecommunication systems require human intervention for command and control. However, considering the range from near Earth to deep space missions, combined with the increase in the number of nodes and advancements in processing capabilities, the benefits from communication autonomy will be immense. Likewise, greater mission science autonomy brings the need for unscheduled, unpredictable communication and network routing. While the terrestrial Internet protocols are highly developed their suitability for space exploration has been questioned. JPL has developed the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) tool to help characterize network designs and protocols. The results will allow future mission planners to better understand the trade offs of communication protocols. This paper discusses various issues with interplanetary network and simulation results of interplanetary networking protocols.

  14. The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

    PubMed Central

    Zylberberg, Ariel; Fernández Slezak, Diego; Roelfsema, Pieter R.; Dehaene, Stanislas; Sigman, Mariano

    2010-01-01

    The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates. PMID:20442869

  15. The brain's router: a cortical network model of serial processing in the primate brain.

    PubMed

    Zylberberg, Ariel; Fernández Slezak, Diego; Roelfsema, Pieter R; Dehaene, Stanislas; Sigman, Mariano

    2010-04-01

    The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100-500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a "router" network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates. PMID:20442869

  16. Integrating botnet simulations with network centric warfare simulations

    NASA Astrophysics Data System (ADS)

    Stytz, Martin R.; Banks, Sheila B.

    2010-04-01

    "Botnets," or "bot armies," are large groups of remotely controlled malicious software designed and operated in order to conduct attacks against government and civilian targets. Bot armies are one of the most serious security threats to networks and computer systems in operation today. Botnets are remotely operated by botmasters who can launch large-scale malicious network activity. While bot army activity has, to date, been largely limited to fraud, blackmail, and other criminal activity, their potential for causing large-scale damage to the entire internet and launching large-scale, coordinated attacks on government computers, networks, and data gathering operations has been underestimated. This paper will not discuss how to build bots but instead discuss ways to use simulation to address the threats they pose. This paper suggests means for addressing the need to provide botnet defense training based upon existing simulation environments and discusses the capabilities needed for training systems for botnet activities. In this paper we discuss botnet technologies and review the capabilities that underlie this threat to network, information, and computer security. The second section of the paper contains background information about bot armies and their foundational technologies. The third section contains a discussion of the techniques we developed for estimating botnet bandwidth consumption and our approach for simulating botnet activities. The fourth section contains a summary and suggestions for additional research.

  17. Cortical and subcortical contributions to sequence retrieval: Schematic coding of temporal context in the neocortical recollection network.

    PubMed

    Hsieh, Liang-Tien; Ranganath, Charan

    2015-11-01

    Episodic memory entails the ability to remember what happened when. Although the available evidence indicates that the hippocampus plays a role in structuring serial order information during retrieval of event sequences, information processed in the hippocampus must be conveyed to other cortical and subcortical areas in order to guide behavior. However, the extent to which other brain regions contribute to the temporal organization of episodic memory remains unclear. Here, we examined multivoxel activity pattern changes during retrieval of learned and random object sequences, focusing on a neocortical "core recollection network" that includes the medial prefrontal cortex, retrosplenial cortex, and angular gyrus, as well as on striatal areas including the caudate nucleus and putamen that have been implicated in processing of sequence information. The results demonstrate that regions of the core recollection network carry information about temporal positions within object sequences, irrespective of object information. This schematic coding of temporal information is in contrast to the putamen, which carried information specific to objects in learned sequences, and the caudate, which carried information about objects, irrespective of sequence context. Our results suggest a role for the cortical recollection network in the representation of temporal structure of events during episodic retrieval, and highlight the possible mechanisms by which the striatal areas may contribute to this process. More broadly, the results indicate that temporal sequence retrieval is a useful paradigm for dissecting the contributions of specific brain regions to episodic memory. PMID:26209802

  18. Enhanced GABAergic network and receptor function in pediatric cortical dysplasia Type IIB compared with Tuberous Sclerosis Complex.

    PubMed

    Cepeda, Carlos; André, Véronique M; Hauptman, Jason S; Yamazaki, Irene; Huynh, My N; Chang, Julia W; Chen, Jane Y; Fisher, Robin S; Vinters, Harry V; Levine, Michael S; Mathern, Gary W

    2012-01-01

    Tuberous Sclerosis Complex (TSC) and cortical dysplasia Type IIB (CDIIB) share histopathologic features that suggest similar epileptogenic mechanisms. This study compared the morphological and electrophysiological properties of cortical cells in tissue from pediatric TSC (n=20) and CDIIB (n=20) patients using whole-cell patch clamp recordings and biocytin staining. Cell types were normal-appearing and dysmorphic-cytomegalic pyramidal neurons, interneurons, and giant/balloon cells, including intermediate neuronal-glial cells. In the cortical mantle, giant/balloon cells occurred more frequently in TSC than in CDIIB cases, whereas cytomegalic pyramidal neurons were found more frequently in CDIIB. Cell morphology and membrane properties were similar in TSC and CDIIB cases. Except for giant/balloon and intermediate cells, all neuronal cell types fired action potentials and displayed spontaneous postsynaptic currents. However, the frequency of spontaneous glutamatergic postsynaptic currents in normal pyramidal neurons and interneurons was significantly lower in CDIIB compared with TSC cases and the GABAergic activity was higher in all neuronal cell types in CDIIB. Further, acutely dissociated pyramidal neurons displayed higher sensitivity to exogenous application of GABA in CDIIB compared with TSC cases. These results indicate that, in spite of similar histopathologic features and basic cell membrane properties, TSC and CDIIB display differences in the topography of abnormal cells, excitatory and inhibitory synaptic network properties, and GABA(A) receptor sensitivity. These differences support the notion that the mechanisms of epileptogenesis could differ in patients with TSC and CDIIB. Consequently, pharmacologic therapies should take these findings into consideration. PMID:21889982

  19. The cortical cytoskeletal network and cell-wall dynamics in the unicellular charophycean green alga Penium margaritaceum

    PubMed Central

    Ochs, Julie; LaRue, Therese; Tinaz, Berke; Yongue, Camille; Domozych, David S.

    2014-01-01

    Background and Aims Penium margaritaceum is a unicellular charophycean green alga with a unique bi-directional polar expansion mechanism that occurs at the central isthmus zone prior to cell division. This entails the focused deposition of cell-wall polymers coordinated by the activities of components of the endomembrane system and cytoskeletal networks. The goal of this study was to elucidate the structural organization of the cortical cytoskeletal network during the cell cycle and identify its specific functional roles during key cell-wall developmental events: pre-division expansion and cell division. Methods Microtubules and actin filaments were labelled during various cell cycle phases with an anti-tubulin antibody and rhodamine phalloidin, respectively. Chemically induced disruption of the cytoskeleton was used to elucidate specific functional roles of microtubules and actin during cell expansion and division. Correlation of cytoskeletal dynamics with cell-wall development included live cell labelling with wall polymer-specific antibodies and electron microscopy. Key Results The cortical cytoplasm of Penium is highlighted by a band of microtubules found at the cell isthmus, i.e. the site of pre-division wall expansion. This band, along with an associated, transient band of actin filaments, probably acts to direct the deposition of new wall material and to mark the plane of the future cell division. Two additional bands of microtubules, which we identify as satellite bands, arise from the isthmus microtubular band at the onset of expansion and displace toward the poles during expansion, ultimately marking the isthmus of future daughter cells. Treatment with microtubule and actin perturbation agents reversibly stops cell division. Conclusions The cortical cytoplasm of Penium contains distinct bands of microtubules and actin filaments that persist through the cell cycle. One of these bands, termed the isthmus microtubule band, or IMB, marks the site of both pre

  20. Modeling and Performance Simulation of the Mass Storage Network Environment

    NASA Technical Reports Server (NTRS)

    Kim, Chan M.; Sang, Janche

    2000-01-01

    This paper describes the application of modeling and simulation in evaluating and predicting the performance of the mass storage network environment. Network traffic is generated to mimic the realistic pattern of file transfer, electronic mail, and web browsing. The behavior and performance of the mass storage network and a typical client-server Local Area Network (LAN) are investigated by modeling and simulation. Performance characteristics in throughput and delay demonstrate the important role of modeling and simulation in network engineering and capacity planning.

  1. Cortical networks for rotational uncertainty effect in mental rotation task by partial directed coherence analysis of EEG.

    PubMed

    Yan, Jing; Guo, Xiaoli; Sun, Junfeng; Tong, Shanbao

    2011-01-01

    Partial directed coherence (PDC) as a frequency-domain representation of Granger casuality (GC) could detect both strength and direction of cortical interactions by multivariate autoregressive (MVAR) model of electroencephalography (EEG). In the present study, we investigate the underlying neural networks mechanisms of "rotational uncertainty effect" during mental rotation (MR) task by PDC analysis of multichannel EEG signals before and after the visual stimuli presented, we found that (i) temporally the "rotational uncertainty effect" involved an activated network before the visual stimuli presented, which could also affect the cognitive process of MR later; (ii) the causality functional connectivity network indicated that the bi-directional frontal [symbol see text] parietal networks played critical roles in maintaining the readiness during the MR task. These findings suggest that functional networks of un-cued preparation before visual stimuli presented are worth to be paid more attention. And these networks provide crucial casuality information to understand the neural mechanism for "rotational uncertainty effect" in MR task. PMID:22254583

  2. Simulation of integrated optical network (IPON) properties

    NASA Astrophysics Data System (ADS)

    Siska, Petr; Koudelka, Petr; Latal, Jan; Vitasek, Jan; Kepak, Stanislav; Vašinek, Vladimír.

    2014-09-01

    There is an increasing pressure nowadays on the efficient use of existing ICT infrastructure in order to provide the latest services for corporate customers or end users. With the increase in number of services, requirements for optical networks of all hierarchies are increasing as well. This increase in the requirements, however, involves risks which must be faced by Internet service providers. These include the maximum use of spectral range, bandwidth and reachable distance, suppression of dispersion effect, route planning efficiency, CAPEX and OPEX costs management, or successful combination of technologies of deployed networks. The aim of this article is to present the problems associated with interconnection of WDM-PON and ver.2 EPON (IEEE 802.3ah standard). The entire simulation is based on real parameters, which were provided by the manufacturers of the technologies and then measured in the laboratory. Then we were able to perform simulations based on more realistic features of these technologies.

  3. Resilience Simulation for Water, Power & Road Networks

    NASA Astrophysics Data System (ADS)

    Clark, S. S.; Seager, T. P.; Chester, M.; Eisenberg, D. A.; Sweet, D.; Linkov, I.

    2014-12-01

    The increasing frequency, scale, and damages associated with recent catastrophic events has called for a shift in focus from evading losses through risk analysis to improving threat preparation, planning, absorption, recovery, and adaptation through resilience. However, neither underlying theory nor analytic tools have kept pace with resilience rhetoric. As a consequence, current approaches to engineering resilience analysis often conflate resilience and robustness or collapse into a deeper commitment to the risk analytic paradigm proven problematic in the first place. This research seeks a generalizable understanding of resilience that is applicable in multiple disciplinary contexts. We adopt a unique investigative perspective by coupling social and technical analysis with human subjects research to discover the adaptive actions, ideas and decisions that contribute to resilience in three socio-technical infrastructure systems: electric power, water, and roadways. Our research integrates physical models representing network objects with examination of the knowledge systems and social interactions revealed by human subjects making decisions in a simulated crisis environment. To ensure a diversity of contexts, we model electric power, water, roadway and knowledge networks for Phoenix AZ and Indianapolis IN. We synthesize this in a new computer-based Resilient Infrastructure Simulation Environment (RISE) to allow individuals, groups (including students) and experts to test different network design configurations and crisis response approaches. By observing simulated failures and best performances, we expect a generalizable understanding of resilience may emerge that yields a measureable understanding of the sensing, anticipating, adapting, and learning processes that are essential to resilient organizations.

  4. Simulation of large systems with neural networks

    SciTech Connect

    Paez, T.L.

    1994-09-01

    Artificial neural networks (ANNs) have been shown capable of simulating the behavior of complex, nonlinear, systems, including structural systems. Under certain circumstances, it is desirable to simulate structures that are analyzed with the finite element method. For example, when we perform a probabilistic analysis with the Monte Carlo method, we usually perform numerous (hundreds or thousands of) repetitions of a response simulation with different input and system parameters to estimate the chance of specific response behaviors. In such applications, efficiency in computation of response is critical, and response simulation with ANNs can be valuable. However, finite element analyses of complex systems involve the use of models with tens or hundreds of thousands of degrees of freedom, and ANNs are practically limited to simulations that involve far fewer variables. This paper develops a technique for reducing the amount of information required to characterize the response of a general structure. We show how the reduced information can be used to train a recurrent ANN. Then the trained ANN can be used to simulate the reduced behavior of the original system, and the reduction transformation can be inverted to provide a simulation of the original system. A numerical example is presented.

  5. Modeling of femoral neck cortical bone for the numerical simulation of ultrasound propagation.

    PubMed

    Grimal, Quentin; Rohrbach, Daniel; Grondin, Julien; Barkmann, Reinhard; Glüer, Claus-C; Raum, Kay; Laugier, Pascal

    2014-05-01

    Quantitative ultrasound assessment of the cortical compartment of the femur neck (FN) is investigated with the goal of achieving enhanced fracture risk prediction. Measurements at the FN are influenced by bone size, shape and material properties. The work described here was aimed at determining which FN material properties have a significant impact on ultrasound propagation around 0.5 MHz and assessing the relevancy of different models. A methodology for the modeling of ultrasound propagation in the FN, with a focus on the modeling of bone elastic properties based on scanning acoustic microscopy data, is introduced. It is found that the first-arriving ultrasound signal measured in through-transmission at the FN is not influenced by trabecular bone properties or by the heterogeneities of the cortical bone mineralized matrix. In contrast, the signal is sensitive to variations in cortical porosity, which can, to a certain extent, be accounted for by effective properties calculated with the Mori-Tanaka method. PMID:24486239

  6. Behavioral training reverses global cortical network dysfunction induced by perinatal antidepressant exposure.

    PubMed

    Zhou, Xiaoming; Lu, Jordan Y-F; Darling, Ryan D; Simpson, Kimberly L; Zhu, Xiaoqing; Wang, Fang; Yu, Liping; Sun, Xinde; Merzenich, Michael M; Lin, Rick C S

    2015-02-17

    Abnormal cortical circuitry and function as well as distortions in the modulatory neurological processes controlling cortical plasticity have been argued to underlie the origin of autism. Here, we chemically distorted those processes using an antidepressant drug-exposure model to generate developmental neurological distortions like those characteristics expressed in autism, and then intensively trained altered young rodents to evaluate the potential for neuroplasticity-driven renormalization. We found that young rats that were injected s.c. with the antidepressant citalopram from postnatal d 1-10 displayed impaired neuronal repetition-rate following capacity in the primary auditory cortex (A1). With a focus on recovering grossly degraded auditory system processing in this model, we showed that targeted temporal processing deficits induced by early-life antidepressant exposure within the A1 were almost completely reversed through implementation of a simple behavioral training strategy (i.e., a modified go/no-go repetition-rate discrimination task). Degraded parvalbumin inhibitory GABAergic neurons and the fast inhibitory actions that they control were also renormalized by training. Importantly, antidepressant-induced degradation of serotonergic and dopaminergic neuromodulatory systems regulating cortical neuroplasticity was sharply reversed. These findings bear important implications for neuroplasticity-based therapeutics in autistic patients. PMID:25646455

  7. Switching auditory attention using spatial and non-spatial features recruits different cortical networks

    PubMed Central

    Larson, Eric; Lee, Adrian KC

    2013-01-01

    Switching attention between different stimuli of interest based on particular task demands is important in many everyday settings. In audition in particular, switching attention between different speakers of interest that are talking concurrently is often necessary for effective communication. Recently, it has been shown by multiple studies that auditory selective attention suppresses the representation of unwanted streams in auditory cortical areas in favor of the target stream of interest. However, the neural processing that guides this selective attention process is not well understood. Here we investigated the cortical mechanisms involved in switching attention based on two different types of auditory features. By combining magneto- and electroencephalography (M-EEG) with an anatomical MRI constraint, we examined the cortical dynamics involved in switching auditory attention based on either spatial or pitch features. We designed a paradigm where listeners were cued in the beginning of each trial to switch or maintain attention halfway through the presentation of concurrent target and masker streams. By allowing listeners time to switch during a gap in the continuous target and masker stimuli, we were able to isolate the mechanisms involved in endogenous, top-down attention switching. Our results show a double dissociation between the involvement of right temporoparietal junction (RTPJ) and the left inferior parietal supramarginal part (LIPSP) in tasks requiring listeners to switch attention based on space and pitch features, respectively, suggesting that switching attention based on these features involves at least partially separate processes or behavioral strategies. PMID:24096028

  8. A cortical core for dynamic integration of functional networks in the resting human brain

    PubMed Central

    de Pasquale, Francesco; Della Penna, Stefania; Snyder, Abraham Z.; Marzetti, Laura; Pizzella, Vittorio; Romani, Gian Luca; Corbetta, Maurizio

    2012-01-01

    Summary We used magneto-encephalography to study the temporal dynamics of band-limited power correlation at rest within and across six brain networks previously defined by prior fMRI studies. Epochs of transiently high within-network BLP correlation were identified and correlation of BLP time-series across networks was assessed in these epochs. These analyses demonstrate that functional networks are not equivalent with respect to cross-network interactions. The default-mode network and the posterior cingulate cortex, in particular, exhibit the highest degree of transient BLP correlation with other networks especially in the 14–25 Hz (beta band) frequency range. Our results indicate that the previously demonstrated neuroanatomical centrality of the PCC and DMN has a physiological counterpart in the temporal dynamics of network interaction at behaviorally relevant time scales. This interaction involved subsets of nodes from other networks during periods in which their internal correlation was low. PMID:22632732

  9. Spiking network simulation code for petascale computers.

    PubMed

    Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M; Plesser, Hans E; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

    2014-01-01

    Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682

  10. Spiking network simulation code for petascale computers

    PubMed Central

    Kunkel, Susanne; Schmidt, Maximilian; Eppler, Jochen M.; Plesser, Hans E.; Masumoto, Gen; Igarashi, Jun; Ishii, Shin; Fukai, Tomoki; Morrison, Abigail; Diesmann, Markus; Helias, Moritz

    2014-01-01

    Brain-scale networks exhibit a breathtaking heterogeneity in the dynamical properties and parameters of their constituents. At cellular resolution, the entities of theory are neurons and synapses and over the past decade researchers have learned to manage the heterogeneity of neurons and synapses with efficient data structures. Already early parallel simulation codes stored synapses in a distributed fashion such that a synapse solely consumes memory on the compute node harboring the target neuron. As petaflop computers with some 100,000 nodes become increasingly available for neuroscience, new challenges arise for neuronal network simulation software: Each neuron contacts on the order of 10,000 other neurons and thus has targets only on a fraction of all compute nodes; furthermore, for any given source neuron, at most a single synapse is typically created on any compute node. From the viewpoint of an individual compute node, the heterogeneity in the synaptic target lists thus collapses along two dimensions: the dimension of the types of synapses and the dimension of the number of synapses of a given type. Here we present a data structure taking advantage of this double collapse using metaprogramming techniques. After introducing the relevant scaling scenario for brain-scale simulations, we quantitatively discuss the performance on two supercomputers. We show that the novel architecture scales to the largest petascale supercomputers available today. PMID:25346682

  11. Analyzing Complex Metabolomic Networks: Experiments and Simulation

    NASA Astrophysics Data System (ADS)

    Steuer, R.; Kurths, J.; Fiehn, O.; Weckwerth, W.

    2002-03-01

    In the recent years, remarkable advances in molecular biology have enabled us to measure the behavior of complex regularity networks underlying biological systems. In particular, high throughput techniques, such as gene expression arrays, allow a fast acquisition of a large number of simultaneously measured variables. Similar to gene expression, the analysis of metabolomic datasets results in a huge number of metabolite co-regulations: Metabolites are the end products of cellular regulatory processes, their level can be regarded as the ultimate response to genetic or environmental changes. In this presentation we focus on the topological description of such networks, using both, experimental data and simulations. In particular, we discuss the possibility to deduce novel links between metabolites, using concepts from (nonlinear) time series analysis and information theory.

  12. A cortical network model of cognitive and emotional influences in human decision making.

    PubMed

    Nazir, Azadeh Hassannejad; Liljenström, Hans

    2015-10-01

    Decision making (DM)(2) is a complex process that appears to involve several brain structures. In particular, amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) seem to be essential in human decision making, where both emotional and cognitive aspects are taken into account. In this paper, we present a computational network model representing the neural information processing of DM, from perception to behavior. We model the population dynamics of the three neural structures (amygdala, OFC and LPFC), as well as their interaction. In our model, the neurodynamic activity of amygdala and OFC represents the neural correlates of secondary emotion, while the activity of certain neural populations in OFC alone represents the outcome expectancy of different options. The cognitive/rational aspect of DM is associated with LPFC. Our model is intended to give insights on the emotional and cognitive processes involved in DM under various internal and external contexts. Different options for actions are represented by the oscillatory activity of cell assemblies, which may change due to experience and learning. Knowledge and experience of the outcome of our decisions and actions can eventually result in changes in our neural structures, attitudes and behaviors. Simulation results may have implications for how we make decisions for our individual actions, as well as for societal choices, where we take examples from transport and its impact on CO2 emissions and climate change. PMID:26184761

  13. Temporal Genetic Modifications after Controlled Cortical Impact--Understanding Traumatic Brain Injury through a Systematic Network Approach.

    PubMed

    Wong, Yung-Hao; Wu, Chia-Chou; Wu, John Chung-Che; Lai, Hsien-Yong; Chen, Kai-Yun; Jheng, Bo-Ren; Chen, Mien-Cheng; Chang, Tzu-Hao; Chen, Bor-Sen

    2016-01-01

    Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein-protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research. PMID:26861311

  14. Temporal Genetic Modifications after Controlled Cortical Impact—Understanding Traumatic Brain Injury through a Systematic Network Approach

    PubMed Central

    Wong, Yung-Hao; Wu, Chia-Chou; Wu, John Chung-Che; Lai, Hsien-Yong; Chen, Kai-Yun; Jheng, Bo-Ren; Chen, Mien-Cheng; Chang, Tzu-Hao; Chen, Bor-Sen

    2016-01-01

    Traumatic brain injury (TBI) is a primary injury caused by external physical force and also a secondary injury caused by biological processes such as metabolic, cellular, and other molecular events that eventually lead to brain cell death, tissue and nerve damage, and atrophy. It is a common disease process (as opposed to an event) that causes disabilities and high death rates. In order to treat all the repercussions of this injury, treatment becomes increasingly complex and difficult throughout the evolution of a TBI. Using high-throughput microarray data, we developed a systems biology approach to explore potential molecular mechanisms at four time points post-TBI (4, 8, 24, and 72 h), using a controlled cortical impact (CCI) model. We identified 27, 50, 48, and 59 significant proteins as network biomarkers at these four time points, respectively. We present their network structures to illustrate the protein–protein interactions (PPIs). We also identified UBC (Ubiquitin C), SUMO1, CDKN1A (cyclindependent kinase inhibitor 1A), and MYC as the core network biomarkers at the four time points, respectively. Using the functional analytical tool MetaCore™, we explored regulatory mechanisms and biological processes and conducted a statistical analysis of the four networks. The analytical results support some recent findings regarding TBI and provide additional guidance and directions for future research. PMID:26861311

  15. Large-scale modeling of epileptic seizures: scaling properties of two parallel neuronal network simulation algorithms.

    PubMed

    Pesce, Lorenzo L; Lee, Hyong C; Hereld, Mark; Visser, Sid; Stevens, Rick L; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determined the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers. PMID:24416069

  16. Large-Scale Modeling of Epileptic Seizures: Scaling Properties of Two Parallel Neuronal Network Simulation Algorithms

    DOE PAGESBeta

    Pesce, Lorenzo L.; Lee, Hyong C.; Hereld, Mark; Visser, Sid; Stevens, Rick L.; Wildeman, Albert; van Drongelen, Wim

    2013-01-01

    Our limited understanding of the relationship between the behavior of individual neurons and large neuronal networks is an important limitation in current epilepsy research and may be one of the main causes of our inadequate ability to treat it. Addressing this problem directly via experiments is impossibly complex; thus, we have been developing and studying medium-large-scale simulations of detailed neuronal networks to guide us. Flexibility in the connection schemas and a complete description of the cortical tissue seem necessary for this purpose. In this paper we examine some of the basic issues encountered in these multiscale simulations. We have determinedmore » the detailed behavior of two such simulators on parallel computer systems. The observed memory and computation-time scaling behavior for a distributed memory implementation were very good over the range studied, both in terms of network sizes (2,000 to 400,000 neurons) and processor pool sizes (1 to 256 processors). Our simulations required between a few megabytes and about 150 gigabytes of RAM and lasted between a few minutes and about a week, well within the capability of most multinode clusters. Therefore, simulations of epileptic seizures on networks with millions of cells should be feasible on current supercomputers.« less

  17. Mobile-ip Aeronautical Network Simulation Study

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Tran, Diepchi T.

    2001-01-01

    NASA is interested in applying mobile Internet protocol (mobile-ip) technologies to its space and aeronautics programs. In particular, mobile-ip will play a major role in the Advanced Aeronautic Transportation Technology (AATT), the Weather Information Communication (WINCOMM), and the Small Aircraft Transportation System (SATS) aeronautics programs. This report presents the results of a simulation study of mobile-ip for an aeronautical network. The study was performed to determine the performance of the transmission control protocol (TCP) in a mobile-ip environment and to gain an understanding of how long delays, handoffs, and noisy channels affect mobile-ip performance.

  18. Learning in innovation networks: Some simulation experiments

    NASA Astrophysics Data System (ADS)

    Gilbert, Nigel; Ahrweiler, Petra; Pyka, Andreas

    2007-05-01

    According to the organizational learning literature, the greatest competitive advantage a firm has is its ability to learn. In this paper, a framework for modeling learning competence in firms is presented to improve the understanding of managing innovation. Firms with different knowledge stocks attempt to improve their economic performance by engaging in radical or incremental innovation activities and through partnerships and networking with other firms. In trying to vary and/or to stabilize their knowledge stocks by organizational learning, they attempt to adapt to environmental requirements while the market strongly selects on the results. The simulation experiments show the impact of different learning activities, underlining the importance of innovation and learning.

  19. Virtual Cortical Resection Reveals Push-Pull Network Control Preceding Seizure Evolution.

    PubMed

    Khambhati, Ankit N; Davis, Kathryn A; Lucas, Timothy H; Litt, Brian; Bassett, Danielle S

    2016-09-01

    In ∼20 million people with drug-resistant epilepsy, focal seizures originating in dysfunctional brain networks will often evolve and spread to surrounding tissue, disrupting function in otherwise normal brain regions. To identify network control mechanisms that regulate seizure spread, we developed a novel tool for pinpointing brain regions that facilitate synchronization in the epileptic network. Our method measures the impact of virtually resecting putative control regions on synchronization in a validated model of the human epileptic network. By applying our technique to time-varying functional networks, we identified brain regions whose topological role is to synchronize or desynchronize the epileptic network. Our results suggest that greater antagonistic push-pull interaction between synchronizing and desynchronizing brain regions better constrains seizure spread. These methods, while applied here to epilepsy, are generalizable to other brain networks and have wide applicability in isolating and mapping functional drivers of brain dynamics in health and disease. PMID:27568515

  20. Three-Dimensional Visualization with Large Data Sets: A Simulation of Spreading Cortical Depression in Human Brain

    PubMed Central

    Ertürk, Korhan Levent; Şengül, Gökhan

    2012-01-01

    We developed 3D simulation software of human organs/tissues; we developed a database to store the related data, a data management system to manage the created data, and a metadata system for the management of data. This approach provides two benefits: first of all the developed system does not require to keep the patient's/subject's medical images on the system, providing less memory usage. Besides the system also provides 3D simulation and modification options, which will help clinicians to use necessary tools for visualization and modification operations. The developed system is tested in a case study, in which a 3D human brain model is created and simulated from 2D MRI images of a human brain, and we extended the 3D model to include the spreading cortical depression (SCD) wave front, which is an electrical phoneme that is believed to cause the migraine. PMID:23258956

  1. Modeling And Simulation Of Multimedia Communication Networks

    NASA Astrophysics Data System (ADS)

    Vallee, Richard; Orozco-Barbosa, Luis; Georganas, Nicolas D.

    1989-05-01

    In this paper, we present a simulation study of a browsing system involving radiological image servers. The proposed IEEE 802.6 DQDB MAN standard is designated as the computer network to transfer radiological images from file servers to medical workstations, and to simultaneously support real time voice communications. Storage and transmission of original raster scanned images and images compressed according to pyramid data structures are considered. Different types of browsing as well as various image sizes and bit rates in the DQDB MAN are also compared. The elapsed time, measured from the time an image request is issued until the image is displayed on the monitor, is the parameter considered to evaluate the system performance. Simulation results show that image browsing can be supported by the DQDB MAN.

  2. Chain networking revealed by molecular dynamics simulation

    NASA Astrophysics Data System (ADS)

    Zheng, Yexin; Tsige, Mesfin; Wang, Shi-Qing

    Based on Kremer-Grest model for entangled polymer melts, we demonstrate how the response of a polymer glass depends critically on the chain length. After quenching two melts of very different chain lengths (350 beads per chain and 30 beads per chain) into deeply glassy states, we subject them to uniaxial extension. Our MD simulations show that the glass of long chains undergoes stable necking after yielding whereas the system of short chains is unable to neck and breaks up after strain localization. During ductile extension of the polymer glass made of long chain significant chain tension builds up in the load-bearing strands (LBSs). Further analysis is expected to reveal evidence of activation of the primary structure during post-yield extension. These results lend support to the recent molecular model 1 and are the simulations to demonstrate the role of chain networking. This work is supported, in part, by a NSF Grant (DMR-EAGER-1444859)

  3. Passive ankle dorsiflexion by an automated device and the reactivity of the motor cortical network.

    PubMed

    Pittaccio, Simone; Zappasodi, Filippo; Tamburro, Gabriella; Viscuso, Stefano; Marzetti, Laura; Garavaglia, Lorenzo; Tecchio, Franca; Pizzella, Vittorio

    2013-01-01

    Gait impairment is an important consequence of neurological disease. Passive mobilization of the affected lower limbs is often prescribed in order to safeguard tissue properties and prevent circulatory sequelae during paresis. However, passive movement could play a role also in stimulating cortical areas of the brain devoted to the control of the lower limb, so that deafferentation and learned non-use can be contrasted. The purpose of the present work is to investigate cortical involvement during active and passive movements of the ankle joint, in an attempt to gain deeper insight in the similarities between these two conditions. A wearable device to mobilize the ankle joint was implemented utilizing rotary shape memory alloy actuators. The technical characteristics of this actuator make it very compatible with the tight limitations on electromagnetic noise imposed by diagnostic instrumentation. Eleven healthy volunteers took part in the pre-clinical phase of the study. According to the protocol, brain activity was recorded by 165-channel magnetoencephalography (MEG) under three different conditions: rest, active dorsiflexion of the ankle and passive mobilization of the same joint. The acquired data were processed to obtain cortical ERD/ERS (Event Related Desynchronization/ Synchronization) maps, which were then compared. The results of this analysis show that there are similar patterns of activity between active and passive movement, particularly in β band, in the contralateral primary sensorimotor, dorsal premotor and supplementary motor areas. This result, albeit obtained from healthy subjects, might suggest that passive motion provides somatosensory afferences that, to some extent, are processed in a similar manner as for voluntary control. Should this evidence be confirmed by further experiments on neurological patients, it could support the prescription of passive exercise as a surrogate of active workout, at least, so long as patients are paretic. PMID

  4. The Role of Oscillations and Synchrony in Cortical Networks and Their Putative Relevance for the Pathophysiology of Schizophrenia

    PubMed Central

    Uhlhaas, Peter J.; Haenschel, Corinna; Nikolić, Danko; Singer, Wolf

    2008-01-01

    Neural oscillations and their synchronization may represent a versatile signal to realize flexible communication within and between cortical areas. By now, there is extensive evidence to suggest that cognitive functions depending on coordination of distributed neural responses, such as perceptual grouping, attention-dependent stimulus selection, subsystem integration, working memory, and consciousness, are associated with synchronized oscillatory activity in the theta-, alpha-, beta-, and gamma-band, suggesting a functional mechanism of neural oscillations in cortical networks. In addition to their role in normal brain functioning, there is increasing evidence that altered oscillatory activity may be associated with certain neuropsychiatric disorders, such as schizophrenia, that involve dysfunctional cognition and behavior. In the following article, we aim to summarize the evidence on the role of neural oscillations during normal brain functioning and their relationship to cognitive processes. In the second part, we review research that has examined oscillatory activity during cognitive and behavioral tasks in schizophrenia. These studies suggest that schizophrenia involves abnormal oscillations and synchrony that are related to cognitive dysfunctions and some of the symptoms of the disorder. Perspectives for future research will be discussed in relationship to methodological issues, the utility of neural oscillations as a biomarker, and the neurodevelopmental hypothesis of schizophrenia. PMID:18562344

  5. Quantitative imaging of microvascular blood flow networks in deep cortical layers by 1310 nm µODT

    PubMed Central

    You, Jiang; Zhang, Qiujia; Park, Kicheon; Du, Congwu

    2016-01-01

    There is growing interest in new neuroimage techniques that permit not only high-resolution quantification of cerebral blood flow velocity (CBFv) in capillaries, but also a large field of view to map the CBFv network dynamics. Such image capabilities are of great importance for decoding the functional difference across multiple cortical layers under stimuli. To tackle the limitation of optical penetration depth, we present a new ultrahigh-resolution optical coherence Doppler tomography (µODT) system at 1310 nm and compare it with a prior 800 nm µODT system for mouse brain 3D CBFv imaging. We show that the new 1310 nm µODT allows for dramatically increased depth (~4 times) of quantitative CBFv imaging to 1.4 mm, thus covering the full thickness of the mouse cortex (i.e., layers I–VI). Interestingly, we show that such a unique 3D CBFv imaging capability allows identification of microcirculatory redistribution across different cortical layers resulting from repeated cocaine exposures. PMID:26371919

  6. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence.

    PubMed

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  7. Comparison of deep neural networks to spatio-temporal cortical dynamics of human visual object recognition reveals hierarchical correspondence

    PubMed Central

    Cichy, Radoslaw Martin; Khosla, Aditya; Pantazis, Dimitrios; Torralba, Antonio; Oliva, Aude

    2016-01-01

    The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain. PMID:27282108

  8. The role of long-range connections on the specificity of the macaque interareal cortical network

    PubMed Central

    Markov, Nikola T.; Ercsey-Ravasz, Maria; Lamy, Camille; Ribeiro Gomes, Ana Rita; Magrou, Loïc; Misery, Pierre; Giroud, Pascale; Barone, Pascal; Dehay, Colette; Toroczkai, Zoltán; Knoblauch, Kenneth; Van Essen, David C.; Kennedy, Henry

    2013-01-01

    We investigated the influence of interareal distance on connectivity patterns in a database obtained from the injection of retrograde tracers in 29 areas distributed over six regions (occipital, temporal, parietal, frontal, prefrontal, and limbic). One-third of the 1,615 pathways projecting to the 29 target areas were reported only recently and deemed new-found projections (NFPs). NFPs are predominantly long-range, low-weight connections. A minimum dominating set analysis (a graph theoretic measure) shows that NFPs play a major role in globalizing input to small groups of areas. Randomization tests show that (i) NFPs make important contributions to the specificity of the connectivity profile of individual cortical areas, and (ii) NFPs share key properties with known connections at the same distance. We developed a similarity index, which shows that intraregion similarity is high, whereas the interregion similarity declines with distance. For area pairs, there is a steep decline with distance in the similarity and probability of being connected. Nevertheless, the present findings reveal an unexpected binary specificity despite the high density (66%) of the cortical graph. This specificity is made possible because connections are largely concentrated over short distances. These findings emphasize the importance of long-distance connections in the connectivity profile of an area. We demonstrate that long-distance connections are particularly prevalent for prefrontal areas, where they may play a prominent role in large-scale communication and information integration. PMID:23479610

  9. Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training.

    PubMed

    Metzler-Baddeley, Claudia; Caeyenberghs, Karen; Foley, Sonya; Jones, Derek K

    2016-04-15

    Novel activities and experiences shape the brain's structure and organisation and, hence, our behaviour. However, evidence from structural plasticity studies remains mixed and the neural correlates of learning and practice are still poorly understood. We conducted a robustly designed study into grey matter plasticity following 2months of working memory training. We generated a priori hypotheses regarding the location of plastic effects across three cognitive control networks (executive, anterior salience and basal ganglia networks), and compared the effects of adaptive training (n=20) with a well-matched active control group (n=20) which differed in training complexity and included extensive cognitive assessment before and after the training. Adaptive training relative to control activities resulted in a complex pattern of subtle and localised structural changes: Training was associated with increases in cortical thickness in right-lateralised executive regions, notably the right caudal middle frontal cortex, as well as increases in the volume of the left pallidum. In addition the training group showed reductions of thickness in the right insula, which were correlated with training-induced improvements in backward digit span performance. Unexpectedly, control activities were associated with reductions in thickness in the right pars triangularis. These results suggest that the direction of activity-induced plastic changes depend on the level of training complexity as well as brain location. These observations are consistent with the view that the brain responds dynamically to environmental demands by focusing resources on task relevant networks and eliminating irrelevant processing for the purpose of energy reduction. PMID:26806288

  10. Nanoindentation testing and finite element simulations of cortical bone allowing for anisotropic elastic and inelastic mechanical response.

    PubMed

    Carnelli, Davide; Lucchini, Riccardo; Ponzoni, Matteo; Contro, Roberto; Vena, Pasquale

    2011-07-01

    Anisotropy is one of the most peculiar aspects of cortical bone mechanical behaviour, and the numerical approach can be successfully used to investigate aspects of bone tissue mechanics that analytical methods solve in approximate way or do not cover. In this work, nanoindentation experimental tests and finite element simulations were employed to investigate the elastic-inelastic anisotropic mechanical properties of cortical bone. The model allows for anisotropic elastic and post-yield behaviour of the tissue. A tension-compression mismatch and direction-dependent yield stresses are allowed for. Indentation experiments along the axial and transverse directions were simulated with the purpose to predict the indentation moduli and hardnesses along multiple orientations. Results showed that the experimental transverse-to-axial ratio of indentation moduli, equal to 0.74, is predicted with a ∼3% discrepancy regardless the post-yield material behaviour; whereas, the transverse-to-axial hardness ratio, equal to 0.86, can be correctly simulated (discrepancy ∼6% w.r.t. the experimental results) only employing an anisotropic post-elastic constitutive model. Further, direct comparison between the experimental and simulated indentation tests evidenced a good agreement in the loading branch of the indentation curves and in the peak loads for a transverse-to-axial yield stress ratio comparable to the experimentally obtained transverse-to-axial hardness ratio. In perspective, the present work results strongly support the coupling between indentation experiments and FEM simulations to get a deeper knowledge of bone tissue mechanical behaviour at the microstructural level. The present model could be used to assess the effect of variations of constitutive parameters due to age, injury, and/or disease on bone mechanical performance in the context of indentation testing. PMID:21570077

  11. Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS, fMRI, EEG Study.

    PubMed

    Anwar, A R; Muthalib, M; Perrey, S; Galka, A; Granert, O; Wolff, S; Heute, U; Deuschl, G; Raethjen, J; Muthuraman, Muthuraman

    2016-09-01

    Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography-EEG) and hemodynamic (functional near infrared spectroscopy-fNIRS; and functional magnetic resonance imaging-fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O2Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O2Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a

  12. DELTAMETHRIN AND ESFENVALERATE INHIBIT SPONTANEOUS NETWORK ACTIVITY IN RAT CORTICAL NEURONS IN VITRO.

    EPA Science Inventory

    Understanding pyrethroid actions on neuronal networks will help to establish a mode of action for these compounds, which is needed for cumulative risk decisions under the Food Quality Protection Act of 1996. However, pyrethroid effects on spontaneous activity in networks of inter...

  13. Programmable multi-node quantum network design and simulation

    NASA Astrophysics Data System (ADS)

    Dasari, Venkat R.; Sadlier, Ronald J.; Prout, Ryan; Williams, Brian P.; Humble, Travis S.

    2016-05-01

    Software-defined networking offers a device-agnostic programmable framework to encode new network functions. Externally centralized control plane intelligence allows programmers to write network applications and to build functional network designs. OpenFlow is a key protocol widely adopted to build programmable networks because of its programmability, flexibility and ability to interconnect heterogeneous network devices. We simulate the functional topology of a multi-node quantum network that uses programmable network principles to manage quantum metadata for protocols such as teleportation, superdense coding, and quantum key distribution. We first show how the OpenFlow protocol can manage the quantum metadata needed to control the quantum channel. We then use numerical simulation to demonstrate robust programmability of a quantum switch via the OpenFlow network controller while executing an application of superdense coding. We describe the software framework implemented to carry out these simulations and we discuss near-term efforts to realize these applications.

  14. Response-dependent dynamics of cell-specific inhibition in cortical networks in vivo

    PubMed Central

    El-Boustani, Sami; Sur, Mriganka

    2014-01-01

    In the visual cortex, inhibitory neurons alter the computations performed by target cells via combination of two fundamental operations, division and subtraction. The origins of these operations have been variously ascribed to differences in neuron classes, synapse location or receptor conductances. Here, by utilizing specific visual stimuli and single optogenetic probe pulses, we show that the function of parvalbumin-expressing and somatostatin-expressing neurons in mice in vivo is governed by the overlap of response timing between these neurons and their targets. In particular, somatostatin-expressing neurons respond at longer latencies to small visual stimuli compared with their target neurons and provide subtractive inhibition. With large visual stimuli, however, they respond at short latencies coincident with their target cells and switch to provide divisive inhibition. These results indicate that inhibition mediated by these neurons is a dynamic property of cortical circuits rather than an immutable property of neuronal classes. PMID:25504329

  15. Patterns of Cortical Oscillations Organize Neural Activity into Whole-Brain Functional Networks Evident in the fMRI BOLD Signal

    PubMed Central

    Whitman, Jennifer C.; Ward, Lawrence M.; Woodward, Todd S.

    2013-01-01

    Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG/MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks. PMID:23504590

  16. Patterns of Cortical Oscillations Organize Neural Activity into Whole-Brain Functional Networks Evident in the fMRI BOLD Signal.

    PubMed

    Whitman, Jennifer C; Ward, Lawrence M; Woodward, Todd S

    2013-01-01

    Recent findings from electrophysiology and multimodal neuroimaging have elucidated the relationship between patterns of cortical oscillations evident in EEG/MEG and the functional brain networks evident in the BOLD signal. Much of the existing literature emphasized how high-frequency cortical oscillations are thought to coordinate neural activity locally, while low-frequency oscillations play a role in coordinating activity between more distant brain regions. However, the assignment of different frequencies to different spatial scales is an oversimplification. A more informative approach is to explore the arrangements by which these low- and high-frequency oscillations work in concert, coordinating neural activity into whole-brain functional networks. When relating such networks to the BOLD signal, we must consider how the patterns of cortical oscillations change at the same speed as cognitive states, which often last less than a second. Consequently, the slower BOLD signal may often reflect the summed neural activity of several transient network configurations. This temporal mismatch can be circumvented if we use spatial maps to assess correspondence between oscillatory networks and BOLD networks. PMID:23504590

  17. Tackling learning intractability through topological organization and regulation of cortical networks.

    PubMed

    Thangavelautham, Jekanthan; D'Eleuterio, Gabriele M T

    2012-04-01

    A key challenge in evolving control systems for robots using neural networks is training tractability. Evolving monolithic fixed topology neural networks is shown to be intractable with limited supervision in high dimensional search spaces. Common strategies to overcome this limitation are to provide more supervision by encouraging particular solution strategies, manually decomposing the task and segmenting the search space and network. These strategies require a supervisor with domain knowledge and may not be feasible for difficult tasks where novel concepts are required. The alternate strategy is to use self-organized task decomposition to solve difficult tasks with limited supervision. The artificial neural tissue (ANT) approach presented here uses self-organized task decomposition to solve tasks. ANT inspired by neurobiology combines standard neural networks with a novel wireless signaling scheme modeling chemical diffusion of neurotransmitters. These chemicals are used to dynamically activate and inhibit wired network of neurons using a coarse-coding framework. Using only a global fitness function that does not encourage a predefined solution, modular networks of neurons are shown to self-organize and perform task decomposition. This approach solves the sign-following task found to be intractable with conventional fixed and variable topology networks. In this paper, key attributes of the ANT architecture that perform self-organized task decomposition are shown. The architecture is robust and scalable to number of neurons, synaptic connections, and initialization parameters. PMID:24805039

  18. Low Frequency Activity of Cortical Networks on Microelectrode Arrays is Differentially Altered by Bicuculline and Carbaryl

    EPA Science Inventory

    Thousands of chemicals need to be characterized for their neurotoxicity potential. Neurons grown on microelectrode arrays (MEAs) are an in vitro model used to screen chemicals for functional effects on neuronal networks. Typically, after removal of low frequency components, effec...

  19. Integrated workflows for spiking neuronal network simulations

    PubMed Central

    Antolík, Ján; Davison, Andrew P.

    2013-01-01

    The increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo, and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organized configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualization stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modeling studies by relieving the user from manual handling of the flow of metadata between the individual workflow stages. PMID

  20. Numeric simulation of plant signaling networks.

    PubMed

    Genoud, T; Trevino Santa Cruz, M B; Métraux, J P

    2001-08-01

    Plants have evolved an intricate signaling apparatus that integrates relevant information and allows an optimal response to environmental conditions. For instance, the coordination of defense responses against pathogens involves sophisticated molecular detection and communication systems. Multiple protection strategies may be deployed differentially by the plant according to the nature of the invading organism. These responses are also influenced by the environment, metabolism, and developmental stage of the plant. Though the cellular signaling processes traditionally have been described as linear sequences of events, it is now evident that they may be represented more accurately as network-like structures. The emerging paradigm can be represented readily with the use of Boolean language. This digital (numeric) formalism allows an accurate qualitative description of the signal transduction processes, and a dynamic representation through computer simulation. Moreover, it provides the required power to process the increasing amount of information emerging from the fields of genomics and proteomics, and from the use of new technologies such as microarray analysis. In this review, we have used the Boolean language to represent and analyze part of the signaling network of disease resistance in Arabidopsis. PMID:11500542

  1. Numeric Simulation of Plant Signaling Networks1

    PubMed Central

    Genoud, Thierry; Trevino Santa Cruz, Marcela B.; Métraux, Jean-Pierre

    2001-01-01

    Plants have evolved an intricate signaling apparatus that integrates relevant information and allows an optimal response to environmental conditions. For instance, the coordination of defense responses against pathogens involves sophisticated molecular detection and communication systems. Multiple protection strategies may be deployed differentially by the plant according to the nature of the invading organism. These responses are also influenced by the environment, metabolism, and developmental stage of the plant. Though the cellular signaling processes traditionally have been described as linear sequences of events, it is now evident that they may be represented more accurately as network-like structures. The emerging paradigm can be represented readily with the use of Boolean language. This digital (numeric) formalism allows an accurate qualitative description of the signal transduction processes, and a dynamic representation through computer simulation. Moreover, it provides the required power to process the increasing amount of information emerging from the fields of genomics and proteomics, and from the use of new technologies such as microarray analysis. In this review, we have used the Boolean language to represent and analyze part of the signaling network of disease resistance in Arabidopsis. PMID:11500542

  2. Prefrontal Cortical Network Connections: Key Site of Vulnerability in Stress and Schizophrenia

    PubMed Central

    Arnsten, Amy F.T.

    2011-01-01

    The symptoms of schizophrenia involve profound dysfunction of the prefrontal cortex (PFC). PFC networks create our “mental sketch pad”, and PFC dysfunction contributes to symptoms such as cognitive deficits, thought disorder, delusions and hallucinations. Neuropathological studies of schizophrenia have shown marked loss of dendritic spines in deep layer III, the sublayer where PFC microcircuits reside. The microcircuits consist of recurrent excitatory pyramidal cell networks that interconnect on spines, and excite each other via NMDA receptor signaling. The pyramidal cell persistent firing is sculpted by lateral inhibition from GABAergic basket and chandelier cells, thus creating tuned, persistent firing needed for accurate representational knowledge (i.e. working memory). The strength of pyramidal cell network connections is markedly and flexibly altered by intracellular signaling pathways in dendritic spines, a process called Dynamic Network Connectivity (DNC). DNC proteins such as HCN channels are concentrated on dendritic spines in deep layer III. Under optimal conditions, network inputs to pyramidal cells are strengthened by noradrenergic alpha-2A inhibition of cAMP-HCN channel signaling, and sculpted by dopamine D1-cAMP-HCN channel weakening of inappropriate inputs. However, with stress exposure, high levels of cAMP-HCN channel signaling produces a collapse in network firing. With chronic stress exposure, spines reduce in size and are lost, and this process involves increased PKC signaling. Importantly, molecules that normally strengthen PFC networks connections and/or reverse the stress response, are often genetically altered in schizophrenia. As exposure to stress is a key factor in the precipitation of schizophrenic symptoms, these dysregulated signaling pathways in deep layer III may interact with already vulnerable circuitry to cause spine loss and the descent into illness. PMID:21345366

  3. Electrophysiological properties of hippocampal-cortical neural networks, role in the processes of learning and memory in rats.

    PubMed

    Li, Chang-Jun; Lu, Yun; Zhou, Mei; Guo, Lian-Jun

    2014-06-01

    The recording of hippocampal and cortical long-term potentiation (LTP) in rats in vivo is an appropriate and commonly used method to describe changes in cellular mechanisms underlying synaptic plasticity. Recently, we introduced a method for the simultaneous recording of LTP in bilateral CA1 regions and parietal association cortex (PtA), and observed differences between the Schaffer collateral-CA1 pathway (SC), Schaffer collateral/associational commissural pathway (SAC) and Schaffer collateral/associational commissural-cortex pathway (SACC). In this study, we found that (1) synaptic transmission of the SAC and SACC pathways depended on hippocampal commissural fibers [dorsal and ventral hippocampal commissural fibers, the medial septum (MS) and hippocampal CA3 commissural fibers], (2) nerve conduction velocity of the SACC pathway might be higher than that of the SAC pathway, (3) the input/output (I/O) curve of the SC pathway was shifted to the left side, compared to that of the SAC and SACC pathways, (4) all three pathways could induce stable LTP; however, LTP of the SAC and SACC pathways was much stronger than that of the SC pathway, (5) the degree of paired-pulse facilitation (PPF) was weaker in the SC pathway than that in the SAC and SACC pathways, (6) after cutting off the corpus callosum and commissural fibers, spatial learning and memory were impaired, and the ability to explore the novel environment and spontaneous locomotor activity were weakened. Taken together, our results suggested that hippocampal commissural fibers were very important for exchanging information between hemispheres, and basic differences in electrophysiological properties of hippocampal-cortical neural networks play a vital role in the processes of learning and memory. PMID:24504908

  4. Simulation of the effects of microtubules in the cortical rotation of amphibian embryos in normal and zero gravity.

    PubMed

    Nouri, Comron; Tuszynski, Jack A; Wiebe, Mark W; Gordon, Richard

    2012-09-01

    This paper reports the results of computer modeling of microtubules that end up in the cortical region of a one-cell amphibian embryo, prior to the first cell division. Microtubules are modeled as initially randomly oriented semi-flexible rods, represented by several lines of point-masses interacting with one another like masses on springs with longitudinal and transverse stiffness. They are also considered to be space-filling rods floating in a viscous fluid (cytoplasm) experiencing drag forces and buoyancy from the fluid under a variable gravity field to test gravitational effects. Their randomly distributed interactions with the surrounding spherical container (the cell membrane) have a statistical nonzero average that creates a torque causing a rotational displacement between the cytoplasm and the rigid cortex. The simulation has been done for zero and normal gravity and it validates the observation that cortical rotation occurs in microgravity as well as on Earth. The speed of rotation depends on gravity, but is still substantial in microgravity. PMID:22677068

  5. A stochastic mean field model for an excitatory and inhibitory synaptic drive cortical neuronal network.

    PubMed

    Hui, Qing; Haddad, Wassim M; Bailey, James M; Hayakawa, Tomohisa

    2014-04-01

    With the advances in biochemistry, molecular biology, and neurochemistry there has been impressive progress in understanding the molecular properties of anesthetic agents. However, there has been little focus on how the molecular properties of anesthetic agents lead to the observed macroscopic property that defines the anesthetic state, that is, lack of responsiveness to noxious stimuli. In this paper, we develop a mean field synaptic drive firing rate cortical neuronal model and demonstrate how the induction of general anesthesia can be explained using multistability; the property whereby the solutions of a dynamical system exhibit multiple attracting equilibria under asymptotically slowly changing inputs or system parameters. In particular, we demonstrate multistability in the mean when the system initial conditions or the system coefficients of the neuronal connectivity matrix are random variables. Uncertainty in the system coefficients is captured by representing system uncertain parameters by a multiplicative white noise model wherein stochastic integration is interpreted in the sense of Itô. Modeling a priori system parameter uncertainty using a multiplicative white noise model is motivated by means of the maximum entropy principle of Jaynes and statistical analysis. PMID:24807952

  6. Cortical network dynamics during source memory retrieval: current density imaging with individual MRI.

    PubMed

    Kim, Young Youn; Roh, Ah Young; Namgoong, Yoon; Jo, Hang Joon; Lee, Jong-Min; Kwon, Jun Soo

    2009-01-01

    We investigated the neural correlates of source memory retrieval using low-resolution electromagnetic tomography (LORETA) with 64 channels EEG and individual MRI as a realistic head model. Event-related potentials (ERPs) were recorded while 13 healthy subjects performed the source memory task for the voice of the speaker in spoken words. The source correct condition of old words elicited more positive-going potentials than the correct rejection condition of new words at 400-700 ms post-stimulus and the old/new effects also appeared in the right anterior region between 1,000 and 1,200 ms. We conducted source reconstruction at mean latencies of 311, 604, 793, and 1,100 ms and used statistical parametric mapping for the statistical analysis. The results of source analysis suggest that the activation of the right inferior parietal region may reflect retrieval of source information. The source elicited by the difference ERPs between the source correct and source incorrect conditions exhibited dynamic change of current density activation in the overall cortices with time during source memory retrieval. These results indicate that multiple neural systems may underlie the ability to recollect context. PMID:17979123

  7. To see or not to see--thalamo-cortical networks during blindsight and perceptual suppression.

    PubMed

    Schmid, Michael C; Maier, Alexander

    2015-03-01

    Even during moments when we fail to be fully aware of our environment, our brains never go silent. Instead, it appears that the brain can also operate in an alternate, unconscious mode. Delineating unconscious from conscious neural processes is a promising first step toward investigating how awareness emerges from brain activity. Here we focus on recent insights into the neuronal processes that contribute to visual function in the absence of a conscious visual percept. Drawing on insights from findings on the phenomenon of blindsight that results from injury to primary visual cortex and the results of experimentally induced perceptual suppression, we describe what kind of visual information the visual system analyzes unconsciously and we discuss the neuronal routing and responses that accompany this process. We conclude that unconscious processing of certain visual stimulus attributes, such as the presence of visual motion or the emotional expression of a face can occur in a geniculo-cortical circuit that runs independent from and in parallel to the predominant route through primary visual cortex. We speculate that in contrast, bidirectional neuronal interactions between cortex and the thalamic pulvinar nucleus that support large-scale neuronal integration and visual awareness are impeded during blindsight and perceptual suppression. PMID:25661166

  8. Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

    PubMed Central

    Gollo, Leonardo L.; Zalesky, Andrew; Hutchison, R. Matthew; van den Heuvel, Martijn; Breakspear, Michael

    2015-01-01

    For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously—elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding ‘feeder’ cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics. PMID:25823864

  9. Cortico-cortical networks in patients with ideomotor apraxia as revealed by EEG coherence analysis

    PubMed Central

    Wheaton, Lewis A.; Bohlhalter, Stephan; Nolte, Guido; Shibasaki, Hiroshi; Hattori, Noriaki; Fridman, Esteban; Vorbach, Sherry; Grafman, Jordan; Hallett, Mark

    2008-01-01

    We sought to determine whether coherent networks which circumvent lesioned cortex are seen in patients with ideomotor apraxia (IMA) while performing tool use pantomimes. Five normal subjects and five patients with IMA (three patients with corticobasal degeneration and two with left hemisphere stroke) underwent 64-channel EEG recording while performing three tool-use pantomimes with their left hand in a self-paced manner. Beta band (20–22 Hz) coherence indicates that normal subjects have a dominant left hemisphere network responsible for praxis preparation, which was absent in patients. Corticobasal degeneration patients showed significant coherence increase between left parietal - right premotor areas. Left hemisphere stroke patients showed significant coherence increases in a right parietofrontal network. The right hemisphere appears to store useable praxis representations in IMA patients with left hemisphere damage. PMID:18249498

  10. Simulation of reliability in multiserver computer networks

    NASA Astrophysics Data System (ADS)

    Minkevičius, Saulius

    2012-11-01

    The performance in terms of reliability of computer multiserver networks motivates this paper. The probability limit theorem is derived on the extreme queue length in open multiserver queueing networks in heavy traffic and applied to a reliability model for multiserver computer networks where we relate the time of failure of a multiserver computer network to the system parameters.

  11. Dynamics of Functional and Effective Connectivity Within Human Cortical Motor Control Networks

    PubMed Central

    Ewen, Joshua B.; Lakshmanan, Balaji M.; Hallett, Mark; Mostofsky, Stewart H.; Crone, Nathan E.; Korzeniewska, Anna

    2014-01-01

    Objective Praxis, the performance of complex motor gestures, is crucial to the development of motor and social/communicative capacities. Praxis relies on a network consisting of inferior parietal and premotor regions, particularly on the left, and is thought to require transformation of spatio-temporal representations (parietal) into movement sequences (premotor). Method We examined praxis network dynamics by measuring EEG effective connectivity while healthy subjects performed a praxis task. Results Propagation from parietal to frontal regions was not statistically greater on the left than the right. However, propagation from left parietal regions to all other regions was significantly greater during gesture preparation than execution. Moreover, during gesture preparation only, propagation from the left parietal region to bilateral frontal regions was greater than reciprocal propagations to the left parietal region. This directional specificity was not observed for the right parietal region. Conclusions These findings represent direct electrophysiological evidence for directionally predominant propagation in left frontal-parietal networks during praxis behavior, which may reflect neural mechanisms by which representations in the human brain select appropriate motor sequences for subsequent execution. Significance In addition to bolstering the classic view of praxis network function, these results also demonstrate the relevance of additional information provided by directed connectivity measures. PMID:25270239

  12. Repeated Stimulation of Cultured Networks of Rat Cortical Neurons Induces Parallel Memory Traces

    ERIC Educational Resources Information Center

    le Feber, Joost; Witteveen, Tim; van Veenendaal, Tamar M.; Dijkstra, Jelle

    2015-01-01

    During systems consolidation, memories are spontaneously replayed favoring information transfer from hippocampus to neocortex. However, at present no empirically supported mechanism to accomplish a transfer of memory from hippocampal to extra-hippocampal sites has been offered. We used cultured neuronal networks on multielectrode arrays and…

  13. Task complexity and location specific changes of cortical thickness in executive and salience networks after working memory training

    PubMed Central

    Metzler-Baddeley, Claudia; Caeyenberghs, Karen; Foley, Sonya; Jones, Derek K.

    2016-01-01

    Novel activities and experiences shape the brain's structure and organisation and, hence, our behaviour. However, evidence from structural plasticity studies remains mixed and the neural correlates of learning and practice are still poorly understood. We conducted a robustly designed study into grey matter plasticity following 2 months of working memory training. We generated a priori hypotheses regarding the location of plastic effects across three cognitive control networks (executive, anterior salience and basal ganglia networks), and compared the effects of adaptive training (n = 20) with a well-matched active control group (n = 20) which differed in training complexity and included extensive cognitive assessment before and after the training. Adaptive training relative to control activities resulted in a complex pattern of subtle and localised structural changes: Training was associated with increases in cortical thickness in right-lateralised executive regions, notably the right caudal middle frontal cortex, as well as increases in the volume of the left pallidum. In addition the training group showed reductions of thickness in the right insula, which were correlated with training-induced improvements in backward digit span performance. Unexpectedly, control activities were associated with reductions in thickness in the right pars triangularis. These results suggest that the direction of activity-induced plastic changes depend on the level of training complexity as well as brain location. These observations are consistent with the view that the brain responds dynamically to environmental demands by focusing resources on task relevant networks and eliminating irrelevant processing for the purpose of energy reduction. PMID:26806288

  14. Segmentation of nanotomographic cortical bone images for quantitative characterization of the osteoctyte lacuno-canalicular network

    NASA Astrophysics Data System (ADS)

    Ciani, A.; Guizar-Sicairos, M.; Diaz, A.; Holler, M.; Pallu, S.; Achiou, Z.; Jennane, R.; Toumi, H.; Lespessailles, E.; Kewish, C. M.

    2016-01-01

    A newly developed data processing method able to characterize the osteocytes lacuno-canalicular network (LCN) is presented. Osteocytes are the most abundant cells in the bone, living in spaces called lacunae embedded inside the bone matrix and connected to each other with an extensive network of canals that allows for the exchange of nutrients and for mechanotransduction functions. The geometrical three-dimensional (3D) architecture is increasingly thought to be related to the macroscopic strength or failure of the bone and it is becoming the focus for investigating widely spread diseases such as osteoporosis. To obtain 3D LCN images non-destructively has been out of reach until recently, since tens-of-nanometers scale resolution is required. Ptychographic tomography was validated for bone imaging in [1], showing clearly the LCN. The method presented here was applied to 3D ptychographic tomographic images in order to extract morphological and geometrical parameters of the lacuno-canalicular structures.

  15. Studies of stimulus parameters for seizure disruption using neural network simulations

    PubMed Central

    Kudela, Pawel; Cho, Ryan J.; Bergey, Gregory K.; Franaszczuk, Piotr

    2009-01-01

    A large scale neural network simulation with realistic cortical architecture has been undertaken to investigate the effects of external electrical stimulation on the propagation and evolution of ongoing seizure activity. This is an effort to explore the parameter space of stimulation variables to uncover promising avenues of research for this therapeutic modality. The model consists of an approximately 800 μm × 800 μm region of simulated cortex, and includes seven neuron classes organized by cortical layer, inhibitory or excitatory properties, and electrophysiological characteristics. The cell dynamics are governed by a modified version of the Hodgkin-Huxley equations in single compartment format. Axonal connections are patterned after histological data and published models of local cortical wiring. Stimulation induced action potentials take place at the axon initial segments, according to threshold requirements on the applied electric field distribution. Stimulation induced action potentials in horizontal axonal branches are also separately simulated. The calculations are performed on a 16 node distributed 32-bit processor system. Clear differences in seizure evolution are presented for stimulated versus the undisturbed rhythmic activity. Data is provided for frequency dependent stimulation effects demonstrating a plateau effect of stimulation efficacy as the applied frequency is increased from 60 Hz to 200 Hz. Timing of the stimulation with respect to the underlying rhythmic activity demonstrates a phase dependent sensitivity. Electrode height and position effects are also presented. Using a dipole stimulation electrode arrangement, clear orientation effects of the dipole with respect to the model connectivity is also demonstrated. A sensitivity analysis of these results as a function of the stimulation threshold is also provided. PMID:17619199

  16. Motor Cortical Networks for Skilled Movements Have Dynamic Properties That Are Related to Accurate Reaching

    PubMed Central

    Putrino, David F.; Chen, Zhe; Ghosh, Soumya; Brown, Emery N.

    2011-01-01

    Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total) were acquired. A point process generalized linear model (GLM) was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors. PMID:22007332

  17. Relating Cortical Atrophy in Temporal Lobe Epilepsy with Graph Diffusion-Based Network Models

    PubMed Central

    Abdelnour, Farras; Mueller, Susanne; Raj, Ashish

    2015-01-01

    Mesial temporal lobe epilepsy (TLE) is characterized by stereotyped origination and spread pattern of epileptogenic activity, which is reflected in stereotyped topographic distribution of neuronal atrophy on magnetic resonance imaging (MRI). Both epileptogenic activity and atrophy spread appear to follow white matter connections. We model the networked spread of activity and atrophy in TLE from first principles via two simple first order network diffusion models. Atrophy distribution is modeled as a simple consequence of the propagation of epileptogenic activity in one model, and as a progressive degenerative process in the other. We show that the network models closely reproduce the regional volumetric gray matter atrophy distribution of two epilepsy cohorts: 29 TLE subjects with medial temporal sclerosis (TLE-MTS), and 50 TLE subjects with normal appearance on MRI (TLE-no). Statistical validation at the group level suggests high correlation with measured atrophy (R = 0.586 for TLE-MTS, R = 0.283 for TLE-no). We conclude that atrophy spread model out-performs the hyperactivity spread model. These results pave the way for future clinical application of the proposed model on individual patients, including estimating future spread of atrophy, identification of seizure onset zones and surgical planning. PMID:26513579

  18. A structure-dynamic approach to cortical organization: number of paths and accessibility.

    PubMed

    Rodrigues, Francisco A; da Fontoura Costa, Luciano

    2009-09-30

    A structure-dynamic approach to cortical systems is reported which is based on the number of paths and the accessibility of each node. The latter measurement is obtained by performing self-avoiding random walks in the respective networks, so as to simulate dynamics, and then calculating the entropies of the transition probabilities for walks starting from each node. Cortical networks of three species, namely cat, macaque and humans, are studied considering structural and dynamical aspects. It is verified that the human cortical network presents the highest accessibility and number of paths (in terms of z-scores). The correlation between the number of paths and accessibility is also investigated as a mean to quantify the level of independence between paths connecting pairs of nodes in cortical networks. By comparing the cortical networks of cat, macaque and humans, it is verified that the human cortical network tends to present the largest number of independent paths of length larger than four. These results suggest that the human cortical network is potentially the most resilient to brain injures. PMID:19591866

  19. Simulation of Lunar Surface Communications Network Exploration Scenarios

    NASA Technical Reports Server (NTRS)

    Linsky, Thomas W.; Bhasin, Kul B.; White, Alex; Palangala, Srihari

    2006-01-01

    Simulations and modeling of surface-based communications networks provides a rapid and cost effective means of requirement analysis, protocol assessments, and tradeoff studies. Robust testing in especially important for exploration systems, where the cost of deployment is high and systems cannot be easily replaced or repaired. However, simulation of the envisioned exploration networks cannot be achieved using commercial off the shelf network simulation software. Models for the nonstandard, non-COTS protocols used aboard space systems are not readily available. This paper will address the simulation of realistic scenarios representative of the activities which will take place on the surface of the Moon, including selection of candidate network architectures, and the development of an integrated simulation tool using OPNET modeler capable of faithfully modeling those communications scenarios in the variable delay, dynamic surface environments. Scenarios for exploration missions, OPNET development, limitations, and simulations results will be provided and discussed.

  20. Research and Simulation on Application of the Mobile IP Network

    NASA Astrophysics Data System (ADS)

    Yibing, Deng; Wei, Hu; Minghui, Li; Feng, Gao; Junyi, Shen

    The paper analysed the mobile node, home agent, and foreign agent of mobile IP network firstly, some key technique, such as mobile IP network basical principle, protocol work principle, agent discovery, registration, and IP packet transmission, were discussed. Then a network simulation model was designed, validating the characteristic of mobile IP network, and some advantages, which were brought by mobile network, were testified. Finally, the conclusion is gained: mobile IP network could realize the expectation of consumer that they can communicate with others anywhere.

  1. Individual-specific multi-scale finite element simulation of cortical bone of human proximal femur

    NASA Astrophysics Data System (ADS)

    Ascenzi, Maria-Grazia; Kawas, Neal P.; Lutz, Andre; Kardas, Dieter; Nackenhorst, Udo; Keyak, Joyce H.

    2013-07-01

    We present an innovative method to perform multi-scale finite element analyses of the cortical component of the femur using the individual's (1) computed tomography scan; and (2) a bone specimen obtained in conjunction with orthopedic surgery. The method enables study of micro-structural characteristics regulating strains and stresses under physiological loading conditions. The analysis of the micro-structural scenarios that cause variation of strain and stress is the first step in understanding the elevated strains and stresses in bone tissue, which are indicative of higher likelihood of micro-crack formation in bone, implicated in consequent remodeling or macroscopic bone fracture. Evidence that micro-structure varies with clinical history and contributes in significant, but poorly understood, ways to bone function, motivates the method's development, as does need for software tools to investigate relationships between macroscopic loading and micro-structure. Three applications - varying region of interest, bone mineral density, and orientation of collagen type I, illustrate the method. We show, in comparison between physiological loading and simple compression of a patient's femur, that strains computed at the multi-scale model's micro-level: (i) differ; and (ii) depend on local collagen-apatite orientation and degree of calcification. Our findings confirm the strain concentration role of osteocyte lacunae, important for mechano-transduction. We hypothesize occurrence of micro-crack formation, leading either to remodeling or macroscopic fracture, when the computed strains exceed the elastic range observed in micro-structural testing.

  2. Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks.

    PubMed

    DeMarse, Thomas B; Pan, Liangbin; Alagapan, Sankaraleengam; Brewer, Gregory J; Wheeler, Bruce C

    2016-01-01

    Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized

  3. Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks

    PubMed Central

    DeMarse, Thomas B.; Pan, Liangbin; Alagapan, Sankaraleengam; Brewer, Gregory J.; Wheeler, Bruce C.

    2016-01-01

    Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura’s and van Rossum’s spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous

  4. Menstrual cycle effects on selective attention and its underlying cortical networks.

    PubMed

    Thimm, M; Weis, S; Hausmann, M; Sturm, W

    2014-01-31

    It was the aim of the present study to investigate menstrual cycle effects on selective attention and its underlying functional cerebral networks. Twenty-one healthy, right-handed, normally cycling women were investigated by means of functional magnetic resonance imaging using a go/no-go paradigm during the menstrual, follicular and luteal phase. On the behavioral level there was a significant interaction between visual half field and cycle phase with reaction times to right-sided compared to left-sided stimuli being faster in the menstrual compared to the follicular phase. These results might argue for a more pronounced functional cerebral asymmetry toward the left hemisphere in selective attention during the menstrual phase with low estradiol and progesterone levels. Functional imaging, however, did not reveal clear-cut menstrual phase-related changes in activation pattern in parallel to these behavioral findings. A functional connectivity analysis identified differences between the menstrual and the luteal phase: During the menstrual phase, left inferior parietal cortex showed a stronger negative correlation with the right middle frontal gyrus while the left medial frontal cortex showed a stronger negative correlation with the left middle frontal gyrus. These results can serve as further evidence of a modulatory effect of steroid hormones on networks of lateralized cognitive functions not only by interhemispheric inhibition but also by affecting intrahemispheric functional connectivity. PMID:24262802

  5. Simulated, Emulated, and Physical Investigative Analysis (SEPIA) of networked systems.

    SciTech Connect

    Burton, David P.; Van Leeuwen, Brian P.; McDonald, Michael James; Onunkwo, Uzoma A.; Tarman, Thomas David; Urias, Vincent E.

    2009-09-01

    This report describes recent progress made in developing and utilizing hybrid Simulated, Emulated, and Physical Investigative Analysis (SEPIA) environments. Many organizations require advanced tools to analyze their information system's security, reliability, and resilience against cyber attack. Today's security analysis utilize real systems such as computers, network routers and other network equipment, computer emulations (e.g., virtual machines) and simulation models separately to analyze interplay between threats and safeguards. In contrast, this work developed new methods to combine these three approaches to provide integrated hybrid SEPIA environments. Our SEPIA environments enable an analyst to rapidly configure hybrid environments to pass network traffic and perform, from the outside, like real networks. This provides higher fidelity representations of key network nodes while still leveraging the scalability and cost advantages of simulation tools. The result is to rapidly produce large yet relatively low-cost multi-fidelity SEPIA networks of computers and routers that let analysts quickly investigate threats and test protection approaches.

  6. BioNSi: A Discrete Biological Network Simulator Tool.

    PubMed

    Rubinstein, Amir; Bracha, Noga; Rudner, Liat; Zucker, Noga; Sloin, Hadas E; Chor, Benny

    2016-08-01

    Modeling and simulation of biological networks is an effective and widely used research methodology. The Biological Network Simulator (BioNSi) is a tool for modeling biological networks and simulating their discrete-time dynamics, implemented as a Cytoscape App. BioNSi includes a visual representation of the network that enables researchers to construct, set the parameters, and observe network behavior under various conditions. To construct a network instance in BioNSi, only partial, qualitative biological data suffices. The tool is aimed for use by experimental biologists and requires no prior computational or mathematical expertise. BioNSi is freely available at http://bionsi.wix.com/bionsi , where a complete user guide and a step-by-step manual can also be found. PMID:27354160

  7. Evaluation of the performance of information theory-based methods and cross-correlation to estimate the functional connectivity in cortical networks.

    PubMed

    Garofalo, Matteo; Nieus, Thierry; Massobrio, Paolo; Martinoia, Sergio

    2009-01-01

    Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these "connectivity methods" on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings. PMID:19652720

  8. Stimulus-induced reversal of information flow through a cortical network for animacy perception.

    PubMed

    Shultz, Sarah; van den Honert, Rebecca N; Engell, Andrew D; McCarthy, Gregory

    2015-01-01

    Decades of research have demonstrated that a region of the right fusiform gyrus (FG) and right posterior superior temporal sulcus (pSTS) responds preferentially to static faces and biological motion, respectively. Despite this view, both regions activate in response to both stimulus categories and to a range of other stimuli, such as goal-directed actions, suggesting that these regions respond to characteristics of animate agents more generally. Here we propose a neural model for animacy detection composed of processing streams that are initially differentially sensitive to cues signaling animacy, but that ultimately act in concert to support reasoning about animate agents. We use dynamic causal modeling, a measure of effective connectivity, to demonstrate that the directional flow of information between the FG and pSTS is initially dependent on the characteristics of the animate agent presented, a key prediction of our proposed network for animacy detection. PMID:24625785

  9. Stimulus-induced reversal of information flow through a cortical network for animacy perception

    PubMed Central

    Shultz, Sarah; van den Honert, Rebecca N.; Engell, Andrew D.

    2015-01-01

    Decades of research have demonstrated that a region of the right fusiform gyrus (FG) and right posterior superior temporal sulcus (pSTS) responds preferentially to static faces and biological motion, respectively. Despite this view, both regions activate in response to both stimulus categories and to a range of other stimuli, such as goal-directed actions, suggesting that these regions respond to characteristics of animate agents more generally. Here we propose a neural model for animacy detection composed of processing streams that are initially differentially sensitive to cues signaling animacy, but that ultimately act in concert to support reasoning about animate agents. We use dynamic causal modeling, a measure of effective connectivity, to demonstrate that the directional flow of information between the FG and pSTS is initially dependent on the characteristics of the animate agent presented, a key prediction of our proposed network for animacy detection. PMID:24625785

  10. Individual-specific multi-scale finite element simulation of cortical bone of human proximal femur

    SciTech Connect

    Ascenzi, Maria-Grazia; Kardas, Dieter; Nackenhorst, Udo; Keyak, Joyce H.

    2013-07-01

    We present an innovative method to perform multi-scale finite element analyses of the cortical component of the femur using the individual’s (1) computed tomography scan; and (2) a bone specimen obtained in conjunction with orthopedic surgery. The method enables study of micro-structural characteristics regulating strains and stresses under physiological loading conditions. The analysis of the micro-structural scenarios that cause variation of strain and stress is the first step in understanding the elevated strains and stresses in bone tissue, which are indicative of higher likelihood of micro-crack formation in bone, implicated in consequent remodeling or macroscopic bone fracture. Evidence that micro-structure varies with clinical history and contributes in significant, but poorly understood, ways to bone function, motivates the method’s development, as does need for software tools to investigate relationships between macroscopic loading and micro-structure. Three applications – varying region of interest, bone mineral density, and orientation of collagen type I, illustrate the method. We show, in comparison between physiological loading and simple compression of a patient’s femur, that strains computed at the multi-scale model’s micro-level: (i) differ; and (ii) depend on local collagen-apatite orientation and degree of calcification. Our findings confirm the strain concentration role of osteocyte lacunae, important for mechano-transduction. We hypothesize occurrence of micro-crack formation, leading either to remodeling or macroscopic fracture, when the computed strains exceed the elastic range observed in micro-structural testing.

  11. Graphical user interface for wireless sensor networks simulator

    NASA Astrophysics Data System (ADS)

    Paczesny, Tomasz; Paczesny, Daniel; Weremczuk, Jerzy

    2008-01-01

    Wireless Sensor Networks (WSN) are currently very popular area of development. It can be suited in many applications form military through environment monitoring, healthcare, home automation and others. Those networks, when working in dynamic, ad-hoc model, need effective protocols which must differ from common computer networks algorithms. Research on those protocols would be difficult without simulation tool, because real applications often use many nodes and tests on such a big networks take much effort and costs. The paper presents Graphical User Interface (GUI) for simulator which is dedicated for WSN studies, especially in routing and data link protocols evaluation.

  12. A Flexible System for Simulating Aeronautical Telecommunication Network

    NASA Technical Reports Server (NTRS)

    Maly, Kurt; Overstreet, C. M.; Andey, R.

    1998-01-01

    At Old Dominion University, we have built Aeronautical Telecommunication Network (ATN) Simulator with NASA being the fund provider. It provides a means to evaluate the impact of modified router scheduling algorithms on the network efficiency, to perform capacity studies on various network topologies and to monitor and study various aspects of ATN through graphical user interface (GUI). In this paper we describe briefly about the proposed ATN model and our abstraction of this model. Later we describe our simulator architecture highlighting some of the design specifications, scheduling algorithms and user interface. At the end, we have provided the results of performance studies on this simulator.

  13. A neural network simulation package in CLIPS

    NASA Technical Reports Server (NTRS)

    Bhatnagar, Himanshu; Krolak, Patrick D.; Mcgee, Brenda J.; Coleman, John

    1990-01-01

    The intrinsic similarity between the firing of a rule and the firing of a neuron has been captured in this research to provide a neural network development system within an existing production system (CLIPS). A very important by-product of this research has been the emergence of an integrated technique of using rule based systems in conjunction with the neural networks to solve complex problems. The systems provides a tool kit for an integrated use of the two techniques and is also extendible to accommodate other AI techniques like the semantic networks, connectionist networks, and even the petri nets. This integrated technique can be very useful in solving complex AI problems.

  14. Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization

    PubMed Central

    Lewis, Christopher M.; Fries, Pascal

    2016-01-01

    Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous interareal coactivation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across the dorsal aspect of several visual areas in one hemisphere in each of two awake monkeys to investigate spatial patterns of local and interareal synchronization. We show that stimulation-induced patterns of interareal coactivation were reactivated in the absence of stimulation for the visual quadrant covered. Reactivation occurred through both interareal cofluctuation of local activity and interareal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of interareal coupling observed during stimulation, i.e., the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha, and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics. PMID:26787906

  15. From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation.

    PubMed

    Cheron, G; Duvinage, M; De Saedeleer, C; Castermans, T; Bengoetxea, A; Petieau, M; Seetharaman, K; Hoellinger, T; Dan, B; Dutoit, T; Sylos Labini, F; Lacquaniti, F; Ivanenko, Y

    2012-01-01

    Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy. PMID:22272380

  16. Role of medial cortical networks for anticipatory processing in obsessive-compulsive disorder.

    PubMed

    Ciesielski, Kristina T; Rauch, Scott L; Ahlfors, Seppo P; Vangel, Mark E; Wilhelm, Sabine; Rosen, Bruce R; Hämäläinen, Matti S

    2012-09-01

    Recurrent anticipation of ominous events is central to obsessions, the core symptom of obsessive-compulsive disorder (OCD), yet the neural basis of intrinsic anticipatory processing in OCD is unknown. We studied nonmedicated adults with OCD and case matched healthy controls in a visual-spatial working memory task with distractor. Magnetoencephalography was used to examine the medial cortex activity during anticipation of to-be-inhibited distractors and to-be-facilitated retrieval stimuli. In OCD anticipatory activation to distractors was abnormally reduced within the posterior cingulate and fusiform gyrus compared to prominent activation in controls. Conversely, OCD subjects displayed significantly increased activation to retrieval stimuli within the anterior cingulate and supplementary motor cortex. This previously unreported discordant pattern of medial anticipatory activation in OCD was accompanied by normal performance accuracy. While increased anterior cortex activation in OCD is commonly viewed as failure of inhibition, the current pattern of data implicates the operation of an anterior compensatory mechanism amending the posterior medial self-regulatory networks disrupted in OCD. PMID:21882299

  17. Simulating Timescale Dynamics of Network Traffic Using Homogeneous Modeling

    PubMed Central

    Yuan, Jian; Mills, Kevin L.

    2006-01-01

    Simulating and understanding traffic dynamics in large networks are difficult and challenging due to the complexity of such networks and the limitations inherent in simulation modeling. Typically, simulation models used to study traffic dynamics include substantial detail representing protocol mechanisms across several layers of functionality. Such models must be restricted in space and time in order to be computationally tractable. We propose an alternative simulation approach that uses homogeneous modeling with an increased level of abstraction, in order to explore networks at larger space-time scales than otherwise feasible and to develop intuition and insight about the space-time dynamics of large networks. To illustrate the utility of our approach, we examine some current understandings of the timescale dynamics of network traffic, and we discuss some speculative results obtained with homogeneous modeling. Using a wavelet-based technique, we show correlation structures, and changes in correlation structures, of network traffic under variations in traffic sources, transport mechanisms, and network structure. Our simulation results justify further investigation of our approach, which might benefit from cross-verifications against more detailed simulation models. PMID:27274931

  18. Parallel discrete-event simulation of FCFS stochastic queueing networks

    NASA Technical Reports Server (NTRS)

    Nicol, David M.

    1988-01-01

    Physical systems are inherently parallel. Intuition suggests that simulations of these systems may be amenable to parallel execution. The parallel execution of a discrete-event simulation requires careful synchronization of processes in order to ensure the execution's correctness; this synchronization can degrade performance. Largely negative results were recently reported in a study which used a well-known synchronization method on queueing network simulations. Discussed here is a synchronization method (appointments), which has proven itself to be effective on simulations of FCFS queueing networks. The key concept behind appointments is the provision of lookahead. Lookahead is a prediction on a processor's future behavior, based on an analysis of the processor's simulation state. It is shown how lookahead can be computed for FCFS queueing network simulations, give performance data that demonstrates the method's effectiveness under moderate to heavy loads, and discuss performance tradeoffs between the quality of lookahead, and the cost of computing lookahead.

  19. A GIS Tool for simulating Nitrogen transport along schematic Network

    NASA Astrophysics Data System (ADS)

    Tavakoly, A. A.; Maidment, D. R.; Yang, Z.; Whiteaker, T.; David, C. H.; Johnson, S.

    2012-12-01

    An automated method called the Arc Hydro Schematic Processor has been developed for water process computation on schematic networks formed from the NHDPlus and similar GIS river networks. The sechemtaic network represents the hydrologic feature on the ground and is a network of links and nodes. SchemaNodes show hydrologic features, such as catchments or stream junctions. SchemaLinks prescripe the connections between nodes. The schematic processor uses the schematic network to pass informatin through a watershed and move water or pollutants dwonstream. In addition, the schematic processor has a capability to use additional programming applied to the passed and/or received values and manipulating data trough network. This paper describes how the schemtic processor can be used to simulate nitrogen transport and transformation on river networks. For this purpose the nitrogen loads is estimated on the NHDPlus river network using the Schematic Processor coupled with the river routing model for the Texas Gulf Coast Hydrologic Region.

  20. A Network Contention Model for the Extreme-scale Simulator

    SciTech Connect

    Engelmann, Christian; Naughton III, Thomas J

    2015-01-01

    The Extreme-scale Simulator (xSim) is a performance investigation toolkit for high-performance computing (HPC) hardware/software co-design. It permits running a HPC application with millions of concurrent execution threads, while observing its performance in a simulated extreme-scale system. This paper details a newly developed network modeling feature for xSim, eliminating the shortcomings of the existing network modeling capabilities. The approach takes a different path for implementing network contention and bandwidth capacity modeling using a less synchronous and accurate enough model design. With the new network modeling feature, xSim is able to simulate on-chip and on-node networks with reasonable accuracy and overheads.

  1. Efficient event-driven simulations shed new light on microtubule organization in the plant cortical array

    NASA Astrophysics Data System (ADS)

    Tindemans, Simon H.; Deinum, Eva E.; Lindeboom, Jelmer J.; Mulder, Bela M.

    2014-04-01

    The dynamics of the plant microtubule cytoskeleton is a paradigmatic example of the complex spatiotemporal processes characterising life at the cellular scale. This system is composed of large numbers of spatially extended particles, each endowed with its own intrinsic stochastic dynamics, and is capable of non-equilibrium self-organisation through collisional interactions of these particles. To elucidate the behaviour of such a complex system requires not only conceptual advances, but also the development of appropriate computational tools to simulate it. As the number of parameters involved is large and the behaviour is stochastic, it is essential that these simulations be fast enough to allow for an exploration of the phase space and the gathering of sufficient statistics to accurately pin down the average behaviour as well as the magnitude of fluctuations around it. Here we describe a simulation approach that meets this requirement by adopting an event-driven methodology that encompasses both the spontaneous stochastic changes in microtubule state as well as the deterministic collisions. In contrast with finite time step simulations this technique is intrinsically exact, as well as several orders of magnitude faster, which enables ordinary PC hardware to simulate systems of ˜ 10^3 microtubules on a time scale ˜ 10^{3} faster than real time. In addition we present new tools for the analysis of microtubule trajectories on curved surfaces. We illustrate the use of these methods by addressing a number of outstanding issues regarding the importance of various parameters on the transition from an isotropic to an aligned and oriented state.

  2. Cortical neuron activation induced by electromagnetic stimulation: a quantitative analysis via modelling and simulation.

    PubMed

    Wu, Tiecheng; Fan, Jie; Lee, Kim Seng; Li, Xiaoping

    2016-02-01

    Previous simulation works concerned with the mechanism of non-invasive neuromodulation has isolated many of the factors that can influence stimulation potency, but an inclusive account of the interplay between these factors on realistic neurons is still lacking. To give a comprehensive investigation on the stimulation-evoked neuronal activation, we developed a simulation scheme which incorporates highly detailed physiological and morphological properties of pyramidal cells. The model was implemented on a multitude of neurons; their thresholds and corresponding activation points with respect to various field directions and pulse waveforms were recorded. The results showed that the simulated thresholds had a minor anisotropy and reached minimum when the field direction was parallel to the dendritic-somatic axis; the layer 5 pyramidal cells always had lower thresholds but substantial variances were also observed within layers; reducing pulse length could magnify the threshold values as well as the variance; tortuosity and arborization of axonal segments could obstruct action potential initiation. The dependence of the initiation sites on both the orientation and the duration of the stimulus implies that the cellular excitability might represent the result of the competition between various firing-capable axonal components, each with a unique susceptibility determined by the local geometry. Moreover, the measurements obtained in simulation intimately resemble recordings in physiological and clinical studies, which seems to suggest that, with minimum simplification of the neuron model, the cable theory-based simulation approach can have sufficient verisimilitude to give quantitatively accurate evaluation of cell activities in response to the externally applied field. PMID:26719168

  3. Cortical dynamics revisited.

    PubMed

    Singer, Wolf

    2013-12-01

    Recent discoveries on the organisation of the cortical connectome together with novel data on the dynamics of neuronal interactions require an extension of classical concepts on information processing in the cerebral cortex. These new insights justify considering the brain as a complex, self-organised system with nonlinear dynamics in which principles of distributed, parallel processing coexist with serial operations within highly interconnected networks. The observed dynamics suggest that cortical networks are capable of providing an extremely high-dimensional state space in which a large amount of evolutionary and ontogenetically acquired information can coexist and be accessible to rapid parallel search. PMID:24139950

  4. Functional connectivity change across multiple cortical networks relates to episodic memory changes in aging.

    PubMed

    Fjell, Anders M; Sneve, Markus H; Grydeland, Håkon; Storsve, Andreas B; de Lange, Ann-Marie Glasø; Amlien, Inge K; Røgeberg, Ole J; Walhovd, Kristine B

    2015-12-01

    A major task of contemporary cognitive neuroscience of aging is to explain why episodic memory declines. Change in resting-state functional connectivity (rsFC) could be a mechanism accounting for reduced function. We addressed this through 3 studies. In study 1, 119 healthy participants (20-83 years) were followed for 3.5 years with verbal recall testing and magnetic resonance imaging. Independent of atrophy, recall change was related to change in rsFC in anatomically widespread areas. Striking age-effects were observed in that a positive relationship between rsFC and memory characterized older participants while a negative relationship was seen among the younger and middle-aged. This suggests that cognitive consequences of rsFC change are not stable across age. In study 2 and 3, the age-dependent differences in rsFC-memory relationship were replicated by use of a simulation model (study 2) and by a cross-sectional experimental recognition memory task (study 3). In conclusion, memory changes were related to altered rsFC in an age-dependent manner, and future research needs to detail the mechanisms behind age-varying relationships. PMID:26363813

  5. Lightweight simulation of air traffic control using simple temporal networks

    NASA Technical Reports Server (NTRS)

    Knight, Russell

    2005-01-01

    We provide a formulation of the air traffic control problem and a solver for this problem that makes use of temporal constraint networks and simple geometric reasoning. We provide results showing that this approach is practical for realistic simulated problems.

  6. Using automatic programming for simulating reliability network models

    NASA Technical Reports Server (NTRS)

    Tseng, Fan T.; Schroer, Bernard J.; Zhang, S. X.; Wolfsberger, John W.

    1988-01-01

    This paper presents the development of an automatic programming system for assisting modelers of reliability networks to define problems and then automatically generate the corresponding code in the target simulation language GPSS/PC.

  7. Neural network simulations of the nervous system.

    PubMed

    van Leeuwen, J L

    1990-01-01

    Present knowledge of brain mechanisms is mainly based on anatomical and physiological studies. Such studies are however insufficient to understand the information processing of the brain. The present new focus on neural network studies is the most likely candidate to fill this gap. The present paper reviews some of the history and current status of neural network studies. It signals some of the essential problems for which answers have to be found before substantial progress in the field can be made. PMID:2245130

  8. Combined exposure to simulated microgravity and acute or chronic radiation reduces neuronal network integrity and cell survival

    NASA Astrophysics Data System (ADS)

    Benotmane, Rafi

    During orbital or interplanetary space flights, astronauts are exposed to cosmic radiations and microgravity. This study aimed at assessing the effect of these combined conditions on neuronal network density, cell morphology and survival, using well-connected mouse cortical neuron cultures. To this end, neurons were exposed to acute low and high doses of low LET (X-rays) radiation or to chronic low dose-rate of high LET neutron irradiation (Californium-252), under the simulated microgravity generated by the Random Positioning Machine (RPM, Dutch space). High content image analysis of cortical neurons positive for the neuronal marker βIII-tubulin unveiled a reduced neuronal network integrity and connectivity, and an altered cell morphology after exposure to acute/chronic radiation or to simulated microgravity. Additionally, in both conditions, a defect in DNA-repair efficiency was revealed by an increased number of γH2AX-positive foci, as well as an increased number of Annexin V-positive apoptotic neurons. Of interest, when combining both simulated space conditions, we noted a synergistic effect on neuronal network density, neuronal morphology, cell survival and DNA repair. Furthermore, these observations are in agreement with preliminary gene expression data, revealing modulations in cytoskeletal and apoptosis-related genes after exposure to simulated microgravity. In conclusion, the observed in vitro changes in neuronal network integrity and cell survival induced by space simulated conditions provide us with mechanistic understanding to evaluate health risks and the development of countermeasures to prevent neurological disorders in astronauts over long-term space travels. Acknowledgements: This work is supported partly by the EU-FP7 projects CEREBRAD (n° 295552)

  9. Pathological effects of cortical architecture on working memory in schizophrenia.

    PubMed

    Gore, C D; Bányai, M; Gray, P J; Diwadkar, V; Erdi, P

    2010-05-01

    Neural connectivity of the prefrontal cortex is essential to working memory. Reduction of prefrontal connectivity and abnormal prefrontal dopamine modulation are common characteristics associated with schizophrenia. Two experiments separately modeled the effects of exaggerated pruning and of synaptic depression to imitate schizophrenic performance in a prefrontal neural network. In the first model, effects of cortical pruning were simulated with a set of scale-free networks of neurons and compared with empirical results from the Sternberg working memory task. The second set of simulations were based on the synaptic theory of working memory. Simulations of this model measured memory duration in relation to synaptic facilitation and depression constants and in relation to the level of neural connectivity. In the first set of simulations, modulating levels of cortical pruning resulted in a gain or loss in accuracy and speed of memory recollection. In the second set of simulations, increased facilitation time constants and decreased inhibitory time constants resulting in longer memory durations, and overly connected networks resulted in very low memory durations. In the first model, the decline in memory performance can be attributed to the emergence of pathological memory behavior brought about by the warping of the basins of attraction. Collectively, the simulations demonstrate that a reduction of prefrontal cortical hubs can lead to schizophrenia like performance in neural networks, and may account for pathological working memory in the disorder. PMID:20480449

  10. Simulation studies of a wide area health care network.

    PubMed Central

    McDaniel, J. G.

    1994-01-01

    There is an increasing number of efforts to install wide area health care networks. Some of these networks are being built to support several applications over a wide user base consisting primarily of medical practices, hospitals, pharmacies, medical laboratories, payors, and suppliers. Although on-line, multi-media telecommunication is desirable for some purposes such as cardiac monitoring, store-and-forward messaging is adequate for many common, high-volume applications. Laboratory test results and payment claims, for example, can be distributed using electronic messaging networks. Several network prototypes have been constructed to determine the technical problems and to assess the effectiveness of electronic messaging in wide area health care networks. Our project, Health Link, developed prototype software that was able to use the public switched telephone network to exchange messages automatically, reliably and securely. The network could be configured to accommodate the many different traffic patterns and cost constraints of its users. Discrete event simulations were performed on several network models. Canonical star and mesh networks, that were composed of nodes operating at steady state under equal loads, were modeled. Both topologies were found to support the throughput of a generic wide area health care network. The mean message delivery time of the mesh network was found to be less than that of the star network. Further simulations were conducted for a realistic large-scale health care network consisting of 1,553 doctors, 26 hospitals, four medical labs, one provincial lab and one insurer. Two network topologies were investigated: one using predominantly peer-to-peer communication, the other using client-server communication.(ABSTRACT TRUNCATED AT 250 WORDS) PMID:7949966

  11. PyNN: A Common Interface for Neuronal Network Simulators

    PubMed Central

    Davison, Andrew P.; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

    2008-01-01

    Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

  12. PyNN: A Common Interface for Neuronal Network Simulators.

    PubMed

    Davison, Andrew P; Brüderle, Daniel; Eppler, Jochen; Kremkow, Jens; Muller, Eilif; Pecevski, Dejan; Perrinet, Laurent; Yger, Pierre

    2008-01-01

    Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or configuration language, leading to considerable difficulty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confidence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefits. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modification on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. PMID:19194529

  13. Effectiveness of Simulation in a Hybrid and Online Networking Course.

    ERIC Educational Resources Information Center

    Cameron, Brian H.

    2003-01-01

    Reports on a study that compares the performance of students enrolled in two sections of a Web-based computer networking course: one utilizing a simulation package and the second utilizing a static, graphical software package. Analysis shows statistically significant improvements in performance in the simulation group compared to the…

  14. Least-squares (LS) deconvolution of a series of overlapping cortical auditory evoked potentials: a simulation and experimental study

    NASA Astrophysics Data System (ADS)

    Bardy, Fabrice; Van Dun, Bram; Dillon, Harvey; Cowan, Robert

    2014-08-01

    Objective. To evaluate the viability of disentangling a series of overlapping ‘cortical auditory evoked potentials’ (CAEPs) elicited by different stimuli using least-squares (LS) deconvolution, and to assess the adaptation of CAEPs for different stimulus onset-asynchronies (SOAs). Approach. Optimal aperiodic stimulus sequences were designed by controlling the condition number of matrices associated with the LS deconvolution technique. First, theoretical considerations of LS deconvolution were assessed in simulations in which multiple artificial overlapping responses were recovered. Second, biological CAEPs were recorded in response to continuously repeated stimulus trains containing six different tone-bursts with frequencies 8, 4, 2, 1, 0.5, 0.25 kHz separated by SOAs jittered around 150 (120-185), 250 (220-285) and 650 (620-685) ms. The control condition had a fixed SOA of 1175 ms. In a second condition, using the same SOAs, trains of six stimuli were separated by a silence gap of 1600 ms. Twenty-four adults with normal hearing (<20 dB HL) were assessed. Main results. Results showed disentangling of a series of overlapping responses using LS deconvolution on simulated waveforms as well as on real EEG data. The use of rapid presentation and LS deconvolution did not however, allow the recovered CAEPs to have a higher signal-to-noise ratio than for slowly presented stimuli. The LS deconvolution technique enables the analysis of a series of overlapping responses in EEG. Significance. LS deconvolution is a useful technique for the study of adaptation mechanisms of CAEPs for closely spaced stimuli whose characteristics change from stimulus to stimulus. High-rate presentation is necessary to develop an understanding of how the auditory system encodes natural speech or other intrinsically high-rate stimuli.

  15. Delay Tolerant Networking - Bundle Protocol Simulation

    NASA Technical Reports Server (NTRS)

    SeGui, John; Jenning, Esther

    2006-01-01

    In this paper, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the useof MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol and discuss statistics gathered concerning the total time needed to simulate numerous bundle transmissions.

  16. Evaluation of Network-Based Minimally Invasive VR Surgery Simulator.

    PubMed

    Tagawa, Kazuyoshi; Tanaka, Hiromi T; Kurumi, Yoshimasa; Komori, Masaru; Morikawa, Shigehiro

    2016-01-01

    In this paper, we report a result of an experiment of a field trial of our network-based minimally invasive surgery simulator. In our previous paper, we proposed a network-based visuohaptic surgery training system for laparoscopic surgery. In addition, we proposed a volume-based haptic communication approach, which allows participants at remote sites on the network to simultaneously interact with the same target object in virtual environments presented by multi-level computer performance systems, by only exchanging a small set of manipulation parameters for the target object and additional packet for synchronization of status of binary tree and deformation of shared volume model. We implemented the approach into our network-based surgery simulator, and field trial of the simulator at three locations was performed. PMID:27046613

  17. Artificial Market Simulation with Embedded Complex Network Structures

    NASA Astrophysics Data System (ADS)

    Uchida, Makoto; Shirayama, Susumu

    We investigate a factor of the `network effect' that affects on communication service markets by a multi-agent based simulation approach. The network effect is one of a market characteristic, whereby the benefit of a service or a product increase with use. So far, the network effect has been studied in terms of macroscopic metrics, and interaction patterns of consumers in the market were often ignored. To investigate an infulence of structures of the interaction patterns, we propose a multi-agent based model for a communication serivce market, in which embedded complex network structures are considered as an interaction pattern of agents. Using several complex network models as the interaction patterns, we study the dynamics of a market in which two providers are competing. By a series of simulations, we show that the structural properties of the complex networks, such as the clustering coefficient and degree correlations, are the major factors of the network effect. We also discuss an adequate model of the interaction pattern for reproducing the market dynamics in the real world by performing simulations exploiting with a real data of social network.

  18. Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

    1997-01-01

    The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

  19. Urban traffic-network performance: flow theory and simulation experiments

    SciTech Connect

    Williams, J.C.

    1986-01-01

    Performance models for urban street networks were developed to describe the response of a traffic network to given travel-demand levels. The three basic traffic flow variables, speed, flow, and concentration, are defined at the network level, and three model systems are proposed. Each system consists of a series of interrelated, consistent functions between the three basic traffic-flow variables as well as the fraction of stopped vehicles in the network. These models are subsequently compared with the results of microscopic simulation of a small test network. The sensitivity of one of the model systems to a variety of network features was also explored. Three categories of features were considered, with the specific features tested listed in parentheses: network topology (block length and street width), traffic control (traffic signal coordination), and traffic characteristics (level of inter-vehicular interaction). Finally, a fundamental issue concerning the estimation of two network-level parameters (from a nonlinear relation in the two-fluid theory) was examined. The principal concern was that of comparability of these parameters when estimated with information from a single vehicle (or small group of vehicles), as done in conjunction with previous field studies, and when estimated with network-level information (i.e., all the vehicles), as is possible with simulation.

  20. Documentation for the token ring network simulation system

    NASA Technical Reports Server (NTRS)

    Peden, Jeffery H.; Weaver, Alfred C.

    1990-01-01

    A manual is presented which describes the language features of the Token Ring Network Simulation System. The simulation system is a powerful simulation tool for token ring networks which allows the specification of various Medium Access Control (MAC) layer protocols as well as the specification of various features of upper layer ISO protocols. In addition to these features, it also allows the user to specify message and station classes virtually to any degree of detail desired. The choice of a language instead of an interactive system to specify network parameters was dictated by both flexibility and time considerations. The language was developed specifically for the simulation system, and is very simple. It is also user friendly in that language elements which do not apply to the case at hand are ignored rather than treated as errors.

  1. CUBICORT: simulation of the visual cortical system for 3D image analysis, synthesis, and hypercompression for digital TV, HDTV, and multimedia

    NASA Astrophysics Data System (ADS)

    Leray, Pascal; Guyot, F.; Marchal, Patrick; Burnod, Yves

    1994-05-01

    We describe simulation elements of a new kind of 3D vision simulator, for preprocessing objects and movement analysis in 3D, using the biological concept of the cortical column paradigm in the visual area. The target simulator is primarily dedicated to ultra high image compression for the telecommunication of digital TV images (MPEG4), HDTV, and 3D TV, but can also be used for automatic modeling, digitizing, robotics, and image synthesis. This simulator extracts 3D objects and movements by using the properties of hypercolumns within the visual cortex for spatio-temporal pyramidal filtering, learning, and performs inter and intra-cooperation between these simulated hypercolumns. The simulation process has four levels for analysis - synthesis: pixels, zones, objects and labels. Final synthesis (reconstruction) is processed by reverse filtering, using non-orthogonal basis filters. Substantial upgrades in terms of compression ratio have been estimated using this algorithm as a whole, or partially, with integrated VLSI.

  2. Spontaneous EEG alpha oscillation interacts with positive and negative BOLD responses in the visual-auditory cortices and default-mode network.

    PubMed

    Mayhew, Stephen D; Ostwald, Dirk; Porcaro, Camillo; Bagshaw, Andrew P

    2013-08-01

    The human brain is continually, dynamically active and spontaneous fluctuations in this activity play a functional role in affecting both behavioural and neuronal responses. However, the mechanisms through which this occurs remain poorly understood. Simultaneous EEG-fMRI is a promising technique to study how spontaneous activity modulates the brain's response to stimulation, as temporal indices of ongoing cortical excitability can be integrated with spatially localised evoked responses. Here we demonstrate an interaction between the ongoing power of the electrophysiological alpha oscillation and the magnitude of both positive (PBR) and negative (NBR) fMRI responses to two contrasts of visual checkerboard reversal. Furthermore, the amplitude of pre-stimulus EEG alpha-power significantly modulated the amplitude and shape of subsequent PBR and NBR to the visual stimulus. A nonlinear reduction of visual PBR and an enhancement of auditory NBR and default-mode network NBR were observed in trials preceded by high alpha-power. These modulated areas formed a functionally connected network during a separate resting-state recording. Our findings suggest that the "baseline" state of the brain exhibits considerable trial-to-trial variability which arises from fluctuations in the balance of cortical inhibition/excitation that are represented by respective increases/decreases in the power of the EEG alpha oscillation. The consequence of this spontaneous electrophysiological variability is modulated amplitudes of both PBR and NBR to stimulation. Fluctuations in alpha-power may subserve a functional relationship in the visual-auditory network, acting as mediator for both short and long-range cortical inhibition, the strength of which is represented in part by NBR. PMID:23507378

  3. Unaltered Network Activity and Interneuronal Firing During Spontaneous Cortical Dynamics In Vivo in a Mouse Model of Severe Myoclonic Epilepsy of Infancy

    PubMed Central

    De Stasi, Angela Michela; Farisello, Pasqualina; Marcon, Iacopo; Cavallari, Stefano; Forli, Angelo; Vecchia, Dania; Losi, Gabriele; Mantegazza, Massimo; Panzeri, Stefano; Carmignoto, Giorgio; Bacci, Alberto; Fellin, Tommaso

    2016-01-01

    Severe myoclonic epilepsy of infancy (SMEI) is associated with loss of function of the SCN1A gene encoding the NaV1.1 sodium channel isoform. Previous studies in Scn1a−/+ mice during the pre-epileptic period reported selective reduction in interneuron excitability and proposed this as the main pathological mechanism underlying SMEI. Yet, the functional consequences of this interneuronal dysfunction at the circuit level in vivo are unknown. Here, we investigated whether Scn1a−/+ mice showed alterations in cortical network function. We found that various forms of spontaneous network activity were similar in Scn1a−/+ during the pre-epileptic period compared with wild-type (WT) in vivo. Importantly, in brain slices from Scn1a−/+ mice, the excitability of parvalbumin (PV) and somatostatin (SST) interneurons was reduced, epileptiform activity propagated more rapidly, and complex synaptic changes were observed. However, in vivo, optogenetic reduction of firing in PV or SST cells in WT mice modified ongoing network activities, and juxtasomal recordings from identified PV and SST interneurons showed unaffected interneuronal firing during spontaneous cortical dynamics in Scn1a−/+ compared with WT. These results demonstrate that interneuronal hypoexcitability is not observed in Scn1a−/+ mice during spontaneous activities in vivo and suggest that additional mechanisms may contribute to homeostatic rearrangements and the pathogenesis of SMEI. PMID:26819275

  4. Artificial neural network simulation of battery performance

    SciTech Connect

    O`Gorman, C.C.; Ingersoll, D.; Jungst, R.G.; Paez, T.L.

    1998-12-31

    Although they appear deceptively simple, batteries embody a complex set of interacting physical and chemical processes. While the discrete engineering characteristics of a battery such as the physical dimensions of the individual components, are relatively straightforward to define explicitly, their myriad chemical and physical processes, including interactions, are much more difficult to accurately represent. Within this category are the diffusive and solubility characteristics of individual species, reaction kinetics and mechanisms of primary chemical species as well as intermediates, and growth and morphology characteristics of reaction products as influenced by environmental and operational use profiles. For this reason, development of analytical models that can consistently predict the performance of a battery has only been partially successful, even though significant resources have been applied to this problem. As an alternative approach, the authors have begun development of a non-phenomenological model for battery systems based on artificial neural networks. Both recurrent and non-recurrent forms of these networks have been successfully used to develop accurate representations of battery behavior. The connectionist normalized linear spline (CMLS) network has been implemented with a self-organizing layer to model a battery system with the generalized radial basis function net. Concurrently, efforts are under way to use the feedforward back propagation network to map the {open_quotes}state{close_quotes} of a battery system. Because of the complexity of battery systems, accurate representation of the input and output parameters has proven to be very important. This paper describes these initial feasibility studies as well as the current models and makes comparisons between predicted and actual performance.

  5. Integrated Circuit For Simulation Of Neural Network

    NASA Technical Reports Server (NTRS)

    Thakoor, Anilkumar P.; Moopenn, Alexander W.; Khanna, Satish K.

    1988-01-01

    Ballast resistors deposited on top of circuit structure. Cascadable, programmable binary connection matrix fabricated in VLSI form as basic building block for assembly of like units into content-addressable electronic memory matrices operating somewhat like networks of neurons. Connections formed during storage of data, and data recalled from memory by prompting matrix with approximate or partly erroneous signals. Redundancy in pattern of connections causes matrix to respond with correct stored data.

  6. Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

    PubMed Central

    Nick, Christoph; Yadav, Sandeep; Joshi, Ravi

    2014-01-01

    Summary The growth of cortical neurons on three dimensional structures of spatially defined (structured) randomly oriented, as well as on vertically aligned, carbon nanotubes (CNT) is studied. Cortical neurons are attracted towards both types of CNT nano-architectures. For both, neurons form clusters in close vicinity to the CNT structures whereupon the randomly oriented CNTs are more closely colonised than the CNT pillars. Neurons develop communication paths via neurites on both nanoarchitectures. These neuron cells attach preferentially on the CNT sidewalls of the vertically aligned CNT architecture instead than onto the tips of the individual CNT pillars. PMID:25247139

  7. Influence of porosity, pore size, and cortical thickness on the propagation of ultrasonic waves guided through the femoral neck cortex: a simulation study.

    PubMed

    Rohde, Kerstin; Rohrbach, Daniel; Glüer, Claus-C; Laugier, Pascal; Grimal, Quentin; Raum, Kay; Barkmann, Reinhard

    2014-02-01

    The femoral neck is a common fracture site in elderly people. The cortical shell is thought to be the major contributor to the mechanical competence of the femoral neck, but its microstructural parameters are not sufficiently accessible under in vivo conditions with current X-ray-based methods. To systematically investigate the influences of pore size, porosity, and thickness of the femoral neck cortex on the propagation of ultrasound, we developed 96 different bone models (combining 6 different pore sizes with 4 different porosities and 4 different thicknesses) and simulated the ultrasound propagation using a finite-difference time-domain algorithm. The simulated single-element emitter and receiver array consisting of 16 elements (8 inferior and 8 superior) were placed at anterior and posterior sides of the bone, respectively (transverse transmission). From each simulation, we analyzed the waveform collected by each of the inferior receiver elements for the one with the shortest time of flight. The first arriving signal of this waveform, which is associated with the wave traveling through the cortical shell, was then evaluated for its three different waveform characteristics (TOF: time point of the first point of inflection of the received signal, Δt: difference between the time point at which the signal first crosses the zero baseline and TOF, and A: amplitude of the first extreme of the first arriving signal). From the analyses of these waveform characteristics, we were able to develop multivariate models to predict pore size, porosity, and cortical thickness, corresponding to the 96 different bone models, with remaining errors in the range of 50 μm for pore size, 1.5% for porosity, and 0.17 mm for cortical thickness. PMID:24474136

  8. Effect of bone cortical thickness on velocity measurements using ultrasonic axial transmission: A 2D simulation study

    NASA Astrophysics Data System (ADS)

    Bossy, Emmanuel; Talmant, Maryline; Laugier, Pascal

    2002-07-01

    In recent years, quantitative ultrasound (QUS) has played an increasing role in the assessment of bone status. The axial transmission technique allows to investigate skeletal sites such as the cortical layer of long bones (radius, tibia), inadequate to through-transmission techniques. Nevertheless, the type of propagation involved along bone specimens has not been clearly elucidated. Axial transmission is investigated here by means of two-dimensional simulations at 1 MHz. We focus our interest on the apparent speed of sound (SOS) of the first arriving signal (FAS). Its dependence on the thickness of the plate is discussed and compared to previous work. Different time criteria are used to derive the apparent SOS of the FAS as a function of source-receiver distance. Frequency-wave number analysis is performed in order to understand the type of propagation involved. For thick plates (thickness>lambdabone, longitudinal wavelength in bone), and for a limited range of source-receiver distances, the FAS corresponds to the lateral wave. Its velocity equals the longitudinal bulk velocity of the bone. For plate thickness less than lambdabone, some plate modes contribute to the FAS, and the apparent SOS decreases with the thickness in a way that depends on both the time criterion and on the source-receiver distance. The FAS corresponds neither to the lateral wave nor to a single plate mode. For very thin plates (thicknessbone)/4, the apparent SOS tends towards the velocity of the lowest order symmetrical vibration mode (S0 Lamb mode). copyright 2002 Acoustical Society of America.

  9. SIMULATING FISH ASSEMBLAGE DYNAMICS IN RIVER NETWORKS

    EPA Science Inventory

    My recently retired colleague, Joan Baker, and I have developed a prototype computer simulation model for studying the effects of human and non-human alterations of habitats and species availability on fish assemblage populations. The fish assemblage model, written in R, is a sp...

  10. Decreased functional connectivity in dorsolateral prefrontal cortical networks in adult macaques with neonatal hippocampal lesions: Relations to visual working memory deficits.

    PubMed

    Meng, Yuguang; Hu, Xiaoping; Bachevalier, Jocelyne; Zhang, Xiaodong

    2016-10-01

    Neonatal hippocampal lesions in monkeys impairs normal performance on both relational and working memory tasks, suggesting that the early lesions have impacted the normal development of prefrontal-hippocampal functional interactions necessary for normal performance on these tasks. Given that working memory processes engage distributed neuronal networks associated with the prefrontal cortex, it is critical to explore the integrity of distributed neural networks of dorsolateral prefrontal cortex (dlPFC) following neonatal hippocampal lesions in monkeys. We used resting-state functional MRI to assess functional connectivity of dlPFC networks in monkeys with neonatal neurotoxic hippocampal lesion (Neo-Hibo, n=4) and sham-operated control animals (Neo-C, n=4). Significant differences in the patterns of dlPFC functional networks were found between Groups Neo-Hibo and Neo-C. The within-group maps and the between-group comparisons yielded a highly coherent picture showing altered interactions of core regions of the working memory network (medial prefrontal cortex and posterior parietal cortex) as well as the dorsal (fundus of superior temporal area and superior temporal cortex) and ventral (V4 and infero-temporal cortex) visual processing areas in animals with Neo-Hibo lesions. Correlations between functional connectivity changes and working memory impairment in the same animals were found only between the dlPFC and visual cortical areas (V4 and infero-temporal cortex). Thus, the impact of the neonatal hippocampal lesions extends to multiple cortical areas interconnected with the dlPFC. PMID:27063864

  11. Neural Network Simulation Package from Ohio State University

    SciTech Connect

    Wickham, K.L.

    1990-08-01

    This report describes the Neural Network Simulation Package acquired from Ohio State University. The package known as Neural Shell V2.1 was evaluated and benchmarked at the INEL Supercomputing Center (ISC). The emphasis was on the Back Propagation Net which is currently considered one of the more promising types of neural networks. This report also provides additional documentation that may be helpful to anyone using the package.

  12. F77NNS - A FORTRAN-77 NEURAL NETWORK SIMULATOR

    NASA Technical Reports Server (NTRS)

    Mitchell, P. H.

    1994-01-01

    F77NNS (A FORTRAN-77 Neural Network Simulator) simulates the popular back error propagation neural network. F77NNS is an ANSI-77 FORTRAN program designed to take advantage of vectorization when run on machines having this capability, but it will run on any computer with an ANSI-77 FORTRAN Compiler. Artificial neural networks are formed from hundreds or thousands of simulated neurons, connected to each other in a manner similar to biological nerve cells. Problems which involve pattern matching or system modeling readily fit the class of problems which F77NNS is designed to solve. The program's formulation trains a neural network using Rumelhart's back-propagation algorithm. Typically the nodes of a network are grouped together into clumps called layers. A network will generally have an input layer through which the various environmental stimuli are presented to the network, and an output layer for determining the network's response. The number of nodes in these two layers is usually tied to features of the problem being solved. Other layers, which form intermediate stops between the input and output layers, are called hidden layers. The back-propagation training algorithm can require massive computational resources to implement a large network such as a network capable of learning text-to-phoneme pronunciation rules as in the famous Sehnowski experiment. The Sehnowski neural network learns to pronounce 1000 common English words. The standard input data defines the specific inputs that control the type of run to be made, and input files define the NN in terms of the layers and nodes, as well as the input/output (I/O) pairs. The program has a restart capability so that a neural network can be solved in stages suitable to the user's resources and desires. F77NNS allows the user to customize the patterns of connections between layers of a network. The size of the neural network to be solved is limited only by the amount of random access memory (RAM) available to the

  13. Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

    PubMed

    Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G

    2016-06-01

    Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits. PMID:27248512

  14. Stochastic Simulation of Biomolecular Networks in Dynamic Environments

    PubMed Central

    Voliotis, Margaritis; Thomas, Philipp; Grima, Ramon; Bowsher, Clive G.

    2016-01-01

    Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits. PMID:27248512

  15. Simulation of dynamic processes with adaptive neural networks.

    SciTech Connect

    Tzanos, C. P.

    1998-02-03

    Many industrial processes are highly non-linear and complex. Their simulation with first-principle or conventional input-output correlation models is not satisfactory, either because the process physics is not well understood, or it is so complex that direct simulation is either not adequately accurate, or it requires excessive computation time, especially for on-line applications. Artificial intelligence techniques (neural networks, expert systems, fuzzy logic) or their combination with simple process-physics models can be effectively used for the simulation of such processes. Feedforward (static) neural networks (FNNs) can be used effectively to model steady-state processes. They have also been used to model dynamic (time-varying) processes by adding to the network input layer input nodes that represent values of input variables at previous time steps. The number of previous time steps is problem dependent and, in general, can be determined after extensive testing. This work demonstrates that for dynamic processes that do not vary fast with respect to the retraining time of the neural network, an adaptive feedforward neural network can be an effective simulator that is free of the complexities introduced by the use of input values at previous time steps.

  16. High Fidelity Simulations of Large-Scale Wireless Networks

    SciTech Connect

    Onunkwo, Uzoma; Benz, Zachary

    2015-11-01

    The worldwide proliferation of wireless connected devices continues to accelerate. There are 10s of billions of wireless links across the planet with an additional explosion of new wireless usage anticipated as the Internet of Things develops. Wireless technologies do not only provide convenience for mobile applications, but are also extremely cost-effective to deploy. Thus, this trend towards wireless connectivity will only continue and Sandia must develop the necessary simulation technology to proactively analyze the associated emerging vulnerabilities. Wireless networks are marked by mobility and proximity-based connectivity. The de facto standard for exploratory studies of wireless networks is discrete event simulations (DES). However, the simulation of large-scale wireless networks is extremely difficult due to prohibitively large turnaround time. A path forward is to expedite simulations with parallel discrete event simulation (PDES) techniques. The mobility and distance-based connectivity associated with wireless simulations, however, typically doom PDES and fail to scale (e.g., OPNET and ns-3 simulators). We propose a PDES-based tool aimed at reducing the communication overhead between processors. The proposed solution will use light-weight processes to dynamically distribute computation workload while mitigating communication overhead associated with synchronizations. This work is vital to the analytics and validation capabilities of simulation and emulation at Sandia. We have years of experience in Sandia’s simulation and emulation projects (e.g., MINIMEGA and FIREWHEEL). Sandia’s current highly-regarded capabilities in large-scale emulations have focused on wired networks, where two assumptions prevent scalable wireless studies: (a) the connections between objects are mostly static and (b) the nodes have fixed locations.

  17. Synthesis of recurrent neural networks for dynamical system simulation.

    PubMed

    Trischler, Adam P; D'Eleuterio, Gabriele M T

    2016-08-01

    We review several of the most widely used techniques for training recurrent neural networks to approximate dynamical systems, then describe a novel algorithm for this task. The algorithm is based on an earlier theoretical result that guarantees the quality of the network approximation. We show that a feedforward neural network can be trained on the vector-field representation of a given dynamical system using backpropagation, then recast it as a recurrent network that replicates the original system's dynamics. After detailing this algorithm and its relation to earlier approaches, we present numerical examples that demonstrate its capabilities. One of the distinguishing features of our approach is that both the original dynamical systems and the recurrent networks that simulate them operate in continuous time. PMID:27182811

  18. Transcranial slow oscillation stimulation during NREM sleep enhances acquisition of the radial maze task and modulates cortical network activity in rats

    PubMed Central

    Binder, Sonja; Rawohl, Julia; Born, Jan; Marshall, Lisa

    2014-01-01

    Slow wave sleep, hallmarked by the occurrence of slow oscillations (SO), plays an important role for the consolidation of hippocampus-dependent memories. Transcranial stimulation by weak electric currents oscillating at the endogenous SO frequency (SO-tDCS) during post-learning sleep was previously shown by us to boost SO activity and improve the consolidation of hippocampus-dependent memory in human subjects. Here, we aimed at replicating and extending these results to a rodent model. Rats were trained for 12 days at the beginning of their inactive phase in the reference memory version of the radial arm maze. In a between subjects design, animals received SO-tDCS over prefrontal cortex (PFC) or sham stimulation within a time frame of 1 h during subsequent non-rapid eye movement (NREM) sleep. Applied over multiple daily sessions SO-tDCS impacted cortical network activity as measured by EEG and behavior: at the EEG level, SO-tDCS enhanced post-stimulation upper delta (2–4 Hz) activity whereby the first stimulations of each day were preferentially affected. Furthermore, commencing on day 8, SO-tDCS acutely decreased theta activity indicating long-term effects on cortical networks. Behaviorally, working memory for baited maze arms was enhanced up to day 4, indicating enhanced consolidation of task-inherent rules, while reference memory errors did not differ between groups. Taken together, we could show here for the first time an effect of SO-tDCS during NREM sleep on cognitive functions and on cortical activity in a rodent model. PMID:24409131

  19. SIRS Dynamics on Random Networks: Simulations and Analytical Models

    NASA Astrophysics Data System (ADS)

    Rozhnova, Ganna; Nunes, Ana

    The standard pair approximation equations (PA) for the Susceptible-Infective-Recovered-Susceptible (SIRS) model of infection spread on a network of homogeneous degree k predict a thin phase of sustained oscillations for parameter values that correspond to diseases that confer long lasting immunity. Here we present a study of the dependence of this oscillatory phase on the parameter k and of its relevance to understand the behaviour of simulations on networks. For k = 4, we compare the phase diagram of the PA model with the results of simulations on regular random graphs (RRG) of the same degree. We show that for parameter values in the oscillatory phase, and even for large system sizes, the simulations either die out or exhibit damped oscillations, depending on the initial conditions. This failure of the standard PA model to capture the qualitative behaviour of the simulations on large RRGs is currently being investigated.

  20. Developmental precursors of social brain networks: the emergence of attentional and cortical sensitivity to facial expressions in 5 to 7 months old infants.

    PubMed

    Yrttiaho, Santeri; Forssman, Linda; Kaatiala, Jussi; Leppänen, Jukka M

    2014-01-01

    Biases in attention towards facial cues during infancy may have an important role in the development of social brain networks. The current study used a longitudinal design to examine the stability of infants' attentional biases towards facial expressions and to elucidate how these biases relate to emerging cortical sensitivity to facial expressions. Event-related potential (ERP) and attention disengagement data were acquired in response to the presentation of fearful, happy, neutral, and phase-scrambled face stimuli from the same infants at 5 and 7 months of age. The tendency to disengage from faces was highly consistent across both ages. However, the modulation of this behavior by fearful facial expressions was uncorrelated between 5 and 7 months. In the ERP data, fear-sensitive activity was observed over posterior scalp regions, starting at the latency of the N290 wave. The scalp distribution of this sensitivity to fear in ERPs was dissociable from the topography of face-sensitive modulation within the same latency range. While attentional bias scores were independent of co-registered ERPs, attention bias towards fearful faces at 5 months of age predicted the fear-sensitivity in ERPs at 7 months of age. The current results suggest that the attention bias towards fear could be involved in the developmental tuning of cortical networks for social signals of emotion. PMID:24968161

  1. On-off intermittency of thalamo-cortical neuronal network oscillations in the electroencephalogram of rodents with genetic predisposition to absence epilepsy

    NASA Astrophysics Data System (ADS)

    Hramov, Alexander E.; Grubov, Vadim V.; Pavlov, Alexey N.; Sitnikova, Evgenija Yu.; Koronovskii, Alexey A.; Runnova, Anastasija E.; Shurugina, Sveltlana A.; Ivanov, Alexey V.

    2013-02-01

    Spike-wave discharges are electroencephalographic hallmarks of absence epilepsy. Spike-wave discharges are known to originate from thalamo-cortical neuronal network that normally produces sleep spindle oscillations. Although both sleep spindles and spike-wave discharges are considered as thalamo-cortical oscillations, functional relationship between them is still uncertain. The present study describes temporal dynamics of spike-wave discharges and sleep spindles as determined in long-time electroencephalograms (EEG) recorded in WAG/Rij rat model of absence epilepsy. We have proposed the wavelet-based method for the automatic detection of spike-wave discharges, sleep spindles (10-15Hz) and 5-9Hz oscillations in EEG. It was found that non-linear dynamics of spike-wave discharges and sleep spindles fits well to the law of 'on-off intermittency'. Intermittency in sleep spindles and spike-wave discharges implies that (1) temporal dynamics of these oscillations are deterministic in nature, and (2) it might be controlled by a system-level mechanism responsible for circadian modulation of neuronal network activity.

  2. Cortical motion deafness.

    PubMed

    Ducommun, Christine Y; Michel, Christoph M; Clarke, Stephanie; Adriani, Michela; Seeck, Margitta; Landis, Theodor; Blanke, Olaf

    2004-09-16

    The extent to which the auditory system, like the visual system, processes spatial stimulus characteristics such as location and motion in separate specialized neuronal modules or in one homogeneously distributed network is unresolved. Here we present a patient with a selective deficit for the perception and discrimination of auditory motion following resection of the right anterior temporal lobe and the right posterior superior temporal gyrus (STG). Analysis of stimulus identity and location within the auditory scene remained intact. In addition, intracranial auditory evoked potentials, recorded preoperatively, revealed motion-specific responses selectively over the resected right posterior STG, and electrical cortical stimulation of this region was experienced by the patient as incoming moving sounds. Collectively, these data present a patient with cortical motion deafness, providing evidence that cortical processing of auditory motion is performed in a specialized module within the posterior STG. PMID:15363389

  3. Introducing FNCS: Framework for Network Co-Simulation

    SciTech Connect

    2014-10-23

    This video provides a basic overview of the PNNL Future Power Grid Initiative-developed Framework for Network Co-Simulation (FNCS). It discusses the increasing amounts of data coming from the power grid, and the need for a tool like FNCS that brings together data, transmission and distribution simulators. Included is a description of the FNCS architecture, and the advantages this new open source tool can bring to grid research and development efforts.

  4. Top-Down Feedback in an HMAX-Like Cortical Model of Object Perception Based on Hierarchical Bayesian Networks and Belief Propagation

    PubMed Central

    Dura-Bernal, Salvador; Wennekers, Thomas; Denham, Susan L.

    2012-01-01

    Hierarchical generative models, such as Bayesian networks, and belief propagation have been shown to provide a theoretical framework that can account for perceptual processes, including feedforward recognition and feedback modulation. The framework explains both psychophysical and physiological experimental data and maps well onto the hierarchical distributed cortical anatomy. However, the complexity required to model cortical processes makes inference, even using approximate methods, very computationally expensive. Thus, existing object perception models based on this approach are typically limited to tree-structured networks with no loops, use small toy examples or fail to account for certain perceptual aspects such as invariance to transformations or feedback reconstruction. In this study we develop a Bayesian network with an architecture similar to that of HMAX, a biologically-inspired hierarchical model of object recognition, and use loopy belief propagation to approximate the model operations (selectivity and invariance). Crucially, the resulting Bayesian network extends the functionality of HMAX by including top-down recursive feedback. Thus, the proposed model not only achieves successful feedforward recognition invariant to noise, occlusions, and changes in position and size, but is also able to reproduce modulatory effects such as illusory contour completion and attention. Our novel and rigorous methodology covers key aspects such as learning using a layerwise greedy algorithm, combining feedback information from multiple parents and reducing the number of operations required. Overall, this work extends an established model of object recognition to include high-level feedback modulation, based on state-of-the-art probabilistic approaches. The methodology employed, consistent with evidence from the visual cortex, can be potentially generalized to build models of hierarchical perceptual organization that include top-down and bottom-up interactions, for

  5. Task- and stimulus-related cortical networks in language production: Exploring similarity of MEG- and fMRI-derived functional connectivity.

    PubMed

    Liljeström, Mia; Stevenson, Claire; Kujala, Jan; Salmelin, Riitta

    2015-10-15

    account when assessing the correspondence between MEG and fMRI networks. Task-driven network hubs, evident in both MEG and fMRI, were found in cortical regions previously associated with language processing, including the posterior temporal cortex and the inferior frontal cortex. Network hubs related to stimulus-driven modulations, however, were found in regions related to object recognition and visual processing, including the lateral occipital cortex. Overall, the results depict a shift in network structure when moving from a task dependent modulation to a stimulus dependent modulation, revealing a reorganization of large-scale functional connectivity during task performance. PMID:26169324

  6. Disparity between dorsal and ventral networks in patients with obsessive-compulsive disorder: evidence revealed by graph theoretical analysis based on cortical thickness from MRI

    PubMed Central

    Kim, Seung-Goo; Jung, Wi Hoon; Kim, Sung Nyun; Jang, Joon Hwan; Kwon, Jun Soo

    2013-01-01

    As one of the most widely accepted neuroanatomical models on obsessive-compulsive disorder (OCD), it has been hypothesized that imbalance between an excitatory direct (ventral) pathway and an inhibitory indirect (dorsal) pathway in cortico-striato-thalamic circuit underlies the emergence of OCD. Here we examine the structural network in drug-free patients with OCD in terms of graph theoretical measures for the first time. We used a measure called efficiency which quantifies how a node transfers information efficiently. To construct brain networks, cortical thickness was automatically estimated using T1-weighted magnetic resonance imaging. We found that the network of the OCD patients was as efficient as that of healthy controls so that the both networks were in the small-world regime. More importantly, however, disparity between the dorsal and the ventral networks in the OCD patients was found in terms of graph theoretical measures, suggesting a positive evidence to the imbalance theory on the underlying pathophysiology of OCD. PMID:23840184

  7. Transmission network expansion planning with simulation optimization

    SciTech Connect

    Bent, Russell W; Berscheid, Alan; Toole, G. Loren

    2010-01-01

    Within the electric power literatW''e the transmi ssion expansion planning problem (TNEP) refers to the problem of how to upgrade an electric power network to meet future demands. As this problem is a complex, non-linear, and non-convex optimization problem, researchers have traditionally focused on approximate models. Often, their approaches are tightly coupled to the approximation choice. Until recently, these approximations have produced results that are straight-forward to adapt to the more complex (real) problem. However, the power grid is evolving towards a state where the adaptations are no longer easy (i.e. large amounts of limited control, renewable generation) that necessitates new optimization techniques. In this paper, we propose a generalization of the powerful Limited Discrepancy Search (LDS) that encapsulates the complexity in a black box that may be queJied for information about the quality of a proposed expansion. This allows the development of a new optimization algOlitlun that is independent of the underlying power model.

  8. Optimization of a Stochastically Simulated Gene Network Model via Simulated Annealing

    PubMed Central

    Tomshine, Jonathan; Kaznessis, Yiannis N.

    2006-01-01

    By rearranging naturally occurring genetic components, gene networks can be created that display novel functions. When designing these networks, the kinetic parameters describing DNA/protein binding are of great importance, as these parameters strongly influence the behavior of the resulting gene network. This article presents an optimization method based on simulated annealing to locate combinations of kinetic parameters that produce a desired behavior in a genetic network. Since gene expression is an inherently stochastic process, the simulation component of simulated annealing optimization is conducted using an accurate multiscale simulation algorithm to calculate an ensemble of network trajectories at each iteration of the simulated annealing algorithm. Using the three-gene repressilator of Elowitz and Leibler as an example, we show that gene network optimizations can be conducted using a mechanistically realistic model integrated stochastically. The repressilator is optimized to give oscillations of an arbitrary specified period. These optimized designs may then provide a starting-point for the selection of genetic components needed to realize an in vivo system. PMID:16920827

  9. Distributed simulation using a real-time shared memory network

    NASA Technical Reports Server (NTRS)

    Simon, Donald L.; Mattern, Duane L.; Wong, Edmond; Musgrave, Jeffrey L.

    1993-01-01

    The Advanced Control Technology Branch of the NASA Lewis Research Center performs research in the area of advanced digital controls for aeronautic and space propulsion systems. This work requires the real-time implementation of both control software and complex dynamical models of the propulsion system. We are implementing these systems in a distributed, multi-vendor computer environment. Therefore, a need exists for real-time communication and synchronization between the distributed multi-vendor computers. A shared memory network is a potential solution which offers several advantages over other real-time communication approaches. A candidate shared memory network was tested for basic performance. The shared memory network was then used to implement a distributed simulation of a ramjet engine. The accuracy and execution time of the distributed simulation was measured and compared to the performance of the non-partitioned simulation. The ease of partitioning the simulation, the minimal time required to develop for communication between the processors and the resulting execution time all indicate that the shared memory network is a real-time communication technique worthy of serious consideration.

  10. Improving a Computer Networks Course Using the Partov Simulation Engine

    ERIC Educational Resources Information Center

    Momeni, B.; Kharrazi, M.

    2012-01-01

    Computer networks courses are hard to teach as there are many details in the protocols and techniques involved that are difficult to grasp. Employing programming assignments as part of the course helps students to obtain a better understanding and gain further insight into the theoretical lectures. In this paper, the Partov simulation engine and…

  11. Simulating fish assemblages in riverine networks - September 2013

    EPA Science Inventory

    We describe a modeling approach for simulating assemblages of fish in riverine landscapes. The approach allows a user to determine the grain and extent of river networks within which fish populations reproduce, move, and survive in response to both environmental drivers and assem...

  12. Using SPEEDES to simulate the blue gene interconnect network

    NASA Technical Reports Server (NTRS)

    Springer, P.; Upchurch, E.

    2003-01-01

    JPL and the Center for Advanced Computer Architecture (CACR) is conducting application and simulation analyses of BG/L in order to establish a range of effectiveness for the Blue Gene/L MPP architecture in performing important classes of computations and to determine the design sensitivity of the global interconnect network in support of real world ASCI application execution.

  13. Towards effective flow simulations in realistic discrete fracture networks

    NASA Astrophysics Data System (ADS)

    Berrone, Stefano; Pieraccini, Sandra; Scialò, Stefano

    2016-04-01

    We focus on the simulation of underground flow in fractured media, modeled by means of Discrete Fracture Networks. Focusing on a new recent numerical approach proposed by the authors for tackling the problem avoiding mesh generation problems, we further improve the new family of methods making a step further towards effective simulations of large, multi-scale, heterogeneous networks. Namely, we tackle the imposition of Dirichlet boundary conditions in weak form, in such a way that geometrical complexity of the DFN is not an issue; we effectively solve DFN problems with fracture transmissivities spanning many orders of magnitude and approaching zero; furthermore, we address several numerical issues for improving the numerical solution also in quite challenging networks.

  14. SIMULATING BIOCHEMICAL SIGNALING NETWORKS IN COMPLEX MOVING GEOMETRIES.

    PubMed

    Strychalski, Wanda; Adalsteinsson, David; Elston, Timothy C

    2010-01-01

    Signaling networks regulate cellular responses to environmental stimuli through cascades of protein interactions. External signals can trigger cells to polarize and move in a specific direction. During migration, spatially localized activity of proteins is maintained. To investigate the effects of morphological changes on intracellular signaling, we developed a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion system. We then apply the method to several biochemical reaction networks in changing geometries. We found that a Turing instability can develop exclusively by cell deformations that maintain constant area. For a Turing system with a geometry-dependent single or double peak solution, simulations in a dynamically changing geometry suggest that a single peak solution is the only stable one, independent of the oscillation frequency. The method is also applied to a model of a signaling network in a migrating fibroblast. PMID:24086102

  15. Molecular Simulations of Actomyosin Network Self-Assembly and Remodeling

    NASA Astrophysics Data System (ADS)

    Komianos, James; Popov, Konstantin; Papoian, Garegin; Papoian Lab Team

    Actomyosin networks are an integral part of the cytoskeleton of eukaryotic cells and play an essential role in determining cellular shape and movement. Actomyosin network growth and remodeling in vivo is based on a large number of chemical and mechanical processes, which are mutually coupled and spatially and temporally resolved. To investigate the fundamental principles behind the self-organization of these networks, we have developed a detailed mechanochemical, stochastic model of actin filament growth dynamics, at a single-molecule resolution, where the nonlinear mechanical rigidity of filaments and their corresponding deformations under internally and externally generated forces are taken into account. Our work sheds light on the interplay between the chemical and mechanical processes governing the cytoskeletal dynamics, and also highlights the importance of diffusional and active transport phenomena. Our simulations reveal how different actomyosin micro-architectures emerge in response to varying the network composition. Support from NSF Grant CHE-1363081.

  16. Conservative parallel simulation of priority class queueing networks

    NASA Technical Reports Server (NTRS)

    Nicol, David M.

    1990-01-01

    A conservative synchronization protocol is described for the parallel simulation of queueing networks having C job priority classes, where a job's class is fixed. This problem has long vexed designers of conservative synchronization protocols because of its seemingly poor ability to compute lookahead: the time of the next departure. For, a job in service having low priority can be preempted at any time by an arrival having higher priority and an arbitrarily small service time. The solution is to skew the event generation activity so that the events for higher priority jobs are generated farther ahead in simulated time than lower priority jobs. Thus, when a lower priority job enters service for the first time, all the higher priority jobs that may preempt it are already known and the job's departure time can be exactly predicted. Finally, the protocol was analyzed and it was demonstrated that good performance can be expected on the simulation of large queueing networks.

  17. Conservative parallel simulation of priority class queueing networks

    NASA Technical Reports Server (NTRS)

    Nicol, David

    1992-01-01

    A conservative synchronization protocol is described for the parallel simulation of queueing networks having C job priority classes, where a job's class is fixed. This problem has long vexed designers of conservative synchronization protocols because of its seemingly poor ability to compute lookahead: the time of the next departure. For, a job in service having low priority can be preempted at any time by an arrival having higher priority and an arbitrarily small service time. The solution is to skew the event generation activity so that the events for higher priority jobs are generated farther ahead in simulated time than lower priority jobs. Thus, when a lower priority job enters service for the first time, all the higher priority jobs that may preempt it are already known and the job's departure time can be exactly predicted. Finally, the protocol was analyzed and it was demonstrated that good performance can be expected on the simulation of large queueing networks.

  18. Large-scale organization of the primate cortical visual system

    NASA Astrophysics Data System (ADS)

    Young, Malcolm P.

    1994-03-01

    The primate cortical visual system is composed of many structurally and functionally distinct areas or processing compartments, each of which receives on average about ten afferent inputs from other cortical areas and sends about the same number of output projections. The visual cortex is thus served by a very large number of cortico-cortical connections, so that the areas and their interconnections form a network of remarkable complexity. The gross organization of this cortical processing system hence represents a formidable topological problem: while the spatial position of the areas in the brain are becoming fairly well established, the gross `processing architecture,' defined by the connections, is much less well understood. I have applied optimization analysis to connectional data on the cortical visual system to address this topological problem. This approach gives qualitative and quantitative insight into the connectional topology of the primate cortical visual system and provides new evidence supporting suggestions that the system is divided into a dorsal `stream' and a ventral `stream' with limited cross-talk, that these two streams reconverge in the region of the principal sulcus (area 46) and in the superior temporal polysensory areas, that the system is hierarchically organized, and that the majority of the connections are from nearest-neighbor and next-door- but-one areas. The robustness of the results is shown by reanalyzing the connection data after various manipulations that simulate gross changes to the neuroanatomical database.

  19. Modeling and simulation of the USAVRE network and radiology operations

    NASA Astrophysics Data System (ADS)

    Martinez, Ralph; Bradford, Daniel Q.; Hatch, Jay; Sochan, John; Chimiak, William J.

    1998-07-01

    The U.S. Army Medical Command, lead by the Brooke Army Medical Center, has embarked on a visionary project. The U.S. Army Virtual Radiology Environment (USAVRE) is a CONUS-based network that connects all the Army's major medical centers and Regional Medical Commands (RMC). The purpose of the USAVRE is to improve the quality, access, and cost of radiology services in the Army via the use of state-of-the-art medical imaging, computer, and networking technologies. The USAVRE contains multimedia viewing workstations; database archive systems are based on a distributed computing environment using Common Object Request Broker Architecture (CORBA) middleware protocols. The underlying telecommunications network is an ATM-based backbone network that connects the RMC regional networks and PACS networks at medical centers and RMC clinics. This project is a collaborative effort between Army, university, and industry centers with expertise in teleradiology and Global PACS applications. This paper describes a model and simulation of the USAVRE for performance evaluation purposes. As a first step the results of a Technology Assessment and Requirements Analysis (TARA) -- an analysis of the workload in Army radiology departments, their equipment and their staffing. Using the TARA data and other workload information, we have developed a very detailed analysis of the workload and workflow patterns of our Medical Treatment Facilities. We are embarking on modeling and simulation strategies, which will form the foundation for the VRE network. The workload analysis is performed for each radiology modality in a RMC site. The workload consists of the number of examinations per modality, type of images per exam, number of images per exam, and size of images. The frequency for store and forward cases, second readings, and interactive consultation cases are also determined. These parameters are translated into the model described below. The model for the USAVRE is hierarchical in nature

  20. ESIM_DSN Web-Enabled Distributed Simulation Network

    NASA Technical Reports Server (NTRS)

    Bedrossian, Nazareth; Novotny, John

    2002-01-01

    In this paper, the eSim(sup DSN) approach to achieve distributed simulation capability using the Internet is presented. With this approach a complete simulation can be assembled from component subsystems that run on different computers. The subsystems interact with each other via the Internet The distributed simulation uses a hub-and-spoke type network topology. It provides the ability to dynamically link simulation subsystem models to different computers as well as the ability to assign a particular model to each computer. A proof-of-concept demonstrator is also presented. The eSim(sup DSN) demonstrator can be accessed at http://www.jsc.draper.com/esim which hosts various examples of Web enabled simulations.

  1. Ketamine Dysregulates the Amplitude and Connectivity of High-Frequency Oscillations in Cortical-Subcortical Networks in Humans: Evidence From Resting-State Magnetoencephalography-Recordings.

    PubMed

    Rivolta, Davide; Heidegger, Tonio; Scheller, Bertram; Sauer, Andreas; Schaum, Michael; Birkner, Katharina; Singer, Wolf; Wibral, Michael; Uhlhaas, Peter J

    2015-09-01

    Hypofunctioning of the N-methyl-D-aspartate receptor (NMDA-R) has been prominently implicated in the pathophysiology of schizophrenia (ScZ). The current study tested the effects of ketamine, a dissociative anesthetic and NMDA-R antagonist, on resting-state activity recorded with magnetoencephalography (MEG) in healthy volunteers. In a single-blind cross-over design, each participant (n = 12) received, on 2 different sessions, a subanesthetic dose of S-ketamine (0.006 mg/Kg) and saline injection. MEG-data were analyzed at sensor- and source-level in the beta (13-30 Hz) and gamma (30-90 Hz) frequency ranges. In addition, connectivity analysis at source-level was performed using transfer entropy (TE). Ketamine increased gamma-power while beta-band activity was decreased. Specifically, elevated 30-90 Hz activity was pronounced in subcortical (thalamus and hippocampus) and cortical (frontal and temporal cortex) regions, whilst reductions in beta-band power were localized to the precuneus, cerebellum, anterior cingulate, temporal and visual cortex. TE analysis demonstrated increased information transfer in a thalamo-cortical network after ketamine administration. The findings are consistent with the pronounced dysregulation of high-frequency oscillations following the inhibition of NMDA-R in animal models of ScZ as well as with evidence from electroencephalogram-data in ScZ-patients and increased functional connectivity during early illness stages. Moreover, our data highlight the potential contribution of thalamo-cortical connectivity patterns towards ketamine-induced neuronal dysregulation, which may be relevant for the understanding of ScZ as a disorder of disinhibition of neural circuits. PMID:25987642

  2. Estimation of Thalamocortical and Intracortical Network Models from Joint Thalamic Single-Electrode and Cortical Laminar-Electrode Recordings in the Rat Barrel System

    PubMed Central

    Blomquist, Patrick; Devor, Anna; Indahl, Ulf G.; Ulbert, Istvan; Einevoll, Gaute T.; Dale, Anders M.

    2009-01-01

    A new method is presented for extraction of population firing-rate models for both thalamocortical and intracortical signal transfer based on stimulus-evoked data from simultaneous thalamic single-electrode and cortical recordings using linear (laminar) multielectrodes in the rat barrel system. Time-dependent population firing rates for granular (layer 4), supragranular (layer 2/3), and infragranular (layer 5) populations in a barrel column and the thalamic population in the homologous barreloid are extracted from the high-frequency portion (multi-unit activity; MUA) of the recorded extracellular signals. These extracted firing rates are in turn used to identify population firing-rate models formulated as integral equations with exponentially decaying coupling kernels, allowing for straightforward transformation to the more common firing-rate formulation in terms of differential equations. Optimal model structures and model parameters are identified by minimizing the deviation between model firing rates and the experimentally extracted population firing rates. For the thalamocortical transfer, the experimental data favor a model with fast feedforward excitation from thalamus to the layer-4 laminar population combined with a slower inhibitory process due to feedforward and/or recurrent connections and mixed linear-parabolic activation functions. The extracted firing rates of the various cortical laminar populations are found to exhibit strong temporal correlations for the present experimental paradigm, and simple feedforward population firing-rate models combined with linear or mixed linear-parabolic activation function are found to provide excellent fits to the data. The identified thalamocortical and intracortical network models are thus found to be qualitatively very different. While the thalamocortical circuit is optimally stimulated by rapid changes in the thalamic firing rate, the intracortical circuits are low-pass and respond most strongly to slowly varying

  3. Computer Simulations of Bottlebrush Melts and Soft Networks

    NASA Astrophysics Data System (ADS)

    Cao, Zhen; Carrillo, Jan-Michael; Sheiko, Sergei; Dobrynin, Andrey

    We have studied dense bottlebrush systems in a melt and network state using a combination of the molecular dynamics simulations and analytical calculations. Our simulations show that the bottlebrush macromolecules in a melt behave as ideal chains with the effective Kuhn length bK. The bottlebrush induced bending rigidity is due to redistribution of the side chains upon backbone bending. Kuhn length of the bottlebrushes increases with increasing the side-chain degree of polymerization nsc as bK ~nsc0 . 46 . This model of bottlebrush macromolecules is extended to describe mechanical properties of bottlebrush networks in linear and nonlinear deformation regimes. In the linear deformation regime, the network shear modulus scales with the degree of polymerization of the side chains as G0 ~nsc + 1 - 1 as long as the ratio of the Kuhn length to the size of the fully extended bottlebrush backbone between crosslinks, Rmax, is smaller than unity, bK /Rmax < < 1 . Bottlebrush networks with bK /Rmax ~ 1 demonstrate behavior similar to that of networks of semiflexible chains with G0 ~nsc- 0 . 5 . In the nonlinear deformation regime, the deformation dependent shear modulus is a universal function of the first strain invariant I1 and bottlebrush backbone deformation ratio β describing stretching ability of the bottlebrush backbone between crosslinks. Nsf DMR-1409710 DMR-1436201.

  4. Computer Simulations of Bottle Brushes: From Melts to Soft Networks

    SciTech Connect

    Cao, Zhen; Carrillo, Jan-Michael Y.; Sheiko, Sergei S.; Dobrynin, Andrey V.

    2015-07-13

    We use a combination of Molecular dynamics simulations and analytical calculations, and study dens bottle-brush systems in a melt and network State. Analysis of our simulation results shows that bottle-brush macromolecules in melt behave as ideal chains with effective Kuhn length bK. Simulations show that the bottle-brush-induced bending rigidity is due to an entropy decrease caused by redistribution of the side chains upon backbone bending. The Kuhn length of the bottle:brushes increases with increasing the side-chain degree of polymerization nsc as bK proportional to nsc0.46. Moreover, this model of bottle brush macromolecules is extended to describe mechanical properties of bottle brush networks in linear and nonlinear deformation regimes. In the linear deformation regime, the network shear modulus scales with the degree of polymerization of the side chains as G0 proportional to (nsc + 1)-1 as long as the ratio of the Kuhn length, bK, to the size of the fully extended bottle-brush backbone between cross-links, R-max, is smaller than unity, bK/Rmax << 1. Bottle-brush networks With bK/Rmax proportional to 1 demonstrate behavior similar to that of networks Of semiflexible chains with G0 proportional to nsc-0.5. Finally, in the nonlinear network deformation regime, the deformation-dependent shear modulus is a universal function of the first strain invariant I1 and bottle-brush backbone deformation ratio beta describing stretching ability of the bottle-brush backbone between cross-links.

  5. Computer Simulations of Bottle Brushes: From Melts to Soft Networks

    DOE PAGESBeta

    Cao, Zhen; Carrillo, Jan-Michael Y.; Sheiko, Sergei S.; Dobrynin, Andrey V.

    2015-07-13

    We use a combination of Molecular dynamics simulations and analytical calculations, and study dens bottle-brush systems in a melt and network State. Analysis of our simulation results shows that bottle-brush macromolecules in melt behave as ideal chains with effective Kuhn length bK. Simulations show that the bottle-brush-induced bending rigidity is due to an entropy decrease caused by redistribution of the side chains upon backbone bending. The Kuhn length of the bottle:brushes increases with increasing the side-chain degree of polymerization nsc as bK proportional to nsc0.46. Moreover, this model of bottle brush macromolecules is extended to describe mechanical properties of bottlemore » brush networks in linear and nonlinear deformation regimes. In the linear deformation regime, the network shear modulus scales with the degree of polymerization of the side chains as G0 proportional to (nsc + 1)-1 as long as the ratio of the Kuhn length, bK, to the size of the fully extended bottle-brush backbone between cross-links, R-max, is smaller than unity, bK/Rmax << 1. Bottle-brush networks With bK/Rmax proportional to 1 demonstrate behavior similar to that of networks Of semiflexible chains with G0 proportional to nsc-0.5. Finally, in the nonlinear network deformation regime, the deformation-dependent shear modulus is a universal function of the first strain invariant I1 and bottle-brush backbone deformation ratio beta describing stretching ability of the bottle-brush backbone between cross-links.« less

  6. On the Strategy of Simulating a GGOS Network

    NASA Astrophysics Data System (ADS)

    Schuh, Harald; Koenig, Rolf; Ampatzidis, Dimitrios; Glaser, Susanne; Flechtner, Frank; Nilsson, Tobias; Heinkelmann, Robert

    2015-04-01

    GGOS-SIM ("Simulation of the Global Geodetic Observing System") is a joint project of the TU Berlin (TUB) and the GFZ German Research Centre for Geosciences. GGOS-SIM aims at creating a tool to simulate the realization of the Terrestrial Reference System from the space-geodetic observations space geodetic techniques: Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS), Global Navigation Satellite System (GNSS), Satellite Laser Ranging (SLR), and Very Long Baseline Interferometry (VLBI). In particular the effects of the space segment, the ground network, the local ties on ground and in space, and technical developments shall be assessed with respect to the goals of GGOS, i.e. point positions with 1 mm and velocities with 1 mm/a accuracy globally. For this we set up the observation scenario which consists of the ground networks, the space segments, and the observation types. We discuss our set-up of the standard networks of each space-geodetic technique and their space segments, and we infer accuracy and availability of the observations from real world data. Then we describe the strategy on how the simulations can be conducted so that the results become realistic. This also concerns the trade-off in choosing the dynamic, geometric, and correction models for simulation and recovery

  7. High Speed Networking and Large-scale Simulation in Geodynamics

    NASA Technical Reports Server (NTRS)

    Kuang, Weijia; Gary, Patrick; Seablom, Michael; Truszkowski, Walt; Odubiyi, Jide; Jiang, Weiyuan; Liu, Dong

    2004-01-01

    Large-scale numerical simulation has been one of the most important approaches for understanding global geodynamical processes. In this approach, peta-scale floating point operations (pflops) are often required to carry out a single physically-meaningful numerical experiment. For example, to model convective flow in the Earth's core and generation of the geomagnetic field (geodynamo), simulation for one magnetic free-decay time (approximately 15000 years) with a modest resolution of 150 in three spatial dimensions would require approximately 0.2 pflops. If such a numerical model is used to predict geomagnetic secular variation over decades and longer, with e.g. an ensemble Kalman filter assimilation approach, approximately 30 (and perhaps more) independent simulations of similar scales would be needed for one data assimilation analysis. Obviously, such a simulation would require an enormous computing resource that exceeds the capacity of a single facility currently available at our disposal. One solution is to utilize a very fast network (e.g. 10Gb optical networks) and available middleware (e.g. Globus Toolkit) to allocate available but often heterogeneous resources for such large-scale computing efforts. At NASA GSFC, we are experimenting with such an approach by networking several clusters for geomagnetic data assimilation research. We shall present our initial testing results in the meeting.

  8. Real-time hierarchically distributed processing network interaction simulation

    NASA Technical Reports Server (NTRS)

    Zimmerman, W. F.; Wu, C.

    1987-01-01

    The Telerobot Testbed is a hierarchically distributed processing system which is linked together through a standard, commercial Ethernet. Standard Ethernet systems are primarily designed to manage non-real-time information transfer. Therefore, collisions on the net (i.e., two or more sources attempting to send data at the same time) are managed by randomly rescheduling one of the sources to retransmit at a later time interval. Although acceptable for transmitting noncritical data such as mail, this particular feature is unacceptable for real-time hierarchical command and control systems such as the Telerobot. Data transfer and scheduling simulations, such as token ring, offer solutions to collision management, but do not appropriately characterize real-time data transfer/interactions for robotic systems. Therefore, models like these do not provide a viable simulation environment for understanding real-time network loading. A real-time network loading model is being developed which allows processor-to-processor interactions to be simulated, collisions (and respective probabilities) to be logged, collision-prone areas to be identified, and network control variable adjustments to be reentered as a means of examining and reducing collision-prone regimes that occur in the process of simulating a complete task sequence.

  9. Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

    NASA Astrophysics Data System (ADS)

    Fels, Meike; Bauer, Robert; Gharabaghi, Alireza

    2015-08-01

    Objective. Novel rehabilitation strategies apply robot-assisted exercises and neurofeedback tasks to facilitate intensive motor training. We aimed to disentangle task-specific and subject-related contributions to the perceived workload of these interventions and the related cortical activation patterns. Approach. We assessed the perceived workload with the NASA Task Load Index in twenty-one subjects who were exposed to two different feedback tasks in a cross-over design: (i) brain-robot interface (BRI) with haptic/proprioceptive feedback of sensorimotor oscillations related to motor imagery, and (ii) control of neuromuscular activity with feedback of the electromyography (EMG) of the same hand. We also used electroencephalography to examine the cortical activation patterns beforehand in resting state and during the training session of each task. Main results. The workload profile of BRI feedback differed from EMG feedback and was particularly characterized by the experience of frustration. The frustration level was highly correlated across tasks, suggesting subject-related relevance of this workload component. Those subjects who were specifically challenged by the respective tasks could be detected by an interhemispheric alpha-band network in resting state before the training and by their sensorimotor theta-band activation pattern during the exercise. Significance. Neurophysiological profiles in resting state and during the exercise may provide task-independent workload markers for monitoring and matching participants’ ability and task difficulty of neurofeedback interventions.

  10. Simulating market dynamics: interactions between consumer psychology and social networks.

    PubMed

    Janssen, Marco A; Jager, Wander

    2003-01-01

    Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network). PMID:14761255

  11. Frontotemporal oxyhemoglobin dynamics predict performance accuracy of dance simulation gameplay: temporal characteristics of top-down and bottom-up cortical activities.

    PubMed

    Ono, Yumie; Nomoto, Yasunori; Tanaka, Shohei; Sato, Keisuke; Shimada, Sotaro; Tachibana, Atsumichi; Bronner, Shaw; Noah, J Adam

    2014-01-15

    We utilized the high temporal resolution of functional near-infrared spectroscopy to explore how sensory input (visual and rhythmic auditory cues) are processed in the cortical areas of multimodal integration to achieve coordinated motor output during unrestricted dance simulation gameplay. Using an open source clone of the dance simulation video game, Dance Dance Revolution, two cortical regions of interest were selected for study, the middle temporal gyrus (MTG) and the frontopolar cortex (FPC). We hypothesized that activity in the FPC would indicate top-down regulatory mechanisms of motor behavior; while that in the MTG would be sustained due to bottom-up integration of visual and auditory cues throughout the task. We also hypothesized that a correlation would exist between behavioral performance and the temporal patterns of the hemodynamic responses in these regions of interest. Results indicated that greater temporal accuracy of dance steps positively correlated with persistent activation of the MTG and with cumulative suppression of the FPC. When auditory cues were eliminated from the simulation, modifications in cortical responses were found depending on the gameplay performance. In the MTG, high-performance players showed an increase but low-performance players displayed a decrease in cumulative amount of the oxygenated hemoglobin response in the no music condition compared to that in the music condition. In the FPC, high-performance players showed relatively small variance in the activity regardless of the presence of auditory cues, while low-performance players showed larger differences in the activity between the no music and music conditions. These results suggest that the MTG plays an important role in the successful integration of visual and rhythmic cues and the FPC may work as top-down control to compensate for insufficient integrative ability of visual and rhythmic cues in the MTG. The relative relationships between these cortical areas indicated

  12. Statistical Comparison of Spike Responses to Natural Stimuli in Monkey Area V1 With Simulated Responses of a Detailed Laminar Network Model for a Patch of V1

    PubMed Central

    Schuch, Klaus; Logothetis, Nikos K.; Maass, Wolfgang

    2011-01-01

    A major goal of computational neuroscience is the creation of computer models for cortical areas whose response to sensory stimuli resembles that of cortical areas in vivo in important aspects. It is seldom considered whether the simulated spiking activity is realistic (in a statistical sense) in response to natural stimuli. Because certain statistical properties of spike responses were suggested to facilitate computations in the cortex, acquiring a realistic firing regimen in cortical network models might be a prerequisite for analyzing their computational functions. We present a characterization and comparison of the statistical response properties of the primary visual cortex (V1) in vivo and in silico in response to natural stimuli. We recorded from multiple electrodes in area V1 of 4 macaque monkeys and developed a large state-of-the-art network model for a 5 × 5-mm patch of V1 composed of 35,000 neurons and 3.9 million synapses that integrates previously published anatomical and physiological details. By quantitative comparison of the model response to the “statistical fingerprint” of responses in vivo, we find that our model for a patch of V1 responds to the same movie in a way which matches the statistical structure of the recorded data surprisingly well. The deviation between the firing regimen of the model and the in vivo data are on the same level as deviations among monkeys and sessions. This suggests that, despite strong simplifications and abstractions of cortical network models, they are nevertheless capable of generating realistic spiking activity. To reach a realistic firing state, it was not only necessary to include both N-methyl-d-aspartate and GABAB synaptic conductances in our model, but also to markedly increase the strength of excitatory synapses onto inhibitory neurons (>2-fold) in comparison to literature values, hinting at the importance to carefully adjust the effect of inhibition for achieving realistic dynamics in current network

  13. Simulation of restricted neural networks with reprogrammable neurons

    SciTech Connect

    Hartline, D.K. )

    1989-05-01

    This paper describes a network model composed of reprogrammable neurons. It incorporates the following design features: spikes can be generated by a model representing repetitive firing at axon (and dendritic) trigger zones; active responses (plateau potentials; delaying mechanisms) are simulated with Hodgkin-huxley type kinetics; synaptic interactions both spike-mediated and non-spiking chemical ('chemotonic'), simulate transmitter release and binding to postsynaptic receptors. Facilitation and antifacilitation of spike-mediated postsynaptic potentials (PSP's) are included. Chemical pools are used to simulate second messenger systems, trapping of ions in extracellular spaces, and electrogenic pumps, as well as biochemical reaction chains of quite general character. Modulation of any of the parameters of any compartment can be effected through the pools. Intracellular messengers of three kinds are simulated explicitly: those produced by voltage-gated processes (e.g. Ca); those dependent on transmitter (or hormone) binding; and those dependent on other internal messengers (e.g., internally released Ca; enzymatically activated pathways).

  14. Pore connectivity, electrical conductivity, and partial water saturation: Network simulations

    NASA Astrophysics Data System (ADS)

    Li, M.; Tang, Y. B.; Bernabé, Y.; Zhao, J. Z.; Li, X. F.; Bai, X. Y.; Zhang, L. H.

    2015-06-01

    The electrical conductivity of brine-saturated rock is predominantly dependent on the geometry and topology of the pore space. When a resistive second phase (e.g., air in the vadose zone and oil/gas in hydrocarbon reservoirs) displaces the brine, the geometry and topology of the pore space occupied by the electrically conductive phase are changed. We investigated the effect of these changes on the electrical conductivity of rock partially saturated with brine. We simulated drainage and imbibition as invasion and bond percolation processes, respectively, in pipe networks assumed to be perfectly water-wet. The simulations included the formation of a water film in the pipes invaded by the nonwetting fluid. During simulated drainage/imbibition, we measured the changes in resistivity index as well as a number of relevant microstructural parameters describing the portion of the pore space saturated with water. Except Euler topological number, all quantities considered here showed a significant level of "universality," i.e., insensitivity to the type of lattice used (simple cubic, body-centered cubic, or face-centered cubic). Hence, the coordination number of the pore network appears to be a more effective measure of connectivity than Euler number. In general, the simulated resistivity index did not obey Archie's simple power law. In log-log scale, the resistivity index curves displayed a substantial downward or upward curvature depending on the presence or absence of a water film. Our network simulations compared relatively well with experimental data sets, which were obtained using experimental conditions and procedures consistent with the simulations. Finally, we verified that the connectivity/heterogeneity model proposed by Bernabé et al. (2011) could be extended to the partial brine saturation case when water films were not present.

  15. A zonal model of cortical functions.

    PubMed

    Green, H S; Triffet, T

    1989-01-01

    A model of cortical functions is developed with the object of simulating the observed behavior of individual neurons organized in unit circuits and functional systems of the cerebellum, the cerebrum and the hippocampal formation. The neuronal model is capable of representing refractory and potentiated states, as well as the firing and lowest resting states. The unit circuits of each system consist of all common types of cells with known synaptic connections. In the cerebral system these unit circuits are interconnected to form columns as well as zones. A new discrete neural network equation, which takes account of interactions with the extracellular field, is proposed to simulate electrical activity in these circuits. A coherent theory of cortical activity and functions is derived that accounts for many of the observed phenomena, including those associated with the development of long-term potentiation and sequential memory. Three appendices are devoted to the theory of extracellular interactions, the derivation of non-linear network equations, and a computer program to simulate learning in the cortex. PMID:2779262

  16. Simulation Modeling and Performance Evaluation of Space Networks

    NASA Technical Reports Server (NTRS)

    Jennings, Esther H.; Segui, John

    2006-01-01

    In space exploration missions, the coordinated use of spacecraft as communication relays increases the efficiency of the endeavors. To conduct trade-off studies of the performance and resource usage of different communication protocols and network designs, JPL designed a comprehensive extendable tool, the Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE). The design and development of MACHETE began in 2000 and is constantly evolving. Currently, MACHETE contains Consultative Committee for Space Data Systems (CCSDS) protocol standards such as Proximity-1, Advanced Orbiting Systems (AOS), Packet Telemetry/Telecommand, Space Communications Protocol Specification (SCPS), and the CCSDS File Delivery Protocol (CFDP). MACHETE uses the Aerospace Corporation s Satellite Orbital Analysis Program (SOAP) to generate the orbital geometry information and contact opportunities. Matlab scripts provide the link characteristics. At the core of MACHETE is a discrete event simulator, QualNet. Delay Tolerant Networking (DTN) is an end-to-end architecture providing communication in and/or through highly stressed networking environments. Stressed networking environments include those with intermittent connectivity, large and/or variable delays, and high bit error rates. To provide its services, the DTN protocols reside at the application layer of the constituent internets, forming a store-and-forward overlay network. The key capabilities of the bundling protocols include custody-based reliability, ability to cope with intermittent connectivity, ability to take advantage of scheduled and opportunistic connectivity, and late binding of names to addresses. In this presentation, we report on the addition of MACHETE models needed to support DTN, namely: the Bundle Protocol (BP) model. To illustrate the use of MACHETE with the additional DTN model, we provide an example simulation to benchmark its performance. We demonstrate the use of the DTN protocol

  17. NCC Simulation Model: Simulating the operations of the network control center, phase 2

    NASA Technical Reports Server (NTRS)

    Benjamin, Norman M.; Paul, Arthur S.; Gill, Tepper L.

    1992-01-01

    The simulation of the network control center (NCC) is in the second phase of development. This phase seeks to further develop the work performed in phase one. Phase one concentrated on the computer systems and interconnecting network. The focus of phase two will be the implementation of the network message dialogues and the resources controlled by the NCC. These resources are requested, initiated, monitored and analyzed via network messages. In the NCC network messages are presented in the form of packets that are routed across the network. These packets are generated, encoded, decoded and processed by the network host processors that generate and service the message traffic on the network that connects these hosts. As a result, the message traffic is used to characterize the work done by the NCC and the connected network. Phase one of the model development represented the NCC as a network of bi-directional single server queues and message generating sources. The generators represented the external segment processors. The served based queues represented the host processors. The NCC model consists of the internal and external processors which generate message traffic on the network that links these hosts. To fully realize the objective of phase two it is necessary to identify and model the processes in each internal processor. These processes live in the operating system of the internal host computers and handle tasks such as high speed message exchanging, ISN and NFE interface, event monitoring, network monitoring, and message logging. Inter process communication is achieved through the operating system facilities. The overall performance of the host is determined by its ability to service messages generated by both internal and external processors.

  18. Design of a neural network simulator on a transputer array

    NASA Technical Reports Server (NTRS)

    Mcintire, Gary; Villarreal, James; Baffes, Paul; Rua, Monica

    1987-01-01

    A brief summary of neural networks is presented which concentrates on the design constraints imposed. Major design issues are discussed together with analysis methods and the chosen solutions. Although the system will be capable of running on most transputer architectures, it currently is being implemented on a 40-transputer system connected to a toroidal architecture. Predictions show a performance level equivalent to that of a highly optimized simulator running on the SX-2 supercomputer.

  19. Computer simulation of a model network for the erythrocyte cytoskeleton.

    PubMed Central

    Boal, D H

    1994-01-01

    The geometry and mechanical properties of the human erythrocyte membrane cytoskeleton are investigated by a computer simulation in which the cytoskeleton is represented by a network of polymer chains. Four elastic moduli as well as the area and thickness are predicted for the chain network as a function of temperature and the number of segments in each chain. Comparisons are made with mean field arguments to examine the importance of steric interactions in determining network properties. Applied to the red blood cell, the simulation predicts that in the bilayer plane the membrane cytoskeleton has a shear modulus of 10 +/- 2 x 10(-6) J/m2 and an areal compression modulus of 17 +/- 2 x 10(-6) J/m2. The volume compression modulus and the transverse Young's modulus of the cytoskeleton are predicted to be 1.2 +/- 0.1 x 10(3) J/m3 and 2.0 +/- 0.1 x 10(3) J/m3, respectively. Elements of the cytoskeleton are predicted to have a mean displacement from the bilayer plane of 15 nm. The simulation agrees with some, but not all, of the shear modulus measurements. The other predicted moduli have not been measured. Images FIGURE 1 PMID:7948670

  20. Cortical thinning in psychopathy

    PubMed Central

    Ly, Martina; Motzkin, Julian C.; Philippi, Carissa L.; Kirk, Gregory R.; Newman, Joseph P.; Kiehl, Kent A.; Koenigs, Michael

    2013-01-01

    Objective Psychopathy is a personality disorder associated with severely antisocial behavior and a host of cognitive and affective deficits. The neuropathological basis of the disorder has not been clearly established. Cortical thickness is a sensitive measure of brain structure that has been used to identify neurobiological abnormalities in a number of psychiatric disorders. The purpose of this study is to evaluate cortical thickness and corresponding functional connectivity in criminal psychopaths. Method Using T1 MRI data, we computed cortical thickness maps in a sample of adult male prison inmates selected based on psychopathy diagnosis (n=21 psychopathic inmates, n=31 non-psychopathic inmates). Using rest-fMRI data from a subset of these inmates (n=20 psychopathic inmates, n=20 non-psychopathic inmates), we then computed functional connectivity within networks exhibiting significant thinning among psychopaths. Results Relative to non-psychopaths, psychopaths exhibited significantly thinner cortex in a number of regions, including left insula and dorsal anterior cingulate cortex, bilateral precentral gyrus, bilateral anterior temporal cortex, and right inferior frontal gyrus. These neurostructural differences were not due to differences in age, IQ, or substance abuse. Psychopaths also exhibited a corresponding reduction in functional connectivity between left insula and left dorsal anterior cingulate cortex. Conclusions Psychopathy is associated with a distinct pattern of cortical thinning and reduced functional connectivity. PMID:22581200

  1. Simulation study of unmanned aerial vehicle communication networks addressing bandwidth disruptions

    NASA Astrophysics Data System (ADS)

    Wei, Sixiao; Ge, Linqiang; Yu, Wei; Chen, Genshe; Pham, Khanh; Blasch, Erik; Shen, Dan; Lu, Chao

    2014-06-01

    To date, Unmanned Aerial Vehicles (UAVs) have been widely used for numerous applications. UAVs can directly connect to ground stations or satellites to transfer data. Multiple UAVs can communicate and cooperate with each other and then construct an ad-hoc network. Multi-UAV systems have the potential to provide reliable and timely services for end users in addition to satellite networks. In this paper, we conduct a simulation study for evaluating the network performance of multi-UAV systems and satellite networks using the ns-2 networking simulation tool. Our simulation results show that UAV communication networks can achieve better network performance than satellite networks and with a lower cost and increased timeliness. We also investigate security resiliency of UAV networks. As a case study, we simulate false data injection attacks against UAV communication networks in ns-2 and demonstrate the impact of false data injection attacks on network performance.

  2. Uncemented Total Hip Replacement Stem Loosening after Long Term Compressive Stress Application: A Simulated FEA Study of Cortical Bone Remodeling

    NASA Astrophysics Data System (ADS)

    Jung, Duk-Young; Tsutsumi, Sadami; Nakai, Ryusuke; Ikeuchi, Ken; Sekel, Ron

    The purpose of this study is to predict with the use of FEA, the differing predisposition to cortical bone resorption and subsequent distal migration of an un-cemented femoral hip replacement stem subjected to long term biomechanical high compressive stresses, while varying the load angles, the material properties of the stem, and the stem length. A two-dimensional hip model was constructed to estimate the minimum principle stresses (P3) and migration magnitudes. Bone remodeling at the interface between the bone and the prosthesis was performed by comparison of the local compressive stress to physiological stress values governing bone resorption. With respect to load angles, migrations of the hip prosthesis did not occur with load angles between 63° and 74° load angle in relation to the longitudinal axis of the bony femur, as the compressive stress generated on the cortical bone was under the criteria threshold for bone resorption (-50MPa). In addition, the magnitude of migration (17%decrease) was relatively more sensitive to changes in stem length than those (92%decrease) of changes of material properties. In conclusion, using an FEA model for bone remodeling, based on the high compressive stresses exerted on distal cortical bone, it is possible to estimate migration magnitudes of cementless hip prostheses in the long term. The load angles have been shown to be an important parameter affecting the migration magnitudes and furthermore, it can be demonstrated that the stiffer materials and reduction of stem length can decrease the migration of cementless hip prosthesis in the long term.

  3. Estimating uncertainty of streamflow simulation using Bayesian neural networks

    NASA Astrophysics Data System (ADS)

    Zhang, Xuesong; Liang, Faming; Srinivasan, Raghavan; van Liew, Michael

    2009-02-01

    Recent studies have shown that Bayesian neural networks (BNNs) are powerful tools for providing reliable hydrologic prediction and quantifying the prediction uncertainty. The reasonable estimation of the prediction uncertainty, a valuable tool for decision making to address water resources management and design problems, is influenced by the techniques used to deal with different uncertainty sources. In this study, four types of BNNs with different treatments of the uncertainties related to parameters (neural network's weights) and model structures were applied for uncertainty estimation of streamflow simulation in two U.S. Department of Agriculture Agricultural Research Service watersheds (Little River Experimental Watershed in Georgia and Reynolds Creek Experimental Watershed in Idaho). An advanced Markov chain Monte Carlo algorithm, evolutionary Monte Carlo, was used to train the BNNs and to estimate uncertainty limits of streamflow simulation. The results obtained in these two case study watersheds show that the 95% uncertainty limits estimated by different types of BNNs are different from each other. The BNNs that only consider the parameter uncertainty with noninformative prior knowledge contain the least number of observed streamflow data in their 95% uncertainty bound. By considering variable model structure and informative prior knowledge, the BNNs can provide more reasonable quantification of the uncertainty of streamflow simulation. This study stresses the need for improving understanding and quantifying methods of different uncertainty sources for effective estimation of uncertainty of hydrologic simulation using BNNs.

  4. Detection, eye–hand coordination and virtual mobility performance in simulated vision for a cortical visual prosthesis device

    PubMed Central

    Srivastava, Nishant R; Troyk, Philip R; Dagnelie, Gislin

    2014-01-01

    In order to assess visual performance using a future cortical prosthesis device, the ability of normally sighted and low vision subjects to adapt to a dotted ‘phosphene’ image was studied. Similar studies have been conduced in the past and adaptation to phosphene maps has been shown but the phosphene maps used have been square or hexagonal in pattern. The phosphene map implemented for this testing is what is expected from a cortical implantation of the arrays of intracortical electrodes, generating multiple phosphenes. The dotted image created depends upon the surgical location of electrodes decided for implantation and the expected cortical response. The subjects under tests were required to perform tasks requiring visual inspection, eye–hand coordination and way finding. The subjects did not have any tactile feedback and the visual information provided was live dotted images captured by a camera on a head-mounted low vision enhancing system and processed through a filter generating images similar to the images we expect the blind persons to perceive. The images were locked to the subject’s gaze by means of video-based pupil tracking. In the detection and visual inspection task, the subject scanned a modified checkerboard and counted the number of square white fields on a square checkerboard, in the eye–hand coordination task, the subject placed black checkers on the white fields of the checkerboard, and in the way-finding task, the subjects maneuvered themselves through a virtual maze using a game controller. The accuracy and the time to complete the task were used as the measured outcome. As per the surgical studies by this research group, it might be possible to implant up to 650 electrodes; hence, 650 dots were used to create images and performance studied under 0% dropout (650 dots), 25% dropout (488 dots) and 50% dropout (325 dots) conditions. It was observed that all the subjects under test were able to learn the given tasks and showed improvement

  5. Detection, eye-hand coordination and virtual mobility performance in simulated vision for a cortical visual prosthesis device

    NASA Astrophysics Data System (ADS)

    Srivastava, Nishant R.; Troyk, Philip R.; Dagnelie, Gislin

    2009-06-01

    In order to assess visual performance using a future cortical prosthesis device, the ability of normally sighted and low vision subjects to adapt to a dotted 'phosphene' image was studied. Similar studies have been conduced in the past and adaptation to phosphene maps has been shown but the phosphene maps used have been square or hexagonal in pattern. The phosphene map implemented for this testing is what is expected from a cortical implantation of the arrays of intracortical electrodes, generating multiple phosphenes. The dotted image created depends upon the surgical location of electrodes decided for implantation and the expected cortical response. The subjects under tests were required to perform tasks requiring visual inspection, eye-hand coordination and way finding. The subjects did not have any tactile feedback and the visual information provided was live dotted images captured by a camera on a head-mounted low vision enhancing system and processed through a filter generating images similar to the images we expect the blind persons to perceive. The images were locked to the subject's gaze by means of video-based pupil tracking. In the detection and visual inspection task, the subject scanned a modified checkerboard and counted the number of square white fields on a square checkerboard, in the eye-hand coordination task, the subject placed black checkers on the white fields of the checkerboard, and in the way-finding task, the subjects maneuvered themselves through a virtual maze using a game controller. The accuracy and the time to complete the task were used as the measured outcome. As per the surgical studies by this research group, it might be possible to implant up to 650 electrodes; hence, 650 dots were used to create images and performance studied under 0% dropout (650 dots), 25% dropout (488 dots) and 50% dropout (325 dots) conditions. It was observed that all the subjects under test were able to learn the given tasks and showed improvement in

  6. A simulation study of TCP performance in ATM networks

    SciTech Connect

    Chien Fang; Chen, Helen; Hutchins, J.

    1994-08-01

    This paper presents a simulation study of TCP performance over congested ATM local area networks. We simulated a variety of schemes for congestion control for ATM LANs, including a simple cell-drop, a credit-based flow control scheme that back-pressures individual VC`s, and two selective cell-drop schemes. Our simulation results for congested ATM LANs show the following: (1) TCP performance is poor under simple cell-drop, (2) the selective cell-drop schemes increase effective link utilization and result in higher TCP throughputs than the simple cell-drop scheme, and (3) the credit-based flow control scheme eliminates cell loss and achieves maximum performance and effective link utilization.

  7. Long-term pollution simulation in combined sewer networks.

    PubMed

    Masse, B; Zug, M; Tabuchi, J P; Tisserand, B

    2001-01-01

    This paper presents results of long term pollution simulations on the example of the sewerage system of Grand-Couronne. This modelling work is part of a study where objective is to develop a method to define the reference flow of a WWTP. The model HYDROWORKS DM has been successfully validated in hydraulics and pollution for the sewer network, for long time simulations. A conceptual model has been built to model the pollution in the tank at the outlet of the combined system. One synthetic year of rain has been used to simulate the working of the "up stream system" of the WWTP (combined sewer + tank + separate sewer + pre-treatments) and has been successfully validated by measurements of the 1998-1999 year. If this paper is focused on the "up stream system", the SIMBA/SIMBAD WWTP model has been successfully calibrated and validated too, and the combination represents a fully validated "Integrated Model" for the sewerage system. PMID:11385878

  8. Efficiently passing messages in distributed spiking neural network simulation

    PubMed Central

    Thibeault, Corey M.; Minkovich, Kirill; O'Brien, Michael J.; Harris, Frederick C.; Srinivasa, Narayan

    2013-01-01

    Efficiently passing spiking messages in a neural model is an important aspect of high-performance simulation. As the scale of networks has increased so has the size of the computing systems required to simulate them. In addition, the information exchange of these resources has become more of an impediment to performance. In this paper we explore spike message passing using different mechanisms provided by the Message Passing Interface (MPI). A specific implementation, MVAPICH, designed for high-performance clusters with Infiniband hardware is employed. The focus is on providing information about these mechanisms for users of commodity high-performance spiking simulators. In addition, a novel hybrid method for spike exchange was implemented and benchmarked. PMID:23772213

  9. Quantum versus simulated annealing in wireless interference network optimization.

    PubMed

    Wang, Chi; Chen, Huo; Jonckheere, Edmond

    2016-01-01

    Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed. PMID:27181056

  10. Realistic modeling of neurons and networks: towards brain simulation

    PubMed Central

    D’Angelo, Egidio; Solinas, Sergio; Garrido, Jesus; Casellato, Claudia; Pedrocchi, Alessandra; Mapelli, Jonathan; Gandolfi, Daniela; Prestori, Francesca

    Summary Realistic modeling is a new advanced methodology for investigating brain functions. Realistic modeling is based on a detailed biophysical description of neurons and synapses, which can be integrated into microcircuits. The latter can, in turn, be further integrated to form large-scale brain networks and eventually to reconstruct complex brain systems. Here we provide a review of the realistic simulation strategy and use the cerebellar network as an example. This network has been carefully investigated at molecular and cellular level and has been the object of intense theoretical investigation. The cerebellum is thought to lie at the core of the forward controller operations of the brain and to implement timing and sensory prediction functions. The cerebellum is well described and provides a challenging field in which one of the most advanced realistic microcircuit models has been generated. We illustrate how these models can be elaborated and embedded into robotic control systems to gain insight into how the cellular properties of cerebellar neurons emerge in integrated behaviors. Realistic network modeling opens up new perspectives for the investigation of brain pathologies and for the neurorobotic field. PMID:24139652

  11. Large-Scale, High-Resolution Multielectrode-Array Recording Depicts Functional Network Differences of Cortical and Hippocampal Cultures

    PubMed Central

    Ito, Shinya; Yeh, Fang-Chin; Hiolski, Emma; Rydygier, Przemyslaw; Gunning, Deborah E.; Hottowy, Pawel; Timme, Nicholas; Litke, Alan M.; Beggs, John M.

    2014-01-01

    Understanding the detailed circuitry of functioning neuronal networks is one of the major goals of neuroscience. Recent improvements in neuronal recording techniques have made it possible to record the spiking activity from hundreds of neurons simultaneously with sub-millisecond temporal resolution. Here we used a 512-channel multielectrode array system to record the activity from hundreds of neurons in organotypic cultures of cortico-hippocampal brain slices from mice. To probe the network structure, we employed a wavelet transform of the cross-correlogram to categorize the functional connectivity in different frequency ranges. With this method we directly compare, for the first time, in any preparation, the neuronal network structures of cortex and hippocampus, on the scale of hundreds of neurons, with sub-millisecond time resolution. Among the three frequency ranges that we investigated, the lower two frequency ranges (gamma (30–80 Hz) and beta (12–30 Hz) range) showed similar network structure between cortex and hippocampus, but there were many significant differences between these structures in the high frequency range (100–1000 Hz). The high frequency networks in cortex showed short tailed degree-distributions, shorter decay length of connectivity density, smaller clustering coefficients, and positive assortativity. Our results suggest that our method can characterize frequency dependent differences of network architecture from different brain regions. Crucially, because these differences between brain regions require millisecond temporal scales to be observed and characterized, these results underscore the importance of high temporal resolution recordings for the understanding of functional networks in neuronal systems. PMID:25126851

  12. Increased functional connectivity between cortical hand areas and praxis network associated with training-related improvements in non-dominant hand precision drawing.

    PubMed

    Philip, Benjamin A; Frey, Scott H

    2016-07-01

    Chronic forced use of the non-dominant left hand yields substantial improvements in the precision and quality of writing and drawing. These changes may arise from increased access by the non-dominant (right) hemisphere to dominant (left) hemisphere mechanisms specialized for end-point precision control. To evaluate this prediction, 22 healthy right-handed adults underwent resting state functional connectivity (FC) MRI scans before and after 10 days of training on a left hand precision drawing task. 89% of participants significantly improved left hand speed, accuracy, and smoothness. Smoothness gains were specific to the trained left hand and persistent: 6 months after training, 71% of participants exhibited above-baseline movement smoothness. Contrary to expectations, we found no evidence of increased FC between right and left hemisphere hand areas. Instead, training-related improvements in left hand movement smoothness were associated with increased FC between both sensorimotor hand areas and a left-lateralized parieto-prefrontal network implicated in manual praxis. By contrast, skill retention at 6 months was predicted by changes including decreased FC between the representation of the trained left hand and bilateral sensorimotor, parietal, and premotor cortices, possibly reflecting consolidation and a disengagement of early learning processes. These data indicate that modest amounts of training (<200min total) can induce substantial, persistent improvements the precision and quality of non-dominant hand control in healthy adults, supported by strengthened connectivity between bilateral sensorimotor hand areas and a left-lateralized parieto-prefrontal praxis network. PMID:27212059

  13. Multi-level characterization of human femoral cortices and their underlying osteocyte network reveal trends in quality of young, aged, osteoporotic and antiresorptive-treated bone.

    PubMed

    Milovanovic, Petar; Zimmermann, Elizabeth A; Riedel, Christoph; vom Scheidt, Annika; Herzog, Lydia; Krause, Matthias; Djonic, Danijela; Djuric, Marija; Püschel, Klaus; Amling, Michael; Ritchie, Robert O; Busse, Björn

    2015-03-01

    Characterization of bone's hierarchical structure in aging, disease and treatment conditions is imperative to understand the architectural and compositional modifications to the material and its mechanical integrity. Here, cortical bone sections from 30 female proximal femurs - a frequent fracture site - were rigorously assessed to characterize the osteocyte lacunar network, osteon density and patterns of bone matrix mineralization by backscatter-electron imaging and Fourier-transform infrared spectroscopy in relation to mechanical properties obtained by reference-point indentation. We show that young, healthy bone revealed the highest resistance to mechanical loading (indentation) along with higher mineralization and preserved osteocyte-lacunar characteristics. In contrast, aging and osteoporosis significantly alter bone material properties, where impairment of the osteocyte-lacunar network was evident through accumulation of hypermineralized osteocyte lacunae with aging and even more in osteoporosis, highlighting increased osteocyte apoptosis and reduced mechanical competence. But antiresorptive treatment led to fewer mineralized lacunae and fewer but larger osteons signifying rejuvenated bone. In summary, multiple structural and compositional changes to the bone material were identified leading to decay or maintenance of bone quality in disease, health and treatment conditions. Clearly, antiresorptive treatment reflected favorable effects on the multifunctional osteocytic cells that are a prerequisite for bone's structural, metabolic and mechanosensory integrity. PMID:25662494

  14. Adaptive hybrid simulations for multiscale stochastic reaction networks

    NASA Astrophysics Data System (ADS)

    Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa

    2015-01-01

    The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.

  15. Adaptive hybrid simulations for multiscale stochastic reaction networks

    SciTech Connect

    Hepp, Benjamin; Gupta, Ankit; Khammash, Mustafa

    2015-01-21

    The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest.

  16. The design and simulation test of wireless antenna protection network

    NASA Astrophysics Data System (ADS)

    Chen, Zipeng; Dai, Yawen; Li, Peng; Li, Zhuoqiu

    2013-03-01

    In this paper, a wireless antenna protection program has been designed. In the program, the TVS diode was used as the first lever for protection, and the π-type high pass filtering network as the second lever. As a result, the program not only has the traditional function of ESD protection, which can avoid the high voltage damage to the internal circuit, but also achieves the purpose of load matching, ensuring the signal source not to distort. The ADS simulation software was used to test the ability of this program for filtering and impedance matching, which proved the feasibility of this program. The wireless antenna protection network has been practically used, and its' performance of anti-electromagnetic interference has been validated.

  17. Neocortical dynamics due to axon propagation delays in cortico-cortical fibers: EEG traveling and standing waves with implications for top-down influences on local networks and white matter disease

    PubMed Central

    Nunez, Paul L.; Srinivasan, Ramesh

    2013-01-01

    The brain is treated as a nested hierarchical complex system with substantial interactions across spatial scales. Local networks are pictured as embedded within global fields of synaptic action and action potentials. Global fields may act top-down on multiple networks, acting to bind remote networks. Because of scale-dependent properties, experimental electrophysiology requires both local and global models that match observational scales. Multiple local alpha rhythms are embedded in a global alpha rhythm. Global models are outlined in which cm-scale dynamic behaviors result largely from propagation delays in cortico-cortical axons and cortical background excitation level, controlled by neuromodulators on long time scales. The idealized global models ignore the bottom-up influences of local networks on global fields so as to employ relatively simple mathematics. The resulting models are transparently related to several EEG and steady state visually evoked potentials correlated with cognitive states, including estimates of neocortical coherence structure, traveling waves, and standing waves. The global models suggest that global oscillatory behavior of self-sustained (limit-cycle) modes lower than about 20 Hz may easily occur in neocortical/white matter systems provided: Background cortical excitability is sufficiently high; the strength of long cortico-cortical axon systems is sufficiently high; and the bottom-up influence of local networks on the global dynamic field is sufficiently weak. The global models provide "entry points" to more detailed studies of global top-down influences, including binding of weakly connected networks, modulation of gamma oscillations by theta or alpha rhythms, and the effects of white matter deficits. PMID:24505628

  18. An AKT3-FOXG1-reelin network underlies defective migration in human focal malformations of cortical development.

    PubMed

    Baek, Seung Tae; Copeland, Brett; Yun, Eun-Jin; Kwon, Seok-Kyu; Guemez-Gamboa, Alicia; Schaffer, Ashleigh E; Kim, Sangwoo; Kang, Hoon-Chul; Song, Saera; Mathern, Gary W; Gleeson, Joseph G

    2015-12-01

    Focal malformations of cortical development (FMCDs) account for the majority of drug-resistant pediatric epilepsy. Postzygotic somatic mutations activating the phosphatidylinositol-4,5-bisphosphate-3-kinase (PI3K)-protein kinase B (AKT)-mammalian target of rapamycin (mTOR) pathway are found in a wide range of brain diseases, including FMCDs. It remains unclear how a mutation in a small fraction of cells disrupts the architecture of the entire hemisphere. Within human FMCD-affected brain, we found that cells showing activation of the PI3K-AKT-mTOR pathway were enriched for the AKT3(E17K) mutation. Introducing the FMCD-causing mutation into mouse brain resulted in electrographic seizures and impaired hemispheric architecture. Mutation-expressing neural progenitors showed misexpression of reelin, which led to a non-cell autonomous migration defect in neighboring cells, due at least in part to derepression of reelin transcription in a manner dependent on the forkhead box (FOX) transcription factor FOXG1. Treatments aimed at either blocking downstream AKT signaling or inactivating reelin restored migration. These findings suggest a central AKT-FOXG1-reelin signaling pathway in FMCD and support pathway inhibitors as potential treatments or therapies for some forms of focal epilepsy. PMID:26523971

  19. An AKT3-FOXG1-Reelin Network Underlies Defective Migration in Human Focal Malformations of Cortical Development

    PubMed Central

    Baek, Seung Tae; Copeland, Brett; Yun, Eun-Jin; Kwon, Seok-Kyu; Guemez-Gamboa, Alicia; Schaffer, Ashleigh E.; Kim, Sangwoo; Kang, Hoon-Chul; Song, Saera; Mathern, Gary W.; Gleeson, Joseph G.

    2016-01-01

    Focal malformations of cortical development (FMCD) account for the majority of drug-resistant pediatric epilepsy. Postzygotic somatic mutations activating the PI3K-AKT-mTOR pathway are found in a wide range of brain diseases, including FMCD. It remains unclear how a mutation in a small fraction of cells can disrupt the architecture of the entire hemisphere. We show that, within human FMCD brain, cells showing activation of this pathway were enriched for the mutation. Introducing the FMCD mutation into mouse brain resulted in electrographic seizures and impaired hemispheric architecture. Mutation-expressing neural progenitors showed reelin misexpression, which led to a non-cell autonomous migration defect in neighboring cells, due at least in part to FOXG1-mediated de-repression of reelin transcription. Treatments aimed at blocking downstream AKT signaling or inactivating reelin restored migration. These findings suggest a central AKT-FOXG1-Reelin signaling pathway in FMCD, and support pathway inhibitors as potential treatments or therapies for some forms of focal epilepsy. PMID:26523971

  20. Network simulation using the simulation language for alternate modeling (SLAM 2)

    NASA Technical Reports Server (NTRS)

    Shen, S.; Morris, D. W.

    1983-01-01

    The simulation language for alternate modeling (SLAM 2) is a general purpose language that combines network, discrete event, and continuous modeling capabilities in a single language system. The efficacy of the system's network modeling is examined and discussed. Examples are given of the symbolism that is used, and an example problem and model are derived. The results are discussed in terms of the ease of programming, special features, and system limitations. The system offers many features which allow rapid model development and provides an informative standardized output. The system also has limitations which may cause undetected errors and misleading reports unless the user is aware of these programming characteristics.

  1. Occipital cortical thickness in very low birth weight born adolescents predicts altered neural specialization of visual semantic category related neural networks.

    PubMed

    Klaver, Peter; Latal, Beatrice; Martin, Ernst

    2015-01-01

    Very low birth weight (VLBW) premature born infants have a high risk to develop visual perceptual and learning deficits as well as widespread functional and structural brain abnormalities during infancy and childhood. Whether and how prematurity alters neural specialization within visual neural networks is still unknown. We used functional and structural brain imaging to examine the visual semantic system of VLBW born (<1250 g, gestational age 25-32 weeks) adolescents (13-15 years, n = 11, 3 males) and matched term born control participants (13-15 years, n = 11, 3 males). Neurocognitive assessment revealed no group differences except for lower scores on an adaptive visuomotor integration test. All adolescents were scanned while viewing pictures of animals and tools and scrambled versions of these pictures. Both groups demonstrated animal and tool category related neural networks. Term born adolescents showed tool category related neural activity, i.e. tool pictures elicited more activity than animal pictures, in temporal and parietal brain areas. Animal category related activity was found in the occipital, temporal and frontal cortex. VLBW born adolescents showed reduced tool category related activity in the dorsal visual stream compared with controls, specifically the left anterior intraparietal sulcus, and enhanced animal category related activity in the left middle occipital gyrus and right lingual gyrus. Lower birth weight of VLBW adolescents correlated with larger thickness of the pericalcarine gyrus in the occipital cortex and smaller surface area of the superior temporal gyrus in the lateral temporal cortex. Moreover, larger thickness of the pericalcarine gyrus and smaller surface area of the superior temporal gyrus correlated with reduced tool category related activity in the parietal cortex. Together, our data suggest that very low birth weight predicts alterations of higher order visual semantic networks, particularly in the dorsal stream. The differences

  2. Network Flow Simulation of Fluid Transients in Rocket Propulsion Systems

    NASA Technical Reports Server (NTRS)

    Bandyopadhyay, Alak; Hamill, Brian; Ramachandran, Narayanan; Majumdar, Alok

    2011-01-01

    Fluid transients, also known as water hammer, can have a significant impact on the design and operation of both spacecraft and launch vehicle propulsion systems. These transients often occur at system activation and shutdown. The pressure rise due to sudden opening and closing of valves of propulsion feed lines can cause serious damage during activation and shutdown of propulsion systems. During activation (valve opening) and shutdown (valve closing), pressure surges must be predicted accurately to ensure structural integrity of the propulsion system fluid network. In the current work, a network flow simulation software (Generalized Fluid System Simulation Program) based on Finite Volume Method has been used to predict the pressure surges in the feed line due to both valve closing and valve opening using two separate geometrical configurations. The valve opening pressure surge results are compared with experimental data available in the literature and the numerical results compared very well within reasonable accuracy (< 5%) for a wide range of inlet-to-initial pressure ratios. A Fast Fourier Transform is preformed on the pressure oscillations to predict the various modal frequencies of the pressure wave. The shutdown problem, i.e. valve closing problem, the simulation results are compared with the results of Method of Characteristics. Most rocket engines experience a longitudinal acceleration, known as "pogo" during the later stage of engine burn. In the shutdown example problem, an accumulator has been used in the feed system to demonstrate the "pogo" mitigation effects in the feed system of propellant. The simulation results using GFSSP compared very well with the results of Method of Characteristics.

  3. Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations

    SciTech Connect

    Nemenman, Ilya; Sinitsyn, Nikolai; Hengartner, Nick

    2008-01-01

    We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, follows from the stochastic path Integral representation of the cumulant generating function of reaction events. In applications with a small number of chemIcal reactions, It produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, Interpretable representation and can be used for coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. As an example, we derive the coarse-grained description for a chain of biochemical reactions, and show that the coarse-grained and the microscopic simulations are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.

  4. Simulating replica exchange simulations of protein folding with a kinetic network model

    PubMed Central

    Zheng, Weihua; Andrec, Michael; Gallicchio, Emilio; Levy, Ronald M.

    2007-01-01

    Replica exchange (RE) is a generalized ensemble simulation method for accelerating the exploration of free-energy landscapes, which define many challenging problems in computational biophysics, including protein folding and binding. Although temperature RE (T-RE) is a parallel simulation technique whose implementation is relatively straightforward, kinetics and the approach to equilibrium in the T-RE ensemble are very complicated; there is much to learn about how to best employ T-RE to protein folding and binding problems. We have constructed a kinetic network model for RE studies of protein folding and used this reduced model to carry out “simulations of simulations” to analyze how the underlying temperature dependence of the conformational kinetics and the basic parameters of RE (e.g., the number of replicas, the RE rate, and the temperature spacing) all interact to affect the number of folding transitions observed. When protein folding follows anti-Arrhenius kinetics, we observe a speed limit for the number of folding transitions observed at the low temperature of interest, which depends on the maximum of the harmonic mean of the folding and unfolding transition rates at high temperature. The results shown here for the network RE model suggest ways to improve atomic-level RE simulations such as the use of “training” simulations to explore some aspects of the temperature dependence for folding of the atomic-level models before performing RE studies. PMID:17878309

  5. Efficient stochastic simulations of complex reaction networks on surfaces.

    PubMed

    Barzel, Baruch; Biham, Ofer

    2007-10-14

    Surfaces serve as highly efficient catalysts for a vast variety of chemical reactions. Typically, such surface reactions involve billions of molecules which diffuse and react over macroscopic areas. Therefore, stochastic fluctuations are negligible and the reaction rates can be evaluated using rate equations, which are based on the mean-field approximation. However, in case that the surface is partitioned into a large number of disconnected microscopic domains, the number of reactants in each domain becomes small and it strongly fluctuates. This is, in fact, the situation in the interstellar medium, where some crucial reactions take place on the surfaces of microscopic dust grains. In this case rate equations fail and the simulation of surface reactions requires stochastic methods such as the master equation. However, in the case of complex reaction networks, the master equation becomes infeasible because the number of equations proliferates exponentially. To solve this problem, we introduce a stochastic method based on moment equations. In this method the number of equations is dramatically reduced to just one equation for each reactive species and one equation for each reaction. Moreover, the equations can be easily constructed using a diagrammatic approach. We demonstrate the method for a set of astrophysically relevant networks of increasing complexity. It is expected to be applicable in many other contexts in which problems that exhibit analogous structure appear, such as surface catalysis in nanoscale systems, aerosol chemistry in stratospheric clouds, and genetic networks in cells. PMID:17935419

  6. A Simulation of Cooperation and Competition in Insurgent Networks

    NASA Astrophysics Data System (ADS)

    Gabbay, Michael

    2014-03-01

    Insurgencies are often characterized by multiple groups who share a common foe in the national government but have independent organizations which may differ with respect to social identities, ideologies, strategies, and their use of violence. These groups may cooperate in various ways such as conducting joint attacks, pooling resources, and establishing formal alliances or mergers. However, they may also compete with each other over popular support, recruitment of fighters, funding, allies, and ultimately military dominance. A network coevolution model of insurgent factional dynamics is presented which accounts for factors driving cooperation and competition. The model is formulated as a system of coupled ODEs which evolves network ties between insurgent groups along with group policies concerning the targets of violence. Simulation results are presented showing sharp transitions in network structure as model parameters are varied. Connections are drawn between the model results and empirical data from the Iraqi insurgency. This work was supported by the Office of Naval Research under grant N00014-13-1-0381.

  7. Quantum versus simulated annealing in wireless interference network optimization

    PubMed Central

    Wang, Chi; Chen, Huo; Jonckheere, Edmond

    2016-01-01

    Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed. PMID:27181056

  8. Quantum versus simulated annealing in wireless interference network optimization

    NASA Astrophysics Data System (ADS)

    Wang, Chi; Chen, Huo; Jonckheere, Edmond

    2016-05-01

    Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.

  9. Simulating Quantitative Cellular Responses Using Asynchronous Threshold Boolean Network Ensembles

    PubMed Central

    2011-01-01

    Background With increasing knowledge about the potential mechanisms underlying cellular functions, it is becoming feasible to predict the response of biological systems to genetic and environmental perturbations. Due to the lack of homogeneity in living tissues it is difficult to estimate the physiological effect of chemicals, including potential toxicity. Here we investigate a biologically motivated model for estimating tissue level responses by aggregating the behavior of a cell population. We assume that the molecular state of individual cells is independently governed by discrete non-deterministic signaling mechanisms. This results in noisy but highly reproducible aggregate level responses that are consistent with experimental data. Results We developed an asynchronous threshold Boolean network simulation algorithm to model signal transduction in a single cell, and then used an ensemble of these models to estimate the aggregate response across a cell population. Using published data, we derived a putative crosstalk network involving growth factors and cytokines - i.e., Epidermal Growth Factor, Insulin, Insulin like Growth Factor Type 1, and Tumor Necrosis Factor α - to describe early signaling events in cell proliferation signal transduction. Reproducibility of the modeling technique across ensembles of Boolean networks representing cell populations is investigated. Furthermore, we compare our simulation results to experimental observations of hepatocytes reported in the literature. Conclusion A systematic analysis of the results following differential stimulation of this model by growth factors and cytokines suggests that: (a) using Boolean network ensembles with asynchronous updating provides biologically plausible noisy individual cellular responses with reproducible mean behavior for large cell populations, and (b) with sufficient data our model can estimate the response to different concentrations of extracellular ligands. Our results suggest that this

  10. Not only … but also: REM sleep creates and NREM Stage 2 instantiates landmark junctions in cortical memory networks.

    PubMed

    Llewellyn, Sue; Hobson, J Allan

    2015-07-01

    This article argues both rapid eye movement (REM) and non-rapid eye movement (NREM) sleep contribute to overnight episodic memory processes but their roles differ. Episodic memory may have evolved from memory for spatial navigation in animals and humans. Equally, mnemonic navigation in world and mental space may rely on fundamentally equivalent processes. Consequently, the basic spatial network characteristics of pathways which meet at omnidirectional nodes or junctions may be conserved in episodic brain networks. A pathway is formally identified with the unidirectional, sequential phases of an episodic memory. In contrast, the function of omnidirectional junctions is not well understood. In evolutionary terms, both animals and early humans undertook tours to a series of landmark junctions, to take advantage of resources (food, water and shelter), whilst trying to avoid predators. Such tours required memory for emotionally significant landmark resource-place-danger associations and the spatial relationships amongst these landmarks. In consequence, these tours may have driven the evolution of both spatial and episodic memory. The environment is dynamic. Resource-place associations are liable to shift and new resource-rich landmarks may be discovered, these changes may require re-wiring in neural networks. To realise these changes, REM may perform an associative, emotional encoding function between memory networks, engendering an omnidirectional landmark junction which is instantiated in the cortex during NREM Stage 2. In sum, REM may preplay associated elements of past episodes (rather than replay individual episodes), to engender an unconscious representation which can be used by the animal on approach to a landmark junction in wake. PMID:25921620

  11. The dorsal prefrontal and dorsal anterior cingulate cortices exert complementary network signatures during encoding and retrieval in associative memory.

    PubMed

    Woodcock, Eric A; White, Richard; Diwadkar, Vaibhav A

    2015-09-01

    Cognitive control includes processes that facilitate execution of effortful cognitive tasks, including associative memory. Regions implicated in cognitive control during associative memory include the dorsal prefrontal (dPFC) and dorsal anterior cingulate cortex (dACC). Here we investigated the relative degrees of network-related interactions originating in the dPFC and dACC during oscillating phases of associative memory: encoding and cued retrieval. Volunteers completed an established object-location associative memory paradigm during fMRI. Psychophysiological interactions modeled modulatory network interactions from the dPFC and dACC during memory encoding and retrieval. Results were evaluated in second level analyses of variance with seed region and memory process as factors. Each seed exerted differentiable modulatory effects during encoding and retrieval. The dACC exhibited greater modulation (than the dPFC) on the fusiform and parahippocampal gyrus during encoding, while the dPFC exhibited greater modulation (than the dACC) on the fusiform, hippocampus, dPFC and basal ganglia. During retrieval, the dPFC exhibited greater modulation (than the dACC) on the parahippocampal gyrus, hippocampus, superior parietal lobule, and dPFC. The most notable finding was a seed by process interaction indicating that the dACC and the dPFC exerted complementary modulatory control on the hippocampus during each of the associative memory processes. These results provide evidence for differentiable, yet complementary, control-related modulation by the dACC and dPFC, while establishing the primacy of dPFC in exerting network control during both associative memory phases. Our approach and findings are relevant for understanding basic processes in human memory and psychiatric disorders that impact associative memory-related networks. PMID:25960314

  12. Climate and change: simulating flooding impacts on urban transport network

    NASA Astrophysics Data System (ADS)

    Pregnolato, Maria; Ford, Alistair; Dawson, Richard

    2015-04-01

    National-scale climate projections indicate that in the future there will be hotter and drier summers, warmer and wetter winters, together with rising sea levels. The frequency of extreme weather events is expected to increase, causing severe damage to the built environment and disruption of infrastructures (Dawson, 2007), whilst population growth and changed demographics are placing new demands on urban infrastructure. It is therefore essential to ensure infrastructure networks are robust to these changes. This research addresses these challenges by focussing on the development of probabilistic tools for managing risk by modelling urban transport networks within the context of extreme weather events. This paper presents a methodology to investigate the impacts of extreme weather events on urban environment, in particular infrastructure networks, through a combination of climate simulations and spatial representations. By overlaying spatial data on hazard thresholds from a flood model and a flood safety function, mitigated by potential adaptation strategies, different levels of disruption to commuting journeys on road networks are evaluated. The method follows the Catastrophe Modelling approach and it consists of a spatial model, combining deterministic loss models and probabilistic risk assessment techniques. It can be applied to present conditions as well as future uncertain scenarios, allowing the examination of the impacts alongside socio-economic and climate changes. The hazard is determined by simulating free surface water flooding, with the software CityCAT (Glenis et al., 2013). The outputs are overlapped to the spatial locations of a simple network model in GIS, which uses journey-to-work (JTW) observations, supplemented with speed and capacity information. To calculate the disruptive effect of flooding on transport networks, a function relating water depth to safe driving car speed has been developed by combining data from experimental reports (Morris et

  13. Early asymmetries in maternal transcript distribution associated with a cortical microtubule network and a polar body in the beetle Tribolium castaneum.

    PubMed

    Peel, Andrew D; Averof, Michalis

    2010-11-01

    The localization of maternal mRNAs during oogenesis plays a central role in axial specification in some insects. Here we describe a polar body-associated asymmetry in maternal transcript distribution in pre-blastoderm eggs of the beetle Tribolium castaneum. Since the position of the polar body marks the future dorsal side of the embryo, we have investigated whether this asymmetry in mRNA distribution plays a role in dorsal-ventral axis specification. Whilst our results suggest polar body-associated transcripts do not play a significant role in specifying the DV axis, at least during early embryogenesis, we do find that the polar body is closely associated with a cortical microtubule network (CMN), which may play a role in the localization of transcripts during oogenesis. Transcripts of the gene T.c.pangolin co-localize with the CMN at the time of their anterior localization during oogenesis and their anterior localization is disrupted by the microtubule-depolymerizing agent colcemid. PMID:20857499

  14. DISCRETE EVENT SIMULATION OF OPTICAL SWITCH MATRIX PERFORMANCE IN COMPUTER NETWORKS

    SciTech Connect

    Imam, Neena; Poole, Stephen W

    2013-01-01

    In this paper, we present application of a Discrete Event Simulator (DES) for performance modeling of optical switching devices in computer networks. Network simulators are valuable tools in situations where one cannot investigate the system directly. This situation may arise if the system under study does not exist yet or the cost of studying the system directly is prohibitive. Most available network simulators are based on the paradigm of discrete-event-based simulation. As computer networks become increasingly larger and more complex, sophisticated DES tool chains have become available for both commercial and academic research. Some well-known simulators are NS2, NS3, OPNET, and OMNEST. For this research, we have applied OMNEST for the purpose of simulating multi-wavelength performance of optical switch matrices in computer interconnection networks. Our results suggest that the application of DES to computer interconnection networks provides valuable insight in device performance and aids in topology and system optimization.

  15. Visualization and simulation of bubble growth in pore networks

    SciTech Connect

    Li, Xuehai; Yortsos, Y.C.

    1994-03-01

    Bubble nucleation and bubble growth in porous media is an important problem encountered in processes, such as pressure depletion and boiling. To understand its basic aspects, experiments and numerical simulations in micromodel geometries were undertaken. Experiments of bubble growth by pressure depletion were carried out in 2-D etched-glass micromodels and in Hele-Shaw cells. Nucleation of bubbles and the subsequent growth of gas clusters were visualized. Contrary to the bulk or to Hele-Shaw cells, gas clusters in the micromodel have irregular and ramified shapes and share many of the features of an external invasion process (e.g. of percolation during drainage). A pore network numerical model was developed to simulate the growth of multiple gas clusters under various conditions. The model is based on the solution of the convection-diffusions equation and also accounts for capillary and viscous forces, which play an important role in determining the growth patterns. Numerical simulation resulted in good agreement with the experimental results.

  16. Negative childhood experiences alter a prefrontal-insular-motor cortical network in healthy adults: A preliminary multimodal rsfMRI-fMRI-MRS-dMRI study

    PubMed Central

    Duncan, Niall W.; Hayes, Dave J.; Wiebking, Christine; Tiret, Brice; Pietruska, Karin; Chen, David Q.; Rainville, Pierre; Marjańska, Malgorzata; Mohammid, Omar; Doyon, Julien; Hodaie, Mojgan; Northoff, Georg

    2016-01-01

    Research in humans and animals has shown that negative childhood experiences (NCE) can have long-term effects on the structure and function of the brain. Alterations have been noted in grey and white matter, in the brain’s resting state, on the glutamatergic system, and on neural and behavioural responses to aversive stimuli. These effects can be linked to psychiatric disorder such as depression and anxiety disorders that are influenced by excessive exposure to early life stressors. The aim of the current study was to investigate the effect of NCEs on these systems. Resting state functional MRI (rsfMRI), aversion task fMRI, glutamate magnetic resonance spectroscopy (MRS), and diffusion magnetic resonance imaging (dMRI) were combined with the Childhood Trauma Questionnaire (CTQ) in healthy subjects to examine the impact of NCEs on the brain. Low CTQ scores, a measure of NCEs, were related to higher resting state glutamate levels and higher resting state entropy in the medial prefrontal cortex (mPFC). CTQ scores, mPFC glutamate and entropy, correlated with neural BOLD responses to the anticipation of aversive stimuli in regions throughout the aversion-related network, with strong correlations between all measures in the motor cortex and left insula. Structural connectivity strength, measured using mean fractional anisotropy, between the mPFC and left insula correlated to aversion-related signal changes in the motor cortex. These findings highlight the impact of NCEs on multiple inter-related brain systems. In particular, they highlight the role of a prefrontal-insular-motor cortical network in the processing and responsivity to aversive stimuli and its potential adaptability by NCEs. PMID:26287448

  17. Negative childhood experiences alter a prefrontal-insular-motor cortical network in healthy adults: A preliminary multimodal rsfMRI-fMRI-MRS-dMRI study.

    PubMed

    Duncan, Niall W; Hayes, Dave J; Wiebking, Christine; Tiret, Brice; Pietruska, Karin; Chen, David Q; Rainville, Pierre; Marjańska, Małgorzata; Ayad, Omar; Doyon, Julien; Hodaie, Mojgan; Northoff, Georg

    2015-11-01

    Research in humans and animals has shown that negative childhood experiences (NCE) can have long-term effects on the structure and function of the brain. Alterations have been noted in grey and white matter, in the brain's resting state, on the glutamatergic system, and on neural and behavioural responses to aversive stimuli. These effects can be linked to psychiatric disorder such as depression and anxiety disorders that are influenced by excessive exposure to early life stressors. The aim of the current study was to investigate the effect of NCEs on these systems. Resting state functional MRI (rsfMRI), aversion task fMRI, glutamate magnetic resonance spectroscopy (MRS), and diffusion magnetic resonance imaging (dMRI) were combined with the Childhood Trauma Questionnaire (CTQ) in healthy subjects to examine the impact of NCEs on the brain. Low CTQ scores, a measure of NCEs, were related to higher resting state glutamate levels and higher resting state entropy in the medial prefrontal cortex (mPFC). CTQ scores, mPFC glutamate and entropy, correlated with neural BOLD responses to the anticipation of aversive stimuli in regions throughout the aversion-related network, with strong correlations between all measures in the motor cortex and left insula. Structural connectivity strength, measured using mean fractional anisotropy, between the mPFC and left insula correlated to aversion-related signal changes in the motor cortex. These findings highlight the impact of NCEs on multiple inter-related brain systems. In particular, they highlight the role of a prefrontal-insular-motor cortical network in the processing and responsivity to aversive stimuli and its potential adaptability by NCEs. PMID:26287448

  18. Low and then high frequency oscillations of distinct right cortical networks are progressively enhanced by medium and long term Satyananda Yoga meditation practice

    PubMed Central

    Thomas, John; Jamieson, Graham; Cohen, Marc

    2014-01-01

    Meditation proficiency is related to trait-like (learned) effects on brain function, developed over time. Previous studies show increases in EEG power in lower frequency bands (theta, alpha) in experienced meditators in both meditation states and baseline conditions. Higher gamma band power has been found in advanced Buddhist meditators, yet it is not known if this occurs in Yoga meditation practices. This study used eLORETA to compare differences in cortical source activity underlying scalp EEG from intermediate (mean experience 4 years) and advanced (mean experience 30 years) Australian meditators from the Satyananda Yoga tradition during a body-steadiness meditation, mantra meditation, and non-meditation mental calculation condition. Intermediate Yoga meditators showed greater source activity in low frequencies (particularly theta and alpha1) during mental calculation, body-steadiness and mantra meditation. A similar spatial pattern of significant differences was found in all conditions but the number of significant voxels was double during body-steadiness and mantra meditation than in the non-meditation (calculation) condition. These differences were greatest in right (R) superior frontal and R precentral gyri and extended back to include the R parietal and occipital lobes. Advanced Yoga meditators showed greater activity in high frequencies (beta and especially gamma) in all conditions but greatly expanded during meditation practice. Across all conditions (meditation and non-meditation) differences were greatest in the same regions: R insula, R inferior frontal gyrus and R anterior temporal lobe. Distinct R core networks were identified in alpha1 (8–10 Hz) and gamma (25–42 Hz) bands, respectively. The voxels recruited to these networks greatly expanded during meditation practice to include homologous regions of the left hemisphere. Functional interpretation parallels traditionally described stages of development in Yoga proficiency. PMID:24959124

  19. Cortical Memory Mechanisms and Language Origins

    ERIC Educational Resources Information Center

    Aboitiz, Francisco; Garcia, Ricardo R.; Bosman, Conrado; Brunetti, Enzo

    2006-01-01

    We have previously proposed that cortical auditory-vocal networks of the monkey brain can be partly homologized with language networks that participate in the phonological loop. In this paper, we suggest that other linguistic phenomena like semantic and syntactic processing also rely on the activation of transient memory networks, which can be…

  20. Monitoring Different Phonological Parameters of Sign Language Engages the Same Cortical Language Network but Distinctive Perceptual Ones.

    PubMed

    Cardin, Velia; Orfanidou, Eleni; Kästner, Lena; Rönnberg, Jerker; Woll, Bencie; Capek, Cheryl M; Rudner, Mary

    2016-01-01

    The study of signed languages allows the dissociation of sensorimotor and cognitive neural components of the language signal. Here we investigated the neurocognitive processes underlying the monitoring of two phonological parameters of sign languages: handshape and location. Our goal was to determine if brain regions processing sensorimotor characteristics of different phonological parameters of sign languages were also involved in phonological processing, with their activity being modulated by the linguistic content of manual actions. We conducted an fMRI experiment using manual actions varying in phonological structure and semantics: (1) signs of a familiar sign language (British Sign Language), (2) signs of an unfamiliar sign language (Swedish Sign Language), and (3) invented nonsigns that violate the phonological rules of British Sign Language and Swedish Sign Language or consist of nonoccurring combinations of phonological parameters. Three groups of participants were tested: deaf native signers, deaf nonsigners, and hearing nonsigners. Results show that the linguistic processing of different phonological parameters of sign language is independent of the sensorimotor characteristics of the language signal. Handshape and location were processed by different perceptual and task-related brain networks but recruited the same language areas. The semantic content of the stimuli did not influence this process, but phonological structure did, with nonsigns being associated with longer RTs and stronger activations in an action observation network in all participants and in the supramarginal gyrus exclusively in deaf signers. These results suggest higher processing demands for stimuli that contravene the phonological rules of a signed language, independently of previous knowledge of signed languages. We suggest that the phonological characteristics of a language may arise as a consequence of more efficient neural processing for its perception and production. PMID

  1. The Use of Simulation for the Design and Analysis of Thermophotovoltaic Networks

    SciTech Connect

    JE Oppenlander; JL Vell; WS Gaes; DM Siganporia; LR Danielson; MW Dashiell

    2004-07-20

    Simulation has provided valuable quantification of the fundamental behavior of thermophotovoltaic cell networks. The results of simulation studies have supported the design and fabrication of small-scale demonstration networks and are expected to guide assembly of large-scale systems. This paper describes the methodology and software simulator developed to address issues in thermophotovoltaic (TPV) networking, including failure analysis, electrical network design, and nonuniform illumination. Results from simulation studies are given illustrating their application to the design and fabrication of small-scale TPV arrays.

  2. Imaging Simulations for the Korean VLBI Network (KVN)

    NASA Astrophysics Data System (ADS)

    Jung, Tae-Hyun; Rhee, Myung-Hyun; Roh, Duk-Gyoo; Kim, Hyun-Goo; Sohn, Bong-Won

    2005-03-01

    The Korean VLBI Network (KVN) will open a new field of research in astronomy, geodesy and earth science using the newest three 21m radio telescopes. This will expand our ability to look at the Universe in the millimeter regime. Imaging capability of radio interferometry is highly dependent upon the antenna configuration, source size, declination and the shape of target. In this paper, imaging simulations are carried out with the KVN system configuration. Five test images were used which were a point source, multi-point sources, a uniform sphere with two different sizes compared to the synthesis beam of the KVN and a Very Large Array (VLA) image of Cygnus A. The declination for the full time simulation was set as +60 degrees and the observation time range was --6 to +6 hours around transit. Simulations have been done at 22GHz, one of the KVN observation frequency. All these simulations and data reductions have been run with the Astronomical Image Processing System (AIPS) software package. As the KVN array has a resolution of about 6 mas (milli arcsecond) at 22GHz, in case of model source being approximately the beam size or smaller, the ratio of peak intensity over RMS shows about 10000:1 and 5000:1. The other case in which model source is larger than the beam size, this ratio shows very low range of about 115:1 and 34:1. This is due to the lack of short baselines and the small number of antenna. We compare the coordinates of the model images with those of the cleaned images. The result shows mostly perfect correspondence except in the case of the 12mas uniform sphere. Therefore, the main astronomical targets for the KVN will be the compact sources and the KVN will have an excellent performance in the astrometry for these sources.

  3. Quantum Monte Carlo simulations with tensor-network states

    NASA Astrophysics Data System (ADS)

    Song, Jeong Pil; Clay, R. T.

    2011-03-01

    Matrix-product states, generated by the density-matrix renormalization group method, are among the most powerful methods for simulation of quasi-one dimensional quantum systems. Direct application of a matrix-product state representation fails for two dimensional systems, although a number of tensor-network states have been proposed to generalize the concept for two dimensions. We introduce a useful approximate method replacing a 4-index tensor by two matrices in order to contract tensors in two dimensions. We use this formalism as a basis for variational quantum Monte Carlo, optimizing the matrix elements stochastically. We present results on a two dimensional spinless fermion model including nearest- neighbor Coulomb interactions, and determine the critical Coulomb interaction for the charge density wave state by finite size scaling. This work was supported by the Department of Energy grant DE-FG02-06ER46315.

  4. Study and Simulation of Traffic Behavior in Cellular Network

    NASA Astrophysics Data System (ADS)

    Madhup, D. K.; Shrestha, C. L.; Sharma, R. K.

    2007-07-01

    Cellular radio systems accommodate a large number of users with a limited radio spectrum. The concept of trunking allows a large number of users to share the relatively small number of channels in a cell by providing access to each user, on demand, from a pool of available channels. Traffic engineering deals with provisioning of communication circuits in a given area for a number of subscribers with a required grade of service. Traffic in any cell depends upon the number of users, the average request rate and average call duration. Certain number of channels is required for the required GOS. To design an optimum capacity cellular system, traffic behavior on that system is important. The number of channel required can be estimated by using Erlang formula and Erlang table. Erlang table is not always useful to calculate the probability of blocking in various complex scenarios such as channel borrowing strategies. When the total number of channel available in a given cell are divided to serve partly for newly generated calls and partly for handover calls, and if they use dynamic channel assignment strategies like channel borrowing, then the probability of blocking can't be calculated from Erlang table. Simulation model of the behavior help us to determine the blocking and the channel utilization while using various channel assignment strategies. The title "Study and Simulation of Traffic Behavior in Cellular Network" entail the study of the blocking probability of traffic in cellular network for static channel assignment strategies and dynamic channel borrowing strategies through MATLAB programming language and graphic user interface (GUI). The result shows that the dynamic scheme can perform better than static maximizing the overall utilization of the circuits and minimizing the overall blocking.

  5. A simulation study of TaMAC protocol using network simulator 2.

    PubMed

    Ullah, Sana; Kwak, Kyung Sup

    2012-10-01

    A Wireless Body Area Network (WBAN) is expected to play a significant role in future healthcare system. It interconnects low-cost and intelligent sensor nodes in, on, or around a human body to serve a variety of medical applications. It can be used to diagnose and treat patients with chronic diseases such as hypertensions, diabetes, and cardiovascular diseases. The lightweight sensor nodes integrated in WBAN require low-power operation, which can be achieved using different optimization techniques. We introduce a Traffic-adaptive MAC protocol (TaMAC) for WBAN that supports dual wakeup mechanisms for normal, emergency, and on-demand traffic. In this letter, the TaMAC protocol is simulated using a well-known Network Simulator 2 (NS-2). The problem of multiple emergency nodes is solved using both wakeup radio and CSMA/CA protocol. The power consumption, delay, and throughput performance are closely compared with beacon-enabled IEEE 802.15.4 MAC protocol using extensive simulations. PMID:21863319

  6. Satellite range delay simulator for a matrix-switched time division multiple-access network simulator

    NASA Technical Reports Server (NTRS)

    Nagy, Lawrence A.

    1989-01-01

    The Systems Integration, Test, and Evaluation (SITE) facility at NASA Lewis Research Center is presently configured as a satellite-switched time division multiple access (SS-TDMA) network simulator. The purpose of SITE is to demonstrate and evaluate advanced communication satellite technologies, presently embodied by POC components developed under NASA contracts in addition to other hardware, such as ground terminals, designed and built in-house at NASA Lewis. Each ground terminal in a satellite communications system will experience a different aspect of the satellite's motion due mainly to daily tidal effects and station keeping, hence a different duration and rate of variation in the range delay. As a result of this and other effects such as local oscillator instability, each ground terminal must constantly adjust its transmit burst timing so that data bursts from separate ground terminals arrive at the satellite in their assigned time slots, preventing overlap and keeping the system in synchronism. On the receiving end, ground terminals must synchronize their local clocks using reference transmissions received through the satellite link. A feature of the SITE facility is its capability to simulate the varying propagation delays and associated Doppler frequency shifts that the ground terminals in the network have to cope with. Delay is achieved by means of two NASA Lewis designed and built range delay simulator (RDS) systems, each independently controlled locally with front panel switches or remotely by an experiment control and monitor (EC/M) computer.

  7. On the Effects on Cortical Spontaneous Activity of the Symmetries of the Network of Pinwheels in Visual Area V1.

    PubMed

    Veltz, Romain; Chossat, Pascal; Faugeras, Olivier

    2015-12-01

    This paper challenges and extends earlier seminal work. We consider the problem of describing mathematically the spontaneous activity of V1 by combining several important experimental observations including (1) the organization of the visual cortex into a spatially periodic network of hypercolumns structured around pinwheels, (2) the difference between short-range and long-range intracortical connections, the first ones being rather isotropic and producing naturally doubly periodic patterns by Turing mechanisms, the second one being patchy, and (3) the fact that the Turing patterns spontaneously produced by the short-range connections and the network of pinwheels have similar periods. By analyzing the PO maps, we are able to classify all possible singular points (the pinwheels) as having symmetries described by a small subset of the wallpaper groups. We then propose a description of the spontaneous activity of V1 using a classical voltage-based neural field model that features isotropic short-range connectivities modulated by non-isotropic long-range connectivities. A key observation is that, with only short-range connections and because the problem has full translational invariance in this case, a spontaneous doubly periodic pattern generates a 2-torus in a suitable functional space which persists as a flow-invariant manifold under small perturbations, for example when turning on the long-range connections. Through a complete analysis of the symmetries of the resulting neural field equation and motivated by a numerical investigation of the bifurcations of their solutions, we conclude that the branches of solutions which are stable over an extended range of parameters are those that correspond to patterns with an hexagonal (or nearly hexagonal) symmetry. The question of which patterns persist when turning on the long-range connections is answered by (1) analyzing the remaining symmetries on the perturbed torus and (2) combining this information with the Poincar

  8. Broadband macroscopic cortical oscillations emerge from intrinsic neuronal response failures

    PubMed Central

    Goldental, Amir; Vardi, Roni; Sardi, Shira; Sabo, Pinhas; Kanter, Ido

    2015-01-01

    Broadband spontaneous macroscopic neural oscillations are rhythmic cortical firing which were extensively examined during the last century, however, their possible origination is still controversial. In this work we show how macroscopic oscillations emerge in solely excitatory random networks and without topological constraints. We experimentally and theoretically show that these oscillations stem from the counterintuitive underlying mechanism—the intrinsic stochastic neuronal response failures (NRFs). These NRFs, which are characterized by short-term memory, lead to cooperation among neurons, resulting in sub- or several- Hertz macroscopic oscillations which coexist with high frequency gamma oscillations. A quantitative interplay between the statistical network properties and the emerging oscillations is supported by simulations of large networks based on single-neuron in-vitro experiments and a Langevin equation describing the network dynamics. Results call for the examination of these oscillations in the presence of inhibition and external drives. PMID:26578893

  9. In vitro screening of metal oxide nanoparticles for effects on neural function using cortical networks on microelectrode arrays.

    PubMed

    Strickland, Jenna D; Lefew, William R; Crooks, James; Hall, Diana; Ortenzio, Jayna Nr; Dreher, Kevin; Shafer, Timothy J

    2016-06-01

    Nanoparticles (NPs) may translocate to the brain following inhalation or oral exposures, yet higher throughput methods to screen NPs for potential neurotoxicity are lacking. The present study examined effects of 5 CeO2 (5- 1288 nm), and 4 TiO2 (6-142 nm) NPs and microparticles (MP) on network function in primary cultures of rat cortex on 12 well microelectrode array (MEA) plates. Particles were without cytotoxicity at concentrations ≤50 µg/ml. After recording 1 h of baseline activity prior to particle (3-50 µg/ml) exposure, changes in the total number of spikes (TS) and # of active electrodes (#AEs) were assessed 1, 24, and 48 h later. Following the 48 h recording, the response to a challenge with the GABAA antagonist bicuculline (BIC; 25 µM) was assessed. In all, particles effects were subtle, but 69 nm CeO2 and 25 nm TiO2 NPs caused concentration-related decreases in TS following 1 h exposure. At 48 h, 5 and 69 nm CeO2 and 25 and 31 nm TiO2 decreased #AE, while the two MPs increased #AEs. Following BIC, only 31 nm TiO2 produced concentration-related decreases in #AEs, while 1288 nm CeO2 caused concentration-related increases in both TS and #AE. The results indicate that some metal oxide particles cause subtle concentration-related changes in spontaneous and/or GABAA receptor-mediated neuronal activity in vitro at times when cytotoxicity is absent, and that MEAs can be used to screen and prioritize nanoparticles for neurotoxicity hazard. PMID:26593696

  10. The Virtual Brain: a simulator of primate brain network dynamics.

    PubMed

    Sanz Leon, Paula; Knock, Stuart A; Woodman, M Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R; Jirsa, Viktor

    2013-01-01

    We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

  11. The Virtual Brain: a simulator of primate brain network dynamics

    PubMed Central

    Sanz Leon, Paula; Knock, Stuart A.; Woodman, M. Marmaduke; Domide, Lia; Mersmann, Jochen; McIntosh, Anthony R.; Jirsa, Viktor

    2013-01-01

    We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications. PMID:23781198

  12. Designing laboratory wind simulations using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Križan, Josip; Gašparac, Goran; Kozmar, Hrvoje; Antonić, Oleg; Grisogono, Branko

    2015-05-01

    While experiments in boundary layer wind tunnels remain to be a major research tool in wind engineering and environmental aerodynamics, designing the modeling hardware required for a proper atmospheric boundary layer (ABL) simulation can be costly and time consuming. Hence, possibilities are sought to speed-up this process and make it more time-efficient. In this study, two artificial neural networks (ANNs) are developed to determine an optimal design of the Counihan hardware, i.e., castellated barrier wall, vortex generators, and surface roughness, in order to simulate the ABL flow developing above urban, suburban, and rural terrains, as previous ANN models were created for one terrain type only. A standard procedure is used in developing those two ANNs in order to further enhance best-practice possibilities rather than to improve existing ANN designing methodology. In total, experimental results obtained using 23 different hardware setups are used when creating ANNs. In those tests, basic barrier height, barrier castellation height, spacing density, and height of surface roughness elements are the parameters that were varied to create satisfactory ABL simulations. The first ANN was used for the estimation of mean wind velocity, turbulent Reynolds stress, turbulence intensity, and length scales, while the second one was used for the estimation of the power spectral density of velocity fluctuations. This extensive set of studied flow and turbulence parameters is unmatched in comparison to the previous relevant studies, as it includes here turbulence intensity and power spectral density of velocity fluctuations in all three directions, as well as the Reynolds stress profiles and turbulence length scales. Modeling results agree well with experiments for all terrain types, particularly in the lower ABL within the height range of the most engineering structures, while exhibiting sensitivity to abrupt changes and data scattering in profiles of wind-tunnel results. The

  13. Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX): comparing multi-electrode recordings from simulated and biological mammalian cortical tissue.

    PubMed

    Tomsett, Richard J; Ainsworth, Matt; Thiele, Alexander; Sanayei, Mehdi; Chen, Xing; Gieselmann, Marc A; Whittington, Miles A; Cunningham, Mark O; Kaiser, Marcus

    2015-07-01

    Local field potentials (LFPs) sampled with extracellular electrodes are frequently used as a measure of population neuronal activity. However, relating such measurements to underlying neuronal behaviour and connectivity is non-trivial. To help study this link, we developed the Virtual Electrode Recording Tool for EXtracellular potentials (VERTEX). We first identified a reduced neuron model that retained the spatial and frequency filtering characteristics of extracellular potentials from neocortical neurons. We then developed VERTEX as an easy-to-use Matlab tool for simulating LFPs from large populations (>100,000 neurons). A VERTEX-based simulation successfully reproduced features of the LFPs from an in vitro multi-electrode array recording of macaque neocortical tissue. Our model, with virtual electrodes placed anywhere in 3D, allows direct comparisons with the in vitro recording setup. We envisage that VERTEX will stimulate experimentalists, clinicians, and computational neuroscientists to use models to understand the mechanisms underlying measured brain dynamics in health and disease. PMID:24863422

  14. Simulating Issue Networks in Small Classes using the World Wide Web.

    ERIC Educational Resources Information Center

    Josefson, Jim; Casey, Kelly

    2000-01-01

    Provides background information on simulations and active learning. Discusses the use of simulations in political science courses. Describes a simulation exercise where students performed specific institutional role playing, simulating the workings of a single congressional issue network, based on the reauthorization of the Endangered Species Act.…

  15. Satellite-matrix-switched, time-division-multiple-access network simulator

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Andro, Monty; Nagy, Lawrence A.; Budinger, James M.; Shalkhauser, Mary JO

    1989-01-01

    A versatile experimental Ka-band network simulator has been implemented at the NASA Lewis Research Center to demonstrate and evaluate a satellite-matrix-switched, time-division-multiple-access (SMS-TDMA) network and to evaluate future digital ground terminals and radiofrequency (RF) components. The simulator was implemented by using proof-of-concept RF components developed under NASA contracts and digital ground terminal and link simulation hardware developed at Lewis. This simulator provides many unique capabilities such as satellite range delay and variation simulation and rain fade simulation. All network parameters (e.g., signal-to-noise ratio, satellite range variation rate, burst density, and rain fade) are controlled and monitored by a central computer. The simulator is presently configured as a three-ground-terminal SMS-TDMA network.

  16. Satellite-matrix-switched, time-division-multiple-access network simulator

    NASA Technical Reports Server (NTRS)

    Ivancic, William D.; Andro, Monty; Nagy, Lawrence A.; Budinger, James M.; Shalkhauser, Mary JO

    1990-01-01

    A versatile experimental Ka-band network simulator has been implemented at the NASA Lewis Research Center to demonstrate and evaluate a satellite-matrix-switched, time-division-multiple-access (SMS-TDMA) network and to evaluate future digital ground terminals and radiofrequency (RF) components. The simulator was implemented by using proof-of-concept RF components developed under NASA contracts and digital ground terminal and link simulation hardware developed at Lewis. This simulator provides many unique capabilities such as satellite range delay and variation simulation and rain fade simulation. All network parameters (e.g., signal-to-noise ratio, satellite range variation rate, burst density, and rain fade) are controlled and monitored by a central computer. The simulator is presently configured as a three-ground-terminal SMS-TDMA network.

  17. Identifying True Cortical Interactions in MEG using the Nulling Beamformer

    PubMed Central

    Hui, Hua Brian; Pantazis, Dimitrios; Bressler, Steven L.; Leahy, Richard M.

    2009-01-01

    Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recording data. This is because most interaction measures are not robust to the cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. In this paper we describe a modified beam-forming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to indicate the performance of our nulling beamforming method. PMID:19896541

  18. Taming Wild Horses: The Need for Virtual Time-based Scheduling of VMs in Network Simulations

    SciTech Connect

    Yoginath, Srikanth B; Perumalla, Kalyan S; Henz, Brian J

    2012-01-01

    The next generation of scalable network simulators employ virtual machines (VMs) to act as high-fidelity models of traffic producer/consumer nodes in simulated networks. However, network simulations could be inaccurate if VMs are not scheduled according to virtual time, especially when many VMs are hosted per simulator core in a multi-core simulator environment. Since VMs are by default free-running, on the outset, it is not clear if, and to what extent, their untamed execution affects the results in simulated scenarios. Here, we provide the first quantitative basis for establishing the need for generalized virtual time scheduling of VMs in network simulators, based on an actual prototyped implementations. To exercise breadth, our system is tested with multiple disparate applications: (a) a set of message passing parallel programs, (b) a computer worm propagation phenomenon, and (c) a mobile ad-hoc wireless network simulation. We define and use error metrics and benchmarks in scaled tests to empirically report the poor match of traditional, fairness-based VM scheduling to VM-based network simulation, and also clearly show the better performance of our simulation-specific scheduler, with up to 64 VMs hosted on a 12-core simulator node.

  19. A new approach to blood flow simulation in vascular networks.

    PubMed

    Tamaddon, Houman; Behnia, Mehrdad; Behnia, Masud; Kritharides, Leonard

    2016-01-01

    A proper analysis of blood flow is contingent upon accurate modelling of the branching pattern and vascular geometry of the network of interest. It is challenging to reconstruct the entire vascular network of any organ experimentally, in particular the pulmonary vasculature, because of its very high number of vessels, complexity of the branching pattern and poor accessibility in vivo. The objective of our research is to develop an innovative approach for the reconstruction of the full pulmonary vascular tree from available morphometric data. Our method consists of the use of morphometric data on those parts of the pulmonary vascular tree that are too small to reconstruct by medical imaging methods. This method is a three-step technique that reconstructs the entire pulmonary arterial tree down to the capillary bed. Vessels greater than 2 mm are reconstructed from direct volume and surface analysis using contrast-enhanced computed tomography. Vessels smaller than 2 mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray's laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray's laws to every vessel bifurcation simultaneously leads to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. In conclusion, the present model provides a morphological foundation for future analysis of blood flow in the pulmonary circulation. PMID:26195135

  20. Unified Approach to Modeling and Simulation of Space Communication Networks and Systems

    NASA Technical Reports Server (NTRS)

    Barritt, Brian; Bhasin, Kul; Eddy, Wesley; Matthews, Seth

    2010-01-01

    Network simulator software tools are often used to model the behaviors and interactions of applications, protocols, packets, and data links in terrestrial communication networks. Other software tools that model the physics, orbital dynamics, and RF characteristics of space systems have matured to allow for rapid, detailed analysis of space communication links. However, the absence of a unified toolset that integrates the two modeling approaches has encumbered the systems engineers tasked with the design, architecture, and analysis of complex space communication networks and systems. This paper presents the unified approach and describes the motivation, challenges, and our solution - the customization of the network simulator to integrate with astronautical analysis software tools for high-fidelity end-to-end simulation. Keywords space; communication; systems; networking; simulation; modeling; QualNet; STK; integration; space networks

  1. Communication and wiring in the cortical connectome

    PubMed Central

    Budd, Julian M. L.; Kisvárday, Zoltán F.

    2012-01-01

    In cerebral cortex, the huge mass of axonal wiring that carries information between near and distant neurons is thought to provide the neural substrate for cognitive and perceptual function. The goal of mapping the connectivity of cortical axons at different spatial scales, the cortical connectome, is to trace the paths of information flow in cerebral cortex. To appreciate the relationship between the connectome and cortical function, we need to discover the nature and purpose of the wiring principles underlying cortical connectivity. A popular explanation has been that axonal length is strictly minimized both within and between cortical regions. In contrast, we have hypothesized the existence of a multi-scale principle of cortical wiring where to optimize communication there is a trade-off between spatial (construction) and temporal (routing) costs. Here, using recent evidence concerning cortical spatial networks we critically evaluate this hypothesis at neuron, local circuit, and pathway scales. We report three main conclusions. First, the axonal and dendritic arbor morphology of single neocortical neurons may be governed by a similar wiring principle, one that balances the conservation of cellular material and conduction delay. Second, the same principle may be observed for fiber tracts connecting cortical regions. Third, the absence of sufficient local circuit data currently prohibits any meaningful assessment of the hypothesis at this scale of cortical organization. To avoid neglecting neuron and microcircuit levels of cortical organization, the connectome framework should incorporate more morphological description. In addition, structural analyses of temporal cost for cortical circuits should take account of both axonal conduction and neuronal integration delays, which appear mostly of the same order of magnitude. We conclude the hypothesized trade-off between spatial and temporal costs may potentially offer a powerful explanation for cortical wiring patterns

  2. A three-dimensional computer simulation model reveals the mechanisms for self-organization of plant cortical microtubules into oblique arrays.

    PubMed