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Sample records for model cortical network

  1. Growth of cortical neuronal network in vitro: Modeling and analysis

    International Nuclear Information System (INIS)

    Lai, P.-Y.; Jia, L. C.; Chan, C. K.

    2006-01-01

    We present a detailed analysis and theoretical growth models to account for recent experimental data on the growth of cortical neuronal networks in vitro [Phys. Rev. Lett. 93, 088101 (2004)]. The experimentally observed synchronized firing frequency of a well-connected neuronal network is shown to be proportional to the mean network connectivity. The growth of the network is consistent with the model of an early enhanced growth of connection, but followed by a retarded growth once the synchronized cluster is formed. Microscopic models with dominant excluded volume interactions are consistent with the observed exponential decay of the mean connection probability as a function of the mean network connectivity. The biological implications of the growth model are also discussed

  2. Biophysical Model of Cortical Network Activity and the Influence of Electrical Stimulation

    Science.gov (United States)

    2015-11-13

    model, multicompartment model, subdural cortical stimulation, anode, cathode, epilepsy REPORT DOCUMENTATION PAGE 11. SPONSOR/MONITOR’S REPORT NUMBER(S...and axon orientation in respect to the electrode position. 4) A single stimulation pulse causes a sequence of action potentials ectopically generated...Bergey, P.J. Franaszczuk. Phase-dependent stimulation effects on bursting activity in a neural network cortical simulation, Epilepsy Research (07 2008

  3. High-conductance states in a mean-field cortical network model

    CERN Document Server

    Lerchner, A; Hertz, J

    2004-01-01

    Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1 due to the tendency of spikes being clustered into bursts. We show that this behavior emerges naturally in a balanced cortical network model with random connectivity and conductance-based synapses. We employ mean field theory with correctly colored noise to describe temporal correlations in the neuronal activity. Our results illuminate the connection between two independent experimental findings: high conductance states of cortical neurons in their natural environment, and variable non-Poissonian spike statistics with Fano factors greater than 1.

  4. Memory in cultured cortical networks: experiment and modeling

    NARCIS (Netherlands)

    Witteveen, Tim; van Veenendaal, Tamar; le Feber, Jakob; Sergeev, A.

    The mechanism behind memory is one of the mysteries in neuroscience. Here we unravel part of the mechanism by showing that cultured neuronal networks develop an activity connectivity balance. External inputs disturb this balance and induce connectivity changes. The new connectivity is no longer

  5. High-conductance states in a mean-field cortical network model

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ahmadi, Mandana; Hertz, John

    2004-01-01

    cortical network model with random connectivity and conductance-based synapses. We employ mean-field theory with correctly colored noise to describe temporal correlations in the neuronal activity. Our results illuminate the connection between two independent experimental findings: high-conductance states......Measured responses from visual cortical neurons show that spike times tend to be correlated rather than exactly Poisson distributed. Fano factors vary and are usually greater than 1, indicating a tendency toward spikes being clustered. We show that this behavior emerges naturally in a balanced...... of cortical neurons in their natural environment, and variable non-Poissonian spike statistics with Fano factors greater than 1. (C) 2004 Elsevier B.V. All rights reserved....

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

    Directory of Open Access Journals (Sweden)

    Maxwell R Bennett

    Full Text Available 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.

  7. Cortical information flow in Parkinson's disease: a composite network/field model

    Directory of Open Access Journals (Sweden)

    Cliff C. Kerr

    2013-04-01

    Full Text Available The basal ganglia play a crucial role in the execution of movements, as demonstrated by the severe motor deficits that accompany Parkinson's disease (PD. Since motor commands originate in the cortex, an important question is how the basal ganglia influence cortical information flow, and how this influence becomes pathological in PD. To explore this, we developed a composite neuronal network/neural field model. The network model consisted of 4950 spiking neurons, divided into 15 excitatory and inhibitory cell populations in the thalamus and cortex. The field model consisted of the cortex, thalamus, striatum, subthalamic nucleus, and globus pallidus. Both models have been separately validated in previous work. Three field models were used: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. Spikes generated by these field models were then used to drive the network model. Compared to the network driven by the healthy model, the PD-driven network had lower firing rates, a shift in spectral power towards lower frequencies, and higher probability of bursting; each of these findings is consistent with empirical data on PD. In the healthy model, we found strong Granger causality in the beta and low gamma bands between cortical layers, but this was largely absent in the PD model. In particular, the reduction in Granger causality from the main "input" layer of the cortex (layer 4 to the main "output" layer (layer 5 was pronounced. This may account for symptoms of PD that seem to reflect deficits in information flow, such as bradykinesia. In general, these results demonstrate that the brain's large-scale oscillatory environment, represented here by the field model, strongly influences the information processing that occurs within its subnetworks. Hence, it may be preferable to drive spiking network models with physiologically realistic inputs rather than

  8. Examining the volume efficiency of the cortical architecture in a multi-processor network model.

    Science.gov (United States)

    Ruppin, E; Schwartz, E L; Yeshurun, Y

    1993-01-01

    The convoluted form of the sheet-like mammalian cortex naturally raises the question whether there is a simple geometrical reason for the prevalence of cortical architecture in the brains of higher vertebrates. Addressing this question, we present a formal analysis of the volume occupied by a massively connected network or processors (neurons) and then consider the pertaining cortical data. Three gross macroscopic features of cortical organization are examined: the segregation of white and gray matter, the circumferential organization of the gray matter around the white matter, and the folded cortical structure. Our results testify to the efficiency of cortical architecture.

  9. Reducing a cortical network to a Potts model yields storage capacity estimates

    Science.gov (United States)

    Naim, Michelangelo; Boboeva, Vezha; Kang, Chol Jun; Treves, Alessandro

    2018-04-01

    An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e. in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback w, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss the implications for semantic memory in humans.

  10. Response variability in balanced cortical networks

    DEFF Research Database (Denmark)

    Lerchner, Alexander; Ursta, C.; Hertz, J.

    2006-01-01

    We study the spike statistics of neurons in a network with dynamically balanced excitation and inhibition. Our model, intended to represent a generic cortical column, comprises randomly connected excitatory and inhibitory leaky integrate-and-fire neurons, driven by excitatory input from an external...

  11. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons.

    Science.gov (United States)

    Xiao, Dongsheng; Vanni, Matthieu P; Mitelut, Catalin C; Chan, Allen W; LeDue, Jeffrey M; Xie, Yicheng; Chen, Andrew Cn; Swindale, Nicholas V; Murphy, Timothy H

    2017-02-04

    Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps.

  12. Fetal brain extracellular matrix boosts neuronal network formation in 3D bioengineered model of cortical brain tissue.

    Science.gov (United States)

    Sood, Disha; Chwalek, Karolina; Stuntz, Emily; Pouli, Dimitra; Du, Chuang; Tang-Schomer, Min; Georgakoudi, Irene; Black, Lauren D; Kaplan, David L

    2016-01-01

    The extracellular matrix (ECM) constituting up to 20% of the organ volume is a significant component of the brain due to its instructive role in the compartmentalization of functional microdomains in every brain structure. The composition, quantity and structure of ECM changes dramatically during the development of an organism greatly contributing to the remarkably sophisticated architecture and function of the brain. Since fetal brain is highly plastic, we hypothesize that the fetal brain ECM may contain cues promoting neural growth and differentiation, highly desired in regenerative medicine. Thus, we studied the effect of brain-derived fetal and adult ECM complemented with matricellular proteins on cortical neurons using in vitro 3D bioengineered model of cortical brain tissue. The tested parameters included neuronal network density, cell viability, calcium signaling and electrophysiology. Both, adult and fetal brain ECM as well as matricellular proteins significantly improved neural network formation as compared to single component, collagen I matrix. Additionally, the brain ECM improved cell viability and lowered glutamate release. The fetal brain ECM induced superior neural network formation, calcium signaling and spontaneous spiking activity over adult brain ECM. This study highlights the difference in the neuroinductive properties of fetal and adult brain ECM and suggests that delineating the basis for this divergence may have implications for regenerative medicine.

  13. Mean field methods for cortical network dynamics

    DEFF Research Database (Denmark)

    Hertz, J.; Lerchner, Alexander; Ahmadi, M.

    2004-01-01

    We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate- and-fire neurons, we can show how the firing irregularity, as measured by the Fano factor, increases...... with the strength of the synapses in the network and with the value to which the membrane potential is reset after a spike. Generalizing the model to include conductance-based synapses gives insight into the connection between the firing statistics and the high- conductance state observed experimentally in visual...

  14. State-dependent intrinsic predictability of cortical network dynamics.

    Directory of Open Access Journals (Sweden)

    Leila Fakhraei

    Full Text Available The information encoded in cortical circuit dynamics is fleeting, changing from moment to moment as new input arrives and ongoing intracortical interactions progress. A combination of deterministic and stochastic biophysical mechanisms governs how cortical dynamics at one moment evolve from cortical dynamics in recently preceding moments. Such temporal continuity of cortical dynamics is fundamental to many aspects of cortex function but is not well understood. Here we study temporal continuity by attempting to predict cortical population dynamics (multisite local field potential based on its own recent history in somatosensory cortex of anesthetized rats and in a computational network-level model. We found that the intrinsic predictability of cortical dynamics was dependent on multiple factors including cortical state, synaptic inhibition, and how far into the future the prediction extends. By pharmacologically tuning synaptic inhibition, we obtained a continuum of cortical states with asynchronous population activity at one extreme and stronger, spatially extended synchrony at the other extreme. Intermediate between these extremes we observed evidence for a special regime of population dynamics called criticality. Predictability of the near future (10-100 ms increased as the cortical state was tuned from asynchronous to synchronous. Predictability of the more distant future (>1 s was generally poor, but, surprisingly, was higher for asynchronous states compared to synchronous states. These experimental results were confirmed in a computational network model of spiking excitatory and inhibitory neurons. Our findings demonstrate that determinism and predictability of network dynamics depend on cortical state and the time-scale of the dynamics.

  15. Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells.

    Directory of Open Access Journals (Sweden)

    Pablo Martínez-Cañada

    2018-01-01

    Full Text Available Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs and interneurons (INs not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY. We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed ('push-pull' and phase-matched ('push-push', as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli

  16. Biophysical network modeling of the dLGN circuit: Effects of cortical feedback on spatial response properties of relay cells

    Science.gov (United States)

    Martínez-Cañada, Pablo; Halnes, Geir; Fyhn, Marianne

    2018-01-01

    Despite half-a-century of research since the seminal work of Hubel and Wiesel, the role of the dorsal lateral geniculate nucleus (dLGN) in shaping the visual signals is not properly understood. Placed on route from retina to primary visual cortex in the early visual pathway, a striking feature of the dLGN circuit is that both the relay cells (RCs) and interneurons (INs) not only receive feedforward input from retinal ganglion cells, but also a prominent feedback from cells in layer 6 of visual cortex. This feedback has been proposed to affect synchronicity and other temporal properties of the RC firing. It has also been seen to affect spatial properties such as the center-surround antagonism of thalamic receptive fields, i.e., the suppression of the response to very large stimuli compared to smaller, more optimal stimuli. Here we explore the spatial effects of cortical feedback on the RC response by means of a a comprehensive network model with biophysically detailed, single-compartment and multicompartment neuron models of RCs, INs and a population of orientation-selective layer 6 simple cells, consisting of pyramidal cells (PY). We have considered two different arrangements of synaptic feedback from the ON and OFF zones in the visual cortex to the dLGN: phase-reversed (‘push-pull’) and phase-matched (‘push-push’), as well as different spatial extents of the corticothalamic projection pattern. Our simulation results support that a phase-reversed arrangement provides a more effective way for cortical feedback to provide the increased center-surround antagonism seen in experiments both for flashing spots and, even more prominently, for patch gratings. This implies that ON-center RCs receive direct excitation from OFF-dominated cortical cells and indirect inhibitory feedback from ON-dominated cortical cells. The increased center-surround antagonism in the model is accompanied by spatial focusing, i.e., the maximum RC response occurs for smaller stimuli when

  17. Modeling cortical circuits.

    Energy Technology Data Exchange (ETDEWEB)

    Rohrer, Brandon Robinson; Rothganger, Fredrick H.; Verzi, Stephen J.; Xavier, Patrick Gordon

    2010-09-01

    The neocortex is perhaps the highest region of the human brain, where audio and visual perception takes place along with many important cognitive functions. An important research goal is to describe the mechanisms implemented by the neocortex. There is an apparent regularity in the structure of the neocortex [Brodmann 1909, Mountcastle 1957] which may help simplify this task. The work reported here addresses the problem of how to describe the putative repeated units ('cortical circuits') in a manner that is easily understood and manipulated, with the long-term goal of developing a mathematical and algorithmic description of their function. The approach is to reduce each algorithm to an enhanced perceptron-like structure and describe its computation using difference equations. We organize this algorithmic processing into larger structures based on physiological observations, and implement key modeling concepts in software which runs on parallel computing hardware.

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

    Science.gov (United States)

    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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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

    NARCIS (Netherlands)

    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

  20. Dynamic Causal Modeling of the Cortical Responses to Wrist Perturbations

    Directory of Open Access Journals (Sweden)

    Yuan Yang

    2017-09-01

    Full Text Available Mechanical perturbations applied to the wrist joint typically evoke a stereotypical sequence of cortical and muscle responses. The early cortical responses (<100 ms are thought be involved in the “rapid” transcortical reaction to the perturbation while the late cortical responses (>100 ms are related to the “slow” transcortical reaction. Although previous studies indicated that both responses involve the primary motor cortex, it remains unclear if both responses are engaged by the same effective connectivity in the cortical network. To answer this question, we investigated the effective connectivity cortical network after a “ramp-and-hold” mechanical perturbation, in both the early (<100 ms and late (>100 ms periods, using dynamic causal modeling. Ramp-and-hold perturbations were applied to the wrist joint while the subject maintained an isometric wrist flexion. Cortical activity was recorded using a 128-channel electroencephalogram (EEG. We investigated how the perturbation modulated the effective connectivity for the early and late periods. Bayesian model comparisons suggested that different effective connectivity networks are engaged in these two periods. For the early period, we found that only a few cortico-cortical connections were modulated, while more complicated connectivity was identified in the cortical network during the late period with multiple modulated cortico-cortical connections. The limited early cortical network likely allows for a rapid muscle response without involving high-level cognitive processes, while the complexity of the late network may facilitate coordinated responses.

  1. Memory in cultured cortical networks

    NARCIS (Netherlands)

    le Feber, Jakob; Witteveen, T.; Stoyanova, Irina; Rutten, Wim

    2012-01-01

    Tetanic stimulation was applied to affect network connectivity, as assessed by conditional firing probabilities. We showed that the first period(s) of titanic stimulation at a certain electrode significantly alters functional connectivity, but subsequent, identical stimuli do not. These findings

  2. Connectivities and synchronous firing in cortical neuronal networks

    International Nuclear Information System (INIS)

    Jia, L.C.; Sano, M.; Lai, P.-Y.; Chan, C.K.

    2004-01-01

    Network connectivities (k-bar) of cortical neural cultures are studied by synchronized firing and determined from measured correlations between fluorescence intensities of firing neurons. The bursting frequency (f) during synchronized firing of the networks is found to be an increasing function of k-bar. With f taken to be proportional to k-bar, a simple random model with a k-bar dependent connection probability p(k-bar) has been constructed to explain our experimental findings successfully

  3. Top-down feedback in an HMAX-like cortical model of object perception based on hierarchical Bayesian networks and belief propagation.

    Directory of Open Access Journals (Sweden)

    Salvador Dura-Bernal

    Full Text Available 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

  4. Network bursts in cortical neuronal cultures: 'noise - versus pacemaker'- driven neural network simulations

    NARCIS (Netherlands)

    Gritsun, T.; Stegenga, J.; le Feber, Jakob; Rutten, Wim

    2009-01-01

    In this paper we address the issue of spontaneous bursting activity in cortical neuronal cultures and explain what might cause this collective behavior using computer simulations of two different neural network models. While the common approach to acivate a passive network is done by introducing

  5. Characterization of Early Cortical Neural Network ...

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentially absent on DIV 2 and developed rapidly between DIV 5 and 12. Spiking activity was primarily sporadic and unorganized at early DIV, and became progressively more organized with time in culture, with bursting parameters, synchrony and network bursting increasing between DIV 5 and 12. We selected 12 features to describe network activity and principal components analysis using these features demonstrated a general segregation of data by age at both the well and plate levels. Using a combination of random forest classifiers and Support Vector Machines, we demonstrated that 4 features (CV of within burst ISI, CV of IBI, network spike rate and burst rate) were sufficient to predict the age (either DIV 5, 7, 9 or 12) of each well recording with >65% accuracy. When restricting the classification problem to a binary decision, we found that classification improved dramatically, e.g. 95% accuracy for discriminating DIV 5 vs DIV 12 wells. Further, we present a novel resampling approach to determine the number of wells that might be needed for conducting comparisons of different treatments using mwMEA plates. Overall, these results demonstrate that network development on mwMEA plates is similar to

  6. Cortical network during deception detection by functional neuroimaging

    International Nuclear Information System (INIS)

    Saito, Keiichi

    2008-01-01

    We examined the coherence of cortical network during deception detection. First, we performed combined EEG-MRI experiments during the Guilty Knowledge Test (GKT) using number cards which has been used to model deception and 5 right-handed healthy participants performed the experiment. The superior frontal gyrus, the anterior cingulate cortex and the inferior parietal lobule were activated and the P 300 event-related brain potential (300-450 ms) was detected at only 'Lie' card. Secondary, we measured magnetoencephalography (MEG) data during GKT and the other 5 right-handed healthy subjects participated in the next experiment. The coherence between the superior frontal gyrus and the inferior parietal lobule showed significant differences between 'Lie' card and 'truth' cards during P 300 emerging. This results indicates that the coherence of cortical network is useful for GKT. (author)

  7. Magnetoencephalography from signals to dynamic cortical networks

    CERN Document Server

    Aine, Cheryl

    2014-01-01

    "Magnetoencephalography (MEG) provides a time-accurate view into human brain function. The concerted action of neurons generates minute magnetic fields that can be detected---totally noninvasively---by sensitive multichannel magnetometers. The obtained millisecond accuracycomplements information obtained by other modern brain-imaging tools. Accurate timing is quintessential in normal brain function, often distorted in brain disorders. The noninvasiveness and time-sensitivityof MEG are great assets to developmental studies, as well. This multiauthored book covers an ambitiously wide range of MEG research from introductory to advanced level, from sensors to signals, and from focal sources to the dynamics of cortical networks. Written by active practioners of this multidisciplinary field, the book contains tutorials for newcomers and chapters of new challenging methods and emerging technologies to advanced MEG users. The reader will obtain a firm grasp of the possibilities of MEG in the study of audition, vision...

  8. Influences of brain development and ageing on cortical interactive networks.

    Science.gov (United States)

    Zhu, Chengyu; Guo, Xiaoli; Jin, Zheng; Sun, Junfeng; Qiu, Yihong; Zhu, Yisheng; Tong, Shanbao

    2011-02-01

    To study the effect of brain development and ageing on the pattern of cortical interactive networks. By causality analysis of multichannel electroencephalograph (EEG) with partial directed coherence (PDC), we investigated the different neural networks involved in the whole cortex as well as the anterior and posterior areas in three age groups, i.e., children (0-10 years), mid-aged adults (26-38 years) and the elderly (56-80 years). By comparing the cortical interactive networks in different age groups, the following findings were concluded: (1) the cortical interactive network in the right hemisphere develops earlier than its left counterpart in the development stage; (2) the cortical interactive network of anterior cortex, especially at C3 and F3, is demonstrated to undergo far more extensive changes, compared with the posterior area during brain development and ageing; (3) the asymmetry of the cortical interactive networks declines during ageing with more loss of connectivity in the left frontal and central areas. The age-related variation of cortical interactive networks from resting EEG provides new insights into brain development and ageing. Our findings demonstrated that the PDC analysis of EEG is a powerful approach for characterizing the cortical functional connectivity during brain development and ageing. Copyright © 2010 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  9. Dynamic causal modeling revealed dysfunctional effective connectivity in both, the cortico-basal-ganglia and the cerebello-cortical motor network in writers' cramp

    Directory of Open Access Journals (Sweden)

    Inken Rothkirch

    demonstrates abnormal reciprocal excitatory connectivity in the cortico-cerebellar circuitry. These results highlight the dysfunctional cerebello-cortical as well as basalganglio-cortical interaction in WC. Keywords: Dynamic causal modeling, Focal hand dystonia, Writer's cramp, Network disorder, Cerebellum

  10. Morphometric Changes in the Cortical Microvascular Network in Alzheimer's Disease

    NARCIS (Netherlands)

    Richard, E.; van Gool, W.A.; Hoozemans, J.J.M.; van Haastert, E.S.; Eikelenboom, P.; Rozemuller, A.J.M.; van de Berg, W.D.J.

    2010-01-01

    Alzheimer's disease (AD) pathology is accompanied by abnormalities of the microvasculature. Despite the potential importance of morphometric changes in the cortical capillary network on neuronal dysfunction and cognitive impairment, few autopsy studies have addressed this issue. In the present

  11. Cortical electrophysiological network dynamics of feedback learning

    NARCIS (Netherlands)

    Cohen, M.X.; Wilmes, K.A.; van de Vijver, I.

    2011-01-01

    Understanding the neurophysiological mechanisms of learning is important for both fundamental and clinical neuroscience. We present a neurophysiologically inspired framework for understanding cortical mechanisms of feedback-guided learning. This framework is based on dynamic changes in systems-level

  12. Altered cortical anatomical networks in temporal lobe epilepsy

    Science.gov (United States)

    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.

  13. Cortical Networks for Visual Self-Recognition

    Science.gov (United States)

    Sugiura, Motoaki

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

  14. Cortical networks for visual self-recognition

    International Nuclear Information System (INIS)

    Sugiura, Motoaki

    2007-01-01

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

  15. Learning in AN Oscillatory Cortical Model

    Science.gov (United States)

    Scarpetta, Silvia; Li, Zhaoping; Hertz, John

    We study a model of generalized-Hebbian learning in asymmetric oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. The learning rule is based on the synaptic plasticity observed experimentally, in particular long-term potentiation and long-term depression of the synaptic efficacies depending on the relative timing of the pre- and postsynaptic activities during learning. The learned memory or representational states can be encoded by both the amplitude and the phase patterns of the oscillating neural populations, enabling more efficient and robust information coding than in conventional models of associative memory or input representation. Depending on the class of nonlinearity of the activation function, the model can function as an associative memory for oscillatory patterns (nonlinearity of class II) or can generalize from or interpolate between the learned states, appropriate for the function of input representation (nonlinearity of class I). In the former case, simulations of the model exhibits a first order transition between the "disordered state" and the "ordered" memory state.

  16. Firing-rate based network modeling of the dLGN circuit: Effects of cortical feedback on spatiotemporal response properties of relay cells.

    Science.gov (United States)

    Mobarhan, Milad Hobbi; Halnes, Geir; Martínez-Cañada, Pablo; Hafting, Torkel; Fyhn, Marianne; Einevoll, Gaute T

    2018-05-01

    Visually evoked signals in the retina pass through the dorsal geniculate nucleus (dLGN) on the way to the visual cortex. This is however not a simple feedforward flow of information: there is a significant feedback from cortical cells back to both relay cells and interneurons in the dLGN. Despite four decades of experimental and theoretical studies, the functional role of this feedback is still debated. Here we use a firing-rate model, the extended difference-of-Gaussians (eDOG) model, to explore cortical feedback effects on visual responses of dLGN relay cells. For this model the responses are found by direct evaluation of two- or three-dimensional integrals allowing for fast and comprehensive studies of putative effects of different candidate organizations of the cortical feedback. Our analysis identifies a special mixed configuration of excitatory and inhibitory cortical feedback which seems to best account for available experimental data. This configuration consists of (i) a slow (long-delay) and spatially widespread inhibitory feedback, combined with (ii) a fast (short-delayed) and spatially narrow excitatory feedback, where (iii) the excitatory/inhibitory ON-ON connections are accompanied respectively by inhibitory/excitatory OFF-ON connections, i.e. following a phase-reversed arrangement. The recent development of optogenetic and pharmacogenetic methods has provided new tools for more precise manipulation and investigation of the thalamocortical circuit, in particular for mice. Such data will expectedly allow the eDOG model to be better constrained by data from specific animal model systems than has been possible until now for cat. We have therefore made the Python tool pyLGN which allows for easy adaptation of the eDOG model to new situations.

  17. Development of global cortical networks in early infancy.

    Science.gov (United States)

    Homae, Fumitaka; Watanabe, Hama; Otobe, Takayuki; Nakano, Tamami; Go, Tohshin; Konishi, Yukuo; Taga, Gentaro

    2010-04-07

    Human cognition and behaviors are subserved by global networks of neural mechanisms. Although the organization of the brain is a subject of interest, the process of development of global cortical networks in early infancy has not yet been clarified. In the present study, we explored developmental changes in these networks from several days to 6 months after birth by examining spontaneous fluctuations in brain activity, using multichannel near-infrared spectroscopy. We set up 94 measurement channels over the frontal, temporal, parietal, and occipital regions of the infant brain. The obtained signals showed complex time-series properties, which were characterized as 1/f fluctuations. To reveal the functional connectivity of the cortical networks, we calculated the temporal correlations of continuous signals between all the pairs of measurement channels. We found that the cortical network organization showed regional dependency and dynamic changes in the course of development. In the temporal, parietal, and occipital regions, connectivity increased between homologous regions in the two hemispheres and within hemispheres; in the frontal regions, it decreased progressively. Frontoposterior connectivity changed to a "U-shaped" pattern within 6 months: it decreases from the neonatal period to the age of 3 months and increases from the age of 3 months to the age of 6 months. We applied cluster analyses to the correlation coefficients and showed that the bilateral organization of the networks begins to emerge during the first 3 months of life. Our findings suggest that these developing networks, which form multiple clusters, are precursors of the functional cerebral architecture.

  18. Nanoscaffold's stiffness affects primary cortical cell network formation

    NARCIS (Netherlands)

    Xie, Sijia; Schurink, Bart; Wolbers, F.; Lüttge, Regina; Hassink, Gerrit Cornelis

    2014-01-01

    Networks of neurons cultured on-chip can provide insights into both normal and disease-state brain function. The ability to guide neuronal growth in specific, artificially designed patterns allows us to study how brain function follows form. Primary cortical cells cultured on nanograting scaffolds,

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

    Science.gov (United States)

    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

  20. Anti-correlated cortical networks of intrinsic connectivity in the rat brain.

    Science.gov (United States)

    Schwarz, Adam J; Gass, Natalia; Sartorius, Alexander; Risterucci, Celine; Spedding, Michael; Schenker, Esther; Meyer-Lindenberg, Andreas; Weber-Fahr, Wolfgang

    2013-01-01

    In humans, resting-state blood oxygen level-dependent (BOLD) signals in the default mode network (DMN) are temporally anti-correlated with those from a lateral cortical network involving the frontal eye fields, secondary somatosensory and posterior insular cortices. Here, we demonstrate the existence of an analogous lateral cortical network in the rat brain, extending laterally from anterior secondary sensorimotor regions to the insular cortex and exhibiting low-frequency BOLD fluctuations that are temporally anti-correlated with a midline "DMN-like" network comprising posterior/anterior cingulate and prefrontal cortices. The primary nexus for this anti-correlation relationship was the anterior secondary motor cortex, close to regions that have been identified with frontal eye fields in the rat brain. The anti-correlation relationship was corroborated after global signal removal, underscoring this finding as a robust property of the functional connectivity signature in the rat brain. These anti-correlated networks demonstrate strong anatomical homology to networks identified in human and monkey connectivity studies, extend the known preserved functional connectivity relationships between rodent and primates, and support the use of resting-state functional magnetic resonance imaging as a translational imaging method between rat models and humans.

  1. Qualia could arise from information processing in local cortical networks.

    Science.gov (United States)

    Orpwood, Roger

    2013-01-01

    Re-entrant feedback, either within sensory cortex or arising from prefrontal areas, has been strongly linked to the emergence of consciousness, both in theoretical and experimental work. This idea, together with evidence for local micro-consciousness, suggests the generation of qualia could in some way result from local network activity under re-entrant activation. This paper explores the possibility by examining the processing of information by local cortical networks. It highlights the difference between the information structure (how the information is physically embodied), and the information message (what the information is about). It focuses on the network's ability to recognize information structures amongst its inputs under conditions of extensive local feedback, and to then assign information messages to those structures. It is shown that if the re-entrant feedback enables the network to achieve an attractor state, then the message assigned in any given pass of information through the network is a representation of the message assigned in the previous pass-through of information. Based on this ability the paper argues that as information is repeatedly cycled through the network, the information message that is assigned evolves from a recognition of what the input structure is, to what it is like, to how it appears, to how it seems. It could enable individual networks to be the site of qualia generation. The paper goes on to show networks in cortical layers 2/3 and 5a have the connectivity required for the behavior proposed, and reviews some evidence for a link between such local cortical cyclic activity and conscious percepts. It concludes with some predictions based on the theory discussed.

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

    Science.gov (United States)

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

    2015-08-01

    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. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  3. Graph properties of synchronized cortical networks during visual working memory maintenance.

    Science.gov (United States)

    Palva, Satu; Monto, Simo; Palva, J Matias

    2010-02-15

    Oscillatory synchronization facilitates communication in neuronal networks and is intimately associated with human cognition. Neuronal activity in the human brain can be non-invasively imaged with magneto- (MEG) and electroencephalography (EEG), but the large-scale structure of synchronized cortical networks supporting cognitive processing has remained uncharacterized. We combined simultaneous MEG and EEG (MEEG) recordings with minimum-norm-estimate-based inverse modeling to investigate the structure of oscillatory phase synchronized networks that were active during visual working memory (VWM) maintenance. Inter-areal phase-synchrony was quantified as a function of time and frequency by single-trial phase-difference estimates of cortical patches covering the entire cortical surfaces. The resulting networks were characterized with a number of network metrics that were then compared between delta/theta- (3-6 Hz), alpha- (7-13 Hz), beta- (16-25 Hz), and gamma- (30-80 Hz) frequency bands. We found several salient differences between frequency bands. Alpha- and beta-band networks were more clustered and small-world like but had smaller global efficiency than the networks in the delta/theta and gamma bands. Alpha- and beta-band networks also had truncated-power-law degree distributions and high k-core numbers. The data converge on showing that during the VWM-retention period, human cortical alpha- and beta-band networks have a memory-load dependent, scale-free small-world structure with densely connected core-like structures. These data further show that synchronized dynamic networks underlying a specific cognitive state can exhibit distinct frequency-dependent network structures that could support distinct functional roles. Copyright 2009 Elsevier Inc. All rights reserved.

  4. Cortical hubs form a module for multisensory integration on top of the hierarchy of cortical networks

    Directory of Open Access Journals (Sweden)

    Gorka Zamora-López

    2010-03-01

    Full Text Available 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.

  5. Alterations of cortical GABA neurons and network oscillations in schizophrenia.

    Science.gov (United States)

    Gonzalez-Burgos, Guillermo; Hashimoto, Takanori; Lewis, David A

    2010-08-01

    The hypothesis that alterations of cortical inhibitory gamma-aminobutyric acid (GABA) neurons are a central element in the pathology of schizophrenia has emerged from a series of postmortem studies. How such abnormalities may contribute to the clinical features of schizophrenia has been substantially informed by a convergence with basic neuroscience studies revealing complex details of GABA neuron function in the healthy brain. Importantly, activity of the parvalbumin-containing class of GABA neurons has been linked to the production of cortical network oscillations. Furthermore, growing knowledge supports the concept that gamma band oscillations (30-80 Hz) are an essential mechanism for cortical information transmission and processing. Herein we review recent studies further indicating that inhibition from parvalbumin-positive GABA neurons is necessary to produce gamma oscillations in cortical circuits; provide an update on postmortem studies documenting that deficits in the expression of glutamic acid decarboxylase67, which accounts for most GABA synthesis in the cortex, are widely observed in schizophrenia; and describe studies using novel, noninvasive approaches directly assessing potential relations between alterations in GABA, oscillations, and cognitive function in schizophrenia.

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

    Science.gov (United States)

    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

  7. Computational modeling of epidural cortical stimulation

    Science.gov (United States)

    Wongsarnpigoon, Amorn; Grill, Warren M.

    2008-12-01

    Epidural cortical stimulation (ECS) is a developing therapy to treat neurological disorders. However, it is not clear how the cortical anatomy or the polarity and position of the electrode affects current flow and neural activation in the cortex. We developed a 3D computational model simulating ECS over the precentral gyrus. With the electrode placed directly above the gyrus, about half of the stimulus current flowed through the crown of the gyrus while current density was low along the banks deep in the sulci. Beneath the electrode, neurons oriented perpendicular to the cortical surface were depolarized by anodic stimulation, and neurons oriented parallel to the boundary were depolarized by cathodic stimulation. Activation was localized to the crown of the gyrus, and neurons on the banks deep in the sulci were not polarized. During regulated voltage stimulation, the magnitude of the activating function was inversely proportional to the thickness of the CSF and dura. During regulated current stimulation, the activating function was not sensitive to the thickness of the dura but was slightly more sensitive than during regulated voltage stimulation to the thickness of the CSF. Varying the width of the gyrus and the position of the electrode altered the distribution of the activating function due to changes in the orientation of the neurons beneath the electrode. Bipolar stimulation, although often used in clinical practice, reduced spatial selectivity as well as selectivity for neuron orientation.

  8. Functional specialisation within the cortical language network: effects of cortical dysfunction.

    Science.gov (United States)

    Vandenberghe, R

    2007-01-01

    In the 1990's neuroanatomical models of language and semantic memory have been mainly based on functional neuroimaging studies of brain activity in healthy volunteers and correlational studies between structural lesions in patients and behavioral deficits. In this paper we present a novel approach where we test models that have been developed in healthy volunteers by means of functional imaging in patients in combination with behavioral studies. Study populations consist of patients with focal cortical stroke (n = 2), amnestic mild cognitive impairment (n = 14) and primary progressive aphasia (n = 18). The experiments provide converging evidence that 1. the integrity of the right mid- and anterior fusiform gyrus is required for full and detailed retrieval of knowledge of visual attributes of concrete entities 2. the left posterior superior temporal sulcus is critically involved in lexical-semantic retrieval 3. the anterior temporal pole to the left functions as an associative structure that links the representations of meaning that are distribured over the cortical brain surface. Our experiments also provide us with new insight into the degradation and re-organisation of the language system in cortical neurodegenerative disease.

  9. UP-DOWN cortical dynamics reflect state transitions in a bistable network.

    Science.gov (United States)

    Jercog, Daniel; Roxin, Alex; Barthó, Peter; Luczak, Artur; Compte, Albert; de la Rocha, Jaime

    2017-08-04

    In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.

  10. Critical fluctuations in cortical models near instability

    Directory of Open Access Journals (Sweden)

    Matthew J. Aburn

    2012-08-01

    Full Text Available Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human EEG, however, have shown significant autocorrelation at time lags on the scale of minutes, indicating the need to consider regimes where nonlinearities influence the dynamics. Statistical properties such as increased autocorrelation length, increased variance, power-law scaling and bistable switching have been suggested as generic indicators of the approach to bifurcation in nonlinear dynamical systems. We study temporal fluctuations in a widely-employed computational model (the Jansen-Rit model of cortical activity, examining the statistical signatures that accompany bifurcations. Approaching supercritical Hopf bifurcations through tuning of the background excitatory input, we find a dramatic increase in the autocorrelation length that depends sensitively on the direction in phase space of the input fluctuations and hence on which neuronal subpopulation is stochastically perturbed. Similar dependence on the input direction is found in the distribution of fluctuation size and duration, which show power law scaling that extends over four orders of magnitude at the Hopf bifurcation. We conjecture that the alignment in phase space between the input noise vector and the center manifold of the Hopf bifurcation is directly linked to these changes. These results are consistent with the possibility of statistical indicators of linear instability being detectable in real EEG time series. However, even in a simple cortical model, we find that these indicators may not necessarily be visible even when bifurcations are present because their expression can depend sensitively on the neuronal pathway of incoming fluctuations.

  11. Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

    Science.gov (United States)

    Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

    2018-02-28

    The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

  12. Population spikes in cortical networks during different functional states.

    Directory of Open Access Journals (Sweden)

    Shirley eMark

    2012-07-01

    Full Text Available Brain computational challenges vary between behavioral states. Engaged animals react according to incoming sensory information, while in relaxed and sleeping states consolidation of the learned information is believed to take place. Different states are characterized by different forms of cortical activity. We study a possible neuronal mechanism for generating these diverse dynamics and suggest their possible functional significance. Previous studies demonstrated that brief synchronized increase in a neural firing (Population Spikes can be generated in homogenous recurrent neural networks with short-term synaptic depression. Here we consider more realistic networks with clustered architecture. We show that the level of synchronization in neural activity can be controlled smoothly by network parameters. The network shifts from asynchronous activity to a regime in which clusters synchronized separately, then, the synchronization between the clusters increases gradually to fully synchronized state. We examine the effects of different synchrony levels on the transmission of information by the network. We find that the regime of intermediate synchronization is preferential for the flow of information between sparsely connected areas. Based on these results, we suggest that the regime of intermediate synchronization corresponds to engaged behavioral state of the animal, while global synchronization is exhibited during relaxed and sleeping states.

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

    Directory of Open Access Journals (Sweden)

    Marie-Eve eLaramée

    2015-01-01

    Full Text Available 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.

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

    Science.gov (United States)

    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

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

    Science.gov (United States)

    Laramée, Marie-Eve; Boire, Denis

    2014-01-01

    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.

  16. Electrophysiological Data and the Biophysical Modelling of Local Cortical Circuits

    Directory of Open Access Journals (Sweden)

    Dimitris Pinotsis

    2014-03-01

    Full Text Available This paper shows how recordings of gamma oscillations – under different experimental conditions or from different subjects – can be combined with a class of population models called neural fields and dynamic causal modeling (DCM to distinguish among alternative hypotheses regarding cortical structure and function. This approach exploits inter-subject variability and trial-specific effects associated with modulations in the peak frequency of gamma oscillations. It draws on the computational power of Bayesian model inversion, when applied to neural field models of cortical dynamics. Bayesian model comparison allows one to adjudicate among different mechanistic hypotheses about cortical excitability, synaptic kinetics and the cardinal topographic features of local cortical circuits. It also provides optimal parameter estimates that quantify neuromodulation and the spatial dispersion of axonal connections or summation of receptive fields in the visual cortex. This paper provides an overview of a family of neural field models that have been recently implemented using the DCM toolbox of the academic freeware Statistical Parametric Mapping (SPM. The SPM software is a popular platform for analyzing neuroimaging data, used by several neuroscience communities worldwide. DCM allows for a formal (Bayesian statistical analysis of cortical network connectivity, based upon realistic biophysical models of brain responses. It is this particular feature of DCM – the unique combination of generative models with optimization techniques based upon (variational Bayesian principles – that furnishes a novel way to characterize functional brain architectures. In particular, it provides answers to questions about how the brain is wired and how it responds to different experimental manipulations. For a review of the general role of neural fields in SPM the reader can consult e.g. see [1]. Neural fields have a long and illustrious history in mathematical

  17. Cortical Thinning and Altered Cortico-Cortical Structural Covariance of the Default Mode Network in Patients with Persistent Insomnia Symptoms.

    Science.gov (United States)

    Suh, Sooyeon; Kim, Hosung; Dang-Vu, Thien Thanh; Joo, Eunyeon; Shin, Chol

    2016-01-01

    Recent studies have suggested that structural abnormalities in insomnia may be linked with alterations in the default-mode network (DMN). This study compared cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia (PI) and good sleepers (GS). The current study used a clinical subsample from the longitudinal community-based Korean Genome and Epidemiology Study (KoGES). Cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia symptoms (PIS; n = 57) were compared to good sleepers (GS; n = 40). All participants underwent MRI acquisition. Based on literature review, we selected cortical regions corresponding to the DMN. A seed-based structural covariance analysis measured cortical thickness correlation between each seed region of the DMN and other cortical areas. Association of cortical thickness and covariance with sleep quality and neuropsychological assessments were further assessed. Compared to GS, cortical thinning was found in PIS in the anterior cingulate cortex, precentral cortex, and lateral prefrontal cortex. Decreased structural connectivity between anterior and posterior regions of the DMN was observed in the PIS group. Decreased structural covariance within the DMN was associated with higher PSQI scores. Cortical thinning in the lateral frontal lobe was related to poor performance in executive function in PIS. Disrupted structural covariance network in PIS might reflect malfunctioning of antero-posterior disconnection of the DMN during the wake to sleep transition that is commonly found during normal sleep. The observed structural network alteration may further implicate commonly observed sustained sleep difficulties and cognitive impairment in insomnia. © 2016 Associated Professional Sleep Societies, LLC.

  18. Math anxiety: Brain cortical network changes in anticipation of doing mathematics.

    Science.gov (United States)

    Klados, Manousos A; Pandria, Niki; Micheloyannis, Sifis; Margulies, Daniel; Bamidis, Panagiotis D

    2017-12-01

    Following our previous work regarding the involvement of math anxiety (MA) in math-oriented tasks, this study tries to explore the differences in the cerebral networks' topology between self-reported low math-anxious (LMA) and high math-anxious (HMA) individuals, during the anticipation phase prior to a mathematical related experiment. For this reason, multichannel EEG recordings were adopted, while the solution of the inverse problem was applied in a generic head model, in order to obtain the cortical signals. The cortical networks have been computed for each band separately, using the magnitude square coherence metric. The main graph theoretical parameters, showed differences in segregation and integration in almost all EEG bands of the HMAs in comparison to LMAs, indicative of a great influence of the anticipatory anxiety prior to mathematical performance. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    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

  20. Order-based representation in random networks of cortical neurons.

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    Goded Shahaf

    2008-11-01

    Full Text Available The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen.

  1. Cortical networks involved in visual awareness independent of visual attention.

    Science.gov (United States)

    Webb, Taylor W; Igelström, Kajsa M; Schurger, Aaron; Graziano, Michael S A

    2016-11-29

    It is now well established that visual attention, as measured with standard spatial attention tasks, and visual awareness, as measured by report, can be dissociated. It is possible to attend to a stimulus with no reported awareness of the stimulus. We used a behavioral paradigm in which people were aware of a stimulus in one condition and unaware of it in another condition, but the stimulus drew a similar amount of spatial attention in both conditions. The paradigm allowed us to test for brain regions active in association with awareness independent of level of attention. Participants performed the task in an MRI scanner. We looked for brain regions that were more active in the aware than the unaware trials. The largest cluster of activity was obtained in the temporoparietal junction (TPJ) bilaterally. Local independent component analysis (ICA) revealed that this activity contained three distinct, but overlapping, components: a bilateral, anterior component; a left dorsal component; and a right dorsal component. These components had brain-wide functional connectivity that partially overlapped the ventral attention network and the frontoparietal control network. In contrast, no significant activity in association with awareness was found in the banks of the intraparietal sulcus, a region connected to the dorsal attention network and traditionally associated with attention control. These results show the importance of separating awareness and attention when testing for cortical substrates. They are also consistent with a recent proposal that awareness is associated with ventral attention areas, especially in the TPJ.

  2. Short-term memory in networks of dissociated cortical neurons.

    Science.gov (United States)

    Dranias, Mark R; Ju, Han; Rajaram, Ezhilarasan; VanDongen, Antonius M J

    2013-01-30

    Short-term memory refers to the ability to store small amounts of stimulus-specific information for a short period of time. It is supported by both fading and hidden memory processes. Fading memory relies on recurrent activity patterns in a neuronal network, whereas hidden memory is encoded using synaptic mechanisms, such as facilitation, which persist even when neurons fall silent. We have used a novel computational and optogenetic approach to investigate whether these same memory processes hypothesized to support pattern recognition and short-term memory in vivo, exist in vitro. Electrophysiological activity was recorded from primary cultures of dissociated rat cortical neurons plated on multielectrode arrays. Cultures were transfected with ChannelRhodopsin-2 and optically stimulated using random dot stimuli. The pattern of neuronal activity resulting from this stimulation was analyzed using classification algorithms that enabled the identification of stimulus-specific memories. Fading memories for different stimuli, encoded in ongoing neural activity, persisted and could be distinguished from each other for as long as 1 s after stimulation was terminated. Hidden memories were detected by altered responses of neurons to additional stimulation, and this effect persisted longer than 1 s. Interestingly, network bursts seem to eliminate hidden memories. These results are similar to those that have been reported from similar experiments in vivo and demonstrate that mechanisms of information processing and short-term memory can be studied using cultured neuronal networks, thereby setting the stage for therapeutic applications using this platform.

  3. Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures

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    Daniel Carl Miner

    2014-11-01

    Full Text Available The neuroanatomical connectivity of cortical circuits is believed to follow certain rules, the exact origins of which are still poorly understood. In particular, numerous nonrandom features, such as common neighbor clustering, overrepresentation of reciprocal connectivity, and overrepresentation of certain triadic graph motifs have been experimentally observed in cortical slice data. Some of these data, particularly regarding bidirectional connectivity are seemingly contradictory, and the reasons for this are unclear. Here we present a simple static geometric network model with distance-dependent connectivity on a realistic scale that naturally gives rise to certain elements of these observed behaviors, and may provide plausible explanations for some of the conflicting findings. Specifically, investigation of the model shows that experimentally measured nonrandom effects, especially bidirectional connectivity, may depend sensitively on experimental parameters such as slice thickness and sampling area, suggesting potential explanations for the seemingly conflicting experimental results.

  4. Slicing, sampling, and distance-dependent effects affect network measures in simulated cortical circuit structures.

    Science.gov (United States)

    Miner, Daniel C; Triesch, Jochen

    2014-01-01

    The neuroanatomical connectivity of cortical circuits is believed to follow certain rules, the exact origins of which are still poorly understood. In particular, numerous nonrandom features, such as common neighbor clustering, overrepresentation of reciprocal connectivity, and overrepresentation of certain triadic graph motifs have been experimentally observed in cortical slice data. Some of these data, particularly regarding bidirectional connectivity are seemingly contradictory, and the reasons for this are unclear. Here we present a simple static geometric network model with distance-dependent connectivity on a realistic scale that naturally gives rise to certain elements of these observed behaviors, and may provide plausible explanations for some of the conflicting findings. Specifically, investigation of the model shows that experimentally measured nonrandom effects, especially bidirectional connectivity, may depend sensitively on experimental parameters such as slice thickness and sampling area, suggesting potential explanations for the seemingly conflicting experimental results.

  5. Low and High-Frequency Field Potentials of Cortical Networks ...

    Science.gov (United States)

    Neural networks grown on microelectrode arrays (MEAs) have become an important, high content in vitro assay for assessing neuronal function. MEA experiments typically examine high- frequency (HF) (>200 Hz) spikes, and bursts which can be used to discriminate between different pharmacological agents/chemicals. However, normal brain activity is additionally composed of integrated low-frequency (0.5-100 Hz) field potentials (LFPs) which are filtered out of MEA recordings. The objective of this study was to characterize the relationship between HF and LFP neural network signals, and to assess the relative sensitivity of LFPs to selected neurotoxicants. Rat primary cortical cultures were grown on glass, single-well MEA chips. Spontaneous activity was sampled at 25 kHz and recorded (5 min) (Multi-Channel Systems) from mature networks (14 days in vitro). HF (spike, mean firing rate, MFR) and LF (power spectrum, amplitude) components were extracted from each network and served as its baseline (BL). Next, each chip was treated with either 1) a positive control, bicuculline (BIC, 25μM) or domoic acid (DA, 0.3μM), 2) or a negative control, acetaminophen (ACE, 100μM) or glyphosate (GLY, 100μM), 3) a solvent control (H2O or DMSO:EtOH), or 4) a neurotoxicant, (carbaryl, CAR 5, 30μM ; lindane, LIN 1, 10μM; permethrin, PERM 25, 50μM; triadimefon, TRI 5, 65μM). Post treatment, 5 mins of spontaneous activity was recorded and analyzed. As expected posit

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

    OpenAIRE

    Chu, Catherine Jean; Leahy, J.; Pathmanathan, Jay Sriram; Kramer, M.A.; Cash, Sydney S.

    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. ...

  7. Complex Networks in Psychological Models

    Science.gov (United States)

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

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

  8. Neural synchrony in cortical networks: history, concept and current status

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    Peter Uhlhaas

    2009-07-01

    Full Text Available 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.

  9. A network of networks model to study phase synchronization using structural connection matrix of human brain

    Science.gov (United States)

    Ferrari, F. A. S.; Viana, R. L.; Reis, A. S.; Iarosz, K. C.; Caldas, I. L.; Batista, A. M.

    2018-04-01

    The cerebral cortex plays a key role in complex cortical functions. It can be divided into areas according to their function (motor, sensory and association areas). In this paper, the cerebral cortex is described as a network of networks (cortex network), we consider that each cortical area is composed of a network with small-world property (cortical network). The neurons are assumed to have bursting properties with the dynamics described by the Rulkov model. We study the phase synchronization of the cortex network and the cortical networks. In our simulations, we verify that synchronization in cortex network is not homogeneous. Besides, we focus on the suppression of neural phase synchronization. Synchronization can be related to undesired and pathological abnormal rhythms in the brain. For this reason, we consider the delayed feedback control to suppress the synchronization. We show that delayed feedback control is efficient to suppress synchronous behavior in our network model when an appropriate signal intensity and time delay are defined.

  10. Simultaneous stability and sensitivity in model cortical networks is achieved through anti-correlations between the in- and out-degree of connectivity

    Directory of Open Access Journals (Sweden)

    Juan Carlos Vasquez

    2013-11-01

    Full Text Available Neuronal networks in rodent barrel cortex are characterized by stable low baseline firing rates. However, they are sensitive to the action potentials of single neurons as suggested by recent single-cell stimulation experiments that report quantifiable behavioral responses in response to short spike trains elicited in single neurons. Hence, these networks are stable against internally generated fluctuations in firing rate but at the same time remain sensitive to similarly-sized externally induced perturbations. We investigated stability and sensitivity in a simple recurrent network of stochastic binary neurons and determined numerically the effects of correlation between the number of afferent (‘in-degree’ and efferent (‘out-degree’ connections in neurons. The key advance reported in this work is that anti-correlation between in-/out-degree distributions increased the stability of the network in comparison to networks with no correlation or positive correlations, while being able to achieve the same level of sensitivity. The experimental characterization of degree distributions is difficult because all presynaptic and postsynaptic neurons have to be identified and counted. We explored whether the statistics of network motifs, which requires the characterization of connections between small subsets of neurons, could be used to detect evidence for degree anti-correlations. We find that the sample frequency of the 3-neuron ‘ring’ motif (1→2→3→1, can be used to detect degree anti-correlation for sub-networks of size 30 using about 50 samples, which is of significance because the necessary measurements are achievable experimentally in the near future.Taken together, we hypothesize that barrel cortex networks exhibit degree anti-correlations and specific network motif statistics.

  11. Critical fluctuations in cortical models near instability

    NARCIS (Netherlands)

    Aburn, M.J.; Holmes, C.A.; Roberts, J.A.; Boonstra, T.W.; Breakspear, M.

    2012-01-01

    Computational studies often proceed from the premise that cortical dynamics operate in a linearly stable domain, where fluctuations dissipate quickly and show only short memory. Studies of human electroencephalography (EEG), however, have shown significant autocorrelation at time lags on the scale

  12. Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

    Science.gov (United States)

    Kong, Ru; Li, Jingwei; Orban, Csaba; Sabuncu, Mert R; Liu, Hesheng; Schaefer, Alexander; Sun, Nanbo; Zuo, Xi-Nian; Holmes, Avram J; Eickhoff, Simon B; Yeo, B T Thomas

    2018-06-06

    Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

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

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    Makii Muthalib

    Full Text Available 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.

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

    Science.gov (United States)

    Muthalib, Makii; Re, Rebecca; 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.

  15. Longitudinal Development of Cortical Thickness, Folding, and Fiber Density Networks in the First 2 Years of Life

    OpenAIRE

    Nie, Jingxin; Li, Gang; Wang, Li; Shi, Feng; Lin, Weili; Gilmore, John H.; Shen, Dinggang

    2013-01-01

    Quantitatively characterizing the development of cortical anatomical networks during the early stage of life plays an important role in revealing the relationship between cortical structural connection and high-level functional development. The development of correlation networks of cortical-thickness, cortical folding, and fiber-density is systematically analyzed in this article to study the relationship between different anatomical properties during the first 2 years of life. Specifically, ...

  16. Altered brain structural networks in attention deficit/hyperactivity disorder children revealed by cortical thickness.

    Science.gov (United States)

    Liu, Tian; Chen, Yanni; Li, Chenxi; Li, Youjun; Wang, Jue

    2017-07-04

    This study investigated the cortical thickness and topological features of human brain anatomical networks related to attention deficit/hyperactivity disorder. Data were collected from 40 attention deficit/hyperactivity disorder children and 40 normal control children. Interregional correlation matrices were established by calculating the correlations of cortical thickness between all pairs of cortical regions (68 regions) of the whole brain. Further thresholds were applied to create binary matrices to construct a series of undirected and unweighted graphs, and global, local, and nodal efficiencies were computed as a function of the network cost. These experimental results revealed abnormal cortical thickness and correlations in attention deficit/hyperactivity disorder, and showed that the brain structural networks of attention deficit/hyperactivity disorder subjects had inefficient small-world topological features. Furthermore, their topological properties were altered abnormally. In particular, decreased global efficiency combined with increased local efficiency in attention deficit/hyperactivity disorder children led to a disorder-related shift of the network topological structure toward regular networks. In addition, nodal efficiency, cortical thickness, and correlation analyses revealed that several brain regions were altered in attention deficit/hyperactivity disorder patients. These findings are in accordance with a hypothesis of dysfunctional integration and segregation of the brain in patients with attention deficit/hyperactivity disorder and provide further evidence of brain dysfunction in attention deficit/hyperactivity disorder patients by observing cortical thickness on magnetic resonance imaging.

  17. Trade-off of cerebello-cortical and cortico-cortical functional networks for planning in 6-year-old children.

    Science.gov (United States)

    Kipping, Judy A; Margulies, Daniel S; Eickhoff, Simon B; Lee, Annie; Qiu, Anqi

    2018-05-03

    Childhood is a critical period for the development of cognitive planning. There is a lack of knowledge on its neural mechanisms in children. This study aimed to examine cerebello-cortical and cortico-cortical functional connectivity in association with planning skills in 6-year-olds (n = 76). We identified the cerebello-cortical and cortico-cortical functional networks related to cognitive planning using activation likelihood estimation (ALE) meta-analysis on existing functional imaging studies on spatial planning, and data-driven independent component analysis (ICA) of children's resting-state functional MRI (rs-fMRI). We investigated associations of cerebello-cortical and cortico-cortical functional connectivity with planning ability in 6-year-olds, as assessed using the Stockings of Cambridge task. Long-range functional connectivity of two cerebellar networks (lobules VI and lateral VIIa) with the prefrontal and premotor cortex were greater in children with poorer planning ability. In contrast, cortico-cortical association networks were not associated with the performance of planning in children. These results highlighted the key contribution of the lateral cerebello-frontal functional connectivity, but not cortico-cortical association functional connectivity, for planning ability in 6-year-olds. Our results suggested that brain adaptation to the acquisition of planning ability during childhood is partially achieved through the engagement of the cerebello-cortical functional connectivity. Copyright © 2018 Elsevier Inc. All rights reserved.

  18. Cortical Network Dynamics of Perceptual Decision-Making in the Human Brain

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    Markus eSiegel

    2011-02-01

    Full Text Available Goal-directed behavior requires the flexible transformation of sensory evidence about our environment into motor actions. Studies of perceptual decision-making have shown that this transformation is distributed across several widely separated brain regions. Yet, little is known about how decision-making emerges from the dynamic interactions among these regions. Here, we review a series of studies, in which we characterized the cortical network interactions underlying a perceptual decision process in the human brain. We used magnetoencephalography (MEG to measure the large-scale cortical population dynamics underlying each of the sub-processes involved in this decision: the encoding of sensory evidence and action plan, the mapping between the two, and the attentional selection of task-relevant evidence. We found that these sub-processes are mediated by neuronal oscillations within specific frequency ranges. Localized gamma-band oscillations in sensory and motor cortices reflect the encoding of the sensory evidence and motor plan. Large-scale oscillations across widespread cortical networks mediate the integrative processes connecting these local networks: Gamma- and beta-band oscillations across frontal, parietal and sensory cortices serve the selection of relevant sensory evidence and its flexible mapping onto action plans. In sum, our results suggest that perceptual decisions are mediated by oscillatory interactions within overlapping local and large-scale cortical networks.

  19. Longitudinal development of cortical thickness, folding, and fiber density networks in the first 2 years of life.

    Science.gov (United States)

    Nie, Jingxin; Li, Gang; Wang, Li; Shi, Feng; Lin, Weili; Gilmore, John H; Shen, Dinggang

    2014-08-01

    Quantitatively characterizing the development of cortical anatomical networks during the early stage of life plays an important role in revealing the relationship between cortical structural connection and high-level functional development. The development of correlation networks of cortical-thickness, cortical folding, and fiber-density is systematically analyzed in this article to study the relationship between different anatomical properties during the first 2 years of life. Specifically, longitudinal MR images of 73 healthy subjects from birth to 2 year old are used. For each subject at each time point, its measures of cortical thickness, cortical folding, and fiber density are projected to its cortical surface that has been partitioned into 78 cortical regions. Then, the correlation matrices for cortical thickness, cortical folding, and fiber density at each time point can be constructed, respectively, by computing the inter-regional Pearson correlation coefficient (of any pair of ROIs) across all 73 subjects. Finally, the presence/absence pattern (i.e., binary pattern) of the connection network is constructed from each inter-regional correlation matrix, and its statistical and anatomical properties are adopted to analyze the longitudinal development of anatomical networks. The results show that the development of anatomical network could be characterized differently by using different anatomical properties (i.e., using cortical thickness, cortical folding, or fiber density). Copyright © 2013 Wiley Periodicals, Inc.

  20. Representing where along with what information in a model of a cortical patch.

    Directory of Open Access Journals (Sweden)

    Yasser Roudi

    2008-03-01

    Full Text Available Behaving in the real world requires flexibly combining and maintaining information about both continuous and discrete variables. In the visual domain, several lines of evidence show that neurons in some cortical networks can simultaneously represent information about the position and identity of objects, and maintain this combined representation when the object is no longer present. The underlying network mechanism for this combined representation is, however, unknown. In this paper, we approach this issue through a theoretical analysis of recurrent networks. We present a model of a cortical network that can retrieve information about the identity of objects from incomplete transient cues, while simultaneously representing their spatial position. Our results show that two factors are important in making this possible: A a metric organisation of the recurrent connections, and B a spatially localised change in the linear gain of neurons. Metric connectivity enables a localised retrieval of information about object identity, while gain modulation ensures localisation in the correct position. Importantly, we find that the amount of information that the network can retrieve and retain about identity is strongly affected by the amount of information it maintains about position. This balance can be controlled by global signals that change the neuronal gain. These results show that anatomical and physiological properties, which have long been known to characterise cortical networks, naturally endow them with the ability to maintain a conjunctive representation of the identity and location of objects.

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

    Science.gov (United States)

    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.

  2. Early-life exposure to caffeine affects the construction and activity of cortical networks in mice.

    Science.gov (United States)

    Fazeli, Walid; Zappettini, Stefania; Marguet, Stephan Lawrence; Grendel, Jasper; Esclapez, Monique; Bernard, Christophe; Isbrandt, Dirk

    2017-09-01

    The consumption of psychoactive drugs during pregnancy can have deleterious effects on newborns. It remains unclear whether early-life exposure to caffeine, the most widely consumed psychoactive substance, alters brain development. We hypothesized that maternal caffeine ingestion during pregnancy and the early postnatal period in mice affects the construction and activity of cortical networks in offspring. To test this hypothesis, we focused on primary visual cortex (V1) as a model neocortical region. In a study design mimicking the daily consumption of approximately three cups of coffee during pregnancy in humans, caffeine was added to the drinking water of female mice and their offspring were compared to control offspring. Caffeine altered the construction of GABAergic neuronal networks in V1, as reflected by a reduced number of somatostatin-containing GABA neurons at postnatal days 6-7, with the remaining ones showing poorly developed dendritic arbors. These findings were accompanied by increased synaptic activity in vitro and elevated network activity in vivo in V1. Similarly, in vivo hippocampal network activity was altered from the neonatal period until adulthood. Finally, caffeine-exposed offspring showed increased seizure susceptibility in a hyperthermia-induced seizure model. In summary, our results indicate detrimental effects of developmental caffeine exposure on mouse brain development. Copyright © 2017 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Chu, C J; Leahy, J; Pathmanathan, J; Kramer, M A; Cash, S S

    2014-07-01

    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. 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. 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. Our results indicate that a predictable program directs the development of key rhythmic components and physiological brain networks over early development. 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. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

  4. A cortical attractor network with Martinotti cells driven by facilitating synapses.

    Directory of Open Access Journals (Sweden)

    Pradeep Krishnamurthy

    Full Text Available The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

  5. Methods for parameter identification in oscillatory networks and application to cortical and thalamic 600 Hz activity.

    Science.gov (United States)

    Leistritz, L; Suesse, T; Haueisen, J; Hilgenfeld, B; Witte, H

    2006-01-01

    Directed information transfer in the human brain occurs presumably by oscillations. As of yet, most approaches for the analysis of these oscillations are based on time-frequency or coherence analysis. The present work concerns the modeling of cortical 600 Hz oscillations, localized within the Brodmann Areas 3b and 1 after stimulation of the nervus medianus, by means of coupled differential equations. This approach leads to the so-called parameter identification problem, where based on a given data set, a set of unknown parameters of a system of ordinary differential equations is determined by special optimization procedures. Some suitable algorithms for this task are presented in this paper. Finally an oscillatory network model is optimally fitted to the data taken from ten volunteers.

  6. Collaborative networks: Reference modeling

    NARCIS (Netherlands)

    Camarinha-Matos, L.M.; Afsarmanesh, H.

    2008-01-01

    Collaborative Networks: Reference Modeling works to establish a theoretical foundation for Collaborative Networks. Particular emphasis is put on modeling multiple facets of collaborative networks and establishing a comprehensive modeling framework that captures and structures diverse perspectives of

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

    Science.gov (United States)

    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.

  8. Developing guinea pig brain as a model for cortical folding.

    Science.gov (United States)

    Hatakeyama, Jun; Sato, Haruka; Shimamura, Kenji

    2017-05-01

    The cerebral cortex in mammals, the neocortex specifically, is highly diverse among species with respect to its size and morphology, likely reflecting the immense adaptiveness of this lineage. In particular, the pattern and number of convoluted ridges and fissures, called gyri and sulci, respectively, on the surface of the cortex are variable among species and even individuals. However, little is known about the mechanism of cortical folding, although there have been several hypotheses proposed. Recent studies on embryonic neurogenesis revealed the differences in cortical progenitors as a critical factor of the process of gyrification. Here, we investigated the gyrification processes using developing guinea pig brains that form a simple but fundamental pattern of gyri. In addition, we established an electroporation-mediated gene transfer method for guinea pig embryos. We introduce the guinea pig brain as a useful model system to understand the mechanisms and basic principle of cortical folding. © 2017 Japanese Society of Developmental Biologists.

  9. Modulation of Cortical-subcortical Networks in Parkinson’s Disease by Applied Field Effects

    Directory of Open Access Journals (Sweden)

    Christopher William Hess

    2013-09-01

    Full Text Available Studies suggest that endogenous field effects may play a role in neuronal oscillations and communication. Non-invasive transcranial electrical stimulation with low-intensity currents can also have direct effects on the underlying cortex as well as distant network effects. While Parkinson's disease (PD is amenable to invasive neuromodulation in the basal ganglia by deep brain stimulation, techniques of non-invasive neuromodulation like transcranial direct current stimulation (tDCS and transcranial alternating current stimulation (tACS are being investigated as possible therapies. tDCS and tACS have the potential to influence the abnormal cortical-subcortical network activity that occurs in PD through sub-threshold changes in cortical excitability or through entrainment or disruption of ongoing rhythmic cortical activity. This may allow for the targeting of specific features of the disease involving abnormal oscillatory activity, as well as the enhancement of potential cortical compensation for basal ganglia dysfunction and modulation of cortical plasticity in neurorehabilitation. However, little is currently known about how cortical stimulation will affect subcortical structures, the size of any effect, and the factors of stimulation that will influence these effects.

  10. Self-sustained firing activities of the cortical network with plastic rules in weak AC electrical fields

    International Nuclear Information System (INIS)

    Qin Ying-Mei; Wang Jiang; Men Cong; Zhao Jia; Wei Xi-Le; Deng Bin

    2012-01-01

    Both external and endogenous electrical fields widely exist in the environment of cortical neurons. The effects of a weak alternating current (AC) field on a neural network model with synaptic plasticity are studied. It is found that self-sustained rhythmic firing patterns, which are closely correlated with the cognitive functions, are significantly modified due to the self-organizing of the network in the weak AC field. The activities of the neural networks are affected by the synaptic connection strength, the external stimuli, and so on. In the presence of learning rules, the synaptic connections can be modulated by the external stimuli, which will further enhance the sensitivity of the network to the external signal. The properties of the external AC stimuli can serve as control parameters in modulating the evolution of the neural network. (interdisciplinary physics and related areas of science and technology)

  11. The Bat as a New Model of Cortical Development.

    Science.gov (United States)

    Martínez-Cerdeño, Verónica; Camacho, Jasmin; Ariza, Jeanelle; Rogers, Hailee; Horton-Sparks, Kayla; Kreutz, Anna; Behringer, Richard; Rasweiler, John J; Noctor, Stephen C

    2017-11-09

    The organization of the mammalian cerebral cortex shares fundamental features across species. However, while the radial thickness of grey matter varies within one order of magnitude, the tangential spread of the cortical sheet varies by orders of magnitude across species. A broader sample of model species may provide additional clues for understanding mechanisms that drive cortical expansion. Here, we introduce the bat Carollia perspicillata as a new model species. The brain of C. perspicillata is similar in size to that of mouse but has a cortical neurogenic period at least 5 times longer than mouse, and nearly as long as that of the rhesus macaque, whose brain is 100 times larger. We describe the development of laminar and regional structures, neural precursor cell identity and distribution, immune cell distribution, and a novel population of Tbr2+ cells in the caudal ganglionic eminence of the developing neocortex of C. perspicillata. Our data indicate that unique mechanisms guide bat cortical development, particularly concerning cell cycle length. The bat model provides new perspective on the evolution of developmental programs that regulate neurogenesis in mammalian cerebral cortex, and offers insight into mechanisms that contribute to tangential expansion and gyri formation in the cerebral cortex. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

    Science.gov (United States)

    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…

  13. Cortical Thinning in Network-Associated Regions in Cognitively Normal and Below-Normal Range Schizophrenia

    Directory of Open Access Journals (Sweden)

    R. Walter Heinrichs

    2017-01-01

    Full Text Available This study assessed whether cortical thickness across the brain and regionally in terms of the default mode, salience, and central executive networks differentiates schizophrenia patients and healthy controls with normal range or below-normal range cognitive performance. Cognitive normality was defined using the MATRICS Consensus Cognitive Battery (MCCB composite score (T=50 ± 10 and structural magnetic resonance imaging was used to generate cortical thickness data. Whole brain analysis revealed that cognitively normal range controls (n=39 had greater cortical thickness than both cognitively normal (n=17 and below-normal range (n=49 patients. Cognitively normal controls also demonstrated greater thickness than patients in regions associated with the default mode and salience, but not central executive networks. No differences on any thickness measure were found between cognitively normal range and below-normal range controls (n=24 or between cognitively normal and below-normal range patients. In addition, structural covariance between network regions was high and similar across subgroups. Positive and negative symptom severity did not correlate with thickness values. Cortical thinning across the brain and regionally in relation to the default and salience networks may index shared aspects of the psychotic psychopathology that defines schizophrenia with no relation to cognitive impairment.

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

    Science.gov (United States)

    We examined the development of neural network activity using microelectrode array (MEA) recordings made in multi-well MEA plates (mwMEAs) over the first 12 days in vitro (DIV). In primary cortical cultures made from postnatal rats, action potential spiking activity was essentiall...

  15. Spatial interactions reveal inhibitory cortical networks in human amblyopia.

    Science.gov (United States)

    Wong, Erwin H; Levi, Dennis M; McGraw, Paul V

    2005-10-01

    Humans with amblyopia have a well-documented loss of sensitivity for first-order, or luminance defined, visual information. Recent studies show that they also display a specific loss of sensitivity for second-order, or contrast defined, visual information; a type of image structure encoded by neurons found predominantly in visual area A18/V2. In the present study, we investigate whether amblyopia disrupts the normal architecture of spatial interactions in V2 by determining the contrast detection threshold of a second-order target in the presence of second-order flanking stimuli. Adjacent flanks facilitated second-order detectability in normal observers. However, in marked contrast, they suppressed detection in each eye of the majority of amblyopic observers. Furthermore, strabismic observers with no loss of visual acuity show a similar pattern of detection suppression. We speculate that amblyopia results in predominantly inhibitory cortical interactions between second-order neurons.

  16. Calcium dynamics of cortical astrocytic networks in vivo.

    Directory of Open Access Journals (Sweden)

    Hajime Hirase

    2004-04-01

    Full Text Available Large and long-lasting cytosolic calcium surges in astrocytes have been described in cultured cells and acute slice preparations. The mechanisms that give rise to these calcium events have been extensively studied in vitro. However, their existence and functions in the intact brain are unknown. We have topically applied Fluo-4 AM on the cerebral cortex of anesthetized rats, and imaged cytosolic calcium fluctuation in astrocyte populations of superficial cortical layers in vivo, using two-photon laser scanning microscopy. Spontaneous [Ca(2+](i events in individual astrocytes were similar to those observed in vitro. Coordination of [Ca(2+](i events among astrocytes was indicated by the broad cross-correlograms. Increased neuronal discharge was associated with increased astrocytic [Ca(2+](i activity in individual cells and a robust coordination of [Ca(2+](i signals in neighboring astrocytes. These findings indicate potential neuron-glia communication in the intact brain.

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

    Science.gov (United States)

    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

  18. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Science.gov (United States)

    El Boustani, Sami; Marre, Olivier; Béhuret, Sébastien; Baudot, Pierre; Yger, Pierre; Bal, Thierry; Destexhe, Alain; Frégnac, Yves

    2009-09-01

    Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m) activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m) reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population signals measured

  19. Network-state modulation of power-law frequency-scaling in visual cortical neurons.

    Directory of Open Access Journals (Sweden)

    Sami El Boustani

    2009-09-01

    Full Text Available Various types of neural-based signals, such as EEG, local field potentials and intracellular synaptic potentials, integrate multiple sources of activity distributed across large assemblies. They have in common a power-law frequency-scaling structure at high frequencies, but it is still unclear whether this scaling property is dominated by intrinsic neuronal properties or by network activity. The latter case is particularly interesting because if frequency-scaling reflects the network state it could be used to characterize the functional impact of the connectivity. In intracellularly recorded neurons of cat primary visual cortex in vivo, the power spectral density of V(m activity displays a power-law structure at high frequencies with a fractional scaling exponent. We show that this exponent is not constant, but depends on the visual statistics used to drive the network. To investigate the determinants of this frequency-scaling, we considered a generic recurrent model of cortex receiving a retinotopically organized external input. Similarly to the in vivo case, our in computo simulations show that the scaling exponent reflects the correlation level imposed in the input. This systematic dependence was also replicated at the single cell level, by controlling independently, in a parametric way, the strength and the temporal decay of the pairwise correlation between presynaptic inputs. This last model was implemented in vitro by imposing the correlation control in artificial presynaptic spike trains through dynamic-clamp techniques. These in vitro manipulations induced a modulation of the scaling exponent, similar to that observed in vivo and predicted in computo. We conclude that the frequency-scaling exponent of the V(m reflects stimulus-driven correlations in the cortical network activity. Therefore, we propose that the scaling exponent could be used to read-out the "effective" connectivity responsible for the dynamical signature of the population

  20. Cerebello-thalamo-cortical networks predict positive symptom progression in individuals at ultra-high risk for psychosis

    Directory of Open Access Journals (Sweden)

    Jessica A. Bernard

    2017-01-01

    Full Text Available Prospective longitudinal evaluation of adolescents at ultra-high-risk (UHR for the development of psychosis enables an enriched neurodevelopmental perspective of disease progression in the absence of many of the factors that typically confound research with formally psychotic patients (antipsychotic medications, drug/alcohol dependence. The cerebellum has been linked to cognitive dysfunction and symptom severity in schizophrenia and recent work from our team suggests that it is a promising target for investigation in UHR individuals as well. However, the cerebellum and cerebello-thalamo-cortical networks have not been investigated developmentally or with respect to disease progression in this critical population. Further, to date, the types of longitudinal multimodal connectivity studies that would substantially inform our understanding of this area have not yet been conducted. In the present investigation 26 UHR and 24 healthy control adolescents were administered structured clinical interviews and scanned at baseline and then again at 12-month time points to investigate both functional and structural connectivity development of cerebello-thalamo-cortical networks in conjunction with symptom progression. Our results provide evidence of abnormal functional and structural cerebellar network development in the UHR group. Crucially, we also found that cerebello-thalamo-cortical network development and connectivity at baseline are associated with positive symptom course, suggesting that cerebellar networks may be a biomarker of disease progression. Together, these findings provide support for neurodevelopmental models of psychotic disorders and suggest that the cerebellum and respective networks with the cortex may be especially important for elucidating the pathophysiology of psychosis and highlighting novel treatment targets.

  1. Modeling the effects of transcranial magnetic stimulation on cortical circuits.

    Science.gov (United States)

    Esser, Steve K; Hill, Sean L; Tononi, Giulio

    2005-07-01

    Transcranial magnetic stimulation (TMS) is commonly used to activate or inactivate specific cortical areas in a noninvasive manner. Because of technical constraints, the precise effects of TMS on cortical circuits are difficult to assess experimentally. Here, this issue is investigated by constructing a detailed model of a portion of the thalamocortical system and examining the effects of the simulated delivery of a TMS pulse. The model, which incorporates a large number of physiological and anatomical constraints, includes 33,000 spiking neurons arranged in a 3-layered motor cortex and over 5 million intra- and interlayer synaptic connections. The model was validated by reproducing several results from the experimental literature. These include the frequency, timing, dose response, and pharmacological modulation of epidurally recorded responses to TMS (the so-called I-waves), as well as paired-pulse response curves consistent with data from several experimental studies. The modeled responses to simulated TMS pulses in different experimental paradigms provide a detailed, self-consistent account of the neural and synaptic activities evoked by TMS within prototypical cortical circuits.

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

    Science.gov (United States)

    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. © 2016 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  4. Repair of Neocortex in a Model of Cortical Dysplasia

    Science.gov (United States)

    2007-03-27

    as dyslexia, intractable epilepsy, and schizophrenia which has been linked to abnormal reelin expression (Grayson et al., 2005; Brigman et al., 2006...exposure to ethanol on glutamate and GABA immunoreactivity in macaque somatosensory and motor cortices: critical timing of exposure. Neuroscience...Rothblat LA (2006) Executive functions in the heterozygous reeler mouse model of schizophrenia . Behav Neurosci 120:984-988. Caldwell MA, He X

  5. Causal hierarchy within the thalamo-cortical network in spike and wave discharges.

    Directory of Open Access Journals (Sweden)

    Anna E Vaudano

    2009-08-01

    Full Text Available Generalised spike wave (GSW discharges are the electroencephalographic (EEG hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges.We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A, ventromedial prefrontal cortex (model B, and precuneus (model C. Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus, to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was approximately 1.Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus -- an index of awareness -- and the occurrence of pathological discharges in epilepsy.

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

    Science.gov (United States)

    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.

  7. Dynamic interactions of the cortical networks during thought suppression.

    Science.gov (United States)

    Aso, Toshihiko; Nishimura, Kazuo; Kiyonaka, Takashi; Aoki, Takaaki; Inagawa, Michiyo; Matsuhashi, Masao; Tobinaga, Yoshikazu; Fukuyama, Hidenao

    2016-08-01

    Thought suppression has spurred extensive research in clinical and preclinical fields, particularly with regard to the paradoxical aspects of this behavior. However, the involvement of the brain's inhibitory system in the dynamics underlying the continuous effort to suppress thoughts has yet to be clarified. This study aims to provide a unified perspective for the volitional suppression of internal events incorporating the current understanding of the brain's inhibitory system. Twenty healthy volunteers underwent functional magnetic resonance imaging while they performed thought suppression blocks alternating with visual imagery blocks. The whole dataset was decomposed by group-independent component analysis into 30 components. After discarding noise components, the 20 valid components were subjected to further analysis of their temporal properties including task-relatedness and between-component residual correlation. Combining a long task period and a data-driven approach, we observed a right-side-dominant, lateral frontoparietal network to be strongly suppression related. This network exhibited increased fluctuation during suppression, which is compatible with the well-known difficulty of suppression maintenance. Between-network correlation provided further insight into the coordinated engagement of the executive control and dorsal attention networks, as well as the reciprocal activation of imagery-related components, thus revealing neural substrates associated with the rivalry between intrusive thoughts and the suppression process.

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

    Science.gov (United States)

    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

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

    Directory of Open Access Journals (Sweden)

    Xiaogeng Wan

    Full Text Available 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.

  10. Altered cortical hubs in functional brain networks in amyotrophic lateral sclerosis.

    Science.gov (United States)

    Ma, Xujing; Zhang, Jiuquan; Zhang, Youxue; Chen, Heng; Li, Rong; Wang, Jian; Chen, Huafu

    2015-11-01

    Cortical hubs are highly connected nodes in functional brain networks that play vital roles in the efficient transfer of information across brain regions. Although altered functional connectivity has been found in amyotrophic lateral sclerosis (ALS), the changing pattern in functional network hubs in ALS remains unknown. In this study, we applied a voxel-wise method to investigate the changing pattern of cortical hubs in ALS. Through resting-state fMRI, we constructed whole-brain voxel-wise functional networks by measuring the temporal correlations of each pair of brain voxels and identified hubs using the graph theory method. Specifically, a functional connectivity strength (FCS) map was derived from the data on 20 patients with ALS and 20 healthy controls. The brain regions with high FCS values were regarded as functional network hubs. Functional hubs were found mainly in the bilateral precuneus, parietal cortex, medial prefrontal cortex, and in several visual regions and temporal areas in both groups. Within the hub regions, the ALS patients exhibited higher FCS in the prefrontal cortex compared with the healthy controls. The FCS value in the significantly abnormal hub regions was correlated with clinical variables. Results indicated the presence of altered cortical hubs in the ALS patients and could therefore shed light on the pathophysiology mechanisms underlying ALS.

  11. Model of Cortical Organization Embodying a Basis for a Theory of Information Processing and Memory Recall

    Science.gov (United States)

    Shaw, Gordon L.; Silverman, Dennis J.; Pearson, John C.

    1985-04-01

    Motivated by V. B. Mountcastle's organizational principle for neocortical function, and by M. E. Fisher's model of physical spin systems, we introduce a cooperative model of the cortical column incorporating an idealized substructure, the trion, which represents a localized group of neurons. Computer studies reveal that typical networks composed of a small number of trions (with symmetric interactions) exhibit striking behavior--e.g., hundreds to thousands of quasi-stable, periodic firing patterns, any of which can be selected out and enhanced with only small changes in interaction strengths by using a Hebb-type algorithm.

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

    Science.gov (United States)

    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

  13. The Cortical Network for Braille Writing in the Blind.

    Science.gov (United States)

    Likova, Lora T; Tyler, Christopher W; Cacciamani, Laura; Mineff, Kristyo; Nicholas, Spero

    2016-01-01

    Fundamental forms of high-order cognition, such as reading and writing, are usually studied in the context of one modality - vision. People without sight, however, use the kinesthetic-based Braille writing, and haptic-based Braille reading. We asked whether the cognitive and motor control mechanisms underlying writing and reading are modality-specific or supramodal. While a number of previous functional Magnetic Resonance Imaging (fMRI) studies have investigated the brain network for Braille reading in the blind, such studies on Braille writing are lacking. Consequently, no comparative network analysis of Braille writing vs. reading exists. Here, we report the first study of Braille writing, and a comparison of the brain organization for Braille writing vs Braille reading. FMRI was conducted in a Siemens 3T Trio scanner. Our custom MRI-compatible drawing/writing lectern was further modified to provide for Braille reading and writing. Each of five paragraphs of novel Braille text describing objects, faces and navigation sequences was read, then reproduced twice by Braille writing from memory, then read a second time. During Braille reading, the haptic-sensing of the Braille letters strongly activated not only the early visual area V1 and V2, but some highly specialized areas, such as the classical visual grapheme area and the Exner motor grapheme area. Braille-writing-from-memory, engaged a significantly more extensive network in dorsal motor, somatosensory/kinesthetic, dorsal parietal and prefrontal cortex. However, in contrast to the largely extended V1 activation in drawing-from-memory in the blind after training (Likova, 2012), Braille writing from memory generated focal activation restricted to the most foveal part of V1, presumably reflecting topographically the focal demands of such a "pin-pricking" task.

  14. Modeling Network Interdiction Tasks

    Science.gov (United States)

    2015-09-17

    118 xiii Table Page 36 Computation times for weighted, 100-node random networks for GAND Approach testing in Python ...in Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 38 Accuracy measures for weighted, 100-node random networks for GAND...networks [15:p. 1]. A common approach to modeling network interdiction is to formulate the problem in terms of a two-stage strategic game between two

  15. Extensive video-game experience alters cortical networks for complex visuomotor transformations.

    Science.gov (United States)

    Granek, Joshua A; Gorbet, Diana J; Sergio, Lauren E

    2010-10-01

    Using event-related functional magnetic resonance imaging (fMRI), we examined the effect of video-game experience on the neural control of increasingly complex visuomotor tasks. Previously, skilled individuals have demonstrated the use of a more efficient movement control brain network, including the prefrontal, premotor, primary sensorimotor and parietal cortices. Our results extend and generalize this finding by documenting additional prefrontal cortex activity in experienced video gamers planning for complex eye-hand coordination tasks that are distinct from actual video-game play. These changes in activation between non-gamers and extensive gamers are putatively related to the increased online control and spatial attention required for complex visually guided reaching. These data suggest that the basic cortical network for processing complex visually guided reaching is altered by extensive video-game play. Crown Copyright © 2009. Published by Elsevier Srl. All rights reserved.

  16. Anti-correlated cortical networks arise from spontaneous neuronal dynamics at slow timescales.

    Science.gov (United States)

    Kodama, Nathan X; Feng, Tianyi; Ullett, James J; Chiel, Hillel J; Sivakumar, Siddharth S; Galán, Roberto F

    2018-01-12

    In the highly interconnected architectures of the cerebral cortex, recurrent intracortical loops disproportionately outnumber thalamo-cortical inputs. These networks are also capable of generating neuronal activity without feedforward sensory drive. It is unknown, however, what spatiotemporal patterns may be solely attributed to intrinsic connections of the local cortical network. Using high-density microelectrode arrays, here we show that in the isolated, primary somatosensory cortex of mice, neuronal firing fluctuates on timescales from milliseconds to tens of seconds. Slower firing fluctuations reveal two spatially distinct neuronal ensembles, which correspond to superficial and deeper layers. These ensembles are anti-correlated: when one fires more, the other fires less and vice versa. This interplay is clearest at timescales of several seconds and is therefore consistent with shifts between active sensing and anticipatory behavioral states in mice.

  17. Synapse-centric mapping of cortical models to the SpiNNaker neuromorphic architecture

    Directory of Open Access Journals (Sweden)

    James Courtney Knight

    2016-09-01

    Full Text Available While the adult human brain has approximately 8.8x10^10 neurons, this number is dwarfed by its 1x10^15 synapses. From the point of view of neuromorphic engineering and neural simulation in general this makes the simulation of these synapses a particularly complex problem. SpiNNaker is a digital, neuromorphic architecture designed for simulating large-scale spiking neural networks at speeds close to biological real-time. Current solutions for simulating spiking neural networks on SpiNNaker are heavily inspired by work on distributed high-performance computing. However, while SpiNNaker shares many characteristics with such distributed systems, its component nodes have much more limited resources and, as the system lacks global synchronization, the computation performed on each node must complete within a fixed time step. We first analyze the performance of the current SpiNNaker neural simulation software and identify several problems that occur when it is used to simulate networks of the type often used to model the cortex which contain large numbers of sparsely connected synapses. We then present a new, more flexible approach for mapping the simulation of such networks to SpiNNaker which solves many of these problems. Finally we analyze the performance of our new approach using both benchmarks, designed to represent cortical connectivity, and larger, functional cortical models. In a benchmark network where neurons receive input from 8000 STDP synapses, our new approach allows more neurons to be simulated on each SpiNNaker core than has been previously possible. We also demonstrate that the largest plastic neural network previously simulated on neuromorphic hardware can be run in real time using our new approach: double the speed that was previously achieved. Additionally this network contains two types of plastic synapse which previously had to be trained separately but, using our new approach, can be trained simultaneously.

  18. Altered modular organization of structural cortical networks in children with autism.

    Directory of Open Access Journals (Sweden)

    Feng Shi

    Full Text Available 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.

  19. Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI.

    Science.gov (United States)

    He, Ye; Lim, Sol; Fortunato, Santo; Sporns, Olaf; Zhang, Lei; Qiu, Jiang; Xie, Peng; Zuo, Xi-Nian

    2018-04-01

    Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system.

  20. Cortical networks for encoding near and far space in the non-human primate.

    Science.gov (United States)

    Cléry, Justine; Guipponi, Olivier; Odouard, Soline; Wardak, Claire; Ben Hamed, Suliann

    2018-04-19

    While extra-personal space is often erroneously considered as a unique entity, early neuropsychological studies report a dissociation between near and far space processing both in humans and in monkeys. Here, we use functional MRI in a naturalistic 3D environment to describe the non-human primate near and far space cortical networks. We describe the co-occurrence of two extended functional networks respectively dedicated to near and far space processing. Specifically, far space processing involves occipital, temporal, parietal, posterior cingulate as well as orbitofrontal regions not activated by near space, possibly subserving the processing of the shape and identity of objects. In contrast, near space processing involves temporal, parietal, prefrontal and premotor regions not activated by far space, possibly subserving the preparation of an arm/hand mediated action in this proximal space. Interestingly, this network also involves somatosensory regions, suggesting a cross-modal anticipation of touch by a nearby object. Last, we also describe cortical regions that process both far and near space with a preference for one or the other. This suggests a continuous encoding of relative distance to the body, in the form of a far-to-near gradient. We propose that these cortical gradients in space representation subserve the physically delineable peripersonal spaces described in numerous psychology and psychophysics studies. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. Alterations in Normal Aging Revealed by Cortical Brain Network Constructed Using IBASPM.

    Science.gov (United States)

    Li, Wan; Yang, Chunlan; Shi, Feng; Wang, Qun; Wu, Shuicai; Lu, Wangsheng; Li, Shaowu; Nie, Yingnan; Zhang, Xin

    2018-04-16

    Normal aging has been linked with the decline of cognitive functions, such as memory and executive skills. One of the prominent approaches to investigate the age-related alterations in the brain is by examining the cortical brain connectome. IBASPM is a toolkit to realize individual atlas-based volume measurement. Hence, this study seeks to determine what further alterations can be revealed by cortical brain networks formed by IBASPM-extracted regional gray matter volumes. We found the reduced strength of connections between the superior temporal pole and middle temporal pole in the right hemisphere, global hubs as the left fusiform gyrus and right Rolandic operculum in the young and aging groups, respectively, and significantly reduced inter-module connection of one module in the aging group. These new findings are consistent with the phenomenon of normal aging mentioned in previous studies and suggest that brain network built with the IBASPM could provide supplementary information to some extent. The individualization of morphometric features extraction deserved to be given more attention in future cortical brain network research.

  2. Optogenetic stimulation of a meso-scale human cortical model

    Science.gov (United States)

    Selvaraj, Prashanth; Szeri, Andrew; Sleigh, Jamie; Kirsch, Heidi

    2015-03-01

    Neurological phenomena like sleep and seizures depend not only on the activity of individual neurons, but on the dynamics of neuron populations as well. Meso-scale models of cortical activity provide a means to study neural dynamics at the level of neuron populations. Additionally, they offer a safe and economical way to test the effects and efficacy of stimulation techniques on the dynamics of the cortex. Here, we use a physiologically relevant meso-scale model of the cortex to study the hypersynchronous activity of neuron populations during epileptic seizures. The model consists of a set of stochastic, highly non-linear partial differential equations. Next, we use optogenetic stimulation to control seizures in a hyperexcited cortex, and to induce seizures in a normally functioning cortex. The high spatial and temporal resolution this method offers makes a strong case for the use of optogenetics in treating meso scale cortical disorders such as epileptic seizures. We use bifurcation analysis to investigate the effect of optogenetic stimulation in the meso scale model, and its efficacy in suppressing the non-linear dynamics of seizures.

  3. The Computational Properties of a Simplified Cortical Column Model.

    Science.gov (United States)

    Cain, Nicholas; Iyer, Ramakrishnan; Koch, Christof; Mihalas, Stefan

    2016-09-01

    The mammalian neocortex has a repetitious, laminar structure and performs functions integral to higher cognitive processes, including sensory perception, memory, and coordinated motor output. What computations does this circuitry subserve that link these unique structural elements to their function? Potjans and Diesmann (2014) parameterized a four-layer, two cell type (i.e. excitatory and inhibitory) model of a cortical column with homogeneous populations and cell type dependent connection probabilities. We implement a version of their model using a displacement integro-partial differential equation (DiPDE) population density model. This approach, exact in the limit of large homogeneous populations, provides a fast numerical method to solve equations describing the full probability density distribution of neuronal membrane potentials. It lends itself to quickly analyzing the mean response properties of population-scale firing rate dynamics. We use this strategy to examine the input-output relationship of the Potjans and Diesmann cortical column model to understand its computational properties. When inputs are constrained to jointly and equally target excitatory and inhibitory neurons, we find a large linear regime where the effect of a multi-layer input signal can be reduced to a linear combination of component signals. One of these, a simple subtractive operation, can act as an error signal passed between hierarchical processing stages.

  4. Modeling a space-variant cortical representation for apparent motion.

    Science.gov (United States)

    Wurbs, Jeremy; Mingolla, Ennio; Yazdanbakhsh, Arash

    2013-08-06

    Receptive field sizes of neurons in early primate visual areas increase with eccentricity, as does temporal processing speed. The fovea is evidently specialized for slow, fine movements while the periphery is suited for fast, coarse movements. In either the fovea or periphery discrete flashes can produce motion percepts. Grossberg and Rudd (1989) used traveling Gaussian activity profiles to model long-range apparent motion percepts. We propose a neural model constrained by physiological data to explain how signals from retinal ganglion cells to V1 affect the perception of motion as a function of eccentricity. Our model incorporates cortical magnification, receptive field overlap and scatter, and spatial and temporal response characteristics of retinal ganglion cells for cortical processing of motion. Consistent with the finding of Baker and Braddick (1985), in our model the maximum flash distance that is perceived as an apparent motion (Dmax) increases linearly as a function of eccentricity. Baker and Braddick (1985) made qualitative predictions about the functional significance of both stimulus and visual system parameters that constrain motion perception, such as an increase in the range of detectable motions as a function of eccentricity and the likely role of higher visual processes in determining Dmax. We generate corresponding quantitative predictions for those functional dependencies for individual aspects of motion processing. Simulation results indicate that the early visual pathway can explain the qualitative linear increase of Dmax data without reliance on extrastriate areas, but that those higher visual areas may serve as a modulatory influence on the exact Dmax increase.

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

    Directory of Open Access Journals (Sweden)

    Peng Fang

    Full Text Available 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.

  6. SynGAP regulates protein synthesis and homeostatic synaptic plasticity in developing cortical networks.

    Directory of Open Access Journals (Sweden)

    Chih-Chieh Wang

    Full Text Available Disrupting the balance between excitatory and inhibitory neurotransmission in the developing brain has been causally linked with intellectual disability (ID and autism spectrum disorders (ASD. Excitatory synapse strength is regulated in the central nervous system by controlling the number of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs. De novo genetic mutations of the synaptic GTPase-activating protein (SynGAP are associated with ID and ASD. SynGAP is enriched at excitatory synapses and genetic suppression of SynGAP increases excitatory synaptic strength. However, exactly how SynGAP acts to maintain synaptic AMPAR content is unclear. We show here that SynGAP limits excitatory synaptic strength, in part, by suppressing protein synthesis in cortical neurons. The data presented here from in vitro, rat and mouse cortical networks, demonstrate that regulation of translation by SynGAP involves ERK, mTOR, and the small GTP-binding protein Rheb. Furthermore, these data show that GluN2B-containing NMDARs and the cognitive kinase CaMKII act upstream of SynGAP and that this signaling cascade is required for proper translation-dependent homeostatic synaptic plasticity of excitatory synapses in developing cortical networks.

  7. Modelling computer networks

    International Nuclear Information System (INIS)

    Max, G

    2011-01-01

    Traffic models in computer networks can be described as a complicated system. These systems show non-linear features and to simulate behaviours of these systems are also difficult. Before implementing network equipments users wants to know capability of their computer network. They do not want the servers to be overloaded during temporary traffic peaks when more requests arrive than the server is designed for. As a starting point for our study a non-linear system model of network traffic is established to exam behaviour of the network planned. The paper presents setting up a non-linear simulation model that helps us to observe dataflow problems of the networks. This simple model captures the relationship between the competing traffic and the input and output dataflow. In this paper, we also focus on measuring the bottleneck of the network, which was defined as the difference between the link capacity and the competing traffic volume on the link that limits end-to-end throughput. We validate the model using measurements on a working network. The results show that the initial model estimates well main behaviours and critical parameters of the network. Based on this study, we propose to develop a new algorithm, which experimentally determines and predict the available parameters of the network modelled.

  8. Comparing Intrinsic Connectivity Models for the Primary Auditory Cortices

    Science.gov (United States)

    Hamid, Khairiah Abdul; Yusoff, Ahmad Nazlim; Mohamad, Mazlyfarina; Hamid, Aini Ismafairus Abd; Manan, Hanani Abd

    2010-07-01

    This fMRI study is about modeling the intrinsic connectivity between Heschl' gyrus (HG) and superior temporal gyrus (STG) in human primary auditory cortices. Ten healthy male subjects participated and required to listen to white noise stimulus during the fMRI scans. Two intrinsic connectivity models comprising bilateral HG and STG were constructed using statistical parametric mapping (SPM) and dynamic causal modeling (DCM). Group Bayes factor (GBF), positive evidence ratio (PER) and Bayesian model selection (BMS) for group studies were used in model comparison. Group results indicated significant bilateral asymmetrical activation (puncorr < 0.001) in HG and STG. Comparison results showed strong evidence of Model 2 as the preferred model (STG as the input center) with GBF value of 5.77 × 1073 The model is preferred by 6 out of 10 subjects. The results were supported by BMS results for group studies. One-sample t-test on connection values obtained from Model 2 indicates unidirectional parallel connections from STG to bilateral HG (p<0.05). Model 2 was determined to be the most probable intrinsic connectivity model between bilateral HG and STG when listening to white noise.

  9. Network Supervision of Adult Experience and Learning Dependent Sensory Cortical Plasticity.

    Science.gov (United States)

    Blake, David T

    2017-06-18

    The brain is capable of remodeling throughout life. The sensory cortices provide a useful preparation for studying neuroplasticity both during development and thereafter. In adulthood, sensory cortices change in the cortical area activated by behaviorally relevant stimuli, by the strength of response within that activated area, and by the temporal profiles of those responses. Evidence supports forms of unsupervised, reinforcement, and fully supervised network learning rules. Studies on experience-dependent plasticity have mostly not controlled for learning, and they find support for unsupervised learning mechanisms. Changes occur with greatest ease in neurons containing α-CamKII, which are pyramidal neurons in layers II/III and layers V/VI. These changes use synaptic mechanisms including long term depression. Synaptic strengthening at NMDA-containing synapses does occur, but its weak association with activity suggests other factors also initiate changes. Studies that control learning find support of reinforcement learning rules and limited evidence of other forms of supervised learning. Behaviorally associating a stimulus with reinforcement leads to a strengthening of cortical response strength and enlarging of response area with poor selectivity. Associating a stimulus with omission of reinforcement leads to a selective weakening of responses. In some preparations in which these associations are not as clearly made, neurons with the most informative discharges are relatively stronger after training. Studies analyzing the temporal profile of responses associated with omission of reward, or of plasticity in studies with different discriminanda but statistically matched stimuli, support the existence of limited supervised network learning. © 2017 American Physiological Society. Compr Physiol 7:977-1008, 2017. Copyright © 2017 John Wiley & Sons, Inc.

  10. Initiation of electrographic seizures by neuronal networks in entorhinal and perirhinal cortices in vitro.

    Science.gov (United States)

    de Guzman, P; D'Antuono, M; Avoli, M

    2004-01-01

    The hippocampus is often considered to play a major role in the pathophysiology of mesial temporal lobe epilepsy. However, emerging clinical and experimental evidence suggests that parahippocampal areas may contribute to a greater extent to limbic seizure initiation, and perhaps epileptogenesis. To date, little is known about the participation of entorhinal and perirhinal networks to epileptiform synchronization. Here, we addressed this issue by using simultaneous field potential recordings in horizontal rat brain slices containing interconnected limbic structures that included the hippocampus proper. Epileptiform discharges were disclosed by bath applying the convulsant drug 4-aminopyridine (50 microM) or by superfusing Mg(2+)-free medium. In the presence of 4-aminopyridine, slow interictal- (duration=2.34+/-0.29 s; interval of occurrence=25.75+/-2.11 s, n=16) and ictal-like (duration=31.25+/-3.34 s; interval of occurrence=196.96+/-21.56 s, n=17) discharges were recorded in entorhinal and perirhinal cortices after abating the propagation of CA3-driven interictal activity to these areas following extended hippocampal knife cuts. Simultaneous recordings obtained from the medial and lateral entorhinal cortex, and from the perirhinal cortex revealed that interictal and ictal discharges could initiate from any of these areas and propagate to the neighboring structure with delays of 8-66 ms. However, slow interictal- and ictal-like events more often originated in the medial entorhinal cortex and perirhinal cortex, respectively. Cutting the connections between entorhinal and perirhinal cortices (n=10), or functional inactivation of cortical areas by local application of a glutamatergic receptor antagonist (n=11) made independent epileptiform activity occur in all areas. These procedures also shortened ictal discharge duration in the entorhinal cortices, but not in the perirhinal area. Similar results could be obtained by applying Mg(2+)-free medium (n=7). These findings

  11. Depth-dependent flow and pressure characteristics in cortical microvascular networks.

    Directory of Open Access Journals (Sweden)

    Franca Schmid

    2017-02-01

    Full Text Available A better knowledge of the flow and pressure distribution in realistic microvascular networks is needed for improving our understanding of neurovascular coupling mechanisms and the related measurement techniques. Here, numerical simulations with discrete tracking of red blood cells (RBCs are performed in three realistic microvascular networks from the mouse cerebral cortex. Our analysis is based on trajectories of individual RBCs and focuses on layer-specific flow phenomena until a cortical depth of 1 mm. The individual RBC trajectories reveal that in the capillary bed RBCs preferentially move in plane. Hence, the capillary flow field shows laminar patterns and a layer-specific analysis is valid. We demonstrate that for RBCs entering the capillary bed close to the cortical surface (< 400 μm the largest pressure drop takes place in the capillaries (37%, while for deeper regions arterioles are responsible for 61% of the total pressure drop. Further flow characteristics, such as capillary transit time or RBC velocity, also vary significantly over cortical depth. Comparison of purely topological characteristics with flow-based ones shows that a combined interpretation of topology and flow is indispensable. Our results provide evidence that it is crucial to consider layer-specific differences for all investigations related to the flow and pressure distribution in the cortical vasculature. These findings support the hypothesis that for an efficient oxygen up-regulation at least two regulation mechanisms must be playing hand in hand, namely cerebral blood flow increase and microvascular flow homogenization. However, the contribution of both regulation mechanisms to oxygen up-regulation likely varies over depth.

  12. Modeling the citation network by network cosmology.

    Science.gov (United States)

    Xie, Zheng; Ouyang, Zhenzheng; Zhang, Pengyuan; Yi, Dongyun; Kong, Dexing

    2015-01-01

    Citation between papers can be treated as a causal relationship. In addition, some citation networks have a number of similarities to the causal networks in network cosmology, e.g., the similar in-and out-degree distributions. Hence, it is possible to model the citation network using network cosmology. The casual network models built on homogenous spacetimes have some restrictions when describing some phenomena in citation networks, e.g., the hot papers receive more citations than other simultaneously published papers. We propose an inhomogenous causal network model to model the citation network, the connection mechanism of which well expresses some features of citation. The node growth trend and degree distributions of the generated networks also fit those of some citation networks well.

  13. Brain Network Modelling

    DEFF Research Database (Denmark)

    Andersen, Kasper Winther

    Three main topics are presented in this thesis. The first and largest topic concerns network modelling of functional Magnetic Resonance Imaging (fMRI) and Diffusion Weighted Imaging (DWI). In particular nonparametric Bayesian methods are used to model brain networks derived from resting state f...... for their ability to reproduce node clustering and predict unseen data. Comparing the models on whole brain networks, BCD and IRM showed better reproducibility and predictability than IDM, suggesting that resting state networks exhibit community structure. This also points to the importance of using models, which...... allow for complex interactions between all pairs of clusters. In addition, it is demonstrated how the IRM can be used for segmenting brain structures into functionally coherent clusters. A new nonparametric Bayesian network model is presented. The model builds upon the IRM and can be used to infer...

  14. Neuroimaging in human MDMA (Ecstasy) users: A cortical model

    Science.gov (United States)

    Cowan, Ronald L; Roberts, Deanne M; Joers, James M

    2009-01-01

    MDMA (3,4 methylenedioxymethamphetamine) has been used by millions of people worldwide as a recreational drug. MDMA and Ecstasy are often used synonymously but it is important to note that the purity of Ecstasy sold as MDMA is not certain. MDMA use is of public health concern, not so much because MDMA produces a common or severe dependence syndrome, but rather because rodent and non-human primate studies have indicated that MDMA (when administered at certain dosages and intervals) can cause long-lasting reductions in markers of brain serotonin (5-HT) that appear specific to fine diameter axons arising largely from the dorsal raphe nucleus (DR). Given the popularity of MDMA, the potential for the drug to produce long-lasting or permanent 5-HT axon damage or loss, and the widespread role of 5-HT function in the brain, there is a great need for a better understanding of brain function in human users of this drug. To this end, neuropsychological, neuroendocrine, and neuroimaging studies have all suggested that human MDMA users may have long-lasting changes in brain function consistent with 5-HT toxicity. Data from animal models leads to testable hypotheses regarding MDMA effects on the human brain. Because neuropsychological and neuroimaging findings have focused on the neocortex, a cortical model is developed to provide context for designing and interpreting neuroimaging studies in MDMA users. Aspects of the model are supported by the available neuroimaging data but there are controversial findings in some areas and most findings have not been replicated across different laboratories and using different modalities. This paper reviews existing findings in the context of a cortical model and suggests directions for future research. PMID:18991874

  15. Modeling Epidemic Network Failures

    DEFF Research Database (Denmark)

    Ruepp, Sarah Renée; Fagertun, Anna Manolova

    2013-01-01

    This paper presents the implementation of a failure propagation model for transport networks when multiple failures occur resulting in an epidemic. We model the Susceptible Infected Disabled (SID) epidemic model and validate it by comparing it to analytical solutions. Furthermore, we evaluate...... the SID model’s behavior and impact on the network performance, as well as the severity of the infection spreading. The simulations are carried out in OPNET Modeler. The model provides an important input to epidemic connection recovery mechanisms, and can due to its flexibility and versatility be used...... to evaluate multiple epidemic scenarios in various network types....

  16. Early magnetic resonance detection of cortical necrosis and acute network injury associated with neonatal and infantile cerebral infarction

    Energy Technology Data Exchange (ETDEWEB)

    Okabe, Tetsuhiko; Aida, Noriko; Nozawa, Kumiko [Kanagawa Children' s Medical Center, Department of Radiology, Yokohama (Japan); Niwa, Tetsu [Kanagawa Children' s Medical Center, Department of Radiology, Yokohama (Japan); Tokai University School of Medicine, Department of Radiology, Isehara (Japan); Shibasaki, Jun [Kanagawa Children' s Medical Center, Department of Neonatology, Yokohama (Japan); Osaka, Hitoshi [Kanagawa Children' s Medical Center, Department of Neurology, Yokohama (Japan)

    2014-05-15

    Knowledge of MRI findings in pediatric cerebral infarction is limited. To determine whether cortical necrosis and network injury appear in the acute phase in post-stroke children and to identify anatomical location of acute network injury and the ages at which these phenomena are seen. Images from 12 children (age range: 0-9 years; neonates [<1 month], n=5; infants [1 month-12 months], n=3; others [≥1 year], n=4) with acute middle cerebral artery (MCA) cortical infarction were retrospectively analyzed. Cortical necrosis was defined as hyperintense cortical lesions on T1-weighted imaging that lacked evidence of hemorrhage. Acute network injury was defined as hyperintense lesions on diffusion-weighted imaging that were not in the MCA territory and had fiber connections with the affected cerebral cortex. MRI was performed within the first week after disease onset. Cortical necrosis was only found in three neonates. Acute network injury was seen in the corticospinal tract (CST), thalamus and corpus callosum. Acute network injury along the CST was found in five neonates and one 7-month-old infant. Acute network injury was evident in the thalamus of four neonates and two infants (ages 4 and 7 months) and in the corpus callosum of five neonates and two infants (ages 4 and 7 months). The entire thalamus was involved in three children when infarction of MCA was complete. In acute MCA cortical infarction, MRI findings indicating cortical necrosis or acute network injury was frequently found in neonates and early infants. Response to injury in a developing brain may be faster than that in a mature one. (orig.)

  17. Early magnetic resonance detection of cortical necrosis and acute network injury associated with neonatal and infantile cerebral infarction

    International Nuclear Information System (INIS)

    Okabe, Tetsuhiko; Aida, Noriko; Nozawa, Kumiko; Niwa, Tetsu; Shibasaki, Jun; Osaka, Hitoshi

    2014-01-01

    Knowledge of MRI findings in pediatric cerebral infarction is limited. To determine whether cortical necrosis and network injury appear in the acute phase in post-stroke children and to identify anatomical location of acute network injury and the ages at which these phenomena are seen. Images from 12 children (age range: 0-9 years; neonates [<1 month], n=5; infants [1 month-12 months], n=3; others [≥1 year], n=4) with acute middle cerebral artery (MCA) cortical infarction were retrospectively analyzed. Cortical necrosis was defined as hyperintense cortical lesions on T1-weighted imaging that lacked evidence of hemorrhage. Acute network injury was defined as hyperintense lesions on diffusion-weighted imaging that were not in the MCA territory and had fiber connections with the affected cerebral cortex. MRI was performed within the first week after disease onset. Cortical necrosis was only found in three neonates. Acute network injury was seen in the corticospinal tract (CST), thalamus and corpus callosum. Acute network injury along the CST was found in five neonates and one 7-month-old infant. Acute network injury was evident in the thalamus of four neonates and two infants (ages 4 and 7 months) and in the corpus callosum of five neonates and two infants (ages 4 and 7 months). The entire thalamus was involved in three children when infarction of MCA was complete. In acute MCA cortical infarction, MRI findings indicating cortical necrosis or acute network injury was frequently found in neonates and early infants. Response to injury in a developing brain may be faster than that in a mature one. (orig.)

  18. Early magnetic resonance detection of cortical necrosis and acute network injury associated with neonatal and infantile cerebral infarction.

    Science.gov (United States)

    Okabe, Tetsuhiko; Aida, Noriko; Niwa, Tetsu; Nozawa, Kumiko; Shibasaki, Jun; Osaka, Hitoshi

    2014-05-01

    Knowledge of MRI findings in pediatric cerebral infarction is limited. To determine whether cortical necrosis and network injury appear in the acute phase in post-stroke children and to identify anatomical location of acute network injury and the ages at which these phenomena are seen. Images from 12 children (age range: 0-9 years; neonates [acute middle cerebral artery (MCA) cortical infarction were retrospectively analyzed. Cortical necrosis was defined as hyperintense cortical lesions on T1-weighted imaging that lacked evidence of hemorrhage. Acute network injury was defined as hyperintense lesions on diffusion-weighted imaging that were not in the MCA territory and had fiber connections with the affected cerebral cortex. MRI was performed within the first week after disease onset. Cortical necrosis was only found in three neonates. Acute network injury was seen in the corticospinal tract (CST), thalamus and corpus callosum. Acute network injury along the CST was found in five neonates and one 7-month-old infant. Acute network injury was evident in the thalamus of four neonates and two infants (ages 4 and 7 months) and in the corpus callosum of five neonates and two infants (ages 4 and 7 months). The entire thalamus was involved in three children when infarction of MCA was complete. In acute MCA cortical infarction, MRI findings indicating cortical necrosis or acute network injury was frequently found in neonates and early infants. Response to injury in a developing brain may be faster than that in a mature one.

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

    Science.gov (United States)

    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. Copyright © 2013 John Wiley & Sons, Ltd.

  20. Differential Motor and Prefrontal Cerebello-Cortical Network Development: Evidence from Multimodal Neuroimaging

    Science.gov (United States)

    Bernard, Jessica A.; Orr, Joseph M.; Mittal, Vijay A.

    2015-01-01

    While our understanding of cerebellar structural development through adolescence and young adulthood has expanded, we still lack knowledge of the developmental patterns of cerebellar networks during this critical portion of the lifespan. Volume in lateral posterior cerebellar regions associated with cognition and the prefrontal cortex develops more slowly, reaching their peak volume in adulthood, particularly as compared to motor Lobule V. We predicted that resting state functional connectivity of the lateral posterior regions would show a similar pattern of development during adolescence and young adulthood. That is, we expected to see changes over time in Crus I and Crus II connectivity with the cortex, but no changes in Lobule V connectivity. Additionally, we were interested in how structural connectivity changes in cerebello-thalamo-cortical white matter are related to changes in functional connectivity. A sample of 23 individuals between 12 and 21 years old underwent neuroimaging scans at baseline and 12-months later. Functional networks of Crus I and Crus II showed significant connectivity decreases over 12-months, though there were no differences in Lobule V. Furthermore, these functional connectivity changes were correlated with increases in white matter structural integrity in the corresponding cerebello-thalamo-cortical white matter tract. We suggest that these functional network changes are due to both later pruning in the prefrontal cortex as well as further development of the white matter tracts linking these brain regions. PMID:26391125

  1. Basal ganglia and cortical networks for sequential ordering and rhythm of complex movements

    Directory of Open Access Journals (Sweden)

    Jeffery G. Bednark

    2015-07-01

    Full Text Available Voluntary actions require the concurrent engagement and coordinated control of complex temporal (e.g. rhythm and ordinal motor processes. Using high-resolution functional magnetic resonance imaging (fMRI and multi-voxel pattern analysis (MVPA, we sought to determine the degree to which these complex motor processes are dissociable in basal ganglia and cortical networks. We employed three different finger-tapping tasks that differed in the demand on the sequential temporal rhythm or sequential ordering of submovements. Our results demonstrate that sequential rhythm and sequential order tasks were partially dissociable based on activation differences. The sequential rhythm task activated a widespread network centered around the SMA and basal-ganglia regions including the dorsomedial putamen and caudate nucleus, while the sequential order task preferentially activated a fronto-parietal network. There was also extensive overlap between sequential rhythm and sequential order tasks, with both tasks commonly activating bilateral premotor, supplementary motor, and superior/inferior parietal cortical regions, as well as regions of the caudate/putamen of the basal ganglia and the ventro-lateral thalamus. Importantly, within the cortical regions that were active for both complex movements, MVPA could accurately classify different patterns of activation for the sequential rhythm and sequential order tasks. In the basal ganglia, however, overlapping activation for the sequential rhythm and sequential order tasks, which was found in classic motor circuits of the putamen and ventro-lateral thalamus, could not be accurately differentiated by MVPA. Overall, our results highlight the convergent architecture of the motor system, where complex motor information that is spatially distributed in the cortex converges into a more compact representation in the basal ganglia.

  2. Structural Covariance Network of Cortical Gyrification in Benign Childhood Epilepsy with Centrotemporal Spikes

    Directory of Open Access Journals (Sweden)

    Lin Jiang

    2018-02-01

    Full Text Available Benign childhood epilepsy with centrotemporal spikes (BECTS is associated with cognitive and language problems. According to recent studies, disruptions in brain structure and function in children with BECTS are beyond a Rolandic focus, suggesting atypical cortical development. However, previous studies utilizing surface-based metrics (e.g., cortical gyrification and their structural covariance networks at high resolution in children with BECTS are limited. Twenty-six children with BECTS (15 males/11 females; 10.35 ± 2.91 years and 26 demographically matched controls (15 males/11 females; 11.35 ± 2.51 years were included in this study and subjected to high-resolution structural brain MRI scans. The gyrification index was calculated, and structural brain networks were reconstructed based on the covariance of the cortical folding. In the BECTS group, significantly increased gyrification was observed in the bilateral Sylvain fissures and the left pars triangularis, temporal, rostral middle frontal, lateral orbitofrontal, and supramarginal areas (cluster-corrected p < 0.05. Global brain network measures were not significantly different between the groups; however, the nodal alterations were most pronounced in the insular, frontal, temporal, and occipital lobes (FDR corrected, p < 0.05. In children with BECTS, brain hubs increased in number and tended to shift to sensorimotor and temporal areas. Furthermore, we observed significantly positive relationships between the gyrification index and age (vertex p < 0.001, cluster-level correction as well as duration of epilepsy (vertex p < 0.001, cluster-level correction. Our results suggest that BECTS may be a condition that features abnormal over-folding of the Sylvian fissures and uncoordinated development of structural wiring, disrupted nodal profiles of centrality, and shifted hub distribution, which potentially represents a neuroanatomical hallmark of BECTS in the

  3. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  4. Role of altered cerebello-thalamo-cortical network in the neurobiology of essential tremor

    Energy Technology Data Exchange (ETDEWEB)

    Lenka, Abhishek; Bhalsing, Ketaki Swapnil; Jhunjhunwala, Ketan [National Institute of Mental Health and Neurosciences, Department of Neurology, Bangalore, Karnataka (India); National Institute of Mental Health and Neurosciences, Department of Clinical Neurosciences, Bangalore, Karnataka (India); Panda, Rajanikant; Saini, Jitender; Bharath, Rose Dawn [National Institute of Mental Health and Neurosciences, Department of Neuroimaging and Interventional Radiology, Bangalore, Karnataka (India); Naduthota, Rajini M.; Yadav, Ravi; Pal, Pramod Kumar [National Institute of Mental Health and Neurosciences, Department of Neurology, Bangalore, Karnataka (India)

    2017-02-15

    Essential tremor (ET) is the most common movement disorder among adults. Although ET has been recognized as a mono-symptomatic benign illness, reports of non-motor symptoms and non-tremor motor symptoms have increased its clinical heterogeneity. The neural correlates of ET are not clearly understood. The aim of this study was to understand the neurobiology of ET using resting state fMRI. Resting state functional MR images of 30 patients with ET and 30 age- and gender-matched healthy controls were obtained. The functional connectivity of the two groups was compared using whole-brain seed-to-voxel-based analysis. The ET group had decreased connectivity of several cortical regions especially of the primary motor cortex and the primary somatosensory cortex with several right cerebellar lobules compared to the controls. The thalamus on both hemispheres had increased connectivity with multiple posterior cerebellar lobules and vermis. Connectivity of several right cerebellar seeds with the cortical and thalamic seeds had significant correlation with an overall score of Fahn-Tolosa-Marin tremor rating scale (FTM-TRS) as well as the subscores for head tremor and limb tremor. Seed-to-voxel resting state connectivity analysis revealed significant alterations in the cerebello-thalamo-cortical network in patients with ET. These alterations correlated with the overall FTM scores as well as the subscores for limb tremor and head tremor in patients with ET. These results further support the previous evidence of cerebellar pathology in ET. (orig.)

  5. Role of altered cerebello-thalamo-cortical network in the neurobiology of essential tremor

    International Nuclear Information System (INIS)

    Lenka, Abhishek; Bhalsing, Ketaki Swapnil; Jhunjhunwala, Ketan; Panda, Rajanikant; Saini, Jitender; Bharath, Rose Dawn; Naduthota, Rajini M.; Yadav, Ravi; Pal, Pramod Kumar

    2017-01-01

    Essential tremor (ET) is the most common movement disorder among adults. Although ET has been recognized as a mono-symptomatic benign illness, reports of non-motor symptoms and non-tremor motor symptoms have increased its clinical heterogeneity. The neural correlates of ET are not clearly understood. The aim of this study was to understand the neurobiology of ET using resting state fMRI. Resting state functional MR images of 30 patients with ET and 30 age- and gender-matched healthy controls were obtained. The functional connectivity of the two groups was compared using whole-brain seed-to-voxel-based analysis. The ET group had decreased connectivity of several cortical regions especially of the primary motor cortex and the primary somatosensory cortex with several right cerebellar lobules compared to the controls. The thalamus on both hemispheres had increased connectivity with multiple posterior cerebellar lobules and vermis. Connectivity of several right cerebellar seeds with the cortical and thalamic seeds had significant correlation with an overall score of Fahn-Tolosa-Marin tremor rating scale (FTM-TRS) as well as the subscores for head tremor and limb tremor. Seed-to-voxel resting state connectivity analysis revealed significant alterations in the cerebello-thalamo-cortical network in patients with ET. These alterations correlated with the overall FTM scores as well as the subscores for limb tremor and head tremor in patients with ET. These results further support the previous evidence of cerebellar pathology in ET. (orig.)

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

    Science.gov (United States)

    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.

  7. Formation and Dynamics of Waves in a Cortical Model of Cholinergic Modulation.

    Directory of Open Access Journals (Sweden)

    James P Roach

    2015-08-01

    Full Text Available Acetylcholine (ACh is a regulator of neural excitability and one of the neurochemical substrates of sleep. Amongst the cellular effects induced by cholinergic modulation are a reduction in spike-frequency adaptation (SFA and a shift in the phase response curve (PRC. We demonstrate in a biophysical model how changes in neural excitability and network structure interact to create three distinct functional regimes: localized asynchronous, traveling asynchronous, and traveling synchronous. Our results qualitatively match those observed experimentally. Cortical activity during slow wave sleep (SWS differs from that during REM sleep or waking states. During SWS there are traveling patterns of activity in the cortex; in other states stationary patterns occur. Our model is a network composed of Hodgkin-Huxley type neurons with a M-current regulated by ACh. Regulation of ACh level can account for dynamical changes between functional regimes. Reduction of the magnitude of this current recreates the reduction in SFA the shift from a type 2 to a type 1 PRC observed in the presence of ACh. When SFA is minimal (in waking or REM sleep state, high ACh patterns of activity are localized and easily pinned by network inhomogeneities. When SFA is present (decreasing ACh, traveling waves of activity naturally arise. A further decrease in ACh leads to a high degree of synchrony within traveling waves. We also show that the level of ACh determines how sensitive network activity is to synaptic heterogeneity. These regimes may have a profound functional significance as stationary patterns may play a role in the proper encoding of external input as memory and traveling waves could lead to synaptic regularization, giving unique insights into the role and significance of ACh in determining patterns of cortical activity and functional differences arising from the patterns.

  8. Cortical networks dynamically emerge with the interplay of slow and fast oscillations for memory of a natural scene.

    Science.gov (United States)

    Mizuhara, Hiroaki; Sato, Naoyuki; Yamaguchi, Yoko

    2015-05-01

    Neural oscillations are crucial for revealing dynamic cortical networks and for serving as a possible mechanism of inter-cortical communication, especially in association with mnemonic function. The interplay of the slow and fast oscillations might dynamically coordinate the mnemonic cortical circuits to rehearse stored items during working memory retention. We recorded simultaneous EEG-fMRI during a working memory task involving a natural scene to verify whether the cortical networks emerge with the neural oscillations for memory of the natural scene. The slow EEG power was enhanced in association with the better accuracy of working memory retention, and accompanied cortical activities in the mnemonic circuits for the natural scene. Fast oscillation showed a phase-amplitude coupling to the slow oscillation, and its power was tightly coupled with the cortical activities for representing the visual images of natural scenes. The mnemonic cortical circuit with the slow neural oscillations would rehearse the distributed natural scene representations with the fast oscillation for working memory retention. The coincidence of the natural scene representations could be obtained by the slow oscillation phase to create a coherent whole of the natural scene in the working memory. Copyright © 2015 Elsevier Inc. All rights reserved.

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

    Directory of Open Access Journals (Sweden)

    Yung-Hao Wong

    2016-02-01

    Full Text Available 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.

  10. Trading speed and accuracy by coding time: a coupled-circuit cortical model.

    Directory of Open Access Journals (Sweden)

    Dominic Standage

    2013-04-01

    Full Text Available Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by 'climbing' activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification.

  11. Pragmatics in action: indirect requests engage theory of mind areas and the cortical motor network.

    Science.gov (United States)

    van Ackeren, Markus J; Casasanto, Daniel; Bekkering, Harold; Hagoort, Peter; Rueschemeyer, Shirley-Ann

    2012-11-01

    Research from the past decade has shown that understanding the meaning of words and utterances (i.e., abstracted symbols) engages the same systems we used to perceive and interact with the physical world in a content-specific manner. For example, understanding the word "grasp" elicits activation in the cortical motor network, that is, part of the neural substrate involved in planned and executing a grasping action. In the embodied literature, cortical motor activation during language comprehension is thought to reflect motor simulation underlying conceptual knowledge [note that outside the embodied framework, other explanations for the link between action and language are offered, e.g., Mahon, B. Z., & Caramazza, A. A critical look at the embodied cognition hypothesis and a new proposal for grouding conceptual content. Journal of Physiology, 102, 59-70, 2008; Hagoort, P. On Broca, brain, and binding: A new framework. Trends in Cognitive Sciences, 9, 416-423, 2005]. Previous research has supported the view that the coupling between language and action is flexible, and reading an action-related word form is not sufficient for cortical motor activation [Van Dam, W. O., van Dijk, M., Bekkering, H., & Rueschemeyer, S.-A. Flexibility in embodied lexical-semantic representations. Human Brain Mapping, doi: 10.1002/hbm.21365, 2011]. The current study goes one step further by addressing the necessity of action-related word forms for motor activation during language comprehension. Subjects listened to indirect requests (IRs) for action during an fMRI session. IRs for action are speech acts in which access to an action concept is required, although it is not explicitly encoded in the language. For example, the utterance "It is hot here!" in a room with a window is likely to be interpreted as a request to open the window. However, the same utterance in a desert will be interpreted as a statement. The results indicate (1) that comprehension of IR sentences activates cortical

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

    Science.gov (United States)

    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

  13. Modeling vocalization with ECoG cortical activity recorded during vocal production in the macaque monkey.

    Science.gov (United States)

    Fukushima, Makoto; Saunders, Richard C; Fujii, Naotaka; Averbeck, Bruno B; Mishkin, Mortimer

    2014-01-01

    Vocal production is an example of controlled motor behavior with high temporal precision. Previous studies have decoded auditory evoked cortical activity while monkeys listened to vocalization sounds. On the other hand, there have been few attempts at decoding motor cortical activity during vocal production. Here we recorded cortical activity during vocal production in the macaque with a chronically implanted electrocorticographic (ECoG) electrode array. The array detected robust activity in motor cortex during vocal production. We used a nonlinear dynamical model of the vocal organ to reduce the dimensionality of `Coo' calls produced by the monkey. We then used linear regression to evaluate the information in motor cortical activity for this reduced representation of calls. This simple linear model accounted for circa 65% of the variance in the reduced sound representations, supporting the feasibility of using the dynamical model of the vocal organ for decoding motor cortical activity during vocal production.

  14. Effects of Stress and Task Difficulty on Working Memory and Cortical Networking.

    Science.gov (United States)

    Kim, Yujin; Woo, Jihwan; Woo, Minjung

    2017-12-01

    This study investigated interactive effects of stress and task difficulty on working memory and cortico-cortical communication during memory encoding. Thirty-eight adolescent participants (mean age of 15.7 ± 1.5 years) completed easy and hard working memory tasks under low- and high-stress conditions. We analyzed the accuracy and reaction time (RT) of working memory performance and inter- and intrahemispheric electroencephalogram coherences during memory encoding. Working memory accuracy was higher, and RT shorter, in the easy versus the hard task. RT was shorter under the high-stress (TENS) versus low-stress (no-TENS) condition, while there was no difference in memory accuracy between the two stress conditions. For electroencephalogram coherence, we found higher interhemispheric coherence in all bands but only at frontal electrode sites in the easy versus the hard task. On the other hand, intrahemispheric coherence was higher in the left hemisphere in the easy (versus hard task) and higher in the right hemisphere (with one exception) in the hard (versus easy task). Inter- and intracoherences were higher in the low- versus high-stress condition. Significant interactions between task difficulty and stress condition were observed in coherences of the beta frequency band. The difference in coherence between low- and high-stress conditions was greater in the hard compared with the easy task, with lower coherence under the high-stress condition relative to the low-stress condition. Stress seemed to cause a decrease in cortical network communications between memory-relevant cortical areas as task difficulty increased.

  15. Fast oscillations in cortical-striatal networks switch frequency following rewarding events and stimulant drugs.

    Science.gov (United States)

    Berke, J D

    2009-09-01

    Oscillations may organize communication between components of large-scale brain networks. Although gamma-band oscillations have been repeatedly observed in cortical-basal ganglia circuits, their functional roles are not yet clear. Here I show that, in behaving rats, distinct frequencies of ventral striatal local field potential oscillations show coherence with different cortical inputs. The approximately 50 Hz gamma oscillations that normally predominate in awake ventral striatum are coherent with piriform cortex, whereas approximately 80-100 Hz high-gamma oscillations are coherent with frontal cortex. Within striatum, entrainment to gamma rhythms is selective to fast-spiking interneurons, with distinct fast-spiking interneuron populations entrained to different gamma frequencies. Administration of the psychomotor stimulant amphetamine or the dopamine agonist apomorphine causes a prolonged decrease in approximately 50 Hz power and increase in approximately 80-100 Hz power. The same frequency switch is observed for shorter epochs spontaneously in awake, undrugged animals and is consistently provoked for reward receipt. Individual striatal neurons can participate in these brief high-gamma bursts with, or without, substantial changes in firing rate. Switching between discrete oscillatory states may allow different modes of information processing during decision-making and reinforcement-based learning, and may also be an important systems-level process by which stimulant drugs affect cognition and behavior.

  16. Cortical atrophy and language network reorganization associated with a novel progranulin mutation.

    Science.gov (United States)

    Cruchaga, Carlos; Fernández-Seara, Maria A; Seijo-Martínez, Manuel; Samaranch, Lluis; Lorenzo, Elena; Hinrichs, Anthony; Irigoyen, Jaione; Maestro, Cristina; Prieto, Elena; Martí-Climent, Josep M; Arbizu, Javier; Pastor, Maria A; Pastor, Pau

    2009-08-01

    Progressive nonfluent aphasia (PNFA) is an early stage of frontotemporal degeneration. We identified a novel Cys521Tyr progranulin gene variant in a PNFA family that potentially disrupts disulphide bridging causing protein misfolding. To identify early neurodegeneration changes, we performed neuropsychological and neuroimaging studies in 6 family members (MRI [magnetic resonance imaging], fMRI [functional MRI], and 18f-fluorodeoxygenlucose positron emission tomography, including 4 mutation carriers, and in 9 unrelated controls. Voxel-based morphometry (VBM) of the carriers compared with controls showed significant cortical atrophy in language areas. Grey matter loss was distributed mainly in frontal lobes, being more prominent on the left. Clusters were located in the superior frontal gyri, left inferior frontal gyrus, left middle frontal gyrus, left middle temporal gyri and left posterior parietal areas, concordant with (18)FDG-PET hypometabolic areas. fMRI during semantic and phonemic covert word generation (CWGTs) and word listening tasks (WLTs) showed recruitment of attentional and working memory networks in the carriers indicative of functional reorganization. During CWGTs, activation in left prefrontal cortex and bilateral anterior insulae was present whereas WLT recruited mesial prefrontal and anterior temporal cortex. These findings suggest that Cys521Tyr could be associated with early brain impairment not limited to language areas and compensated by recruitment of bilateral auxiliary cortical areas.

  17. Modeling Pediatric Brain Trauma: Piglet Model of Controlled Cortical Impact.

    Science.gov (United States)

    Pareja, Jennifer C Munoz; Keeley, Kristen; Duhaime, Ann-Christine; Dodge, Carter P

    2016-01-01

    The brain has different responses to traumatic injury as a function of its developmental stage. As a model of injury to the immature brain, the piglet shares numerous similarities in regards to morphology and neurodevelopmental sequence compared to humans. This chapter describes a piglet scaled focal contusion model of traumatic brain injury that accounts for the changes in mass and morphology of the brain as it matures, facilitating the study of age-dependent differences in response to a comparable mechanical trauma.

  18. Modelling of Cortical Bone Tissue as a Fluid Saturated Double-Porous Material - Parametric Study

    Directory of Open Access Journals (Sweden)

    Jana TURJANICOVÁ

    2013-06-01

    Full Text Available In this paper, the cortical bone tissue is considered as a poroelastic material with periodic structure represented at microscopic and mesoscopic levels. The pores of microscopic scale are connected with the pores of mesoscopic scale creating one system of connected network filled with compressible fluid. The method of asymptotic homogenization is applied to upscale the microscopic model of the fluid-solid interaction under a static loading. Obtained homogenized coefficients describe material properties of the poroelastic matrix fractured by fluid-filled pores whose geometry is described at the mesoscopic level. The second-level upscaling provides homogenized poroelastic coefficients relevant on the macroscopic scale. Furthermore, we study the dependence of these coefficients on geometrical parameters on related microscopic and macroscopic scales.

  19. Synaptic integration of transplanted interneuron progenitor cells into native cortical networks.

    Science.gov (United States)

    Howard, MacKenzie A; Baraban, Scott C

    2016-08-01

    Interneuron-based cell transplantation is a powerful method to modify network function in a variety of neurological disorders, including epilepsy. Whether new interneurons integrate into native neural networks in a subtype-specific manner is not well understood, and the therapeutic mechanisms underlying interneuron-based cell therapy, including the role of synaptic inhibition, are debated. In this study, we tested subtype-specific integration of transplanted interneurons using acute cortical brain slices and visualized patch-clamp recordings to measure excitatory synaptic inputs, intrinsic properties, and inhibitory synaptic outputs. Fluorescently labeled progenitor cells from the embryonic medial ganglionic eminence (MGE) were used for transplantation. At 5 wk after transplantation, MGE-derived parvalbumin-positive (PV+) interneurons received excitatory synaptic inputs, exhibited mature interneuron firing properties, and made functional synaptic inhibitory connections to native pyramidal cells that were comparable to those of native PV+ interneurons. These findings demonstrate that MGE-derived PV+ interneurons functionally integrate into subtype-appropriate physiological niches within host networks following transplantation. Copyright © 2016 the American Physiological Society.

  20. Dynamic brain glucose metabolism identifies anti-correlated cortical-cerebellar networks at rest.

    Science.gov (United States)

    Tomasi, Dardo G; Shokri-Kojori, Ehsan; Wiers, Corinde E; Kim, Sunny W; Demiral, Şukru B; Cabrera, Elizabeth A; Lindgren, Elsa; Miller, Gregg; Wang, Gene-Jack; Volkow, Nora D

    2017-12-01

    It remains unclear whether resting state functional magnetic resonance imaging (rfMRI) networks are associated with underlying synchrony in energy demand, as measured by dynamic 2-deoxy-2-[ 18 F]fluoroglucose (FDG) positron emission tomography (PET). We measured absolute glucose metabolism, temporal metabolic connectivity (t-MC) and rfMRI patterns in 53 healthy participants at rest. Twenty-two rfMRI networks emerged from group independent component analysis (gICA). In contrast, only two anti-correlated t-MC emerged from FDG-PET time series using gICA or seed-voxel correlations; one included frontal, parietal and temporal cortices, the other included the cerebellum and medial temporal regions. Whereas cerebellum, thalamus, globus pallidus and calcarine cortex arose as the strongest t-MC hubs, the precuneus and visual cortex arose as the strongest rfMRI hubs. The strength of the t-MC linearly increased with the metabolic rate of glucose suggesting that t-MC measures are strongly associated with the energy demand of the brain tissue, and could reflect regional differences in glucose metabolism, counterbalanced metabolic network demand, and/or differential time-varying delivery of FDG. The mismatch between metabolic and functional connectivity patterns computed as a function of time could reflect differences in the temporal characteristics of glucose metabolism as measured with PET-FDG and brain activation as measured with rfMRI.

  1. Impairment of GABA transporter GAT-1 terminates cortical recurrent network activity via enhanced phasic inhibition

    Directory of Open Access Journals (Sweden)

    Daniel Simon Razik

    2013-09-01

    Full Text Available In the central nervous system, GABA transporters (GATs very efficiently clear synaptically released GABA from the extracellular space, and thus exert a tight control on GABAergic inhibition. In neocortex, GABAergic inhibition is heavily recruited during recurrent phases of spontaneous action potential activity which alternate with neuronally quiet periods. Therefore, such activity should be quite sensitive to minute alterations of GAT function. Here, we explored the effects of a gradual impairment of GAT-1 and GAT-2/3 on spontaneous recurrent network activity – termed network bursts and silent periods – in organotypic slice cultures of rat neocortex. The GAT-1 specific antagonist NO-711 depressed activity already at nanomolar concentrations (IC50 for depression of spontaneous multiunit firing rate of 42 nM, reaching a level of 80% at 500-1000 nM. By contrast, the GAT-2/3 preferring antagonist SNAP-5114 had weaker and less consistent effects. Several lines of evidence pointed towards an enhancement of phasic GABAergic inhibition as the dominant activity-depressing mechanism: network bursts were drastically shortened, phasic GABAergic currents decayed slower, and neuronal excitability during ongoing activity was diminished. In silent periods, NO-711 had little effect on neuronal excitability or membrane resistance, quite in contrast to the effects of muscimol, a GABA mimetic which activates GABAA receptors tonically. Our results suggest that an enhancement of phasic GABAergic inhibition efficiently curtails cortical recurrent activity and may mediate antiepileptic effects of therapeutically relevant concentrations of GAT-1 antagonists.

  2. Cortical neurons and networks are dormant but fully responsive during isoelectric brain state.

    Science.gov (United States)

    Altwegg-Boussac, Tristan; Schramm, Adrien E; Ballestero, Jimena; Grosselin, Fanny; Chavez, Mario; Lecas, Sarah; Baulac, Michel; Naccache, Lionel; Demeret, Sophie; Navarro, Vincent; Mahon, Séverine; Charpier, Stéphane

    2017-09-01

    A continuous isoelectric electroencephalogram reflects an interruption of endogenously-generated activity in cortical networks and systematically results in a complete dissolution of conscious processes. This electro-cerebral inactivity occurs during various brain disorders, including hypothermia, drug intoxication, long-lasting anoxia and brain trauma. It can also be induced in a therapeutic context, following the administration of high doses of barbiturate-derived compounds, to interrupt a hyper-refractory status epilepticus. Although altered sensory responses can be occasionally observed on an isoelectric electroencephalogram, the electrical membrane properties and synaptic responses of individual neurons during this cerebral state remain largely unknown. The aim of the present study was to characterize the intracellular correlates of a barbiturate-induced isoelectric electroencephalogram and to analyse the sensory-evoked synaptic responses that can emerge from a brain deprived of spontaneous electrical activity. We first examined the sensory responsiveness from patients suffering from intractable status epilepticus and treated by administration of thiopental. Multimodal sensory responses could be evoked on the flat electroencephalogram, including visually-evoked potentials that were significantly amplified and delayed, with a high trial-to-trial reproducibility compared to awake healthy subjects. Using an analogous pharmacological procedure to induce prolonged electro-cerebral inactivity in the rat, we could describe its cortical and subcortical intracellular counterparts. Neocortical, hippocampal and thalamo-cortical neurons were all silent during the isoelectric state and displayed a flat membrane potential significantly hyperpolarized compared with spontaneously active control states. Nonetheless, all recorded neurons could fire action potentials in response to intracellularly injected depolarizing current pulses and their specific intrinsic

  3. Cortical and sub-cortical effects in primate models of cocaine use: implications for addiction and the increased risk of psychiatric illness.

    Science.gov (United States)

    Bradberry, Charles W

    2011-02-01

    Drug abuse is a serious risk factor for the incidence and severity of multiple psychiatric illnesses. Understanding the neurobiological consequences of repeated exposure to abused drugs can help to inform how those risks are manifested in terms of specific neurochemical mechanisms and brain networks. This review examines selective studies in non-human primates that employed a cocaine self-administration model. Neurochemical consequences of chronic exposure appear to differ from observations in rodent studies. Whereas chronic intermittent exposure in the rodent is usually associated with a dose-dependent increase in dopaminergic response to a cocaine challenge, in the rhesus monkey, high cumulative exposure was not observed to cause a sensitized dopamine response. These non-human primate observations are concordant with clinical findings in human users. The results of cue exposure studies on dopaminergic transmission are also reviewed. Direct microdialysis measurements indicate that there is not a sustained increase in dopamine associated with cocaine-linked cues. As an alternative to striatal dopaminergic mechanisms mediating cue effects, single unit studies in prefrontal cortex during self-administration in monkeys suggests the orbitofrontal and anterior cingulate cortex are strongly engaged by cocaine cues. Based on the strong clinical imaging literature on cortical and cognitive dysfunction associated with addiction, it is proposed that the strong engagement of cortical systems during repeated cocaine reinforcement results in maladaptive changes that contribute to the risks of drug use for exacerbation of other psychiatric disorders.

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

    Science.gov (United States)

    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...

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

    Science.gov (United States)

    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. © 2010 Wiley-Liss, Inc.

  6. Acupuncture analgesia involves modulation of pain-induced gamma oscillations and cortical network connectivity.

    Science.gov (United States)

    Hauck, Michael; Schröder, Sven; Meyer-Hamme, Gesa; Lorenz, Jürgen; Friedrichs, Sunja; Nolte, Guido; Gerloff, Christian; Engel, Andreas K

    2017-11-24

    Recent studies support the view that cortical sensory, limbic and executive networks and the autonomic nervous system might interact in distinct manners under the influence of acupuncture to modulate pain. We performed a double-blind crossover design study to investigate subjective ratings, EEG and ECG following experimental laser pain under the influence of sham and verum acupuncture in 26 healthy volunteers. We analyzed neuronal oscillations and inter-regional coherence in the gamma band of 128-channel-EEG recordings as well as heart rate variability (HRV) on two experimental days. Pain ratings and pain-induced gamma oscillations together with vagally-mediated power in the high-frequency bandwidth (vmHF) of HRV decreased significantly stronger during verum than sham acupuncture. Gamma oscillations were localized in the prefrontal cortex (PFC), mid-cingulate cortex (MCC), primary somatosensory cortex and insula. Reductions of pain ratings and vmHF-power were significantly correlated with increase of connectivity between the insula and MCC. In contrast, connectivity between left and right PFC and between PFC and insula correlated positively with vmHF-power without a relationship to acupuncture analgesia. Overall, these findings highlight the influence of the insula in integrating activity in limbic-saliency networks with vagally mediated homeostatic control to mediate antinociception under the influence of acupuncture.

  7. The slow oscillation in cortical and thalamic networks: mechanisms and functions

    Directory of Open Access Journals (Sweden)

    Garrett T. Neske

    2016-01-01

    Full Text Available 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.

  8. Free association transitions in models of cortical latching dynamics

    International Nuclear Information System (INIS)

    Russo, Eleonora; Treves, Alessandro; Kropff, Emilio; Namboodiri, Vijay M K

    2008-01-01

    Potts networks, in certain conditions, hop spontaneously from one discrete attractor state to another, a process we have called latching dynamics. When continuing indefinitely, latching can serve as a model of infinite recursion, which is nontrivial if the matrix of transition probabilities presents a structure, i.e. a rudimentary grammar. We show here, with computer simulations, that latching transitions cluster in a number of distinct classes: effectively random transitions between weakly correlated attractors; structured, history-dependent transitions between attractors with intermediate correlations; and oscillations between pairs of closely overlapping attractors. Each type can be described by a reduced set of equations of motion, which, once numerically integrated, matches simulations results. We propose that the analysis of such equations may offer clues on how to embed meaningful grammatical structures into more realistic models of specific recursive processes

  9. Free association transitions in models of cortical latching dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Russo, Eleonora; Treves, Alessandro; Kropff, Emilio [SISSA, Cognitive Neuroscience, via Beirut 4, 34014 Trieste (Italy); Namboodiri, Vijay M K [Department of Physics, IIT Bombay, Powai, Mumbai, India 400076 (India)], E-mail: russo@sissa.it, E-mail: vijay_mkn@iitb.ac.in, E-mail: ale@sissa.it, E-mail: kropff@sissa.it

    2008-01-15

    Potts networks, in certain conditions, hop spontaneously from one discrete attractor state to another, a process we have called latching dynamics. When continuing indefinitely, latching can serve as a model of infinite recursion, which is nontrivial if the matrix of transition probabilities presents a structure, i.e. a rudimentary grammar. We show here, with computer simulations, that latching transitions cluster in a number of distinct classes: effectively random transitions between weakly correlated attractors; structured, history-dependent transitions between attractors with intermediate correlations; and oscillations between pairs of closely overlapping attractors. Each type can be described by a reduced set of equations of motion, which, once numerically integrated, matches simulations results. We propose that the analysis of such equations may offer clues on how to embed meaningful grammatical structures into more realistic models of specific recursive processes.

  10. Statistical Models for Social Networks

    NARCIS (Netherlands)

    Snijders, Tom A. B.; Cook, KS; Massey, DS

    2011-01-01

    Statistical models for social networks as dependent variables must represent the typical network dependencies between tie variables such as reciprocity, homophily, transitivity, etc. This review first treats models for single (cross-sectionally observed) networks and then for network dynamics. For

  11. Is There a Canonical Cortical Circuit for the Cholinergic System? Anatomical Differences Across Common Model Systems.

    Science.gov (United States)

    Coppola, Jennifer J; Disney, Anita A

    2018-01-01

    Acetylcholine (ACh) is believed to act as a neuromodulator in cortical circuits that support cognition, specifically in processes including learning, memory consolidation, vigilance, arousal and attention. The cholinergic modulation of cortical processes is studied in many model systems including rodents, cats and primates. Further, these studies are performed in cortical areas ranging from the primary visual cortex to the prefrontal cortex and using diverse methodologies. The results of these studies have been combined into singular models of function-a practice based on an implicit assumption that the various model systems are equivalent and interchangeable. However, comparative anatomy both within and across species reveals important differences in the structure of the cholinergic system. Here, we will review anatomical data including innervation patterns, receptor expression, synthesis and release compared across species and cortical area with a focus on rodents and primates. We argue that these data suggest no canonical cortical model system exists for the cholinergic system. Further, we will argue that as a result, care must be taken both in combining data from studies across cortical areas and species, and in choosing the best model systems to improve our understanding and support of human health.

  12. Is There a Canonical Cortical Circuit for the Cholinergic System? Anatomical Differences Across Common Model Systems

    Directory of Open Access Journals (Sweden)

    Jennifer J. Coppola

    2018-01-01

    Full Text Available Acetylcholine (ACh is believed to act as a neuromodulator in cortical circuits that support cognition, specifically in processes including learning, memory consolidation, vigilance, arousal and attention. The cholinergic modulation of cortical processes is studied in many model systems including rodents, cats and primates. Further, these studies are performed in cortical areas ranging from the primary visual cortex to the prefrontal cortex and using diverse methodologies. The results of these studies have been combined into singular models of function—a practice based on an implicit assumption that the various model systems are equivalent and interchangeable. However, comparative anatomy both within and across species reveals important differences in the structure of the cholinergic system. Here, we will review anatomical data including innervation patterns, receptor expression, synthesis and release compared across species and cortical area with a focus on rodents and primates. We argue that these data suggest no canonical cortical model system exists for the cholinergic system. Further, we will argue that as a result, care must be taken both in combining data from studies across cortical areas and species, and in choosing the best model systems to improve our understanding and support of human health.

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

    Directory of Open Access Journals (Sweden)

    Dan eShlosberg

    2012-02-01

    Full Text Available 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 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 4X objective. Histochemistry for NADPH diaphorase, 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 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 m from the stimulation, but not when inhibition was non-selectively weakened with the GABAA 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.

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

    Science.gov (United States)

    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.

  15. Brainlab: A Python Toolkit to Aid in the Design, Simulation, and Analysis of Spiking Neural Networks with the NeoCortical Simulator.

    Science.gov (United States)

    Drewes, Rich; Zou, Quan; Goodman, Philip H

    2009-01-01

    Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading "glue" tool for managing all sorts of complex programmatic tasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS (NeoCortical Simulator) environment in particular. Brainlab is an integrated model-building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS.

  16. Brainlab: a Python toolkit to aid in the design, simulation, and analysis of spiking neural networks with the NeoCortical Simulator

    Directory of Open Access Journals (Sweden)

    Richard P Drewes

    2009-05-01

    Full Text Available Neuroscience modeling experiments often involve multiple complex neural network and cell model variants, complex input stimuli and input protocols, followed by complex data analysis. Coordinating all this complexity becomes a central difficulty for the experimenter. The Python programming language, along with its extensive library packages, has emerged as a leading ``glue'' tool for managing all sorts of complex programmatictasks. This paper describes a toolkit called Brainlab, written in Python, that leverages Python's strengths for the task of managing the general complexity of neuroscience modeling experiments. Brainlab was also designed to overcome the major difficulties of working with the NCS environment in particular. Brainlab is an integrated model building, experimentation, and data analysis environment for the powerful parallel spiking neural network simulator system NCS (the NeoCortical Simulator.

  17. Statistical mechanics of attractor neural network models with synaptic depression

    International Nuclear Information System (INIS)

    Igarashi, Yasuhiko; Oizumi, Masafumi; Otsubo, Yosuke; Nagata, Kenji; Okada, Masato

    2009-01-01

    Synaptic depression is known to control gain for presynaptic inputs. Since cortical neurons receive thousands of presynaptic inputs, and their outputs are fed into thousands of other neurons, the synaptic depression should influence macroscopic properties of neural networks. We employ simple neural network models to explore the macroscopic effects of synaptic depression. Systems with the synaptic depression cannot be analyzed due to asymmetry of connections with the conventional equilibrium statistical-mechanical approach. Thus, we first propose a microscopic dynamical mean field theory. Next, we derive macroscopic steady state equations and discuss the stabilities of steady states for various types of neural network models.

  18. Effects of an environmentally-relevant mixture of pyrethroid insecticides on spontaneous activity in primary cortical networks on microelectrode arrays.

    Science.gov (United States)

    Johnstone, Andrew F M; Strickland, Jenna D; Crofton, Kevin M; Gennings, Chris; Shafer, Timothy J

    2017-05-01

    Pyrethroid insecticides exert their insecticidal and toxicological effects primarily by disrupting voltage-gated sodium channel (VGSC) function, resulting in altered neuronal excitability. Numerous studies of individual pyrethroids have characterized effects on mammalian VGSC function and neuronal excitability, yet studies examining effects of complex pyrethroid mixtures in mammalian neurons, especially in environmentally relevant mixture ratios, are limited. In the present study, concentration-response functions were characterized for five pyrethroids (permethrin, deltamethrin, cypermethrin, β-cyfluthrin and esfenvalerate) in an in vitro preparation containing cortical neurons and glia. As a metric of neuronal network activity, spontaneous mean network firing rates (MFR) were measured using microelectorde arrays (MEAs). In addition, the effect of a complex and exposure relevant mixture of the five pyrethroids (containing 52% permethrin, 28.8% cypermethrin, 12.9% β-cyfluthrin, 3.4% deltamethrin and 2.7% esfenvalerate) was also measured. Data were modeled to determine whether effects of the pyrethroid mixture were predicted by dose-addition. At concentrations up to 10μM, all compounds except permethrin reduced MFR. Deltamethrin and β-cyfluthrin were the most potent and reduced MFR by as much as 60 and 50%, respectively, while cypermethrin and esfenvalerate were of approximately equal potency and reduced MFR by only ∼20% at the highest concentration. Permethrin caused small (∼24% maximum), concentration-dependent increases in MFR. Effects of the environmentally relevant mixture did not depart from the prediction of dose-addition. These data demonstrate that an environmentally relevant mixture caused dose-additive effects on spontaneous neuronal network activity in vitro, and is consistent with other in vitro and in vivo assessments of pyrethroid mixtures. Published by Elsevier B.V.

  19. GABA neurons and the mechanisms of network oscillations: implications for understanding cortical dysfunction in schizophrenia.

    Science.gov (United States)

    Gonzalez-Burgos, Guillermo; Lewis, David A

    2008-09-01

    Synchronization of neuronal activity in the neocortex may underlie the coordination of neural representations and thus is critical for optimal cognitive function. Because cognitive deficits are the major determinant of functional outcome in schizophrenia, identifying their neural basis is important for the development of new therapeutic interventions. Here we review the data suggesting that phasic synaptic inhibition mediated by specific subtypes of cortical gamma-aminobutyric acid (GABA) neurons is essential for the production of synchronized network oscillations. We also discuss evidence indicating that GABA neurotransmission is altered in schizophrenia and propose mechanisms by which such alterations can decrease the strength of inhibitory connections in a cell-type-specific manner. We suggest that some alterations observed in the neocortex of schizophrenia subjects may be compensatory responses that partially restore inhibitory synaptic efficacy. The findings of altered neural synchrony and impaired cognitive function in schizophrenia suggest that such compensatory responses are insufficient and that interventions aimed at augmenting the efficacy of GABA neurotransmission might be of therapeutic value.

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

    Science.gov (United States)

    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.

  1. Functional characterization of GABAA receptor-mediated modulation of cortical neuron network activity in microelectrode array recordings

    DEFF Research Database (Denmark)

    Bader, Benjamin M; Steder, Anne; Klein, Anders Bue

    2017-01-01

    The numerous γ-aminobutyric acid type A receptor (GABAAR) subtypes are differentially expressed and mediate distinct functions at neuronal level. In this study we have investigated GABAAR-mediated modulation of the spontaneous activity patterns of primary neuronal networks from murine frontal...... of the information extractable from the MEA recordings offers interesting insights into the contributions of various GABAAR subtypes/subgroups to cortical network activity and the putative functional interplay between these receptors in these neurons....... cortex by characterizing the effects induced by a wide selection of pharmacological tools at a plethora of activity parameters in microelectrode array (MEA) recordings. The basic characteristics of the primary cortical neurons used in the recordings were studied in some detail, and the expression levels...

  2. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim

    2014-12-03

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  3. Coevolutionary modeling in network formation

    KAUST Repository

    Al-Shyoukh, Ibrahim; Chasparis, Georgios; Shamma, Jeff S.

    2014-01-01

    Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

  4. A computational growth model for measuring dynamic cortical development in the first year of life.

    Science.gov (United States)

    Nie, Jingxin; Li, Gang; Wang, Li; Gilmore, John H; Lin, Weili; Shen, Dinggang

    2012-10-01

    Human cerebral cortex develops extremely fast in the first year of life. Quantitative measurement of cortical development during this early stage plays an important role in revealing the relationship between cortical structural and high-level functional development. This paper presents a computational growth model to simulate the dynamic development of the cerebral cortex from birth to 1 year old by modeling the cerebral cortex as a deformable elastoplasticity surface driven via a growth model. To achieve a high accuracy, a guidance model is also incorporated to estimate the growth parameters and cortical shapes at later developmental stages. The proposed growth model has been applied to 10 healthy subjects with longitudinal brain MR images acquired at every 3 months from birth to 1 year old. The experimental results show that our proposed method can capture the dynamic developmental process of the cortex, with the average surface distance error smaller than 0.6 mm compared with the ground truth surfaces, and the results also show that 1) the curvedness and sharpness decrease from 2 weeks to 12 months and 2) the frontal lobe shows rapidly increasing cortical folding during this period, with relatively slower increase of the cortical folding in the occipital and parietal lobes.

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

    Science.gov (United States)

    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

  6. Cortical Amyloid Beta in Cognitively Normal Elderly Adults is Associated with Decreased Network Efficiency within the Cerebro-Cerebellar System.

    Science.gov (United States)

    Steininger, Stefanie C; Liu, Xinyang; Gietl, Anton; Wyss, Michael; Schreiner, Simon; Gruber, Esmeralda; Treyer, Valerie; Kälin, Andrea; Leh, Sandra; Buck, Alfred; Nitsch, Roger M; Prüssmann, Klaas P; Hock, Christoph; Unschuld, Paul G

    2014-01-01

    Deposition of cortical amyloid beta (Aβ) is a correlate of aging and a risk factor for Alzheimer disease (AD). While several higher order cognitive processes involve functional interactions between cortex and cerebellum, this study aims to investigate effects of cortical Aβ deposition on coupling within the cerebro-cerebellar system. We included 15 healthy elderly subjects with normal cognitive performance as assessed by neuropsychological testing. Cortical Aβ was quantified using (11)carbon-labeled Pittsburgh compound B positron-emission-tomography late frame signals. Volumes of brain structures were assessed by applying an automated parcelation algorithm to three dimensional magnetization-prepared rapid gradient-echo T1-weighted images. Basal functional network activity within the cerebro-cerebellar system was assessed using blood-oxygen-level dependent resting state functional magnetic resonance imaging at the high field strength of 7 T for measuring coupling between cerebellar seeds and cerebral gray matter. A bivariate regression approach was applied for identification of brain regions with significant effects of individual cortical Aβ load on coupling. Consistent with earlier reports, a significant degree of positive and negative coupling could be observed between cerebellar seeds and cerebral voxels. Significant positive effects of cortical Aβ load on cerebro-cerebellar coupling resulted for cerebral brain regions located in inferior temporal lobe, prefrontal cortex, hippocampus, parahippocampal gyrus, and thalamus. Our findings indicate that brain amyloidosis in cognitively normal elderly subjects is associated with decreased network efficiency within the cerebro-cerebellar system. While the identified cerebral regions are consistent with established patterns of increased sensitivity for Aβ-associated neurodegeneration, additional studies are needed to elucidate the relationship between dysfunction of the cerebro-cerebellar system and risk for AD.

  7. Modeling online social signed networks

    Science.gov (United States)

    Li, Le; Gu, Ke; Zeng, An; Fan, Ying; Di, Zengru

    2018-04-01

    People's online rating behavior can be modeled by user-object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user-object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user's signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

  8. Assessment of compressive failure process of cortical bone materials using damage-based model.

    Science.gov (United States)

    Ng, Theng Pin; R Koloor, S S; Djuansjah, J R P; Abdul Kadir, M R

    2017-02-01

    The main failure factors of cortical bone are aging or osteoporosis, accident and high energy trauma or physiological activities. However, the mechanism of damage evolution coupled with yield criterion is considered as one of the unclear subjects in failure analysis of cortical bone materials. Therefore, this study attempts to assess the structural response and progressive failure process of cortical bone using a brittle damaged plasticity model. For this reason, several compressive tests are performed on cortical bone specimens made of bovine femur, in order to obtain the structural response and mechanical properties of the material. Complementary finite element (FE) model of the sample and test is prepared to simulate the elastic-to-damage behavior of the cortical bone using the brittle damaged plasticity model. The FE model is validated in a comparative method using the predicted and measured structural response as load-compressive displacement through simulation and experiment. FE results indicated that the compressive damage initiated and propagated at central region where maximum equivalent plastic strain is computed, which coincided with the degradation of structural compressive stiffness followed by a vast amount of strain energy dissipation. The parameter of compressive damage rate, which is a function dependent on damage parameter and the plastic strain is examined for different rates. Results show that considering a similar rate to the initial slope of the damage parameter in the experiment would give a better sense for prediction of compressive failure. Copyright © 2016 Elsevier Ltd. All rights reserved.

  9. Mutation of Semaphorin-6A disrupts limbic and cortical connectivity and models neurodevelopmental psychopathology.

    LENUS (Irish Health Repository)

    2011-01-01

    Psychiatric disorders such as schizophrenia and autism are characterised by cellular disorganisation and dysconnectivity across the brain and can be caused by mutations in genes that control neurodevelopmental processes. To examine how neurodevelopmental defects can affect brain function and behaviour, we have comprehensively investigated the consequences of mutation of one such gene, Semaphorin-6A, on cellular organisation, axonal projection patterns, behaviour and physiology in mice. These analyses reveal a spectrum of widespread but subtle anatomical defects in Sema6A mutants, notably in limbic and cortical cellular organisation, lamination and connectivity. These mutants display concomitant alterations in the electroencephalogram and hyper-exploratory behaviour, which are characteristic of models of psychosis and reversible by the antipsychotic clozapine. They also show altered social interaction and deficits in object recognition and working memory. Mice with mutations in Sema6A or the interacting genes may thus represent a highly informative model for how neurodevelopmental defects can lead to anatomical dysconnectivity, resulting, either directly or through reactive mechanisms, in dysfunction at the level of neuronal networks with associated behavioural phenotypes of relevance to psychiatric disorders. The biological data presented here also make these genes plausible candidates to explain human linkage findings for schizophrenia and autism.

  10. Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

    Science.gov (United States)

    Tan, Weng Chun; Mat Isa, Nor Ashidi

    2016-01-01

    In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state-of-the-art segmentation methods. The proposed method resulted in rates of 98.14%, 98.82%, 86.46% and 99.81% in accuracy, sensitivity, specificity and precision, respectively, after testing with 1200 sperms. The proposed algorithm is expected to be implemented in analysing sperm motility because of the robustness and capability of this algorithm.

  11. Improved diagnostic accuracy of Alzheimer's disease by combining regional cortical thickness and default mode network functional connectivity: Validated in the Alzheimer's disease neuroimaging initiative set

    International Nuclear Information System (INIS)

    Park, Ji Eun; Park, Bum Woo; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Jung; Oh, Joo Young; Shim, Woo Hyun; Lee, Jae Hong; Roh, Jee Hoon

    2017-01-01

    To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease

  12. Using a virtual cortical module implementing a neural field model to modulate brain rhythms in Parkinson's disease

    Directory of Open Access Journals (Sweden)

    Julien Modolo

    2010-06-01

    Full Text Available We propose a new method for selective modulation of cortical rhythms based on neural field theory, in which the activity of a cortical area is extensively monitored using a two-dimensional microelectrode array. The example of Parkinson's disease illustrates the proposed method, in which a neural field model is assumed to accurately describe experimentally recorded activity. In addition, we propose a new closed-loop stimulation signal that is both space- and time- dependent. This method is especially designed to specifically modulate a targeted brain rhythm, without interfering with other rhythms. A new class of neuroprosthetic devices is also proposed, in which the multielectrode array is seen as an artificial neural network interacting with biological tissue. Such a bio-inspired approach may provide a solution to optimize interactions between the stimulation device and the cortex aiming to attenuate or augment specific cortical rhythms. The next step will be to validate this new approach experimentally in patients with Parkinson's disease.

  13. Resistor mesh model of a spherical head: part 2: a review of applications to cortical mapping.

    Science.gov (United States)

    Chauveau, N; Morucci, J P; Franceries, X; Celsis, P; Rigaud, B

    2005-11-01

    A resistor mesh model (RMM) has been validated with reference to the analytical model by consideration of a set of four dipoles close to the cortex. The application of the RMM to scalp potential interpolation was detailed in Part 1. Using the RMM and the same four dipoles, the different methods of cortical mapping were compared and have shown the potentiality of this RMM for obtaining current and potential cortical distributions. The lead-field matrices are well-adapted tools, but the use of a square matrix of high dimension does not permit the inverse solution to be improved in the presence of noise, as a regularisation technique is necessary with noisy data. With the RMM, the transfer matrix and the cortical imaging technique proved to be easy to implement. Further development of the RMM will include application to more realistic head models with more accurate conductivities.

  14. A neighbourhood evolving network model

    International Nuclear Information System (INIS)

    Cao, Y.J.; Wang, G.Z.; Jiang, Q.Y.; Han, Z.X.

    2006-01-01

    Many social, technological, biological and economical systems are best described by evolved network models. In this short Letter, we propose and study a new evolving network model. The model is based on the new concept of neighbourhood connectivity, which exists in many physical complex networks. The statistical properties and dynamics of the proposed model is analytically studied and compared with those of Barabasi-Albert scale-free model. Numerical simulations indicate that this network model yields a transition between power-law and exponential scaling, while the Barabasi-Albert scale-free model is only one of its special (limiting) cases. Particularly, this model can be used to enhance the evolving mechanism of complex networks in the real world, such as some social networks development

  15. Model cortical responses for the detection of perceptual onsets and beat tracking in singing

    NARCIS (Netherlands)

    Coath, M.; Denham, S.L.; Smith, L.M.; Honing, H.; Hazan, A.; Holonowicz, P.; Purwins, H.

    2009-01-01

    We describe a biophysically motivated model of auditory salience based on a model of cortical responses and present results that show that the derived measure of salience can be used to identify the position of perceptual onsets in a musical stimulus successfully. The salience measure is also shown

  16. Model cortical association fields account for the time course and dependence on target complexity of human contour perception.

    Directory of Open Access Journals (Sweden)

    Vadas Gintautas

    2011-10-01

    Full Text Available Can lateral connectivity in the primary visual cortex account for the time dependence and intrinsic task difficulty of human contour detection? To answer this question, we created a synthetic image set that prevents sole reliance on either low-level visual features or high-level context for the detection of target objects. Rendered images consist of smoothly varying, globally aligned contour fragments (amoebas distributed among groups of randomly rotated fragments (clutter. The time course and accuracy of amoeba detection by humans was measured using a two-alternative forced choice protocol with self-reported confidence and variable image presentation time (20-200 ms, followed by an image mask optimized so as to interrupt visual processing. Measured psychometric functions were well fit by sigmoidal functions with exponential time constants of 30-91 ms, depending on amoeba complexity. Key aspects of the psychophysical experiments were accounted for by a computational network model, in which simulated responses across retinotopic arrays of orientation-selective elements were modulated by cortical association fields, represented as multiplicative kernels computed from the differences in pairwise edge statistics between target and distractor images. Comparing the experimental and the computational results suggests that each iteration of the lateral interactions takes at least [Formula: see text] ms of cortical processing time. Our results provide evidence that cortical association fields between orientation selective elements in early visual areas can account for important temporal and task-dependent aspects of the psychometric curves characterizing human contour perception, with the remaining discrepancies postulated to arise from the influence of higher cortical areas.

  17. Regional vulnerability of longitudinal cortical association connectivity: Associated with structural network topology alterations in preterm children with cerebral palsy.

    Science.gov (United States)

    Ceschin, Rafael; Lee, Vince K; Schmithorst, Vince; Panigrahy, Ashok

    2015-01-01

    Preterm born children with spastic diplegia type of cerebral palsy and white matter injury or periventricular leukomalacia (PVL), are known to have motor, visual and cognitive impairments. Most diffusion tensor imaging (DTI) studies performed in this group have demonstrated widespread abnormalities using averaged deterministic tractography and voxel-based DTI measurements. Little is known about structural network correlates of white matter topography and reorganization in preterm cerebral palsy, despite the availability of new therapies and the need for brain imaging biomarkers. Here, we combined novel post-processing methodology of probabilistic tractography data in this preterm cohort to improve spatial and regional delineation of longitudinal cortical association tract abnormalities using an along-tract approach, and compared these data to structural DTI cortical network topology analysis. DTI images were acquired on 16 preterm children with cerebral palsy (mean age 5.6 ± 4) and 75 healthy controls (mean age 5.7 ± 3.4). Despite mean tract analysis, Tract-Based Spatial Statistics (TBSS) and voxel-based morphometry (VBM) demonstrating diffusely reduced fractional anisotropy (FA) reduction in all white matter tracts, the along-tract analysis improved the detection of regional tract vulnerability. The along-tract map-structural network topology correlates revealed two associations: (1) reduced regional posterior-anterior gradient in FA of the longitudinal visual cortical association tracts (inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, optic radiation, posterior thalamic radiation) correlated with reduced posterior-anterior gradient of intra-regional (nodal efficiency) metrics with relative sparing of frontal and temporal regions; and (2) reduced regional FA within frontal-thalamic-striatal white matter pathways (anterior limb/anterior thalamic radiation, superior longitudinal fasciculus and cortical spinal tract) correlated with

  18. Altered Gradients of Glutamate and Gamma-Aminobutyric Acid Transcripts in the Cortical Visuospatial Working Memory Network in Schizophrenia.

    Science.gov (United States)

    Hoftman, Gil D; Dienel, Samuel J; Bazmi, Holly H; Zhang, Yun; Chen, Kehui; Lewis, David A

    2018-04-15

    Visuospatial working memory (vsWM), which is impaired in schizophrenia, requires information transfer across multiple nodes in the cerebral cortex, including visual, posterior parietal, and dorsolateral prefrontal regions. Information is conveyed across these regions via the excitatory projections of glutamatergic pyramidal neurons located in layer 3, whose activity is modulated by local inhibitory gamma-aminobutyric acidergic (GABAergic) neurons. Key properties of these neurons differ across these cortical regions. Consequently, in schizophrenia, alterations in the expression of gene products regulating these properties could disrupt vsWM function in different ways, depending on the region(s) affected. Here, we quantified the expression of markers of glutamate and GABA neurotransmission selectively in layer 3 of four cortical regions in the vsWM network from 20 matched pairs of schizophrenia and unaffected comparison subjects. In comparison subjects, levels of glutamate transcripts tended to increase, whereas GABA transcript levels tended to decrease, from caudal to rostral, across cortical regions of the vsWM network. Composite measures across all transcripts revealed a significant effect of region, with the glutamate measure lowest in the primary visual cortex and highest in the dorsolateral prefrontal cortex, whereas the GABA measure showed the opposite pattern. In schizophrenia subjects, the expression levels of many of these transcripts were altered. However, this disease effect differed across regions, such that the caudal-to-rostral increase in the glutamate measure was blunted and the caudal-to-rostral decline in the GABA measure was enhanced in the illness. Differential alterations in layer 3 glutamate and GABA neurotransmission across cortical regions may contribute to vsWM deficits in schizophrenia. Copyright © 2017 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  19. Model-driven harmonic parameterization of the cortical surface: HIP-HOP.

    Science.gov (United States)

    Auzias, G; Lefèvre, J; Le Troter, A; Fischer, C; Perrot, M; Régis, J; Coulon, O

    2013-05-01

    In the context of inter subject brain surface matching, we present a parameterization of the cortical surface constrained by a model of cortical organization. The parameterization is defined via an harmonic mapping of each hemisphere surface to a rectangular planar domain that integrates a representation of the model. As opposed to previous landmark-based registration methods we do not match folds between individuals but instead optimize the fit between cortical sulci and specific iso-coordinate axis in the model. This strategy overcomes some limitation to sulcus-based registration techniques such as topological variability in sulcal landmarks across subjects. Experiments on 62 subjects with manually traced sulci are presented and compared with the result of the Freesurfer software. The evaluation involves a measure of dispersion of sulci with both angular and area distortions. We show that the model-based strategy can lead to a natural, efficient and very fast (less than 5 min per hemisphere) method for defining inter subjects correspondences. We discuss how this approach also reduces the problems inherent to anatomically defined landmarks and open the way to the investigation of cortical organization through the notion of orientation and alignment of structures across the cortex.

  20. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.

    Science.gov (United States)

    Berkes, Pietro; Orbán, Gergo; Lengyel, Máté; Fiser, József

    2011-01-07

    The brain maintains internal models of its environment to interpret sensory inputs and to prepare actions. Although behavioral studies have demonstrated that these internal models are optimally adapted to the statistics of the environment, the neural underpinning of this adaptation is unknown. Using a Bayesian model of sensory cortical processing, we related stimulus-evoked and spontaneous neural activities to inferences and prior expectations in an internal model and predicted that they should match if the model is statistically optimal. To test this prediction, we analyzed visual cortical activity of awake ferrets during development. Similarity between spontaneous and evoked activities increased with age and was specific to responses evoked by natural scenes. This demonstrates the progressive adaptation of internal models to the statistics of natural stimuli at the neural level.

  1. Dampened hippocampal oscillations and enhanced spindle activity in an asymptomatic model of developmental cortical malformations

    Directory of Open Access Journals (Sweden)

    Elena eCid

    2014-04-01

    Full Text Available Developmental cortical malformations comprise a large spectrum of histopathological brain abnormalities and syndromes. Their genetic, developmental and clinical complexity suggests they should be better understood in terms of the complementary action of independently timed perturbations (i.e. the multiple-hit hypothesis. However, understanding the underlying biological processes remains puzzling. Here we induced developmental cortical malformations in offspring, after intraventricular injection of methylazoxymethanol (MAM in utero in mice. We combined extensive histological and electrophysiological studies to characterize the model. We found that MAM injections at E14 and E15 induced a range of cortical and hippocampal malformations resembling histological alterations of specific genetic mutations and transplacental mitotoxic agent injections. However, in contrast to most of these models, intraventricularly MAM-injected mice remained asymptomatic and showed no clear epilepsy-related phenotype as tested in long-term chronic recordings and with pharmacological manipulations. Instead, they exhibited a non-specific reduction of hippocampal-related brain oscillations (mostly in CA1; including theta, gamma and HFOs; and enhanced thalamocortical spindle activity during non-REM sleep. These data suggest that developmental cortical malformations do not necessarily correlate with epileptiform activity. We propose that the intraventricular in utero MAM approach exhibiting a range of rhythmopathies is a suitable model for multiple-hit studies of associated neurological disorders.

  2. Developing Personal Network Business Models

    DEFF Research Database (Denmark)

    Saugstrup, Dan; Henten, Anders

    2006-01-01

    The aim of the paper is to examine the issue of business modeling in relation to personal networks, PNs. The paper builds on research performed on business models in the EU 1ST MAGNET1 project (My personal Adaptive Global NET). The paper presents the Personal Network concept and briefly reports...

  3. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.; Byrne, H.M.; King, J.R.; Bennett, M.J.

    2013-01-01

    methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more

  4. Epileptic Networks in Focal Cortical Dysplasia Revealed Using Electroencephalography–Functional Magnetic Resonance Imaging

    Science.gov (United States)

    Thornton, Rachel; Vulliemoz, Serge; Rodionov, Roman; Carmichael, David W; Chaudhary, Umair J; Diehl, Beate; Laufs, Helmut; Vollmar, Christian; McEvoy, Andrew W; Walker, Matthew C; Bartolomei, Fabrice; Guye, Maxime; Chauvel, Patrick; Duncan, John S; Lemieux, Louis

    2011-01-01

    Objective Surgical treatment of focal epilepsy in patients with focal cortical dysplasia (FCD) is most successful if all epileptogenic tissue is resected. This may not be evident on structural magnetic resonance imaging (MRI), so intracranial electroencephalography (icEEG) is needed to delineate the seizure onset zone (SOZ). EEG-functional MRI (fMRI) can reveal interictal discharge (IED)-related hemodynamic changes in the irritative zone (IZ). We assessed the value of EEG-fMRI in patients with FCD-associated focal epilepsy by examining the relationship between IED-related hemodynamic changes, icEEG findings, and postoperative outcome. Methods Twenty-three patients with FCD-associated focal epilepsy undergoing presurgical evaluation including icEEG underwent simultaneous EEG-fMRI at 3T. IED-related hemodynamic changes were modeled, and results were overlaid on coregistered T1-weighted MRI scans fused with computed tomography scans showing the intracranial electrodes. IED-related hemodynamic changes were compared with the SOZ on icEEG and postoperative outcome at 1 year. Results Twelve of 23 patients had IEDs during recording, and 11 of 12 had significant IED-related hemodynamic changes. The fMRI results were concordant with the SOZ in 5 of 11 patients, all of whom had a solitary SOZ on icEEG. Four of 5 had >50% reduction in seizure frequency following resective surgery. The remaining 6 of 11 patients had widespread or discordant regions of IED-related fMRI signal change. Five of 6 had either a poor surgical outcome (<50% reduction in seizure frequency) or widespread SOZ precluding surgery. Interpretation Comparison of EEG-fMRI with icEEG suggests that EEG-fMRI may provide useful additional information about the SOZ in FCD. Widely distributed discordant regions of IED-related hemodynamic change appear to be associated with a widespread SOZ and poor postsurgical outcome. ANN NEUROL 2011 PMID:22162063

  5. Thalamo–cortical network underlying deep brain stimulation of centromedian thalamic nuclei in intractable epilepsy: a multimodal imaging analysis

    Directory of Open Access Journals (Sweden)

    Kim SH

    2017-10-01

    Full Text Available Seong Hoon Kim,1 Sung Chul Lim,1 Dong Won Yang,1 Jeong Hee Cho,1 Byung-Chul Son,2 Jiyeon Kim,3 Seung Bong Hong,4 Young-Min Shon4 1Department of Neurology, 2Department of Neurosurgery, Catholic Neuroscience Institute, College of Medicine, The Catholic University of Korea, Seoul, 3Department of Neurology, Korea University Ansan Hospital, College of Medicine, Korea University, Ansan, 4Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea Objective: Deep brain stimulation (DBS of the centromedian thalamic nucleus (CM can be an alternative treatment option for intractable epilepsy patients. Since CM may be involved in widespread cortico-subcortical networks, identification of the cortical sub-networks specific to the target stimuli may provide further understanding on the underlying mechanisms of CM DBS. Several brain structures have distinguishing brain connections that may be related to the pivotal propagation and subsequent clinical effect of DBS.Methods: To explore core structures and their connections relevant to CM DBS, we applied electroencephalogram (EEG and diffusion tensor imaging (DTI to 10 medically intractable patients – three generalized epilepsy (GE and seven multifocal epilepsy (MFE patients unsuitable for resective surgery. Spatiotemporal activation pattern was mapped from scalp EEG by delivering low-frequency stimuli (5 Hz. Structural connections between the CM and the cortical activation spots were assessed using DTI.Results: We confirmed an average 72% seizure reduction after CM DBS and its clinical efficiency remained consistent during the observation period (mean 21 months. EEG data revealed sequential source propagation from the anterior cingulate, followed by the frontotemporal regions bilaterally. In addition, maximal activation was found in the left cingulate gyrus and the right medial frontal cortex during the right and left CM stimulation, respectively

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

    Directory of Open Access Journals (Sweden)

    David F. Putrino

    2011-01-01

    Full Text Available 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.

  7. A model of coauthorship networks

    Science.gov (United States)

    Zhou, Guochang; Li, Jianping; Xie, Zonglin

    2017-10-01

    A natural way of representing the coauthorship of authors is to use a generalization of graphs known as hypergraphs. A random geometric hypergraph model is proposed here to model coauthorship networks, which is generated by placing nodes on a region of Euclidean space randomly and uniformly, and connecting some nodes if the nodes satisfy particular geometric conditions. Two kinds of geometric conditions are designed to model the collaboration patterns of academic authorities and basic researches respectively. The conditions give geometric expressions of two causes of coauthorship: the authority and similarity of authors. By simulation and calculus, we show that the forepart of the degree distribution of the network generated by the model is mixture Poissonian, and the tail is power-law, which are similar to these of some coauthorship networks. Further, we show more similarities between the generated network and real coauthorship networks: the distribution of cardinalities of hyperedges, high clustering coefficient, assortativity, and small-world property

  8. Telecommunications network modelling, planning and design

    CERN Document Server

    Evans, Sharon

    2003-01-01

    Telecommunication Network Modelling, Planning and Design addresses sophisticated modelling techniques from the perspective of the communications industry and covers some of the major issues facing telecommunications network engineers and managers today. Topics covered include network planning for transmission systems, modelling of SDH transport network structures and telecommunications network design and performance modelling, as well as network costs and ROI modelling and QoS in 3G networks.

  9. Oscillatory neuronal activity reflects lexical-semantic feature integration within and across sensory modalities in distributed cortical networks.

    Science.gov (United States)

    van Ackeren, Markus J; Schneider, Till R; Müsch, Kathrin; Rueschemeyer, Shirley-Ann

    2014-10-22

    Research from the previous decade suggests that word meaning is partially stored in distributed modality-specific cortical networks. However, little is known about the mechanisms by which semantic content from multiple modalities is integrated into a coherent multisensory representation. Therefore we aimed to characterize differences between integration of lexical-semantic information from a single modality compared with two sensory modalities. We used magnetoencephalography in humans to investigate changes in oscillatory neuronal activity while participants verified two features for a given target word (e.g., "bus"). Feature pairs consisted of either two features from the same modality (visual: "red," "big") or different modalities (auditory and visual: "red," "loud"). The results suggest that integrating modality-specific features of the target word is associated with enhanced high-frequency power (80-120 Hz), while integrating features from different modalities is associated with a sustained increase in low-frequency power (2-8 Hz). Source reconstruction revealed a peak in the anterior temporal lobe for low-frequency and high-frequency effects. These results suggest that integrating lexical-semantic knowledge at different cortical scales is reflected in frequency-specific oscillatory neuronal activity in unisensory and multisensory association networks. Copyright © 2014 the authors 0270-6474/14/3314318-06$15.00/0.

  10. Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders

    Science.gov (United States)

    Kana, Rajesh K.; Libero, Lauren E.; Moore, Marie S.

    2011-12-01

    such as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD.

  11. Disrupted cortical connectivity theory as an explanatory model for autism spectrum disorders.

    Science.gov (United States)

    Kana, Rajesh K; Libero, Lauren E; Moore, Marie S

    2011-12-01

    as Theory-of-Mind, cognitive flexibility, and information processing; and 2) how connection abnormalities relate to, and may determine, behavioral symptoms hallmarked by the triad of Impairments in ASD. Furthermore, we will relate the disrupted cortical connectivity model to existing cognitive and neural models of ASD. Published by Elsevier B.V.

  12. Campus network security model study

    Science.gov (United States)

    Zhang, Yong-ku; Song, Li-ren

    2011-12-01

    Campus network security is growing importance, Design a very effective defense hacker attacks, viruses, data theft, and internal defense system, is the focus of the study in this paper. This paper compared the firewall; IDS based on the integrated, then design of a campus network security model, and detail the specific implementation principle.

  13. Evidence for a cerebral cortical thickness network anti-correlated with amygdalar volume in healthy youths: implications for the neural substrates of emotion regulation.

    Science.gov (United States)

    Albaugh, Matthew D; Ducharme, Simon; Collins, D Louis; Botteron, Kelly N; Althoff, Robert R; Evans, Alan C; Karama, Sherif; Hudziak, James J

    2013-05-01

    Recent functional connectivity studies have demonstrated that, in resting humans, activity in a dorsally-situated neocortical network is inversely associated with activity in the amygdalae. Similarly, in human neuroimaging studies, aspects of emotion regulation have been associated with increased activity in dorsolateral, dorsomedial, orbital and ventromedial prefrontal regions, as well as concomitant decreases in amygdalar activity. These findings indicate the presence of two countervailing systems in the human brain that are reciprocally related: a dorsally-situated cognitive control network, and a ventrally-situated limbic network. We investigated the extent to which this functional reciprocity between limbic and dorsal neocortical regions is recapitulated from a purely structural standpoint. Specifically, we hypothesized that amygdalar volume would be related to cerebral cortical thickness in cortical regions implicated in aspects of emotion regulation. In 297 typically developing youths (162 females, 135 males; 572 MRIs), the relationship between cortical thickness and amygdalar volume was characterized. Amygdalar volume was found to be inversely associated with thickness in bilateral dorsolateral and dorsomedial prefrontal, inferior parietal, as well as bilateral orbital and ventromedial prefrontal cortices. Our findings are in line with previous work demonstrating that a predominantly dorsally-centered neocortical network is reciprocally related to core limbic structures such as the amygdalae. Future research may benefit from investigating the extent to which such cortical-limbic morphometric relations are qualified by the presence of mood and anxiety psychopathology. Copyright © 2012 Elsevier Inc. All rights reserved.

  14. Generalized Network Psychometrics : Combining Network and Latent Variable Models

    NARCIS (Netherlands)

    Epskamp, S.; Rhemtulla, M.; Borsboom, D.

    2017-01-01

    We introduce the network model as a formal psychometric model, conceptualizing the covariance between psychometric indicators as resulting from pairwise interactions between observable variables in a network structure. This contrasts with standard psychometric models, in which the covariance between

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

    Science.gov (United States)

    Larson, Eric; Lee, Adrian K C

    2014-01-01

    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 electro-encephalography (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. © 2013 Elsevier Inc. All rights reserved.

  16. An experimental approach towards the development of an in vitro cortical-thalamic co-culture model.

    Science.gov (United States)

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

    2011-01-01

    In this paper, we propose an experimental approach to develop an in vitro dissociated cortical-thalamic co-culture model using a dual compartment neurofluidic device. The device has two compartments separated by 10 μm wide and 3 μm high microchannels. The microchannels provide a physical isolation of neurons allowing only neurites to grow between the compartments. Long-term viable co-culture was maintained in the compartmented device, neurite growth through the microchannels was verified using immunofluorescence staining, and electrophysiological recordings from the co-culture system was investigated. Preliminary analysis of spontaneous activities from the co-culture shows a distinctively different firing pattern associated with cultures of individual cell types and further analysis is proposed for a deeper understanding of the dynamics involved in the network connectivity in such a co-culture system.

  17. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  18. Modeling of fluctuating reaction networks

    International Nuclear Information System (INIS)

    Lipshtat, A.; Biham, O.

    2004-01-01

    Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press

  19. Vision first? The development of primary visual cortical networks is more rapid than the development of primary motor networks in humans.

    Directory of Open Access Journals (Sweden)

    Patricia Gervan

    Full Text Available The development of cortical functions and the capacity of the mature brain to learn are largely determined by the establishment and maintenance of neocortical networks. Here we address the human development of long-range connectivity in primary visual and motor cortices, using well-established behavioral measures--a Contour Integration test and a Finger-tapping task--that have been shown to be related to these specific primary areas, and the long-range neural connectivity within those. Possible confounding factors, such as different task requirements (complexity, cognitive load are eliminated by using these tasks in a learning paradigm. We find that there is a temporal lag between the developmental timing of primary sensory vs. motor areas with an advantage of visual development; we also confirm that human development is very slow in both cases, and that there is a retained capacity for practice induced plastic changes in adults. This pattern of results seems to point to human-specific development of the "canonical circuits" of primary sensory and motor cortices, probably reflecting the ecological requirements of human life.

  20. Decomposing neural synchrony: toward an explanation for near-zero phase-lag in cortical oscillatory networks.

    Directory of Open Access Journals (Sweden)

    Rajasimhan Rajagovindan

    Full Text Available BACKGROUND: Synchronized oscillation in cortical networks has been suggested as a mechanism for diverse functions ranging from perceptual binding to memory formation to sensorimotor integration. Concomitant with synchronization is the occurrence of near-zero phase-lag often observed between network components. Recent theories have considered the importance of this phenomenon in establishing an effective communication framework among neuronal ensembles. METHODOLOGY/PRINCIPAL FINDINGS: Two factors, among possibly others, can be hypothesized to contribute to the near-zero phase-lag relationship: (1 positively correlated common input with no significant relative time delay and (2 bidirectional interaction. Thus far, no empirical test of these hypotheses has been possible for lack of means to tease apart the specific causes underlying the observed synchrony. In this work simulation examples were first used to illustrate the ideas. A quantitative method that decomposes the statistical interdependence between two cortical areas into a feed-forward, a feed-back and a common-input component was then introduced and applied to test the hypotheses on multichannel local field potential recordings from two behaving monkeys. CONCLUSION/SIGNIFICANCE: The near-zero phase-lag phenomenon is important in the study of large-scale oscillatory networks. A rigorous mathematical theorem is used for the first time to empirically examine the factors that contribute to this phenomenon. Given the critical role that oscillatory activity is likely to play in the regulation of biological processes at all levels, the significance of the proposed method may extend beyond systems neuroscience, the level at which the present analysis is conceived and performed.

  1. Canonical Cortical Circuit Model Explains Rivalry, Intermittent Rivalry, and Rivalry Memory.

    Directory of Open Access Journals (Sweden)

    Shashaank Vattikuti

    2016-05-01

    Full Text Available It has been shown that the same canonical cortical circuit model with mutual inhibition and a fatigue process can explain perceptual rivalry and other neurophysiological responses to a range of static stimuli. However, it has been proposed that this model cannot explain responses to dynamic inputs such as found in intermittent rivalry and rivalry memory, where maintenance of a percept when the stimulus is absent is required. This challenges the universality of the basic canonical cortical circuit. Here, we show that by including an overlooked realistic small nonspecific background neural activity, the same basic model can reproduce intermittent rivalry and rivalry memory without compromising static rivalry and other cortical phenomena. The background activity induces a mutual-inhibition mechanism for short-term memory, which is robust to noise and where fine-tuning of recurrent excitation or inclusion of sub-threshold currents or synaptic facilitation is unnecessary. We prove existence conditions for the mechanism and show that it can explain experimental results from the quartet apparent motion illusion, which is a prototypical intermittent rivalry stimulus.

  2. Disrupted Cortical Connectivity as an Explanatory Model for Autism Spectrum Disorder

    Directory of Open Access Journals (Sweden)

    Jenniefer Drude Borup

    2014-02-01

    Full Text Available The aim of this article is to explain the theory of Disrupted Cortical Connectivity and discuss whether or not it can integrate the following three theories: Theory of Mind, Executive Functioning, and Weak Central Coherence that dominate the field of autism spectrum disorder research. Due to a lack of existing literature discussing this potential integration, we have consequentially undertaken such an endeavour. In our opinion, integration appears to be possible since this explanatory model can account for difficulties in both social cognition and executive functioning commonly found in autism spectrum disorder. Moreover, the theory of Disrupted Cortical Connectivity could be described as an extension of the theory of Weak Central Coherence.

  3. SEP-induced activity and its thermographic cortical representation in a murine model.

    Science.gov (United States)

    Hoffmann, Klaus-Peter; Ruff, Roman; Kirsch, Matthias

    2013-06-01

    This article is a methodical report on the generation of reproducible changes in brain activity in a murine model. Somatosensory evoked potentials (SEP) are used to generate synchronized cortical activity. After electrical stimulation of mice forelimbs, the potentials were recorded with a flexible thin-film polyimide electrode structure directly from the cortex. Every registration included a simultaneous recording from both hemispheres that repeated four times to reproduce and compare the results. The SEPs in the murine model were shown to generate a very stable signal. The latency of the second positive wave (P2 wave) ranged between 16 and 19 ms, and the N1-P2 amplitude ranged between 39 and 48 µV. In addition, the temperature distribution of the cortex was acquired using infrared thermography. Surface cortical temperature changed during electrical stimulation without a clear hemispheric correlation. These initial results could be a step toward a better understanding of the different synchronized cortical activities and basic methods of evaluation of various mathematical algorithms to detect them.

  4. Analysis of Amygdalar-Cortical Network Covariance During Pre- versus Post-menopausal Estrogen Levels: Potential Relevance to Resting State Networks, Mood, and Cognition

    Science.gov (United States)

    Ottowitz, William E.; Derro, David; Dougherty, Darin D.; Lindquist, Martin A.; Fischman, Alan J.; Hall, Janet E.

    2014-01-01

    Objectives 1.) Expand the scope of neuroendocrine applications of functional neuroimaging techniques. 2.) Compare the covariance of amygdalar activity with that of the rest of the brain during pre- and post-menopausal levels of estrogen (E2). Based on the distribution of cortical E2 receptors and the neocortical regions where E2 has been shown to preferentially accumulate, we predict that E2 infusion will increase covariance of amygdalar activity with that of the temporal and frontal cortices. Design This basic physiology study employed a within-subject design. All participants were post-menopausal women (n =7). Analysis of covariance between whole brain and amygdalar regional cerebral glucose consumption (CMRglc) was conducted in a voxel-wise manner by means of the basic regression option in SPM2 and was applied to FDG-PET scans acquired at baseline and after a 24 hour graded E2 infusion. Setting an academic medical center; Massachusetts General Hospital, Boston, Massachusetts. Results E2 levels (mean ± sem) were significantly greater at 24 hours (257.9 pg/mL ± 29.7) than at 0 hours (28.1 pg/mL ± 3.4). Right amygdalar CMRglc showed a significant covariance with activity of three different regions of the temporal cortex during E2 infusion, but none at baseline. In addition, right amygdalar CMRglc covaried with that of the right medial and superior frontal gyri only during E2 infusion. Conclusions In addition to suggesting changes in amygdalar-cortical network connectivity as a result of short-term E2 exposure, these analyses provide evidence that basic neuroendocrine research may benefit from further use of FDG-PET and other functional neuroimaging modalities for network level analyses. PMID:18766152

  5. Enriched encoding: reward motivation organizes cortical networks for hippocampal detection of unexpected events.

    Science.gov (United States)

    Murty, Vishnu P; Adcock, R Alison

    2014-08-01

    Learning how to obtain rewards requires learning about their contexts and likely causes. How do long-term memory mechanisms balance the need to represent potential determinants of reward outcomes with the computational burden of an over-inclusive memory? One solution would be to enhance memory for salient events that occur during reward anticipation, because all such events are potential determinants of reward. We tested whether reward motivation enhances encoding of salient events like expectancy violations. During functional magnetic resonance imaging, participants performed a reaction-time task in which goal-irrelevant expectancy violations were encountered during states of high- or low-reward motivation. Motivation amplified hippocampal activation to and declarative memory for expectancy violations. Connectivity of the ventral tegmental area (VTA) with medial prefrontal, ventrolateral prefrontal, and visual cortices preceded and predicted this increase in hippocampal sensitivity. These findings elucidate a novel mechanism whereby reward motivation can enhance hippocampus-dependent memory: anticipatory VTA-cortical-hippocampal interactions. Further, the findings integrate literatures on dopaminergic neuromodulation of prefrontal function and hippocampus-dependent memory. We conclude that during reward motivation, VTA modulation induces distributed neural changes that amplify hippocampal signals and records of expectancy violations to improve predictions-a potentially unique contribution of the hippocampus to reward learning. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  6. Enhanced limbic/impaired cortical-loop connection onto the hippocampus of NHE rats: Application of resting-state functional connectivity in a preclinical ADHD model.

    Science.gov (United States)

    Zoratto, F; Palombelli, G M; Ruocco, L A; Carboni, E; Laviola, G; Sadile, A G; Adriani, W; Canese, R

    2017-08-30

    Due to a hyperfunctioning mesocorticolimbic system, Naples-High-Excitability (NHE) rats have been proposed to model for the meso-cortical variant of attention deficit/hyperactivity disorder (ADHD). Compared to Naples Random-Bred (NRB) controls, NHE rats show hyperactivity, impaired non-selective attention (Aspide et al., 1998), and impaired selective spatial attention (Ruocco et al., 2009a, 2014). Alteration in limbic functions has been proposed; however, resulting unbalance among forebrain areas has not been assessed yet. By resting-state functional Magnetic-Resonance Imaging (fMRI) in vivo, we investigated the connectivity of neuronal networks belonging to limbic vs. cortical loops in NHE and NRB rats (n=10 each). Notably, resting-state fMRI was applied using a multi-slice sagittal, gradient-echo sequence. Voxel-wise connectivity maps at rest, based on temporal correlation among fMRI time-series, were computed by seeding the hippocampus (Hip), nucleus accumbens (NAcc), dorsal striatum (dStr), amygdala (Amy) and dorsal/medial prefrontal cortex (PFC), both hemispheres. To summarize patterns of altered connection, clearly directional connectivity was evident within the cortical loop: bilaterally and specularly, from orbital and dorsal PFCs through dStr and hence towards Hip. Such network communication was reduced in NHE rats (also, with less mesencephalic/pontine innervation). Conversely, enhanced network activity emerged within the limbic loop of NHE rats: from left PFC, both through the NAcc and directly, to the Hip (all of which received greater ventral tegmental innervation, likely dopamine). Together with tuned-down cortical loop, this potentiated limbic loop may serve a major role in controlling ADHD-like behavioral symptoms in NHE rats. Copyright © 2017 Elsevier B.V. All rights reserved.

  7. Network model of security system

    Directory of Open Access Journals (Sweden)

    Adamczyk Piotr

    2016-01-01

    Full Text Available The article presents the concept of building a network security model and its application in the process of risk analysis. It indicates the possibility of a new definition of the role of the network models in the safety analysis. Special attention was paid to the development of the use of an algorithm describing the process of identifying the assets, vulnerability and threats in a given context. The aim of the article is to present how this algorithm reduced the complexity of the problem by eliminating from the base model these components that have no links with others component and as a result and it was possible to build a real network model corresponding to reality.

  8. Current approaches to gene regulatory network modelling

    Directory of Open Access Journals (Sweden)

    Brazma Alvis

    2007-09-01

    Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.

  9. Inter-individual variability in cortical excitability and motor network connectivity following multiple blocks of rTMS.

    Science.gov (United States)

    Nettekoven, Charlotte; Volz, Lukas J; Leimbach, Martha; Pool, Eva-Maria; Rehme, Anne K; Eickhoff, Simon B; Fink, Gereon R; Grefkes, Christian

    2015-09-01

    The responsiveness to non-invasive neuromodulation protocols shows high inter-individual variability, the reasons of which remain poorly understood. We here tested whether the response to intermittent theta-burst stimulation (iTBS) - an effective repetitive transcranial magnetic stimulation (rTMS) protocol for increasing cortical excitability - depends on network properties of the cortical motor system. We furthermore investigated whether the responsiveness to iTBS is dose-dependent. To this end, we used a sham-stimulation controlled, single-blinded within-subject design testing for the relationship between iTBS aftereffects and (i) motor-evoked potentials (MEPs) as well as (ii) resting-state functional connectivity (rsFC) in 16 healthy subjects. In each session, three blocks of iTBS were applied, separated by 15min. We found that non-responders (subjects not showing an MEP increase of ≥10% after one iTBS block) featured stronger rsFC between the stimulated primary motor cortex (M1) and premotor areas before stimulation compared to responders. However, only the group of responders showed increases in rsFC and MEPs, while most non-responders remained close to baseline levels after all three blocks of iTBS. Importantly, there was still a large amount of variability in both groups. Our data suggest that responsiveness to iTBS at the local level (i.e., M1 excitability) depends upon the pre-interventional network connectivity of the stimulated region. Of note, increasing iTBS dose did not turn non-responders into responders. The finding that higher levels of pre-interventional connectivity precluded a response to iTBS could reflect a ceiling effect underlying non-responsiveness to iTBS at the systems level. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. The significance of calcified fibrocartilage on the cortical endplate of the translational sheep spine model.

    Science.gov (United States)

    Sinclair, Sarina K; Bell, Spencer; Epperson, Richard Tyler; Bloebaum, Roy D

    2013-05-01

    To gain an understanding of the vertebral cortical endplate and factors that may affect the ability to achieve skeletal attachment to intervertebral implants and fusion, this study aimed to characterize the hypermineralized tissue on the cortical endplate of the vertebral body on a commonly used animal model. Skeletally mature sheep were injected with tetracycline prior to euthanasia and the C2-C3, T5-T6, and L2-L3 spinal motion segments were excised and prepared. Vertebral tissues were imaged using backscatter electron (BSE) imaging, histology, and tetracycline labeling was used to assess bone remodeling within different tissue layers. It was determined that the hypermineralized tissue layer was calcified fibrocartilage (CFC). No tetracycline labels were identified in the CFC layer, in contrast to single and double labels that were present in the underlying bone, indicating the CFC present on the cortical endplate was not being actively remodeled. The average thickness of the CFC layer was 146.3 ± 70.53 µm in the cervical region, 98.2 ± 40.29 µm in the thoracic region, and 150.89 ± 69.25 µm in the lumbar region. This difference in thickness may be attributed to the regional biomechanical properties of the spine. Results from this investigation indicate the presence of a nonremodeling tissue on the cortical endplate of the vertebral body in sheep spines, which attaches the intervertebral disc to the vertebrae. This tissue, if not removed, would likely prevent successful bony attachment to an intervertebral device in spinal fusion studies and total disc replacement surgeries. Copyright © 2013 Wiley Periodicals, Inc.

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

    Science.gov (United States)

    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. A laminar cortical model of stereopsis and 3D surface perception: closure and da Vinci stereopsis.

    Science.gov (United States)

    Cao, Yongqiang; Grossberg, Stephen

    2005-01-01

    A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model describes how monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the LGN and cortical areas V1, V2, and V4. It proposes how interactions between layers 4, 3B, and 2/3 in V1 and V2 contribute to stereopsis, and how binocular and monocular information combine to form 3D boundary and surface representations. The model includes two main new developments: (1) It clarifies how surface-to-boundary feedback from V2 thin stripes to pale stripes helps to explain data about stereopsis. This feedback has previously been used to explain data about 3D figure-ground perception. (2) It proposes that the binocular false match problem is subsumed under the Gestalt grouping problem. In particular, the disparity filter, which helps to solve the correspondence problem by eliminating false matches, is realized using inhibitory interneurons as part of the perceptual grouping process by horizontal connections in layer 2/3 of cortical area V2. The enhanced model explains all the psychophysical data previously simulated by Grossberg and Howe (2003), such as contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, and da Vinci stereopsis. It also explains psychophysical data about perceptual closure and variations of da Vinci stereopsis that previous models cannot yet explain.

  13. Target-Centric Network Modeling

    DEFF Research Database (Denmark)

    Mitchell, Dr. William L.; Clark, Dr. Robert M.

    In Target-Centric Network Modeling: Case Studies in Analyzing Complex Intelligence Issues, authors Robert Clark and William Mitchell take an entirely new approach to teaching intelligence analysis. Unlike any other book on the market, it offers case study scenarios using actual intelligence...... reporting formats, along with a tested process that facilitates the production of a wide range of analytical products for civilian, military, and hybrid intelligence environments. Readers will learn how to perform the specific actions of problem definition modeling, target network modeling......, and collaborative sharing in the process of creating a high-quality, actionable intelligence product. The case studies reflect the complexity of twenty-first century intelligence issues by dealing with multi-layered target networks that cut across political, economic, social, technological, and military issues...

  14. Low and High-Frequency Field Potentials of Cortical Networks Exhibit Distinct Responses to Chemicals

    Science.gov (United States)

    Neural networks grown on microelectrode arrays (MEAs) have become an important, high content in vitro assay for assessing neuronal function. MEA experiments typically examine high- frequency (HF) (>200 Hz) spikes, and bursts which can be used to discriminate between differ...

  15. Patterns of cortical oscillations organize neural activity into whole-brain functional networks evident in the fMRI BOLD signal

    Directory of Open Access Journals (Sweden)

    Jennifer C Whitman

    2013-03-01

    Full Text Available 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.

  16. Computer modelling of RF ablation in cortical osteoid osteoma: Assessment of the insulating effect of the reactive zone

    OpenAIRE

    Irastorza, Ramiro M.; Trujillo Guillen, Macarena; Martel Villagran, Jose; Berjano, Enrique

    2016-01-01

    This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Hyperthermia on 10 Feb 2016, available online: http://www.tandfonline.com/10.3109/02656736.2015.1135998 Purpose: The aim was to study by computer simulations the insulating role of the reactive zone surrounding a cortical osteoid osteoma (OO) in terms of electrical and thermal performance during radiofrequency ablation (RFA). Material and methods: We modelled a cortical OO consi...

  17. Model for the orientational ordering of the plant microtubule cortical array

    Science.gov (United States)

    Hawkins, Rhoda J.; Tindemans, Simon H.; Mulder, Bela M.

    2010-07-01

    The plant microtubule cortical array is a striking feature of all growing plant cells. It consists of a more or less homogeneously distributed array of highly aligned microtubules connected to the inner side of the plasma membrane and oriented transversely to the cell growth axis. Here, we formulate a continuum model to describe the origin of orientational order in such confined arrays of dynamical microtubules. The model is based on recent experimental observations that show that a growing cortical microtubule can interact through angle dependent collisions with pre-existing microtubules that can lead either to co-alignment of the growth, retraction through catastrophe induction or crossing over the encountered microtubule. We identify a single control parameter, which is fully determined by the nucleation rate and intrinsic dynamics of individual microtubules. We solve the model analytically in the stationary isotropic phase, discuss the limits of stability of this isotropic phase, and explicitly solve for the ordered stationary states in a simplified version of the model.

  18. Microstructural changes in ischemic cortical gray matter predicted by a model of diffusion-weighted MRI

    DEFF Research Database (Denmark)

    Vestergaard-Poulsen, Peter; Hansen, Brian; Østergaard, Leif

    2007-01-01

    compartment. A global optimum was found from a wide range of parameter permutations using cluster computing. We also present simulations of cell swelling and changes of exchange rate and intracellular diffusion as possible cellular mechanisms in ischemia. RESULTS: Our model estimates an extracellular volume...... compartments and slow water exchange. Our model reproduces the signal changes observed in ischemia via physiologically credible mechanisms. CONCLUSION: Our modeling suggests that transverse relaxation has a profound influence on the diffusion attenuated MR signal. Our simulations indicate cell swelling...... model to the diffusion-weighted MR signal obtained from cortical gray matter in healthy subjects. Our model includes variable volume fractions, intracellular restriction effects, and exchange between compartments in addition to individual diffusion coefficients and transverse relaxation rates for each...

  19. The Controlled Cortical Impact Model of Experimental Brain Trauma: Overview, Research Applications, and Protocol.

    Science.gov (United States)

    Osier, Nicole; Dixon, C Edward

    2016-01-01

    Controlled cortical impact (CCI) is a commonly used and highly regarded model of brain trauma that uses a pneumatically or electromagnetically controlled piston to induce reproducible and well-controlled injury. The CCI model was originally used in ferrets and it has since been scaled for use in many other species. This chapter will describe the historical development of the CCI model, compare and contrast the pneumatic and electromagnetic models, and summarize key short- and long-term consequences of TBI that have been gleaned using this model. In accordance with the recent efforts to promote high-quality evidence through the reporting of common data elements (CDEs), relevant study details-that should be reported in CCI studies-will be noted.

  20. Cortical surface-based analysis reduces bias and variance in kinetic modeling of brain PET data

    DEFF Research Database (Denmark)

    Greve, Douglas N; Svarer, Claus; Fisher, Patrick M

    2014-01-01

    Exploratory (i.e., voxelwise) spatial methods are commonly used in neuroimaging to identify areas that show an effect when a region-of-interest (ROI) analysis cannot be performed because no strong a priori anatomical hypothesis exists. However, noise at a single voxel is much higher than noise...... in a ROI making noise management critical to successful exploratory analysis. This work explores how preprocessing choices affect the bias and variability of voxelwise kinetic modeling analysis of brain positron emission tomography (PET) data. These choices include the use of volume- or cortical surface...

  1. Continuum Model for River Networks

    Science.gov (United States)

    Giacometti, Achille; Maritan, Amos; Banavar, Jayanth R.

    1995-07-01

    The effects of erosion, avalanching, and random precipitation are captured in a simple stochastic partial differential equation for modeling the evolution of river networks. Our model leads to a self-organized structured landscape and to abstraction and piracy of the smaller tributaries as the evolution proceeds. An algebraic distribution of the average basin areas and a power law relationship between the drainage basin area and the river length are found.

  2. Linear distributed source modeling of local field potentials recorded with intra-cortical electrode arrays.

    Directory of Open Access Journals (Sweden)

    Rikkert Hindriks

    Full Text Available Planar intra-cortical electrode (Utah arrays provide a unique window into the spatial organization of cortical activity. Reconstruction of the current source density (CSD underlying such recordings, however, requires "inverting" Poisson's equation. For inter-laminar recordings, this is commonly done by the CSD method, which consists in taking the second-order spatial derivative of the recorded local field potentials (LFPs. Although the CSD method has been tremendously successful in mapping the current generators underlying inter-laminar LFPs, its application to planar recordings is more challenging. While for inter-laminar recordings the CSD method seems reasonably robust against violations of its assumptions, is it unclear as to what extent this holds for planar recordings. One of the objectives of this study is to characterize the conditions under which the CSD method can be successfully applied to Utah array data. Using forward modeling, we find that for spatially coherent CSDs, the CSD method yields inaccurate reconstructions due to volume-conducted contamination from currents in deeper cortical layers. An alternative approach is to "invert" a constructed forward model. The advantage of this approach is that any a priori knowledge about the geometrical and electrical properties of the tissue can be taken into account. Although several inverse methods have been proposed for LFP data, the applicability of existing electroencephalographic (EEG and magnetoencephalographic (MEG inverse methods to LFP data is largely unexplored. Another objective of our study therefore, is to assess the applicability of the most commonly used EEG/MEG inverse methods to Utah array data. Our main conclusion is that these inverse methods provide more accurate CSD reconstructions than the CSD method. We illustrate the inverse methods using event-related potentials recorded from primary visual cortex of a macaque monkey during a motion discrimination task.

  3. A New Rat Model of Epileptic Spasms Based on Methylazoxymethanol-Induced Malformations of Cortical Development

    Directory of Open Access Journals (Sweden)

    Eun-Hee Kim

    2017-06-01

    Full Text Available Malformations of cortical development (MCDs can cause medically intractable epilepsies and cognitive disabilities in children. We developed a new model of MCD-associated epileptic spasms by treating rats prenatally with methylazoxymethanol acetate (MAM to induce cortical malformations and postnatally with N-methyl-d-aspartate (NMDA to induce spasms. To produce cortical malformations to infant rats, two dosages of MAM (15 mg/kg, intraperitoneally were injected to pregnant rats at gestational day 15. In prenatally MAM-exposed rats and the controls, spasms were triggered by single (6 mg/kg on postnatal day 12 (P12 or 10 mg/kg on P13 or 15 mg/kg on P15 or multiple doses (P12, P13, and P15 of NMDA. In prenatally MAM-exposed rats with single NMDA-provoked spasms at P15, we obtain the intracranial electroencephalography and examine the pretreatment response to adrenocorticotropic hormone (ACTH or vigabatrin. Rat pups prenatally exposed to MAM exhibited a significantly greater number of spasms in response to single and multiple postnatal NMDA doses than vehicle-exposed controls. Vigabatrin treatment prior to a single NMDA dose on P15 significantly suppressed spasms in MAM group rats (p < 0.05, while ACTH did not. The MAM group also showed significantly higher fast oscillation (25–100 Hz power during NMDA-induced spasms than controls (p = 0.047. This new model of MCD-based epileptic spasms with corresponding features of human spasms will be valuable for future research of the developmental epilepsy.

  4. Automatic Detection of Cortical Arousals in Sleep using Bi-direction LSTM Networks

    DEFF Research Database (Denmark)

    Brink-Kjaer, A.; Olesen, Alexander Neergaard; Jespersen, C. A.

    2018-01-01

    ) and chin electromyography (EMG) to compute a probability of arousals through a bi-directional long short-term memory neural network. The study used a dataset of 233 nocturnal PSGs of population-based samples from Wisconsin Sleep Cohort (WSC) and 30 nocturnal PSGs of clinical samples from the Stanford Sleep...

  5. Phase-dependent effects of stimuli locked to oscillatory activity in cultured cortical networks

    NARCIS (Netherlands)

    Stegenga, J.; le Feber, Jakob; Marani, Enrico; Rutten, Wim

    The archetypal activity pattern in cultures of dissociated neurons is spontaneous network-wide bursting. Bursts may interfere with controlled activation of synaptic plasticity, but can be suppressed by the application of stimuli at a sufficient rate. We sinusoidally modulated (4 Hz) the pulse rate

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

    Science.gov (United States)

    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…

  7. Resting-state functional under-connectivity within and between large-scale cortical networks across three low-frequency bands in adolescents with autism.

    Science.gov (United States)

    Duan, Xujun; Chen, Heng; He, Changchun; Long, Zhiliang; Guo, Xiaonan; Zhou, Yuanyue; Uddin, Lucina Q; Chen, Huafu

    2017-10-03

    Although evidence is accumulating that autism spectrum disorder (ASD) is associated with disruption of functional connections between and within brain networks, it remains largely unknown whether these abnormalities are related to specific frequency bands. To address this question, network contingency analysis was performed on brain functional connectomes obtained from 213 adolescent participants across nine sites in the Autism Brain Imaging Data Exchange (ABIDE) multisite sample, to determine the disrupted connections between and within seven major cortical networks in adolescents with ASD at Slow-5, Slow-4 and Slow-3 frequency bands and further assess whether the aberrant intra- and inter-network connectivity varied as a function of ASD symptoms. Overall under-connectivity within and between large-scale intrinsic networks in ASD was revealed across the three frequency bands. Specifically, decreased connectivity strength within the default mode network (DMN), between DMN and visual network (VN), ventral attention network (VAN), and between dorsal attention network (DAN) and VAN was observed in the lower frequency band (slow-5, slow-4), while decreased connectivity between limbic network (LN) and frontal-parietal network (FPN) was observed in the higher frequency band (slow-3). Furthermore, weaker connectivity within and between specific networks correlated with poorer communication and social interaction skills in the slow-5 band, uniquely. These results demonstrate intrinsic under-connectivity within and between multiple brain networks within predefined frequency bands in ASD, suggesting that frequency-related properties underlie abnormal brain network organization in the disorder. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo

    2015-09-15

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation and angiogenesis) and ion transportation networks (e.g., neural networks) is explained in detail and basic analytical features like the gradient flow structure of the fluid transportation network model and the impact of the model parameters on the geometry and topology of network formation are analyzed. We also present a numerical finite-element based discretization scheme and discuss sample cases of network formation simulations.

  9. Network modelling methods for FMRI.

    Science.gov (United States)

    Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

    2011-01-15

    There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.

  10. Neurophysiology of the cortical pain network: revisiting the role of S1 in subjective pain perception via standardized low-resolution brain electromagnetic tomography (sLORETA).

    Science.gov (United States)

    Nir, Rony-Reuven; Lev, Rina; Moont, Ruth; Granovsky, Yelena; Sprecher, Elliot; Yarnitsky, David

    2008-11-01

    Multiple studies have supported the usefulness of standardized low-resolution brain electromagnetic tomography (sLORETA) in localizing generators of scalp-recorded potentials. The current study implemented sLORETA on pain event-related potentials, primarily aiming at validating this technique for pain research by identifying well-known pain-related regions. Subsequently, we pointed at investigating the still-debated and ambiguous topic of pain intensity coding at these regions, focusing on their relative impact on subjective pain perception. sLORETA revealed significant activations of the bilateral primary somatosensory (SI) and anterior cingulate cortices and of the contralateral operculoinsular and dorsolateral prefrontal (DLPFC) cortices (P < .05 for each). Activity of these regions, excluding DLPFC, correlated with subjective numerical pain scores (P < .05 for each). However, a multivariate regression analysis (R = .80; P = .024) distinguished the contralateral SI as the only region whose activation magnitude significantly predicted the subjective perception of noxious stimuli (P = .020), further substantiated by a reduced regression model (R = .75, P = .008). Based on (1) correspondence of the pain-activated regions identified by sLORETA with the acknowledged imaging-based pain-network and (2) the contralateral SI proving to be the most contributing region in pain intensity coding, we found sLORETA to be an appropriate tool for relevant pain research and further substantiated the role of SI in pain perception. Because the literature of pain intensity coding offers inconsistent findings, the current article used a novel tool for revisiting this controversial issue. Results suggest that it is the activation magnitude of SI, which solely establishes the significant correlation with subjective pain ratings, in accordance with the classical clinical thinking, relating SI lesions to diminished perception of pain. Although this study cannot support a causal relation

  11. Lacunar-canalicular network in femoral cortical bone is reduced in aged women and is predominantly due to a loss of canalicular porosity

    Directory of Open Access Journals (Sweden)

    A.M. Ashique

    2017-12-01

    Full Text Available The lacunar-canalicular network (LCN of bone contains osteocytes and their dendritic extensions, which allow for intercellular communication, and are believed to serve as the mechanosensors that coordinate the processes of bone modeling and remodeling. Imbalances in remodeling, for example, are linked to bone disease, including fragility associated with aging. We have reported that there is a reduction in scale for one component of the LCN, osteocyte lacunar volume, across the human lifespan in females. In the present study, we explore the hypothesis that canalicular porosity also declines with age. To visualize the LCN and to determine how its components are altered with aging, we examined samples from young (age: 20–23 y; n = 5 and aged (age: 70–86 y; n = 6 healthy women donors utilizing a fluorescent labelling technique in combination with confocal laser scanning microscopy. A large cross-sectional area of cortical bone spanning the endosteal to periosteal surfaces from the anterior proximal femoral shaft was examined in order to account for potential trans-cortical variation in the LCN. Overall, we found that LCN areal fraction was reduced by 40.6% in the samples from aged women. This reduction was due, in part, to a reduction in lacunar density (21.4% decline in lacunae number per given area of bone, but much more so due to a 44.6% decline in canalicular areal fraction. While the areal fraction of larger vascular canals was higher in endosteal vs. periosteal regions for both age groups, no regional differences were observed in the areal fractions of the LCN and its components for either age group. Our data indicate that the LCN is diminished in aged women, and is largely due to a decline in the canalicular areal fraction, and that, unlike vascular canal porosity, this diminished LCN is uniform across the cortex.

  12. Turing-like structures in a functional model of cortical spreading depression

    Science.gov (United States)

    Verisokin, A. Yu.; Verveyko, D. V.; Postnov, D. E.

    2017-12-01

    Cortical spreading depression (CSD) along with migraine waves and spreading depolarization events with stroke or injures are the front-line examples of extreme physiological behaviors of the brain cortex which manifest themselves via the onset and spreading of localized areas of neuronal hyperactivity followed by their depression. While much is known about the physiological pathways involved, the dynamical mechanisms of the formation and evolution of complex spatiotemporal patterns during CSD are still poorly understood, in spite of the number of modeling studies that have been already performed. Recently we have proposed a relatively simple mathematical model of cortical spreading depression which counts the effects of neurovascular coupling and cerebral blood flow redistribution during CSD. In the present study, we address the main dynamical consequences of newly included pathways, namely, the changes in the formation and propagation speed of the CSD front and the pattern formation features in two dimensions. Our most notable finding is that the combination of vascular-mediated spatial coupling with local regulatory mechanisms results in the formation of stationary Turing-like patterns during a CSD event.

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

    Energy Technology Data Exchange (ETDEWEB)

    Ciani, A.; Kewish, C. M. [Synchrotron Soleil, L’Orme des Merisiers, 91192 Saint-Aubin (France); Guizar-Sicairos, M.; Diaz, A.; Holler, M. [Paul Scherrer Institut, 5232 Villigen PSI (Switzerland); Pallu, S.; Achiou, Z.; Jennane, R.; Toumi, H.; Lespessailles, E. [Univ Orléans, I3MTO, Ea 4708, 45000 Orléans (France)

    2016-01-28

    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.

  14. Modulation of cortical-subcortical networks in Parkinson’s disease by applied field effects

    OpenAIRE

    Hess, Christopher W.

    2013-01-01

    Studies suggest that endogenous field effects may play a role in neuronal oscillations and communication. Non-invasive transcranial electrical stimulation with low-intensity currents can also have direct effects on the underlying cortex as well as distant network effects. While Parkinson’s disease (PD) is amenable to invasive neuromodulation in the basal ganglia by deep brain stimulation (DBS), techniques of non-invasive neuromodulation like transcranial direct current stimulation (tDCS) and ...

  15. Research on the model of home networking

    Science.gov (United States)

    Yun, Xiang; Feng, Xiancheng

    2007-11-01

    It is the research hotspot of current broadband network to combine voice service, data service and broadband audio-video service by IP protocol to transport various real time and mutual services to terminal users (home). Home Networking is a new kind of network and application technology which can provide various services. Home networking is called as Digital Home Network. It means that PC, home entertainment equipment, home appliances, Home wirings, security, illumination system were communicated with each other by some composing network technology, constitute a networking internal home, and connect with WAN by home gateway. It is a new network technology and application technology, and can provide many kinds of services inside home or between homes. Currently, home networking can be divided into three kinds: Information equipment, Home appliances, Communication equipment. Equipment inside home networking can exchange information with outer networking by home gateway, this information communication is bidirectional, user can get information and service which provided by public networking by using home networking internal equipment through home gateway connecting public network, meantime, also can get information and resource to control the internal equipment which provided by home networking internal equipment. Based on the general network model of home networking, there are four functional entities inside home networking: HA, HB, HC, and HD. (1) HA (Home Access) - home networking connects function entity; (2) HB (Home Bridge) Home networking bridge connects function entity; (3) HC (Home Client) - Home networking client function entity; (4) HD (Home Device) - decoder function entity. There are many physical ways to implement four function entities. Based on theses four functional entities, there are reference model of physical layer, reference model of link layer, reference model of IP layer and application reference model of high layer. In the future home network

  16. Mathematical Modelling Plant Signalling Networks

    KAUST Repository

    Muraro, D.

    2013-01-01

    During the last two decades, molecular genetic studies and the completion of the sequencing of the Arabidopsis thaliana genome have increased knowledge of hormonal regulation in plants. These signal transduction pathways act in concert through gene regulatory and signalling networks whose main components have begun to be elucidated. Our understanding of the resulting cellular processes is hindered by the complex, and sometimes counter-intuitive, dynamics of the networks, which may be interconnected through feedback controls and cross-regulation. Mathematical modelling provides a valuable tool to investigate such dynamics and to perform in silico experiments that may not be easily carried out in a laboratory. In this article, we firstly review general methods for modelling gene and signalling networks and their application in plants. We then describe specific models of hormonal perception and cross-talk in plants. This mathematical analysis of sub-cellular molecular mechanisms paves the way for more comprehensive modelling studies of hormonal transport and signalling in a multi-scale setting. © EDP Sciences, 2013.

  17. Effect of canagliflozin and metformin on cortical neurotransmitters in a diabetic rat model.

    Science.gov (United States)

    Arafa, Nadia M S; Marie, Mohamed-Assem S; AlAzimi, Sara Abdullah Mubarak

    2016-10-25

    The rapid economic development in the Arabian Gulf has resulted in lifestyle changes that have increased the prevalence of obesity and type 2 diabetes, with the greatest increases observed in Kuwait. Dyslipidemia and diabetes are risk factors for disruptions in cortical neurotransmitter homeostasis. This study investigated the effect of the antidiabetic medications canagliflozin (CAN) and metformin (MET) on the levels of cortical neurotransmitters in a diabetic rat model. The rats were assigned to the control (C) group, the diabetic group that did not receive treatment (D) or the diabetic group treated with either CAN (10 mg/kg) or MET (100 mg/kg) for 2 or 4 weeks. Blood and urine glucose levels and cortical acetylcholinesterase (AChE) activity were assayed, and amino acid and monoamine levels were measured using HPLC. The diabetic group exhibited a significant increase in AChE activity and a decrease in monoamine and amino acid neurotransmitter levels. In the CAN group, AChE was significantly lower than that in the D and D + MET groups after 2 weeks of treatment. In addition, a significant increase in some cortical monoamines and amino acids was observed in the D + MET and D + CAN groups compared with the D group. Histopathological analysis revealed the presence of severe focal hemorrhage, neuronal degeneration, and cerebral blood vessel congestion, with gliosis in the cerebrum of rats in the D group. The CAN-treated group exhibited severe cerebral blood vessel congestion after 2 weeks of treatment and focal gliosis in the cerebrum after 4 weeks of treatment. Focal gliosis in the cerebrum of rats in the MET-treated group was observed after 2 and 4 weeks of treatment. We conclude that the effect of CAN and MET on neurotransmitters is potentially mediated by their antihyperglycemic and antihyperlipidemic effects. In addition, the effects of CAN on neurotransmitters might be associated with its receptor activity, and the effect of MET on neurotransmitters

  18. Energy modelling in sensor networks

    Science.gov (United States)

    Schmidt, D.; Krämer, M.; Kuhn, T.; Wehn, N.

    2007-06-01

    Wireless sensor networks are one of the key enabling technologies for the vision of ambient intelligence. Energy resources for sensor nodes are very scarce. A key challenge is the design of energy efficient communication protocols. Models of the energy consumption are needed to accurately simulate the efficiency of a protocol or application design, and can also be used for automatic energy optimizations in a model driven design process. We propose a novel methodology to create models for sensor nodes based on few simple measurements. In a case study the methodology was used to create models for MICAz nodes. The models were integrated in a simulation environment as well as in a SDL runtime framework of a model driven design process. Measurements on a test application that was created automatically from an SDL specification showed an 80% reduction in energy consumption compared to an implementation without power saving strategies.

  19. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks

    DEFF Research Database (Denmark)

    Hagen, Espen; Dahmen, David; Stavrinou, Maria L

    2016-01-01

    on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network......With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical...... and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely...

  20. A Comparative Study of Theoretical Graph Models for Characterizing Structural Networks of Human Brain

    Directory of Open Access Journals (Sweden)

    Xiaojin Li

    2013-01-01

    Full Text Available Previous studies have investigated both structural and functional brain networks via graph-theoretical methods. However, there is an important issue that has not been adequately discussed before: what is the optimal theoretical graph model for describing the structural networks of human brain? In this paper, we perform a comparative study to address this problem. Firstly, large-scale cortical regions of interest (ROIs are localized by recently developed and validated brain reference system named Dense Individualized Common Connectivity-based Cortical Landmarks (DICCCOL to address the limitations in the identification of the brain network ROIs in previous studies. Then, we construct structural brain networks based on diffusion tensor imaging (DTI data. Afterwards, the global and local graph properties of the constructed structural brain networks are measured using the state-of-the-art graph analysis algorithms and tools and are further compared with seven popular theoretical graph models. In addition, we compare the topological properties between two graph models, namely, stickiness-index-based model (STICKY and scale-free gene duplication model (SF-GD, that have higher similarity with the real structural brain networks in terms of global and local graph properties. Our experimental results suggest that among the seven theoretical graph models compared in this study, STICKY and SF-GD models have better performances in characterizing the structural human brain network.

  1. Biological transportation networks: Modeling and simulation

    KAUST Repository

    Albi, Giacomo; Artina, Marco; Foransier, Massimo; Markowich, Peter A.

    2015-01-01

    We present a model for biological network formation originally introduced by Cai and Hu [Adaptation and optimization of biological transport networks, Phys. Rev. Lett. 111 (2013) 138701]. The modeling of fluid transportation (e.g., leaf venation

  2. Preparing hearts and minds: cardiac slowing and a cortical inhibitory network.

    Science.gov (United States)

    Jennings, J R; van der Molen, M W; Tanase, C

    2009-11-01

    Preparing for a cued, speeded response induces a set of physiological changes. A review of the psychophysiology of preparation suggested that inhibition of action was an important process among the constellation of changes constituting attentive preparation. The current experiment combined event-related functional magnetic resonance imaging and cardiac inter-beat interval measures in an experiment that compared preparing for a response, watching stimuli without responding, and responding in the absence of preparation. Ten college-aged participants were tested in an initial psychophysiological experiment followed by two scanning sessions during which reverse spiral imaging was performed concurrent with inter-beat interval measurement. Two analytic approaches were used to confirm blood oxygenation level dependent responses during preparation, and these converged to show inferior prefrontal and related subthalamic nuclei activity in the context of other known changes related to brain attentional networks. Subthalamic nuclei changes were related to the depth of preparatory cardiac deceleration. This pattern of findings suggests that preparation involves the activation of a complex inhibitory neural network implicating brain and autonomic nervous systems.

  3. An evolving network model with community structure

    International Nuclear Information System (INIS)

    Li Chunguang; Maini, Philip K

    2005-01-01

    Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

  4. Brand Marketing Model on Social Networks

    Directory of Open Access Journals (Sweden)

    Jolita Jezukevičiūtė

    2014-04-01

    Full Text Available The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalysis of a single case study revealed a brand marketingsocial networking tools that affect consumers the most. Basedon information analysis and methodological studies, develop abrand marketing model on social networks.

  5. The pairwise phase consistency in cortical network and its relationship with neuronal activation

    Directory of Open Access Journals (Sweden)

    Wang Daming

    2017-01-01

    Full Text Available Gamma-band neuronal oscillation and synchronization with the range of 30-90 Hz are ubiquitous phenomenon across numerous brain areas and various species, and correlated with plenty of cognitive functions. The phase of the oscillation, as one aspect of CTC (Communication through Coherence hypothesis, underlies various functions for feature coding, memory processing and behaviour performing. The PPC (Pairwise Phase Consistency, an improved coherence measure, statistically quantifies the strength of phase synchronization. In order to evaluate the PPC and its relationships with input stimulus, neuronal activation and firing rate, a simplified spiking neuronal network is constructed to simulate orientation columns in primary visual cortex. If the input orientation stimulus is preferred for a certain orientation column, neurons within this corresponding column will obtain higher firing rate and stronger neuronal activation, which consequently engender higher PPC values, with higher PPC corresponding to higher firing rate. In addition, we investigate the PPC in time resolved analysis with a sliding window.

  6. Microstructural changes in ischemic cortical gray matter predicted by a model of diffusion-weighted MRI.

    Science.gov (United States)

    Vestergaard-Poulsen, Peter; Hansen, Brian; Ostergaard, Leif; Jakobsen, Rikke

    2007-09-01

    To understand the diffusion attenuated MR signal from normal and ischemic brain tissue in order to extract structural and physiological information using mathematical modeling, taking into account the transverse relaxation rates in gray matter. We fit our diffusion model to the diffusion-weighted MR signal obtained from cortical gray matter in healthy subjects. Our model includes variable volume fractions, intracellular restriction effects, and exchange between compartments in addition to individual diffusion coefficients and transverse relaxation rates for each compartment. A global optimum was found from a wide range of parameter permutations using cluster computing. We also present simulations of cell swelling and changes of exchange rate and intracellular diffusion as possible cellular mechanisms in ischemia. Our model estimates an extracellular volume fraction of 0.19 in accordance with the accepted value from histology. The absolute apparent diffusion coefficient obtained from the model was similar to that of experiments. The model and the experimental results indicate significant differences in diffusion and transverse relaxation between the tissue compartments and slow water exchange. Our model reproduces the signal changes observed in ischemia via physiologically credible mechanisms. Our modeling suggests that transverse relaxation has a profound influence on the diffusion attenuated MR signal. Our simulations indicate cell swelling as the primary cause of the diffusion changes seen in the acute phase of brain ischemia. (c) 2007 Wiley-Liss, Inc.

  7. A novel Direct Small World network model

    Directory of Open Access Journals (Sweden)

    LIN Tao

    2016-10-01

    Full Text Available There is a certain degree of redundancy and low efficiency of existing computer networks.This paper presents a novel Direct Small World network model in order to optimize networks.In this model,several nodes construct a regular network.Then,randomly choose and replot some nodes to generate Direct Small World network iteratively.There is no change in average distance and clustering coefficient.However,the network performance,such as hops,is improved.The experiments prove that compared to traditional small world network,the degree,average of degree centrality and average of closeness centrality are lower in Direct Small World network.This illustrates that the nodes in Direct Small World networks are closer than Watts-Strogatz small world network model.The Direct Small World can be used not only in the communication of the community information,but also in the research of epidemics.

  8. RMBNToolbox: random models for biochemical networks

    Directory of Open Access Journals (Sweden)

    Niemi Jari

    2007-05-01

    Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

  9. Increased susceptibility to cortical spreading depression in the mouse model of familial hemiplegic migraine type 2.

    Directory of Open Access Journals (Sweden)

    Loredana Leo

    2011-06-01

    Full Text Available Familial hemiplegic migraine type 2 (FHM2 is an autosomal dominant form of migraine with aura that is caused by mutations of the α2-subunit of the Na,K-ATPase, an isoform almost exclusively expressed in astrocytes in the adult brain. We generated the first FHM2 knock-in mouse model carrying the human W887R mutation in the Atp1a2 orthologous gene. Homozygous Atp1a2(R887/R887 mutants died just after birth, while heterozygous Atp1a2(+/R887 mice showed no apparent clinical phenotype. The mutant α2 Na,K-ATPase protein was barely detectable in the brain of homozygous mutants and strongly reduced in the brain of heterozygous mutants, likely as a consequence of endoplasmic reticulum retention and subsequent proteasomal degradation, as we demonstrate in transfected cells. In vivo analysis of cortical spreading depression (CSD, the phenomenon underlying migraine aura, revealed a decreased induction threshold and an increased velocity of propagation in the heterozygous FHM2 mouse. Since several lines of evidence involve a specific role of the glial α2 Na,K pump in active reuptake of glutamate from the synaptic cleft, we hypothesize that CSD facilitation in the FHM2 mouse model is sustained by inefficient glutamate clearance by astrocytes and consequent increased cortical excitatory neurotransmission. The demonstration that FHM2 and FHM1 mutations share the ability to facilitate induction and propagation of CSD in mouse models further support the role of CSD as a key migraine trigger.

  10. Adaptation, expertise, and giftedness: towards an understanding of cortical, subcortical, and cerebellar network contributions.

    Science.gov (United States)

    Koziol, Leonard F; Budding, Deborah Ely; Chidekel, Dana

    2010-12-01

    Current cortico-centric models of cognition lack a cohesive neuroanatomic framework that sufficiently considers overlapping levels of function, from "pathological" through "normal" to "gifted" or exceptional ability. While most cognitive theories presume an evolutionary context, few actively consider the process of adaptation, including concepts of neurodevelopment. Further, the frequent co-occurrence of "gifted" and "pathological" function is difficult to explain from a cortico-centric point of view. This comprehensive review paper proposes a framework that includes the brain's vertical organization and considers "giftedness" from an evolutionary and neurodevelopmental vantage point. We begin by discussing the current cortico-centric model of cognition and its relationship to intelligence. We then review an integrated, dual-tiered model of cognition that better explains the process of adaptation by simultaneously allowing for both stimulus-based processing and higher-order cognitive control. We consider the role of the basal ganglia within this model, particularly in relation to reward circuitry and instrumental learning. We review the important role of white matter tracts in relation to speed of adaptation and development of behavioral mastery. We examine the cerebellum's critical role in behavioral refinement and in cognitive and behavioral automation, particularly in relation to expertise and giftedness. We conclude this integrated model of brain function by considering the savant syndrome, which we believe is best understood within the context of a dual-tiered model of cognition that allows for automaticity in adaptation as well as higher-order executive control.

  11. From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation

    Directory of Open Access Journals (Sweden)

    G. Cheron

    2012-01-01

    Full Text Available 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.

  12. Brand Marketing Model on Social Networks

    OpenAIRE

    Jolita Jezukevičiūtė; Vida Davidavičienė

    2014-01-01

    The paper analyzes the brand and its marketing solutions onsocial networks. This analysis led to the creation of improvedbrand marketing model on social networks, which will contributeto the rapid and cheap organization brand recognition, increasecompetitive advantage and enhance consumer loyalty. Therefore,the brand and a variety of social networks are becoming a hotresearch area for brand marketing model on social networks.The world‘s most successful brand marketing models exploratoryanalys...

  13. Brand marketing model on social networks

    OpenAIRE

    Jezukevičiūtė, Jolita; Davidavičienė, Vida

    2014-01-01

    Paper analyzes the brand and its marketing solutions on social networks. This analysis led to the creation of improved brand marketing model on social networks, which will contribute to the rapid and cheap organization brand recognition, increase competitive advantage and enhance consumer loyalty. Therefore, the brand and a variety of social networks are becoming a hot research area for brand marketing model on social networks. The world‘s most successful brand marketing models exploratory an...

  14. Spiking cortical model-based nonlocal means method for speckle reduction in optical coherence tomography images

    Science.gov (United States)

    Zhang, Xuming; Li, Liu; Zhu, Fei; Hou, Wenguang; Chen, Xinjian

    2014-06-01

    Optical coherence tomography (OCT) images are usually degraded by significant speckle noise, which will strongly hamper their quantitative analysis. However, speckle noise reduction in OCT images is particularly challenging because of the difficulty in differentiating between noise and the information components of the speckle pattern. To address this problem, the spiking cortical model (SCM)-based nonlocal means method is presented. The proposed method explores self-similarities of OCT images based on rotation-invariant features of image patches extracted by SCM and then restores the speckled images by averaging the similar patches. This method can provide sufficient speckle reduction while preserving image details very well due to its effectiveness in finding reliable similar patches under high speckle noise contamination. When applied to the retinal OCT image, this method provides signal-to-noise ratio improvements of >16 dB with a small 5.4% loss of similarity.

  15. Network Bandwidth Utilization Forecast Model on High Bandwidth Network

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wucherl; Sim, Alex

    2014-07-07

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2percent. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  16. Network bandwidth utilization forecast model on high bandwidth networks

    Energy Technology Data Exchange (ETDEWEB)

    Yoo, Wuchert (William) [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sim, Alex [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

    2015-03-30

    With the increasing number of geographically distributed scientific collaborations and the scale of the data size growth, it has become more challenging for users to achieve the best possible network performance on a shared network. We have developed a forecast model to predict expected bandwidth utilization for high-bandwidth wide area network. The forecast model can improve the efficiency of resource utilization and scheduling data movements on high-bandwidth network to accommodate ever increasing data volume for large-scale scientific data applications. Univariate model is developed with STL and ARIMA on SNMP path utilization data. Compared with traditional approach such as Box-Jenkins methodology, our forecast model reduces computation time by 83.2%. It also shows resilience against abrupt network usage change. The accuracy of the forecast model is within the standard deviation of the monitored measurements.

  17. Synaptic Changes in AMPA Receptor Subunit Expression in Cortical Parvalbumin Interneurons in the Stargazer Model of Absence Epilepsy

    Directory of Open Access Journals (Sweden)

    Nadia K. Adotevi

    2017-12-01

    Full Text Available Feedforward inhibition is essential to prevent run away excitation within the brain. Recent evidence suggests that a loss of feed-forward inhibition in the corticothalamocortical circuitry may underlie some absence seizures. However, it is unclear if this aberration is specifically linked to loss of synaptic excitation onto local fast-spiking parvalbumin-containing (PV+ inhibitory interneurons, which are responsible for mediating feedforward inhibition within cortical networks. We recently reported a global tissue loss of AMPA receptors (AMPARs, and a specific mistrafficking of these AMPARs in PV+ interneurons in the stargazer somatosensory cortex. The current study was aimed at investigating if cellular changes in AMPAR expression were translated into deficits in receptors at specific synapses in the feedforward inhibitory microcircuit. Using western blot immunolabeling on biochemically isolated synaptic fractions, we demonstrate a loss of AMPAR GluA1–4 subunits in the somatosensory cortex of stargazers compared to non-epileptic control mice. Furthermore, using double post-embedding immunogold-cytochemistry, we show a loss of GluA1–4-AMPARs at excitatory synapses onto cortical PV+ interneurons. Altogether, these data indicate a loss of synaptic AMPAR-mediated excitation of cortical PV+ inhibitory neurons. As the cortex is considered the site of initiation of spike wave discharges (SWDs within the corticothalamocortical circuitry, loss of AMPARs at cortical PV+ interneurons likely impairs feed-forward inhibitory output, and contributes to the generation of SWDs and absence seizures in stargazers.

  18. Population decoding of motor cortical activity using a generalized linear model with hidden states.

    Science.gov (United States)

    Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas; Paninski, Liam

    2010-06-15

    Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (reducing the mean square error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. Copyright (c) 2010 Elsevier B.V. All rights reserved.

  19. Population Decoding of Motor Cortical Activity using a Generalized Linear Model with Hidden States

    Science.gov (United States)

    Lawhern, Vernon; Wu, Wei; Hatsopoulos, Nicholas G.; Paninski, Liam

    2010-01-01

    Generalized linear models (GLMs) have been developed for modeling and decoding population neuronal spiking activity in the motor cortex. These models provide reasonable characterizations between neural activity and motor behavior. However, they lack a description of movement-related terms which are not observed directly in these experiments, such as muscular activation, the subject's level of attention, and other internal or external states. Here we propose to include a multi-dimensional hidden state to address these states in a GLM framework where the spike count at each time is described as a function of the hand state (position, velocity, and acceleration), truncated spike history, and the hidden state. The model can be identified by an Expectation-Maximization algorithm. We tested this new method in two datasets where spikes were simultaneously recorded using a multi-electrode array in the primary motor cortex of two monkeys. It was found that this method significantly improves the model-fitting over the classical GLM, for hidden dimensions varying from 1 to 4. This method also provides more accurate decoding of hand state (lowering the Mean Square Error by up to 29% in some cases), while retaining real-time computational efficiency. These improvements on representation and decoding over the classical GLM model suggest that this new approach could contribute as a useful tool to motor cortical decoding and prosthetic applications. PMID:20359500

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

    DEFF Research Database (Denmark)

    Herz, Damian M.; Haagensen, Brian N.; Christensen, Mark S.

    2015-01-01

    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...

  1. Improved diagnostic accuracy of Alzheimer's disease by combining regional cortical thickness and default mode network functional connectivity: Validated in the Alzheimer's disease neuroimaging initiative set

    Energy Technology Data Exchange (ETDEWEB)

    Park, Ji Eun; Park, Bum Woo; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Jung; Oh, Joo Young; Shim, Woo Hyun [Dept. of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of); Lee, Jae Hong; Roh, Jee Hoon [University of Ulsan College of Medicine, Asan Medical Center, Seoul (Korea, Republic of)

    2017-11-15

    To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease.

  2. An acoustical model based monitoring network

    NARCIS (Netherlands)

    Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

    2010-01-01

    In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

  3. Integrating microRNA and mRNA expression profiles of neuronal progenitors to identify regulatory networks underlying the onset of cortical neurogenesis

    Directory of Open Access Journals (Sweden)

    Barker Jeffery L

    2009-08-01

    Full Text Available Abstract Background Cortical development is a complex process that includes sequential generation of neuronal progenitors, which proliferate and migrate to form the stratified layers of the developing cortex. To identify the individual microRNAs (miRNAs and mRNAs that may regulate the genetic network guiding the earliest phase of cortical development, the expression profiles of rat neuronal progenitors obtained at embryonic day 11 (E11, E12 and E13 were analyzed. Results Neuronal progenitors were purified from telencephalic dissociates by a positive-selection strategy featuring surface labeling with tetanus-toxin and cholera-toxin followed by fluorescence-activated cell sorting. Microarray analyses revealed the fractions of miRNAs and mRNAs that were up-regulated or down-regulated in these neuronal progenitors at the beginning of cortical development. Nearly half of the dynamically expressed miRNAs were negatively correlated with the expression of their predicted target mRNAs. Conclusion These data support a regulatory role for miRNAs during the transition from neuronal progenitors into the earliest differentiating cortical neurons. In addition, by supplying a robust data set in which miRNA and mRNA profiles originate from the same purified cell type, this empirical study may facilitate the development of new algorithms to integrate various "-omics" data sets.

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

    Directory of Open Access Journals (Sweden)

    Guido Gigante

    2015-11-01

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

  5. A Model for Cortical 40 Hz oscillations invokes inter-area interactions

    DEFF Research Database (Denmark)

    Cotterill, Rodney M J; Helix Nielsen, Claus

    1991-01-01

    COMPUTER simulation of the dynamics of neuronal assemblies within minicolumns, and of the interactions between minicolumns in different cortical areas, has produced a quantitative explanation of the 35-60 Hz oscillations recently observed in adult cat striate cortices. The observed behavior...

  6. Spinal Cord Injury Model System Information Network

    Science.gov (United States)

    ... the UAB-SCIMS More The UAB-SCIMS Information Network The University of Alabama at Birmingham Spinal Cord Injury Model System (UAB-SCIMS) maintains this Information Network as a resource to promote knowledge in the ...

  7. Eight challenges for network epidemic models

    Directory of Open Access Journals (Sweden)

    Lorenzo Pellis

    2015-03-01

    Full Text Available Networks offer a fertile framework for studying the spread of infection in human and animal populations. However, owing to the inherent high-dimensionality of networks themselves, modelling transmission through networks is mathematically and computationally challenging. Even the simplest network epidemic models present unanswered questions. Attempts to improve the practical usefulness of network models by including realistic features of contact networks and of host–pathogen biology (e.g. waning immunity have made some progress, but robust analytical results remain scarce. A more general theory is needed to understand the impact of network structure on the dynamics and control of infection. Here we identify a set of challenges that provide scope for active research in the field of network epidemic models.

  8. Age- and function-related regional changes in cortical folding of the default mode network in older adults.

    Science.gov (United States)

    Jockwitz, Christiane; Caspers, Svenja; Lux, Silke; Jütten, Kerstin; Schleicher, Axel; Eickhoff, Simon B; Amunts, Katrin; Zilles, Karl

    2017-01-01

    Healthy aging is accompanied by changes in the functional architecture of the default mode network (DMN), e.g. a posterior to anterior shift (PASA) of activations. The putative structural correlate for this functional reorganization, however, is largely unknown. Changes in gyrification, i.e. decreases of cortical folding were found to be a marker of atrophy of the brain in later decades of life. Therefore, the present study assessed local gyrification indices of the DMN in relation to age and cognitive performance in 749 older adults aged 55-85 years. Age-related decreases in local gyrification indices were found in the anterior part of the DMN [particularly; medial prefrontal cortex (mPFC)] of the right hemisphere, and the medial posterior parts of the DMN [particularly; posterior cingulate cortex (PCC)/precuneus] of both hemispheres. Positive correlations between cognitive performance and local gyrification indices were found for (1) selective attention and left PCC/precuneus, (2) visual/visual-spatial working memory and bilateral PCC/precuneus and right angular gyrus (AG), and (3) semantic verbal fluency and right AG and right mPFC. The more pronounced age-related decrease in local gyrification indices of the posterior parts of the DMN supports the functionally motivated PASA theory by correlated structural changes. Surprisingly, the prominent age-related decrease in local gyrification indices in right hemispheric ROIs provides evidence for a structural underpinning of the right hemi-aging hypothesis. Noticeably, the performance-related changes in local gyrification largely involved the same parts of the DMN that were subject to age-related local gyrification decreases. Thus, the present study lends support for a combined structural and functional theory of aging, in that the functional changes in the DMN during aging are accompanied by comparably localized structural alterations.

  9. A neural model of motion processing and visual navigation by cortical area MST.

    Science.gov (United States)

    Grossberg, S; Mingolla, E; Pack, C

    1999-12-01

    Cells in the dorsal medial superior temporal cortex (MSTd) process optic flow generated by self-motion during visually guided navigation. A neural model shows how interactions between well-known neural mechanisms (log polar cortical magnification, Gaussian motion-sensitive receptive fields, spatial pooling of motion-sensitive signals and subtractive extraretinal eye movement signals) lead to emergent properties that quantitatively simulate neurophysiological data about MSTd cell properties and psychophysical data about human navigation. Model cells match MSTd neuron responses to optic flow stimuli placed in different parts of the visual field, including position invariance, tuning curves, preferred spiral directions, direction reversals, average response curves and preferred locations for stimulus motion centers. The model shows how the preferred motion direction of the most active MSTd cells can explain human judgments of self-motion direction (heading), without using complex heading templates. The model explains when extraretinal eye movement signals are needed for accurate heading perception, and when retinal input is sufficient, and how heading judgments depend on scene layouts and rotation rates.

  10. Computational model of cerebral blood flow redistribution during cortical spreading depression

    Science.gov (United States)

    Verisokin, Andrey Y.; Verveyko, Darya V.; Postnov, Dmitry E.

    2016-04-01

    In recent decades modelling studies on cortical spreading depression (CSD) and migraine waves successfully contributed to formation of modern view on these fundamental phenomena of brain physiology. However, due to the extreme complexity of object under study (brain cortex) and the diversity of involved physiological pathways, the development of new mathematical models of CSD is still a very relevant and challenging research problem. In our study we follow the functional modelling approach aimed to map the action of known physiological pathways to the specific nonlinear mechanisms that govern formation and evolution of CSD wave patterns. Specifically, we address the role of cerebral blood flow (CBF) redistribution that is caused by excessive neuronal activity by means of neurovascular coupling and mediates a spatial pattern of oxygen and glucose delivery. This in turn changes the local metabolic status of neural tissue. To build the model we simplify the web of known cell-to-cell interactions within a neurovascular unit by selecting the most relevant ones, such as local neuron-induced elevation of extracellular potassium concentration and biphasic response of arteriole radius. We propose the lumped description of distance-dependent hemodynamic coupling that fits the most recent experimental findings.

  11. When long-range zero-lag synchronization is feasible in cortical networks

    Directory of Open Access Journals (Sweden)

    Atthaphon eViriyopase

    2012-07-01

    Full Text Available Many studies have reported long-range synchronization of neuronal activity between brain areas, in particular in the gamma-band with frequencies in the range of 40-80 Hz. Several studies have reported synchrony with zero phase lag, which is remarkable considering the synaptic and conduction delays inherent in the connections between distant brain areas. This result has led to many speculations about the possible functional role of zero-lag synchrony, e.g., for neuronal communication in attention, memory and feature binding. However, recent studies using recordings of single-unit activity and local field potentials report that neuronal synchronization occurs with non-zero phase lags. This raises the questions whether zero-lag synchrony can occur in the brain and, if so, under which conditions.We used analytical methods and computer simulations to investigate which connectivity between neuronal populations allows or prohibits zero-lag synchrony. We did so for a model where two oscillators interact via a relay oscillator. Analytical results and computer simulations were obtained for both type I Mirollo-Strogatz neurons and type II Hodgkin-Huxley neurons. We have investigated the dynamics of the model for various types of synaptic coupling and importantly considered the potential impact of Spike-Timing Dependent Plasticity (STDP and its learning window. We confirm previous results that zero-lag synchrony can be achieved in this configuration. This is much easier to achieve with Hodgkin-Huxley neurons, which have a biphasic phase response curve, than for type I neurons. STDP facilitates zero-lag synchrony as it adjusts the synaptic strengths such that zero-lag synchrony is feasible for a much larger range of parameters than without STDP.

  12. Entropy Characterization of Random Network Models

    Directory of Open Access Journals (Sweden)

    Pedro J. Zufiria

    2017-06-01

    Full Text Available This paper elaborates on the Random Network Model (RNM as a mathematical framework for modelling and analyzing the generation of complex networks. Such framework allows the analysis of the relationship between several network characterizing features (link density, clustering coefficient, degree distribution, connectivity, etc. and entropy-based complexity measures, providing new insight on the generation and characterization of random networks. Some theoretical and computational results illustrate the utility of the proposed framework.

  13. The model of social crypto-network

    Directory of Open Access Journals (Sweden)

    Марк Миколайович Орел

    2015-06-01

    Full Text Available The article presents the theoretical model of social network with the enhanced mechanism of privacy policy. It covers the problems arising in the process of implementing the mentioned type of network. There are presented the methods of solving problems arising in the process of building the social network with privacy policy. It was built a theoretical model of social networks with enhanced information protection methods based on information and communication blocks

  14. Introducing Synchronisation in Deterministic Network Models

    DEFF Research Database (Denmark)

    Schiøler, Henrik; Jessen, Jan Jakob; Nielsen, Jens Frederik D.

    2006-01-01

    The paper addresses performance analysis for distributed real time systems through deterministic network modelling. Its main contribution is the introduction and analysis of models for synchronisation between tasks and/or network elements. Typical patterns of synchronisation are presented leading...... to the suggestion of suitable network models. An existing model for flow control is presented and an inherent weakness is revealed and remedied. Examples are given and numerically analysed through deterministic network modelling. Results are presented to highlight the properties of the suggested models...

  15. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network

    Directory of Open Access Journals (Sweden)

    Adam ePonzi

    2012-03-01

    Full Text Available The striatal medium spiny neuron (MSNs network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri stimulus time histograms (PSTH of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioural task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviourally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would in when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and delineate the range of parameters where this behaviour is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response

  16. Input dependent cell assembly dynamics in a model of the striatal medium spiny neuron network.

    Science.gov (United States)

    Ponzi, Adam; Wickens, Jeff

    2012-01-01

    The striatal medium spiny neuron (MSN) network is sparsely connected with fairly weak GABAergic collaterals receiving an excitatory glutamatergic cortical projection. Peri-stimulus time histograms (PSTH) of MSN population response investigated in various experimental studies display strong firing rate modulations distributed throughout behavioral task epochs. In previous work we have shown by numerical simulation that sparse random networks of inhibitory spiking neurons with characteristics appropriate for UP state MSNs form cell assemblies which fire together coherently in sequences on long behaviorally relevant timescales when the network receives a fixed pattern of constant input excitation. Here we first extend that model to the case where cortical excitation is composed of many independent noisy Poisson processes and demonstrate that cell assembly dynamics is still observed when the input is sufficiently weak. However if cortical excitation strength is increased more regularly firing and completely quiescent cells are found, which depend on the cortical stimulation. Subsequently we further extend previous work to consider what happens when the excitatory input varies as it would when the animal is engaged in behavior. We investigate how sudden switches in excitation interact with network generated patterned activity. We show that sequences of cell assembly activations can be locked to the excitatory input sequence and outline the range of parameters where this behavior is shown. Model cell population PSTH display both stimulus and temporal specificity, with large population firing rate modulations locked to elapsed time from task events. Thus the random network can generate a large diversity of temporally evolving stimulus dependent responses even though the input is fixed between switches. We suggest the MSN network is well suited to the generation of such slow coherent task dependent response which could be utilized by the animal in behavior.

  17. Bayesian Network Webserver: a comprehensive tool for biological network modeling.

    Science.gov (United States)

    Ziebarth, Jesse D; Bhattacharya, Anindya; Cui, Yan

    2013-11-01

    The Bayesian Network Webserver (BNW) is a platform for comprehensive network modeling of systems genetics and other biological datasets. It allows users to quickly and seamlessly upload a dataset, learn the structure of the network model that best explains the data and use the model to understand relationships between network variables. Many datasets, including those used to create genetic network models, contain both discrete (e.g. genotype) and continuous (e.g. gene expression traits) variables, and BNW allows for modeling hybrid datasets. Users of BNW can incorporate prior knowledge during structure learning through an easy-to-use structural constraint interface. After structure learning, users are immediately presented with an interactive network model, which can be used to make testable hypotheses about network relationships. BNW, including a downloadable structure learning package, is available at http://compbio.uthsc.edu/BNW. (The BNW interface for adding structural constraints uses HTML5 features that are not supported by current version of Internet Explorer. We suggest using other browsers (e.g. Google Chrome or Mozilla Firefox) when accessing BNW). ycui2@uthsc.edu. Supplementary data are available at Bioinformatics online.

  18. Linear summation of outputs in a balanced network model of motor cortex.

    Science.gov (United States)

    Capaday, Charles; van Vreeswijk, Carl

    2015-01-01

    Given the non-linearities of the neural circuitry's elements, we would expect cortical circuits to respond non-linearly when activated. Surprisingly, when two points in the motor cortex are activated simultaneously, the EMG responses are the linear sum of the responses evoked by each of the points activated separately. Additionally, the corticospinal transfer function is close to linear, implying that the synaptic interactions in motor cortex must be effectively linear. To account for this, here we develop a model of motor cortex composed of multiple interconnected points, each comprised of reciprocally connected excitatory and inhibitory neurons. We show how non-linearities in neuronal transfer functions are eschewed by strong synaptic interactions within each point. Consequently, the simultaneous activation of multiple points results in a linear summation of their respective outputs. We also consider the effects of reduction of inhibition at a cortical point when one or more surrounding points are active. The network response in this condition is linear over an approximately two- to three-fold decrease of inhibitory feedback strength. This result supports the idea that focal disinhibition allows linear coupling of motor cortical points to generate movement related muscle activation patterns; albeit with a limitation on gain control. The model also explains why neural activity does not spread as far out as the axonal connectivity allows, whilst also explaining why distant cortical points can be, nonetheless, functionally coupled by focal disinhibition. Finally, we discuss the advantages that linear interactions at the cortical level afford to motor command synthesis.

  19. A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences

    Science.gov (United States)

    Rudd, Michael E.

    2014-01-01

    Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4. PMID:25202253

  20. A Cortical Edge-integration Model of Object-Based Lightness Computation that Explains Effects of Spatial Context and Individual Differences

    Directory of Open Access Journals (Sweden)

    Michael E Rudd

    2014-08-01

    Full Text Available Previous work demonstrated that perceived surface reflectance (lightness can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatial integrates these steps along paths through the image to compute lightness (Rudd & Zemach, 2004, 2005, 2007. This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013 suggests that the human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010 further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer’s interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd & Zemach, 2005. Here, I show how the separate influences of grouping and attention on lightness can be together modeled by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013, and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.

  1. A cortical edge-integration model of object-based lightness computation that explains effects of spatial context and individual differences.

    Science.gov (United States)

    Rudd, Michael E

    2014-01-01

    Previous work has demonstrated that perceived surface reflectance (lightness) can be modeled in simple contexts in a quantitatively exact way by assuming that the visual system first extracts information about local, directed steps in log luminance, then spatially integrates these steps along paths through the image to compute lightness (Rudd and Zemach, 2004, 2005, 2007). This method of computing lightness is called edge integration. Recent evidence (Rudd, 2013) suggests that human vision employs a default strategy to integrate luminance steps only along paths from a common background region to the targets whose lightness is computed. This implies a role for gestalt grouping in edge-based lightness computation. Rudd (2010) further showed the perceptual weights applied to edges in lightness computation can be influenced by the observer's interpretation of luminance steps as resulting from either spatial variation in surface reflectance or illumination. This implies a role for top-down factors in any edge-based model of lightness (Rudd and Zemach, 2005). Here, I show how the separate influences of grouping and attention on lightness can be modeled in tandem by a cortical mechanism that first employs top-down signals to spatially select regions of interest for lightness computation. An object-based network computation, involving neurons that code for border-ownership, then automatically sets the neural gains applied to edge signals surviving the earlier spatial selection stage. Only the borders that survive both processing stages are spatially integrated to compute lightness. The model assumptions are consistent with those of the cortical lightness model presented earlier by Rudd (2010, 2013), and with neurophysiological data indicating extraction of local edge information in V1, network computations to establish figure-ground relations and border ownership in V2, and edge integration to encode lightness and darkness signals in V4.

  2. An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

    Science.gov (United States)

    Caffrey, James R; Hughes, Barry D; Britto, Joanne M; Landman, Kerry A

    2014-01-01

    The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration). A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

  3. An in silico agent-based model demonstrates Reelin function in directing lamination of neurons during cortical development.

    Directory of Open Access Journals (Sweden)

    James R Caffrey

    Full Text Available The characteristic six-layered appearance of the neocortex arises from the correct positioning of pyramidal neurons during development and alterations in this process can cause intellectual disabilities and developmental delay. Malformations in cortical development arise when neurons either fail to migrate properly from the germinal zones or fail to cease migration in the correct laminar position within the cortical plate. The Reelin signalling pathway is vital for correct neuronal positioning as loss of Reelin leads to a partially inverted cortex. The precise biological function of Reelin remains controversial and debate surrounds its role as a chemoattractant or stop signal for migrating neurons. To investigate this further we developed an in silico agent-based model of cortical layer formation. Using this model we tested four biologically plausible hypotheses for neuron motility and four biologically plausible hypotheses for the loss of neuron motility (conversion from migration. A matrix of 16 combinations of motility and conversion rules was applied against the known structure of mouse cortical layers in the wild-type cortex, the Reelin-null mutant, the Dab1-null mutant and a conditional Dab1 mutant. Using this approach, many combinations of motility and conversion mechanisms can be rejected. For example, the model does not support Reelin acting as a repelling or as a stopping signal. In contrast, the study lends very strong support to the notion that the glycoprotein Reelin acts as a chemoattractant for neurons. Furthermore, the most viable proposition for the conversion mechanism is one in which conversion is affected by a motile neuron sensing in the near vicinity neurons that have already converted. Therefore, this model helps elucidate the function of Reelin during neuronal migration and cortical development.

  4. How to model wireless mesh networks topology

    International Nuclear Information System (INIS)

    Sanni, M L; Hashim, A A; Anwar, F; Ali, S; Ahmed, G S M

    2013-01-01

    The specification of network connectivity model or topology is the beginning of design and analysis in Computer Network researches. Wireless Mesh Networks is an autonomic network that is dynamically self-organised, self-configured while the mesh nodes establish automatic connectivity with the adjacent nodes in the relay network of wireless backbone routers. Researches in Wireless Mesh Networks range from node deployment to internetworking issues with sensor, Internet and cellular networks. These researches require modelling of relationships and interactions among nodes including technical characteristics of the links while satisfying the architectural requirements of the physical network. However, the existing topology generators model geographic topologies which constitute different architectures, thus may not be suitable in Wireless Mesh Networks scenarios. The existing methods of topology generation are explored, analysed and parameters for their characterisation are identified. Furthermore, an algorithm for the design of Wireless Mesh Networks topology based on square grid model is proposed in this paper. The performance of the topology generated is also evaluated. This research is particularly important in the generation of a close-to-real topology for ensuring relevance of design to the intended network and validity of results obtained in Wireless Mesh Networks researches

  5. Murine model of acute myocarditis and cerebral cortical neuron edema induced by coxsackievirus B4

    Directory of Open Access Journals (Sweden)

    Zhao-Peng Dong

    2018-01-01

    Full Text Available Globally, coxsackievirus B4 (CV-B4 has been continuously isolated and evidence suggests an association with the development of pancreatitis and type I diabetes. In addition, CV-B4 is also associated with myocarditis and severe central nervous system (CNS complications, which remain poorly studied and understood. In the present study, we established an ICR mouse model of CV-B4 infection and examined whether CV-B4 infection resulted in a predisposition to myocarditis and CNS infection. We found high survival in both the treatment and control group, with no significant differences in clinical outcomes observed. However, pathological lesions were evident in both brain and heart tissue of the CV-B4-infected mice. In addition, high viral loads were found in the neural and cardiac tissues as early as 2 d postinfection. Expressions of IFN-γ and IL-6 in sera were significantly higher in CV-B4-infected mice compared to uninfected negative controls, suggesting the involvement of these cytokines in the development of histopathological lesions. Our murine model successfully reproduced the acute myocarditis and cerebral cortical neuron edema induced by CV-B4, and may be useful for the evaluation of vaccine candidates and potential antivirals against CV-B4 infection.

  6. Model checking mobile ad hoc networks

    NARCIS (Netherlands)

    Ghassemi, Fatemeh; Fokkink, Wan

    2016-01-01

    Modeling arbitrary connectivity changes within mobile ad hoc networks (MANETs) makes application of automated formal verification challenging. We use constrained labeled transition systems as a semantic model to represent mobility. To model check MANET protocols with respect to the underlying

  7. Formulaic language in cortical and subcortical disease: Evidence of the dual process model.

    Directory of Open Access Journals (Sweden)

    Kelly Bridges

    2014-04-01

    Full Text Available Introduction: It is known that an intact cortical left hemisphere is crucial for language production. Recently, more credit is given to the right hemisphere and subcortical areas in the production of non-novel language, including formulaic language. John Hughlings Jackson (1874/1958, first described how propositional and non-propositional speech are differentially affected by neural impairment. Non-propositional language is often preserved following left hemisphere stroke even when aphasia is present (Code, 1982; Sidtis et al., 2009; Van Lancker Sidtis & Postman, 2006. With right hemisphere and subcortical stroke, formulaic language is reduced (Sidtis et al., 2009; Van Lancker Sidtis & Postman, 2006; Speedie et al., 1993. The dual process model of language competence states that propositional and non-propositional speech are processed differently in the brain, with novel speech controlled by the left hemisphere, and a right hemisphere/subcortical circuit modulating formulaic language (Van Lancker Sidtis, 2004; 2012. Two studies of formulaic language will be presented as further evidence of the dual process model: a study of formulaic language in Alzheimer’s disease, and a study of recited speech in Parkinson’s disease. Formulaic language includes overlearned words, phrases or longer linguistic units that are known to the native speaker, occur naturally in discourse, and are important for normal social interaction (Fillmore, 1979; Pawley & Syder, 1983; Van Lancker, 1988; Van Lancker Sidtis, 2004; Wray, 2002. Formulaic expressions include conversational speech formulas, idioms, proverbs, expletives, pause fillers, discourse elements, and sentence stems (stereotyped sentence-initials. Longer units of linguistic material, such as prayers, rhymes, and poems, termed recited speech, is another subtype of formulaic language that is learned in childhood and recited periodically throughout life. Cortical disease: Alzheimer’s disease and formulaic

  8. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  9. Computer modelling of RF ablation in cortical osteoid osteoma: Assessment of the insulating effect of the reactive zone.

    Science.gov (United States)

    Irastorza, Ramiro M; Trujillo, Macarena; Martel Villagrán, Jose; Berjano, Enrique

    2016-05-01

    The aim was to study by computer simulations the insulating role of the reactive zone surrounding a cortical osteoid osteoma (OO) in terms of electrical and thermal performance during radiofrequency ablation (RFA). We modelled a cortical OO consisting of a nidus (10 mm diameter) enclosed by a reactive zone. The OO was near a layer of cortical bone 1.5 mm thick. Trabecular bone partially surrounds the OO and there was muscle around the cortical bone layer. We modelled RF ablations with a non-cooled-tip 17-gauge needle electrode (300 s duration and 90 °C target temperature). Sensitivity analyses were conducted assuming a reactive zone electrical conductivity value (σrz) within the limits of the cortical and trabecular bone, i.e. 0.02 S/m and 0.087 S/m, respectively. In this way we were really modelling the different degrees of osteosclerosis associated with the reactive zone. The presence of the reactive zone drastically reduced the maximum temperature reached outside it. The temperature drop was proportional to the thickness of the reactive zone: from 68 °C when it was absent to 44 °C when it is 7.5 mm thick. Higher nidus conductivity values (σn) implied higher temperatures, while lower temperatures meant higher σrz values. Changing σrz from 0.02 S/m to 0.087 S/m reduced lesion diameters from 2.4 cm to 1.8 cm. The computer results suggest that the reactive zone plays the role of insulator in terms of reducing the temperature in the surrounding area.

  10. Cortical movement of Bicoid in early Drosophila embryos is actin- and microtubule-dependent and disagrees with the SDD diffusion model.

    Directory of Open Access Journals (Sweden)

    Xiaoli Cai

    Full Text Available The Bicoid (Bcd protein gradient in Drosophila serves as a paradigm for gradient formation in textbooks. The SDD model (synthesis, diffusion, degradation was proposed to explain the formation of the gradient. The SDD model states that the bcd mRNA is located at the anterior pole of the embryo at all times and serves a source for translation of the Bicoid protein, coupled with diffusion and uniform degradation throughout the embryo. Recently, the ARTS model (active RNA transport, synthesis challenged the SDD model. In this model, the mRNA is transported at the cortex along microtubules to form a mRNA gradient which serves as template for the production of Bcd, hence little Bcd movement is involved. To test the validity of the SDD model, we developed a sensitive assay to monitor the movement of Bcd during early nuclear cycles. We observed that Bcd moved along the cortex and not in a broad front towards the posterior as the SDD model would have predicted. We subjected embryos to hypoxia where the mRNA remained strictly located at the tip at all times, while the protein was allowed to move freely, thus conforming to an ideal experimental setup to test the SDD model. Unexpectedly, Bcd still moved along the cortex. Moreover, cortical Bcd movement was sparse, even under longer hypoxic conditions. Hypoxic embryos treated with drugs compromising microtubule and actin function affected Bcd cortical movement and stability. Vinblastine treatment allowed the simulation of an ideal SDD model whereby the protein moved throughout the embryo in a broad front. In unfertilized embryos, the Bcd protein followed the mRNA which itself was transported into the interior of the embryo utilizing a hitherto undiscovered microtubular network. Our data suggest that the Bcd gradient formation is probably more complex than previously anticipated.

  11. Nonparametric Bayesian Modeling of Complex Networks

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Mørup, Morten

    2013-01-01

    an infinite mixture model as running example, we go through the steps of deriving the model as an infinite limit of a finite parametric model, inferring the model parameters by Markov chain Monte Carlo, and checking the model?s fit and predictive performance. We explain how advanced nonparametric models......Modeling structure in complex networks using Bayesian nonparametrics makes it possible to specify flexible model structures and infer the adequate model complexity from the observed data. This article provides a gentle introduction to nonparametric Bayesian modeling of complex networks: Using...

  12. Network structure exploration via Bayesian nonparametric models

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Xiang, X; Tang, B Z; Bu, J Z

    2015-01-01

    Complex networks provide a powerful mathematical representation of complex systems in nature and society. To understand complex networks, it is crucial to explore their internal structures, also called structural regularities. The task of network structure exploration is to determine how many groups there are in a complex network and how to group the nodes of the network. Most existing structure exploration methods need to specify either a group number or a certain type of structure when they are applied to a network. In the real world, however, the group number and also the certain type of structure that a network has are usually unknown in advance. To explore structural regularities in complex networks automatically, without any prior knowledge of the group number or the certain type of structure, we extend a probabilistic mixture model that can handle networks with any type of structure but needs to specify a group number using Bayesian nonparametric theory. We also propose a novel Bayesian nonparametric model, called the Bayesian nonparametric mixture (BNPM) model. Experiments conducted on a large number of networks with different structures show that the BNPM model is able to explore structural regularities in networks automatically with a stable, state-of-the-art performance. (paper)

  13. Deep supervised, but not unsupervised, models may explain IT cortical representation.

    Directory of Open Access Journals (Sweden)

    Seyed-Mahdi Khaligh-Razavi

    2014-11-01

    Full Text Available Inferior temporal (IT cortex in human and nonhuman primates serves visual object recognition. Computational object-vision models, although continually improving, do not yet reach human performance. It is unclear to what extent the internal representations of computational models can explain the IT representation. Here we investigate a wide range of computational model representations (37 in total, testing their categorization performance and their ability to account for the IT representational geometry. The models include well-known neuroscientific object-recognition models (e.g. HMAX, VisNet along with several models from computer vision (e.g. SIFT, GIST, self-similarity features, and a deep convolutional neural network. We compared the representational dissimilarity matrices (RDMs of the model representations with the RDMs obtained from human IT (measured with fMRI and monkey IT (measured with cell recording for the same set of stimuli (not used in training the models. Better performing models were more similar to IT in that they showed greater clustering of representational patterns by category. In addition, better performing models also more strongly resembled IT in terms of their within-category representational dissimilarities. Representational geometries were significantly correlated between IT and many of the models. However, the categorical clustering observed in IT was largely unexplained by the unsupervised models. The deep convolutional network, which was trained by supervision with over a million category-labeled images, reached the highest categorization performance and also best explained IT, although it did not fully explain the IT data. Combining the features of this model with appropriate weights and adding linear combinations that maximize the margin between animate and inanimate objects and between faces and other objects yielded a representation that fully explained our IT data. Overall, our results suggest that explaining

  14. Spiking cortical model based non-local means method for despeckling multiframe optical coherence tomography data

    Science.gov (United States)

    Gu, Yameng; Zhang, Xuming

    2017-05-01

    Optical coherence tomography (OCT) images are severely degraded by speckle noise. Existing methods for despeckling multiframe OCT data cannot deliver sufficient speckle suppression while preserving image details well. To address this problem, the spiking cortical model (SCM) based non-local means (NLM) method has been proposed in this letter. In the proposed method, the considered frame and two neighboring frames are input into three SCMs to generate the temporal series of pulse outputs. The normalized moment of inertia (NMI) of the considered patches in the pulse outputs is extracted to represent the rotational and scaling invariant features of the corresponding patches in each frame. The pixel similarity is computed based on the Euclidean distance between the NMI features and used as the weight. Each pixel in the considered frame is restored by the weighted averaging of all pixels in the pre-defined search window in the three frames. Experiments on the real multiframe OCT data of the pig eye demonstrate the advantage of the proposed method over the frame averaging method, the multiscale sparsity based tomographic denoising method, the wavelet-based method and the traditional NLM method in terms of visual inspection and objective metrics such as signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), equivalent number of looks (ENL) and cross-correlation (XCOR).

  15. Early development of synchrony in cortical activations in the human.

    Science.gov (United States)

    Koolen, N; Dereymaeker, A; Räsänen, O; Jansen, K; Vervisch, J; Matic, V; Naulaers, G; De Vos, M; Van Huffel, S; Vanhatalo, S

    2016-05-13

    Early intermittent cortical activity is thought to play a crucial role in the growth of neuronal network development, and large scale brain networks are known to provide the basis for higher brain functions. Yet, the early development of the large scale synchrony in cortical activations is unknown. Here, we tested the hypothesis that the early intermittent cortical activations seen in the human scalp EEG show a clear developmental course during the last trimester of pregnancy, the period of intensive growth of cortico-cortical connections. We recorded scalp EEG from altogether 22 premature infants at post-menstrual age between 30 and 44 weeks, and the early cortical synchrony was quantified using recently introduced activation synchrony index (ASI). The developmental correlations of ASI were computed for individual EEG signals as well as anatomically and mathematically defined spatial subgroups. We report two main findings. First, we observed a robust and statistically significant increase in ASI in all cortical areas. Second, there were significant spatial gradients in the synchrony in fronto-occipital and left-to-right directions. These findings provide evidence that early cortical activity is increasingly synchronized across the neocortex. The ASI-based metrics introduced in our work allow direct translational comparison to in vivo animal models, as well as hold promise for implementation as a functional developmental biomarker in future research on human neonates. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

    Directory of Open Access Journals (Sweden)

    Christoph Nick

    2014-09-01

    Full Text Available 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.

  17. Modelling the structure of complex networks

    DEFF Research Database (Denmark)

    Herlau, Tue

    networks has been independently studied as mathematical objects in their own right. As such, there has been both an increased demand for statistical methods for complex networks as well as a quickly growing mathematical literature on the subject. In this dissertation we explore aspects of modelling complex....... The next chapters will treat some of the various symmetries, representer theorems and probabilistic structures often deployed in the modelling complex networks, the construction of sampling methods and various network models. The introductory chapters will serve to provide context for the included written...

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

    Science.gov (United States)

    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).

  19. Cortical and hippocampal correlates of deliberation during model-based decisions for rewards in humans.

    Directory of Open Access Journals (Sweden)

    Aaron M Bornstein

    Full Text Available How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward - such as when planning routes using a cognitive map or chess moves using predicted countermoves - and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to

  20. Building functional networks of spiking model neurons.

    Science.gov (United States)

    Abbott, L F; DePasquale, Brian; Memmesheimer, Raoul-Martin

    2016-03-01

    Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

  1. Modeling, Optimization & Control of Hydraulic Networks

    DEFF Research Database (Denmark)

    Tahavori, Maryamsadat

    2014-01-01

    . The nonlinear network model is derived based on the circuit theory. A suitable projection is used to reduce the state vector and to express the model in standard state-space form. Then, the controllability of nonlinear nonaffine hydraulic networks is studied. The Lie algebra-based controllability matrix is used......Water supply systems consist of a number of pumping stations, which deliver water to the customers via pipeline networks and elevated reservoirs. A huge amount of drinking water is lost before it reaches to end-users due to the leakage in pipe networks. A cost effective solution to reduce leakage...... in water network is pressure management. By reducing the pressure in the water network, the leakage can be reduced significantly. Also it reduces the amount of energy consumption in water networks. The primary purpose of this work is to develop control algorithms for pressure control in water supply...

  2. Simple cortical and thalamic neuron models for digital arithmetic circuit implementation

    Directory of Open Access Journals (Sweden)

    Takuya eNanami

    2016-05-01

    Full Text Available Trade-off between reproducibility of neuronal activities and computational efficiency is one ofcrucial subjects in computational neuroscience and neuromorphic engineering. A wide variety ofneuronal models have been studied from different viewpoints. The digital spiking silicon neuron(DSSN model is a qualitative model that focuses on efficient implementation by digital arithmeticcircuits. We expanded the DSSN model and found appropriate parameter sets with which itreproduces the dynamical behaviors of the ionic-conductance models of four classes of corticaland thalamic neurons. We first developed a 4-variable model by reducing the number of variablesin the ionic-conductance models and elucidated its mathematical structures using bifurcationanalysis. Then, expanded DSSN models were constructed that reproduce these mathematicalstructures and capture the characteristic behavior of each neuron class. We confirmed thatstatistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductancemodels. Computational cost of the DSSN model is larger than that of the recent sophisticatedIntegrate-and-Fire-based models, but smaller than the ionic-conductance models. This modelis intended to provide another meeting point for above trade-off that satisfies the demand forlarge-scale neuronal network simulation with closer-to-biology models.

  3. Dynamic neural network models of the premotoneuronal circuitry controlling wrist movements in primates.

    Science.gov (United States)

    Maier, M A; Shupe, L E; Fetz, E E

    2005-10-01

    Dynamic recurrent neural networks were derived to simulate neuronal populations generating bidirectional wrist movements in the monkey. The models incorporate anatomical connections of cortical and rubral neurons, muscle afferents, segmental interneurons and motoneurons; they also incorporate the response profiles of four populations of neurons observed in behaving monkeys. The networks were derived by gradient descent algorithms to generate the eight characteristic patterns of motor unit activations observed during alternating flexion-extension wrist movements. The resulting model generated the appropriate input-output transforms and developed connection strengths resembling those in physiological pathways. We found that this network could be further trained to simulate additional tasks, such as experimentally observed reflex responses to limb perturbations that stretched or shortened the active muscles, and scaling of response amplitudes in proportion to inputs. In the final comprehensive network, motor units are driven by the combined activity of cortical, rubral, spinal and afferent units during step tracking and perturbations. The model displayed many emergent properties corresponding to physiological characteristics. The resulting neural network provides a working model of premotoneuronal circuitry and elucidates the neural mechanisms controlling motoneuron activity. It also predicts several features to be experimentally tested, for example the consequences of eliminating inhibitory connections in cortex and red nucleus. It also reveals that co-contraction can be achieved by simultaneous activation of the flexor and extensor circuits without invoking features specific to co-contraction.

  4. Port Hamiltonian modeling of Power Networks

    NARCIS (Netherlands)

    van Schaik, F.; van der Schaft, Abraham; Scherpen, Jacquelien M.A.; Zonetti, Daniele; Ortega, R

    2012-01-01

    In this talk a full nonlinear model for the power network in port–Hamiltonian framework is derived to study its stability properties. For this we use the modularity approach i.e., we first derive the models of individual components in power network as port-Hamiltonian systems and then we combine all

  5. Modelling traffic congestion using queuing networks

    Indian Academy of Sciences (India)

    Flow-density curves; uninterrupted traffic; Jackson networks. ... ness - also suffer from a big handicap vis-a-vis the Indian scenario: most of these models do .... more well-known queuing network models and onsite data, a more exact Road Cell ...

  6. Settings in Social Networks : a Measurement Model

    NARCIS (Netherlands)

    Schweinberger, Michael; Snijders, Tom A.B.

    2003-01-01

    A class of statistical models is proposed that aims to recover latent settings structures in social networks. Settings may be regarded as clusters of vertices. The measurement model is based on two assumptions. (1) The observed network is generated by hierarchically nested latent transitive

  7. Network interconnections: an architectural reference model

    NARCIS (Netherlands)

    Butscher, B.; Lenzini, L.; Morling, R.; Vissers, C.A.; Popescu-Zeletin, R.; van Sinderen, Marten J.; Heger, D.; Krueger, G.; Spaniol, O.; Zorn, W.

    1985-01-01

    One of the major problems in understanding the different approaches in interconnecting networks of different technologies is the lack of reference to a general model. The paper develops the rationales for a reference model of network interconnection and focuses on the architectural implications for

  8. Performance modeling of network data services

    Energy Technology Data Exchange (ETDEWEB)

    Haynes, R.A.; Pierson, L.G.

    1997-01-01

    Networks at major computational organizations are becoming increasingly complex. The introduction of large massively parallel computers and supercomputers with gigabyte memories are requiring greater and greater bandwidth for network data transfers to widely dispersed clients. For networks to provide adequate data transfer services to high performance computers and remote users connected to them, the networking components must be optimized from a combination of internal and external performance criteria. This paper describes research done at Sandia National Laboratories to model network data services and to visualize the flow of data from source to sink when using the data services.

  9. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo; Burger, Martin; Haskovec, Jan; Markowich, Peter A.; Schlottbom, Matthias

    2017-01-01

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes

  10. Network models in economics and finance

    CERN Document Server

    Pardalos, Panos; Rassias, Themistocles

    2014-01-01

    Using network models to investigate the interconnectivity in modern economic systems allows researchers to better understand and explain some economic phenomena. This volume presents contributions by known experts and active researchers in economic and financial network modeling. Readers are provided with an understanding of the latest advances in network analysis as applied to economics, finance, corporate governance, and investments. Moreover, recent advances in market network analysis  that focus on influential techniques for market graph analysis are also examined. Young researchers will find this volume particularly useful in facilitating their introduction to this new and fascinating field. Professionals in economics, financial management, various technologies, and network analysis, will find the network models presented in this book beneficial in analyzing the interconnectivity in modern economic systems.

  11. Continuous Online Sequence Learning with an Unsupervised Neural Network Model.

    Science.gov (United States)

    Cui, Yuwei; Ahmad, Subutar; Hawkins, Jeff

    2016-09-14

    The ability to recognize and predict temporal sequences of sensory inputs is vital for survival in natural environments. Based on many known properties of cortical neurons, hierarchical temporal memory (HTM) sequence memory recently has been proposed as a theoretical framework for sequence learning in the cortex. In this letter, we analyze properties of HTM sequence memory and apply it to sequence learning and prediction problems with streaming data. We show the model is able to continuously learn a large number of variableorder temporal sequences using an unsupervised Hebbian-like learning rule. The sparse temporal codes formed by the model can robustly handle branching temporal sequences by maintaining multiple predictions until there is sufficient disambiguating evidence. We compare the HTM sequence memory with other sequence learning algorithms, including statistical methods: autoregressive integrated moving average; feedforward neural networks-time delay neural network and online sequential extreme learning machine; and recurrent neural networks-long short-term memory and echo-state networks on sequence prediction problems with both artificial and real-world data. The HTM model achieves comparable accuracy to other state-of-the-art algorithms. The model also exhibits properties that are critical for sequence learning, including continuous online learning, the ability to handle multiple predictions and branching sequences with high-order statistics, robustness to sensor noise and fault tolerance, and good performance without task-specific hyperparameter tuning. Therefore, the HTM sequence memory not only advances our understanding of how the brain may solve the sequence learning problem but is also applicable to real-world sequence learning problems from continuous data streams.

  12. Periosteal PTHrP regulates cortical bone modeling during linear growth in mice.

    Science.gov (United States)

    Wang, Meina; VanHouten, Joshua N; Nasiri, Ali R; Tommasini, Steven M; Broadus, Arthur E

    2014-07-01

    The modeling of long bone surfaces during linear growth is a key developmental process, but its regulation is poorly understood. We report here that parathyroid hormone-related peptide (PTHrP) expressed in the fibrous layer of the periosteum (PO) drives the osteoclastic (OC) resorption that models the metaphyseal-diaphyseal junction (MDJ) in the proximal tibia and fibula during linear growth. PTHrP was conditionally deleted (cKO) in the PO via Scleraxis gene targeting (Scx-Cre). In the lateral tibia, cKO of PTHrP led to a failure of modeling, such that the normal concave MDJ was replaced by a mound-like deformity. This was accompanied by a failure to induce receptor activator of NF-kB ligand (RANKL) and a 75% reduction in OC number (P ≤ 0.001) on the cortical surface. The MDJ also displayed a curious threefold increase in endocortical osteoblast mineral apposition rate (P ≤ 0.001) and a thickened cortex, suggesting some form of coupling of endocortical bone formation to events on the PO surface. Because it fuses distally, the fibula is modeled only proximally and does so at an extraordinary rate, with an anteromedial cortex in CD-1 mice that was so moth-eaten that a clear PO surface could not be identified. The cKO fibula displayed a remarkable phenotype, with a misshapen club-like metaphysis and an enlargement in the 3D size of the entire bone, manifest as a 40-45% increase in the PO circumference at the MDJ (P ≤ 0.001) as well as the mid-diaphysis (P ≤ 0.001). These tibial and fibular phenotypes were reproduced in a Scx-Cre-driven RANKL cKO mouse. We conclude that PTHrP in the fibrous PO mediates the modeling of the MDJ of long bones during linear growth, and that in a highly susceptible system such as the fibula this surface modeling defines the size and shape of the entire bone. © 2014 Anatomical Society.

  13. Synergistic effects in threshold models on networks

    Science.gov (United States)

    Juul, Jonas S.; Porter, Mason A.

    2018-01-01

    Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

  14. Gossip spread in social network Models

    Science.gov (United States)

    Johansson, Tobias

    2017-04-01

    Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

  15. Evaluation of EOR Processes Using Network Models

    DEFF Research Database (Denmark)

    Winter, Anatol; Larsen, Jens Kjell; Krogsbøll, Anette

    1998-01-01

    The report consists of the following parts: 1) Studies of wetting properties of model fluids and fluid mixtures aimed at an optimal selection of candidates for micromodel experiments. 2) Experimental studies of multiphase transport properties using physical models of porous networks (micromodels......) including estimation of their "petrophysical" properties (e.g. absolute permeability). 3) Mathematical modelling and computer studies of multiphase transport through pore space using mathematical network models. 4) Investigation of link between pore-scale and macroscopic recovery mechanisms....

  16. Towards reproducible descriptions of neuronal network models.

    Directory of Open Access Journals (Sweden)

    Eilen Nordlie

    2009-08-01

    Full Text Available Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.

  17. Improved Maximum Parsimony Models for Phylogenetic Networks.

    Science.gov (United States)

    Van Iersel, Leo; Jones, Mark; Scornavacca, Celine

    2018-05-01

    Phylogenetic networks are well suited to represent evolutionary histories comprising reticulate evolution. Several methods aiming at reconstructing explicit phylogenetic networks have been developed in the last two decades. In this article, we propose a new definition of maximum parsimony for phylogenetic networks that permits to model biological scenarios that cannot be modeled by the definitions currently present in the literature (namely, the "hardwired" and "softwired" parsimony). Building on this new definition, we provide several algorithmic results that lay the foundations for new parsimony-based methods for phylogenetic network reconstruction.

  18. Modeling, robust and distributed model predictive control for freeway networks

    NARCIS (Netherlands)

    Liu, S.

    2016-01-01

    In Model Predictive Control (MPC) for traffic networks, traffic models are crucial since they are used as prediction models for determining the optimal control actions. In order to reduce the computational complexity of MPC for traffic networks, macroscopic traffic models are often used instead of

  19. Hybrid Scheme for Modeling Local Field Potentials from Point-Neuron Networks.

    Science.gov (United States)

    Hagen, Espen; Dahmen, David; Stavrinou, Maria L; Lindén, Henrik; Tetzlaff, Tom; van Albada, Sacha J; Grün, Sonja; Diesmann, Markus; Einevoll, Gaute T

    2016-12-01

    With rapidly advancing multi-electrode recording technology, the local field potential (LFP) has again become a popular measure of neuronal activity in both research and clinical applications. Proper understanding of the LFP requires detailed mathematical modeling incorporating the anatomical and electrophysiological features of neurons near the recording electrode, as well as synaptic inputs from the entire network. Here we propose a hybrid modeling scheme combining efficient point-neuron network models with biophysical principles underlying LFP generation by real neurons. The LFP predictions rely on populations of network-equivalent multicompartment neuron models with layer-specific synaptic connectivity, can be used with an arbitrary number of point-neuron network populations, and allows for a full separation of simulated network dynamics and LFPs. We apply the scheme to a full-scale cortical network model for a ∼1 mm 2 patch of primary visual cortex, predict laminar LFPs for different network states, assess the relative LFP contribution from different laminar populations, and investigate effects of input correlations and neuron density on the LFP. The generic nature of the hybrid scheme and its public implementation in hybridLFPy form the basis for LFP predictions from other and larger point-neuron network models, as well as extensions of the current application with additional biological detail. © The Author 2016. Published by Oxford University Press.

  20. Tool wear modeling using abductive networks

    Science.gov (United States)

    Masory, Oren

    1992-09-01

    A tool wear model based on Abductive Networks, which consists of a network of `polynomial' nodes, is described. The model relates the cutting parameters, components of the cutting force, and machining time to flank wear. Thus real time measurements of the cutting force can be used to monitor the machining process. The model is obtained by a training process in which the connectivity between the network's nodes and the polynomial coefficients of each node are determined by optimizing a performance criteria. Actual wear measurements of coated and uncoated carbide inserts were used for training and evaluating the established model.

  1. Regulation of the fear network by mediators of stress: Norepinephrine alters the balance between Cortical and Subcortical afferent excitation of the Lateral Amygdala

    Directory of Open Access Journals (Sweden)

    Luke R Johnson

    2011-05-01

    Full Text Available Pavlovian auditory fear conditioning crucially involves the integration of information about and acoustic conditioned stimulus (CS and an aversive unconditioned stimulus (US in the lateral nucleus of the amygdala (LA. The auditory CS reaches the LA subcortically via a direct connection from the auditory thalamus and also from the auditory association cortex itself. How neural modulators, especially those activated during stress, such as norepinephrine (NE, regulate synaptic transmission and plasticity in this network is poorly understood. Here we show that NE inhibits synaptic transmission in both the subcortical and cortical input pathway but that sensory processing is biased towards the subcortical pathway. In addition binding of NE to β-adrenergic receptors further dissociates sensory processing in the LA. These findings suggest a network mechanism that shifts sensory balance towards the faster but more primitive subcortical input.

  2. Functional connectivity of motor cortical network in patients with brachial plexus avulsion injury after contralateral cervical nerve transfer: a resting-state fMRI study

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Aihong; Cheng, Xiaoguang; Liang, Wei; Bai, Rongjie [The 4th Medical College of Peking University, Department of Radiology, Beijing Jishuitan Hospital, Xicheng Qu, Beijing (China); Wang, Shufeng; Xue, Yunhao; Li, Wenjun [The 4th Medical College of Peking University, Department of Hand Surgery, Beijing Jishuitan Hospital, Beijing (China)

    2017-03-15

    The purpose of this study is to assess the functional connectivity of the motor cortical network in patients with brachial plexus avulsion injury (BPAI) after contralateral C7 nerve transfer, using resting-state functional magnetic resonance imaging (RS-fMRI). Twelve patients with total brachial plexus root avulsion underwent RS-fMRI after contralateral C7 nerve transfer. Seventeen healthy volunteers were also included in this fMRI study as controls. The hand motor seed regions were defined as region of interests in the bilateral hemispheres. The seed-based functional connectivity was calculated in all the subjects. Differences in functional connectivity of the motor cortical network between patients and healthy controls were compared. The inter-hemispheric functional connectivity of the M1 areas was increased in patients with BPAI compared with the controls. The inter-hemispheric functional connectivity between the supplementary motor areas was reduced bilaterally. The resting-state inter-hemispheric functional connectivity of the bilateral M1 areas is altered in patients after contralateral C7 nerve transfer, suggesting a functional reorganization of cerebral cortex. (orig.)

  3. Functional connectivity of motor cortical network in patients with brachial plexus avulsion injury after contralateral cervical nerve transfer: a resting-state fMRI study

    International Nuclear Information System (INIS)

    Yu, Aihong; Cheng, Xiaoguang; Liang, Wei; Bai, Rongjie; Wang, Shufeng; Xue, Yunhao; Li, Wenjun

    2017-01-01

    The purpose of this study is to assess the functional connectivity of the motor cortical network in patients with brachial plexus avulsion injury (BPAI) after contralateral C7 nerve transfer, using resting-state functional magnetic resonance imaging (RS-fMRI). Twelve patients with total brachial plexus root avulsion underwent RS-fMRI after contralateral C7 nerve transfer. Seventeen healthy volunteers were also included in this fMRI study as controls. The hand motor seed regions were defined as region of interests in the bilateral hemispheres. The seed-based functional connectivity was calculated in all the subjects. Differences in functional connectivity of the motor cortical network between patients and healthy controls were compared. The inter-hemispheric functional connectivity of the M1 areas was increased in patients with BPAI compared with the controls. The inter-hemispheric functional connectivity between the supplementary motor areas was reduced bilaterally. The resting-state inter-hemispheric functional connectivity of the bilateral M1 areas is altered in patients after contralateral C7 nerve transfer, suggesting a functional reorganization of cerebral cortex. (orig.)

  4. Modelling of virtual production networks

    Directory of Open Access Journals (Sweden)

    2011-03-01

    Full Text Available Nowadays many companies, especially small and medium-sized enterprises (SMEs, specialize in a limited field of production. It requires forming virtual production networks of cooperating enterprises to manufacture better, faster and cheaper. Apart from that, some production orders cannot be realized, because there is not a company of sufficient production potential. In this case the virtual production networks of cooperating companies can realize these production orders. These networks have larger production capacity and many different resources. Therefore it can realize many more production orders together than each of them separately. Such organization allows for executing high quality product. The maintenance costs of production capacity and used resources are not so high. In this paper a methodology of rapid prototyping of virtual production networks is proposed. It allows to execute production orders on time considered existing logistic constraints.

  5. A Network Disruption Modeling Tool

    National Research Council Canada - National Science Library

    Leinart, James

    1998-01-01

    Given that network disruption has been identified as a military objective and C2-attack has been identified as the mechanism to accomplish this objective, a target set must be acquired and priorities...

  6. Modeling Epidemics Spreading on Social Contact Networks.

    Science.gov (United States)

    Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua

    2015-09-01

    Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.

  7. Spatial Epidemic Modelling in Social Networks

    Science.gov (United States)

    Simoes, Joana Margarida

    2005-06-01

    The spread of infectious diseases is highly influenced by the structure of the underlying social network. The target of this study is not the network of acquaintances, but the social mobility network: the daily movement of people between locations, in regions. It was already shown that this kind of network exhibits small world characteristics. The model developed is agent based (ABM) and comprehends a movement model and a infection model. In the movement model, some assumptions are made about its structure and the daily movement is decomposed into four types: neighborhood, intra region, inter region and random. The model is Geographical Information Systems (GIS) based, and uses real data to define its geometry. Because it is a vector model, some optimization techniques were used to increase its efficiency.

  8. Implementing network constraints in the EMPS model

    Energy Technology Data Exchange (ETDEWEB)

    Helseth, Arild; Warland, Geir; Mo, Birger; Fosso, Olav B.

    2010-02-15

    This report concerns the coupling of detailed market and network models for long-term hydro-thermal scheduling. Currently, the EPF model (Samlast) is the only tool available for this task for actors in the Nordic market. A new prototype for solving the coupled market and network problem has been developed. The prototype is based on the EMPS model (Samkjoeringsmodellen). Results from the market model are distributed to a detailed network model, where a DC load flow detects if there are overloads on monitored lines or intersections. In case of overloads, network constraints are generated and added to the market problem. Theoretical and implementation details for the new prototype are elaborated in this report. The performance of the prototype is tested against the EPF model on a 20-area Nordic dataset. (Author)

  9. Role models for complex networks

    Science.gov (United States)

    Reichardt, J.; White, D. R.

    2007-11-01

    We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.

  10. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  11. Latent variable models are network models.

    Science.gov (United States)

    Molenaar, Peter C M

    2010-06-01

    Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

  12. Homophyly/Kinship Model: Naturally Evolving Networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng; Yin, Xianchen; Yong, Xi

    2015-10-01

    It has been a challenge to understand the formation and roles of social groups or natural communities in the evolution of species, societies and real world networks. Here, we propose the hypothesis that homophyly/kinship is the intrinsic mechanism of natural communities, introduce the notion of the affinity exponent and propose the homophyly/kinship model of networks. We demonstrate that the networks of our model satisfy a number of topological, probabilistic and combinatorial properties and, in particular, that the robustness and stability of natural communities increase as the affinity exponent increases and that the reciprocity of the networks in our model decreases as the affinity exponent increases. We show that both homophyly/kinship and reciprocity are essential to the emergence of cooperation in evolutionary games and that the homophyly/kinship and reciprocity determined by the appropriate affinity exponent guarantee the emergence of cooperation in evolutionary games, verifying Darwin’s proposal that kinship and reciprocity are the means of individual fitness. We propose the new principle of structure entropy minimisation for detecting natural communities of networks and verify the functional module property and characteristic properties by a healthy tissue cell network, a citation network, some metabolic networks and a protein interaction network.

  13. Reduced GABAergic inhibition explains cortical hyperexcitability in the wobbler mouse model of ALS

    DEFF Research Database (Denmark)

    Nieto-Gonzalez, Jose Luis; Moser, Jakob; Lauritzen, Martin

    2011-01-01

    mice. Also, miniature inhibitory postsynaptic currents recorded under blockade of action potentials were decreased by 64%. Tonic inhibition mediated by extrasynaptic GABA(A) receptors was reduced by 87%. In agreement, we found a decreased density of parvalbumin- and somatostatin-positive inhibitory...... inhibition, which might explain the cortical hyperexcitability in wobbler mice....

  14. Cortical Thought Theory: A Working Model of the Human Gestalt Mechanism.

    Science.gov (United States)

    1985-07-01

    2. The Artificial Inteligence Perspective • -° 2.1 Introduction: Chapter Overview This chapter addresses the development of a...6 . . 2. The Artificial Intelligence Perspective ... .......... 9 2.1 Introduction: Chapter Overview .... ........... 9 2.2 The Problem 9...new unified theory of human brain function called Cortical Thought Theory (CTT). The analysis integrates the disciplines of Artificial Intelligence

  15. Neural network tagging in a toy model

    International Nuclear Information System (INIS)

    Milek, Marko; Patel, Popat

    1999-01-01

    The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

  16. An endogenous model of the credit network

    Science.gov (United States)

    He, Jianmin; Sui, Xin; Li, Shouwei

    2016-01-01

    In this paper, an endogenous credit network model of firm-bank agents is constructed. The model describes the endogenous formation of firm-firm, firm-bank and bank-bank credit relationships. By means of simulations, the model is capable of showing some obvious similarities with empirical evidence found by other scholars: the upper-tail of firm size distribution can be well fitted with a power-law; the bank size distribution can be lognormally distributed with a power-law tail; the bank in-degrees of the interbank credit network as well as the firm-bank credit network fall into two-power-law distributions.

  17. Modelling and designing electric energy networks

    International Nuclear Information System (INIS)

    Retiere, N.

    2003-11-01

    The author gives an overview of his research works in the field of electric network modelling. After a brief overview of technological evolutions from the telegraph to the all-electric fly-by-wire aircraft, he reports and describes various works dealing with a simplified modelling of electric systems and with fractal simulation. Then, he outlines the challenges for the design of electric networks, proposes a design process, gives an overview of various design models, methods and tools, and reports an application in the design of electric networks for future jumbo jets

  18. Queueing Models for Mobile Ad Hoc Networks

    NARCIS (Netherlands)

    de Haan, Roland

    2009-01-01

    This thesis presents models for the performance analysis of a recent communication paradigm: \\emph{mobile ad hoc networking}. The objective of mobile ad hoc networking is to provide wireless connectivity between stations in a highly dynamic environment. These dynamics are driven by the mobility of

  19. Modeling GMPLS and Optical MPLS Networks

    DEFF Research Database (Denmark)

    Christiansen, Henrik Lehrmann; Wessing, Henrik

    2003-01-01

    . The MPLS concept is attractive because it can work as a unifying control structure. covering all technologies. This paper describes how a novel scheme for optical MPLS and circuit switched GMPLS based networks can incorporated in such multi-domain, MPLS-based scenarios and how it could be modeled. Network...

  20. Cyber threat model for tactical radio networks

    Science.gov (United States)

    Kurdziel, Michael T.

    2014-05-01

    The shift to a full information-centric paradigm in the battlefield has allowed ConOps to be developed that are only possible using modern network communications systems. Securing these Tactical Networks without impacting their capabilities has been a challenge. Tactical networks with fixed infrastructure have similar vulnerabilities to their commercial counterparts (although they need to be secure against adversaries with greater capabilities, resources and motivation). However, networks with mobile infrastructure components and Mobile Ad hoc Networks (MANets) have additional unique vulnerabilities that must be considered. It is useful to examine Tactical Network based ConOps and use them to construct a threat model and baseline cyber security requirements for Tactical Networks with fixed infrastructure, mobile infrastructure and/or ad hoc modes of operation. This paper will present an introduction to threat model assessment. A definition and detailed discussion of a Tactical Network threat model is also presented. Finally, the model is used to derive baseline requirements that can be used to design or evaluate a cyber security solution that can be scaled and adapted to the needs of specific deployments.

  1. Modeling documents with Generative Adversarial Networks

    OpenAIRE

    Glover, John

    2016-01-01

    This paper describes a method for using Generative Adversarial Networks to learn distributed representations of natural language documents. We propose a model that is based on the recently proposed Energy-Based GAN, but instead uses a Denoising Autoencoder as the discriminator network. Document representations are extracted from the hidden layer of the discriminator and evaluated both quantitatively and qualitatively.

  2. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  3. Modeling trust context in networks

    CERN Document Server

    Adali, Sibel

    2013-01-01

    We make complex decisions every day, requiring trust in many different entities for different reasons. These decisions are not made by combining many isolated trust evaluations. Many interlocking factors play a role, each dynamically impacting the others.? In this brief, 'trust context' is defined as the system level description of how the trust evaluation process unfolds.Networks today are part of almost all human activity, supporting and shaping it. Applications increasingly incorporate new interdependencies and new trust contexts. Social networks connect people and organizations throughout

  4. Mathematical model of highways network optimization

    Science.gov (United States)

    Sakhapov, R. L.; Nikolaeva, R. V.; Gatiyatullin, M. H.; Makhmutov, M. M.

    2017-12-01

    The article deals with the issue of highways network design. Studies show that the main requirement from road transport for the road network is to ensure the realization of all the transport links served by it, with the least possible cost. The goal of optimizing the network of highways is to increase the efficiency of transport. It is necessary to take into account a large number of factors that make it difficult to quantify and qualify their impact on the road network. In this paper, we propose building an optimal variant for locating the road network on the basis of a mathematical model. The article defines the criteria for optimality and objective functions that reflect the requirements for the road network. The most fully satisfying condition for optimality is the minimization of road and transport costs. We adopted this indicator as a criterion of optimality in the economic-mathematical model of a network of highways. Studies have shown that each offset point in the optimal binding road network is associated with all other corresponding points in the directions providing the least financial costs necessary to move passengers and cargo from this point to the other corresponding points. The article presents general principles for constructing an optimal network of roads.

  5. Non-Invasive Brain Stimulation to Enhance Upper Limb Motor Practice Poststroke: A Model for Selection of Cortical Site

    Directory of Open Access Journals (Sweden)

    Michelle L. Harris-Love

    2017-05-01

    Full Text Available Motor practice is an essential part of upper limb motor recovery following stroke. To be effective, it must be intensive with a high number of repetitions. Despite the time and effort required, gains made from practice alone are often relatively limited, and substantial residual impairment remains. Using non-invasive brain stimulation to modulate cortical excitability prior to practice could enhance the effects of practice and provide greater returns on the investment of time and effort. However, determining which cortical area to target is not trivial. The implications of relevant conceptual frameworks such as Interhemispheric Competition and Bimodal Balance Recovery are discussed. In addition, we introduce the STAC (Structural reserve, Task Attributes, Connectivity framework, which incorporates patient-, site-, and task-specific factors. An example is provided of how this framework can assist in selecting a cortical region to target for priming prior to reaching practice poststroke. We suggest that this expanded patient-, site-, and task-specific approach provides a useful model for guiding the development of more successful approaches to neuromodulation for enhancing motor recovery after stroke.

  6. Multiple Regions of a Cortical Network Commonly Encode the Meaning of Words in Multiple Grammatical Positions of Read Sentences.

    Science.gov (United States)

    Anderson, Andrew James; Lalor, Edmund C; Lin, Feng; Binder, Jeffrey R; Fernandino, Leonardo; Humphries, Colin J; Conant, Lisa L; Raizada, Rajeev D S; Grimm, Scott; Wang, Xixi

    2018-05-16

    Deciphering how sentence meaning is represented in the brain remains a major challenge to science. Semantically related neural activity has recently been shown to arise concurrently in distributed brain regions as successive words in a sentence are read. However, what semantic content is represented by different regions, what is common across them, and how this relates to words in different grammatical positions of sentences is weakly understood. To address these questions, we apply a semantic model of word meaning to interpret brain activation patterns elicited in sentence reading. The model is based on human ratings of 65 sensory/motor/emotional and cognitive features of experience with words (and their referents). Through a process of mapping functional Magnetic Resonance Imaging activation back into model space we test: which brain regions semantically encode content words in different grammatical positions (e.g., subject/verb/object); and what semantic features are encoded by different regions. In left temporal, inferior parietal, and inferior/superior frontal regions we detect the semantic encoding of words in all grammatical positions tested and reveal multiple common components of semantic representation. This suggests that sentence comprehension involves a common core representation of multiple words' meaning being encoded in a network of regions distributed across the brain.

  7. Graphical Model Theory for Wireless Sensor Networks

    International Nuclear Information System (INIS)

    Davis, William B.

    2002-01-01

    Information processing in sensor networks, with many small processors, demands a theory of computation that allows the minimization of processing effort, and the distribution of this effort throughout the network. Graphical model theory provides a probabilistic theory of computation that explicitly addresses complexity and decentralization for optimizing network computation. The junction tree algorithm, for decentralized inference on graphical probability models, can be instantiated in a variety of applications useful for wireless sensor networks, including: sensor validation and fusion; data compression and channel coding; expert systems, with decentralized data structures, and efficient local queries; pattern classification, and machine learning. Graphical models for these applications are sketched, and a model of dynamic sensor validation and fusion is presented in more depth, to illustrate the junction tree algorithm

  8. Modeling Network Traffic in Wavelet Domain

    Directory of Open Access Journals (Sweden)

    Sheng Ma

    2004-12-01

    Full Text Available This work discovers that although network traffic has the complicated short- and long-range temporal dependence, the corresponding wavelet coefficients are no longer long-range dependent. Therefore, a "short-range" dependent process can be used to model network traffic in the wavelet domain. Both independent and Markov models are investigated. Theoretical analysis shows that the independent wavelet model is sufficiently accurate in terms of the buffer overflow probability for Fractional Gaussian Noise traffic. Any model, which captures additional correlations in the wavelet domain, only improves the performance marginally. The independent wavelet model is then used as a unified approach to model network traffic including VBR MPEG video and Ethernet data. The computational complexity is O(N for developing such wavelet models and generating synthesized traffic of length N, which is among the lowest attained.

  9. Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions

    Directory of Open Access Journals (Sweden)

    Jyotika Bahuguna

    2017-08-01

    Full Text Available The classical model of basal ganglia has been refined in recent years with discoveries of subpopulations within a nucleus and previously unknown projections. One such discovery is the presence of subpopulations of arkypallidal and prototypical neurons in external globus pallidus, which was previously considered to be a primarily homogeneous nucleus. Developing a computational model of these multiple interconnected nuclei is challenging, because the strengths of the connections are largely unknown. We therefore use a genetic algorithm to search for the unknown connectivity parameters in a firing rate model. We apply a binary cost function derived from empirical firing rate and phase relationship data for the physiological and Parkinsonian conditions. Our approach generates ensembles of over 1,000 configurations, or homologies, for each condition, with broad distributions for many of the parameter values and overlap between the two conditions. However, the resulting effective weights of connections from or to prototypical and arkypallidal neurons are consistent with the experimental data. We investigate the significance of the weight variability by manipulating the parameters individually and cumulatively, and conclude that the correlation observed between the parameters is necessary for generating the dynamics of the two conditions. We then investigate the response of the networks to a transient cortical stimulus, and demonstrate that networks classified as physiological effectively suppress activity in the internal globus pallidus, and are not susceptible to oscillations, whereas parkinsonian networks show the opposite tendency. Thus, we conclude that the rates and phase relationships observed in the globus pallidus are predictive of experimentally observed higher level dynamical features of the physiological and parkinsonian basal ganglia, and that the multiplicity of solutions generated by our method may well be indicative of a natural

  10. Sparsity in Model Gene Regulatory Networks

    International Nuclear Information System (INIS)

    Zagorski, M.

    2011-01-01

    We propose a gene regulatory network model which incorporates the microscopic interactions between genes and transcription factors. In particular the gene's expression level is determined by deterministic synchronous dynamics with contribution from excitatory interactions. We study the structure of networks that have a particular '' function '' and are subject to the natural selection pressure. The question of network robustness against point mutations is addressed, and we conclude that only a small part of connections defined as '' essential '' for cell's existence is fragile. Additionally, the obtained networks are sparse with narrow in-degree and broad out-degree, properties well known from experimental study of biological regulatory networks. Furthermore, during sampling procedure we observe that significantly different genotypes can emerge under mutation-selection balance. All the preceding features hold for the model parameters which lay in the experimentally relevant range. (author)

  11. Evolving Models of Pavlovian Conditioning: Cerebellar Cortical Dynamics in Awake Behaving Mice

    Directory of Open Access Journals (Sweden)

    Michiel M. ten Brinke

    2015-12-01

    Full Text Available Three decades of electrophysiological research on cerebellar cortical activity underlying Pavlovian conditioning have expanded our understanding of motor learning in the brain. Purkinje cell simple spike suppression is considered to be crucial in the expression of conditional blink responses (CRs. However, trial-by-trial quantification of this link in awake behaving animals is lacking, and current hypotheses regarding the underlying plasticity mechanisms have diverged from the classical parallel fiber one to the Purkinje cell synapse LTD hypothesis. Here, we establish that acquired simple spike suppression, acquired conditioned stimulus (CS-related complex spike responses, and molecular layer interneuron (MLI activity predict the expression of CRs on a trial-by-trial basis using awake behaving mice. Additionally, we show that two independent transgenic mouse mutants with impaired MLI function exhibit motor learning deficits. Our findings suggest multiple cerebellar cortical plasticity mechanisms underlying simple spike suppression, and they implicate the broader involvement of the olivocerebellar module within the interstimulus interval.

  12. Abnormal Development of the Earliest Cortical Circuits in a Mouse Model of Autism Spectrum Disorder.

    Science.gov (United States)

    Nagode, Daniel A; Meng, Xiangying; Winkowski, Daniel E; Smith, Ed; Khan-Tareen, Hamza; Kareddy, Vishnupriya; Kao, Joseph P Y; Kanold, Patrick O

    2017-01-31

    Autism spectrum disorder (ASD) involves deficits in speech and sound processing. Cortical circuit changes during early development likely contribute to such deficits. Subplate neurons (SPNs) form the earliest cortical microcircuits and are required for normal development of thalamocortical and intracortical circuits. Prenatal valproic acid (VPA) increases ASD risk, especially when present during a critical time window coinciding with SPN genesis. Using optical circuit mapping in mouse auditory cortex, we find that VPA exposure on E12 altered the functional excitatory and inhibitory connectivity of SPNs. Circuit changes manifested as "patches" of mostly increased connection probability or strength in the first postnatal week and as general hyper-connectivity after P10, shortly after ear opening. These results suggest that prenatal VPA exposure severely affects the developmental trajectory of cortical circuits and that sensory-driven activity may exacerbate earlier, subtle connectivity deficits. Our findings identify the subplate as a possible common pathophysiological substrate of deficits in ASD. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

  13. Hierarchical Winner-Take-All Particle Swarm Optimization Social Network for Neural Model Fitting

    Science.gov (United States)

    Coventry, Brandon S.; Parthasarathy, Aravindakshan; Sommer, Alexandra L.; Bartlett, Edward L.

    2016-01-01

    Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models. PMID:27726048

  14. Hierarchical winner-take-all particle swarm optimization social network for neural model fitting.

    Science.gov (United States)

    Coventry, Brandon S; Parthasarathy, Aravindakshan; Sommer, Alexandra L; Bartlett, Edward L

    2017-02-01

    Particle swarm optimization (PSO) has gained widespread use as a general mathematical programming paradigm and seen use in a wide variety of optimization and machine learning problems. In this work, we introduce a new variant on the PSO social network and apply this method to the inverse problem of input parameter selection from recorded auditory neuron tuning curves. The topology of a PSO social network is a major contributor to optimization success. Here we propose a new social network which draws influence from winner-take-all coding found in visual cortical neurons. We show that the winner-take-all network performs exceptionally well on optimization problems with greater than 5 dimensions and runs at a lower iteration count as compared to other PSO topologies. Finally we show that this variant of PSO is able to recreate auditory frequency tuning curves and modulation transfer functions, making it a potentially useful tool for computational neuroscience models.

  15. The QKD network: model and routing scheme

    Science.gov (United States)

    Yang, Chao; Zhang, Hongqi; Su, Jinhai

    2017-11-01

    Quantum key distribution (QKD) technology can establish unconditional secure keys between two communicating parties. Although this technology has some inherent constraints, such as the distance and point-to-point mode limits, building a QKD network with multiple point-to-point QKD devices can overcome these constraints. Considering the development level of current technology, the trust relaying QKD network is the first choice to build a practical QKD network. However, the previous research didn't address a routing method on the trust relaying QKD network in detail. This paper focuses on the routing issues, builds a model of the trust relaying QKD network for easily analysing and understanding this network, and proposes a dynamical routing scheme for this network. From the viewpoint of designing a dynamical routing scheme in classical network, the proposed scheme consists of three components: a Hello protocol helping share the network topology information, a routing algorithm to select a set of suitable paths and establish the routing table and a link state update mechanism helping keep the routing table newly. Experiments and evaluation demonstrates the validity and effectiveness of the proposed routing scheme.

  16. A Model of Network Porosity

    Science.gov (United States)

    2016-11-09

    Figure 1. We generally express such networks in terms of the services running in each enclave as well as the routing and firewall rules between the...compromise a server, they can compromise other devices in the same subnet or protected enclave. They probe attached firewalls and routers for open ports and...spam and malware filter would prevent this content from reaching its destination. Content filtering provides another layer of defense to other controls

  17. Thermal conductivity model for nanofiber networks

    Science.gov (United States)

    Zhao, Xinpeng; Huang, Congliang; Liu, Qingkun; Smalyukh, Ivan I.; Yang, Ronggui

    2018-02-01

    Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

  18. Thermal conductivity model for nanofiber networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Xinpeng [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Huang, Congliang [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221116, China; Liu, Qingkun [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Smalyukh, Ivan I. [Department of Physics, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Yang, Ronggui [Department of Mechanical Engineering, University of Colorado, Boulder, Colorado 80309, USA; Materials Science and Engineering Program, University of Colorado, Boulder, Colorado 80309, USA; Buildings and Thermal Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, USA

    2018-02-28

    Understanding thermal transport in nanofiber networks is essential for their applications in thermal management, which are used extensively as mechanically sturdy thermal insulation or high thermal conductivity materials. In this study, using the statistical theory and Fourier's law of heat conduction while accounting for both the inter-fiber contact thermal resistance and the intrinsic thermal resistance of nanofibers, an analytical model is developed to predict the thermal conductivity of nanofiber networks as a function of their geometric and thermal properties. A scaling relation between the thermal conductivity and the geometric properties including volume fraction and nanofiber length of the network is revealed. This model agrees well with both numerical simulations and experimental measurements found in the literature. This model may prove useful in analyzing the experimental results and designing nanofiber networks for both high and low thermal conductivity applications.

  19. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  20. Persistence and storage of activity patterns in spiking recurrent cortical networks: modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine.

    Science.gov (United States)

    Palma, Jesse; Grossberg, Stephen; Versace, Massimiliano

    2012-01-01

    Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM). Theorems in the 1970's showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP) currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh) can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 ms or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all (WTA) stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners when the network

  1. Persistence and storage of activity patterns in spiking recurrent cortical networks:Modulation of sigmoid signals by after-hyperpolarization currents and acetylcholine

    Directory of Open Access Journals (Sweden)

    Jesse ePalma

    2012-06-01

    Full Text Available Many cortical networks contain recurrent architectures that transform input patterns before storing them in short-term memory (STM. Theorems in the 1970’s showed how feedback signal functions in rate-based recurrent on-center off-surround networks control this process. A sigmoid signal function induces a quenching threshold below which inputs are suppressed as noise and above which they are contrast-enhanced before pattern storage. This article describes how changes in feedback signaling, neuromodulation, and recurrent connectivity may alter pattern processing in recurrent on-center off-surround networks of spiking neurons. In spiking neurons, fast, medium, and slow after-hyperpolarization (AHP currents control sigmoid signal threshold and slope. Modulation of AHP currents by acetylcholine (ACh can change sigmoid shape and, with it, network dynamics. For example, decreasing signal function threshold and increasing slope can lengthen the persistence of a partially contrast-enhanced pattern, increase the number of active cells stored in STM, or, if connectivity is distance-dependent, cause cell activities to cluster. These results clarify how cholinergic modulation by the basal forebrain may alter the vigilance of category learning circuits, and thus their sensitivity to predictive mismatches, thereby controlling whether learned categories code concrete or abstract features, as predicted by Adaptive Resonance Theory. The analysis includes global, distance-dependent, and interneuron-mediated circuits. With an appropriate degree of recurrent excitation and inhibition, spiking networks maintain a partially contrast-enhanced pattern for 800 milliseconds or longer after stimuli offset, then resolve to no stored pattern, or to winner-take-all stored patterns with one or multiple winners. Strengthening inhibition prolongs a partially contrast-enhanced pattern by slowing the transition to stability, while strengthening excitation causes more winners

  2. Combinatorial explosion in model gene networks

    Science.gov (United States)

    Edwards, R.; Glass, L.

    2000-09-01

    The explosive growth in knowledge of the genome of humans and other organisms leaves open the question of how the functioning of genes in interacting networks is coordinated for orderly activity. One approach to this problem is to study mathematical properties of abstract network models that capture the logical structures of gene networks. The principal issue is to understand how particular patterns of activity can result from particular network structures, and what types of behavior are possible. We study idealized models in which the logical structure of the network is explicitly represented by Boolean functions that can be represented by directed graphs on n-cubes, but which are continuous in time and described by differential equations, rather than being updated synchronously via a discrete clock. The equations are piecewise linear, which allows significant analysis and facilitates rapid integration along trajectories. We first give a combinatorial solution to the question of how many distinct logical structures exist for n-dimensional networks, showing that the number increases very rapidly with n. We then outline analytic methods that can be used to establish the existence, stability and periods of periodic orbits corresponding to particular cycles on the n-cube. We use these methods to confirm the existence of limit cycles discovered in a sample of a million randomly generated structures of networks of 4 genes. Even with only 4 genes, at least several hundred different patterns of stable periodic behavior are possible, many of them surprisingly complex. We discuss ways of further classifying these periodic behaviors, showing that small mutations (reversal of one or a few edges on the n-cube) need not destroy the stability of a limit cycle. Although these networks are very simple as models of gene networks, their mathematical transparency reveals relationships between structure and behavior, they suggest that the possibilities for orderly dynamics in such

  3. Predicting workload profiles of brain-robot interface and electromygraphic neurofeedback with cortical resting-state networks: personal trait or task-specific challenge?

    Science.gov (United States)

    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.

  4. Complex networks under dynamic repair model

    Science.gov (United States)

    Chaoqi, Fu; Ying, Wang; Kun, Zhao; Yangjun, Gao

    2018-01-01

    Invulnerability is not the only factor of importance when considering complex networks' security. It is also critical to have an effective and reasonable repair strategy. Existing research on network repair is confined to the static model. The dynamic model makes better use of the redundant capacity of repaired nodes and repairs the damaged network more efficiently than the static model; however, the dynamic repair model is complex and polytropic. In this paper, we construct a dynamic repair model and systematically describe the energy-transfer relationships between nodes in the repair process of the failure network. Nodes are divided into three types, corresponding to three structures. We find that the strong coupling structure is responsible for secondary failure of the repaired nodes and propose an algorithm that can select the most suitable targets (nodes or links) to repair the failure network with minimal cost. Two types of repair strategies are identified, with different effects under the two energy-transfer rules. The research results enable a more flexible approach to network repair.

  5. Performance modeling, stochastic networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi R

    2013-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of introducing an appropriate mathematical framework for modeling and analysis as well as understanding the phenomenon of statistical multiplexing. The models, techniques, and results presented form the core of traffic engineering methods used to design, control and allocate resources in communication networks.The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the importan

  6. Network Modeling and Simulation A Practical Perspective

    CERN Document Server

    Guizani, Mohsen; Khan, Bilal

    2010-01-01

    Network Modeling and Simulation is a practical guide to using modeling and simulation to solve real-life problems. The authors give a comprehensive exposition of the core concepts in modeling and simulation, and then systematically address the many practical considerations faced by developers in modeling complex large-scale systems. The authors provide examples from computer and telecommunication networks and use these to illustrate the process of mapping generic simulation concepts to domain-specific problems in different industries and disciplines. Key features: Provides the tools and strate

  7. Modeling acquaintance networks based on balance theory

    Directory of Open Access Journals (Sweden)

    Vukašinović Vida

    2014-09-01

    Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models

  8. Optimal transportation networks models and theory

    CERN Document Server

    Bernot, Marc; Morel, Jean-Michel

    2009-01-01

    The transportation problem can be formalized as the problem of finding the optimal way to transport a given measure into another with the same mass. In contrast to the Monge-Kantorovitch problem, recent approaches model the branched structure of such supply networks as minima of an energy functional whose essential feature is to favour wide roads. Such a branched structure is observable in ground transportation networks, in draining and irrigation systems, in electrical power supply systems and in natural counterparts such as blood vessels or the branches of trees. These lectures provide mathematical proof of several existence, structure and regularity properties empirically observed in transportation networks. The link with previous discrete physical models of irrigation and erosion models in geomorphology and with discrete telecommunication and transportation models is discussed. It will be mathematically proven that the majority fit in the simple model sketched in this volume.

  9. Flood routing modelling with Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    R. Peters

    2006-01-01

    Full Text Available For the modelling of the flood routing in the lower reaches of the Freiberger Mulde river and its tributaries the one-dimensional hydrodynamic modelling system HEC-RAS has been applied. Furthermore, this model was used to generate a database to train multilayer feedforward networks. To guarantee numerical stability for the hydrodynamic modelling of some 60 km of streamcourse an adequate resolution in space requires very small calculation time steps, which are some two orders of magnitude smaller than the input data resolution. This leads to quite high computation requirements seriously restricting the application – especially when dealing with real time operations such as online flood forecasting. In order to solve this problem we tested the application of Artificial Neural Networks (ANN. First studies show the ability of adequately trained multilayer feedforward networks (MLFN to reproduce the model performance.

  10. Linear approximation model network and its formation via ...

    Indian Academy of Sciences (India)

    To overcome the deficiency of `local model network' (LMN) techniques, an alternative `linear approximation model' (LAM) network approach is proposed. Such a network models a nonlinear or practical system with multiple linear models fitted along operating trajectories, where individual models are simply networked ...

  11. Modeling Security Aspects of Network

    Science.gov (United States)

    Schoch, Elmar

    With more and more widespread usage of computer systems and networks, dependability becomes a paramount requirement. Dependability typically denotes tolerance or protection against all kinds of failures, errors and faults. Sources of failures can basically be accidental, e.g., in case of hardware errors or software bugs, or intentional due to some kind of malicious behavior. These intentional, malicious actions are subject of security. A more complete overview on the relations between dependability and security can be found in [31]. In parallel to the increased use of technology, misuse also has grown significantly, requiring measures to deal with it.

  12. Modeling and optimization of an electric power distribution network ...

    African Journals Online (AJOL)

    Modeling and optimization of an electric power distribution network planning system using ... of the network was modelled with non-linear mathematical expressions. ... given feasible locations, re-conductoring of existing feeders in the network, ...

  13. Distribution and Network of Basal Temporal Language Areas: A Study of the Combination of Electric Cortical Stimulation and Diffusion Tensor Imaging.

    Science.gov (United States)

    Enatsu, Rei; Kanno, Aya; Ookawa, Satoshi; Ochi, Satoko; Ishiai, Sumio; Nagamine, Takashi; Mikuni, Nobuhiro

    2017-10-01

    The basal temporal language area (BTLA) is considered to have several functions in language processing; however, its brain network is still unknown. This study investigated the distribution and networks of the BTLA using a combination of electric cortical stimulation and diffusion tensor imaging (DTI). 10 patients with intractable focal epilepsy who underwent presurgical evaluation with subdural electrodes were enrolled in this study (language dominant side: 6 patients, language nondominant side: 4 patients). Electric stimulation at 50 Hz was applied to the electrodes during Japanese sentence reading, morphograms (kanji) reading, and syllabograms (kana) reading tasks to identify the BTLA. DTI was used to identify the subcortical fibers originating from the BTLA found by electric stimulation. The BTLA was found in 6 patients who underwent implantation of the subdural electrodes in the dominant hemisphere. The BTLA was located anywhere between 20 mm and 56 mm posterior to the temporal tips. In 3 patients, electric stimulation of some or all areas within the BTLA induced disturbance in reading of kanji words only. DTI detected the inferior longitudinal fasciculus (ILF) in all patients and the uncinate fasciculus (UF) in 1 patient, originating from the BTLA. ILF was detected from both kanji-specific areas and kanji-nonspecific areas. This study indicates that the network of the BTLA is a part of a ventral stream and is mainly composed of the ILF, which acts as a critical structure for lexical retrieval. ILF is also associated with the specific processing of kanji words. Copyright © 2017 Elsevier Inc. All rights reserved.

  14. An evolving network model with modular growth

    International Nuclear Information System (INIS)

    Zou Zhi-Yun; Liu Peng; Lei Li; Gao Jian-Zhi

    2012-01-01

    In this paper, we propose an evolving network model growing fast in units of module, according to the analysis of the evolution characteristics in real complex networks. Each module is a small-world network containing several interconnected nodes and the nodes between the modules are linked by preferential attachment on degree of nodes. We study the modularity measure of the proposed model, which can be adjusted by changing the ratio of the number of inner-module edges and the number of inter-module edges. In view of the mean-field theory, we develop an analytical function of the degree distribution, which is verified by a numerical example and indicates that the degree distribution shows characteristics of the small-world network and the scale-free network distinctly at different segments. The clustering coefficient and the average path length of the network are simulated numerically, indicating that the network shows the small-world property and is affected little by the randomness of the new module. (interdisciplinary physics and related areas of science and technology)

  15. Modeling of contact tracing in social networks

    Science.gov (United States)

    Tsimring, Lev S.; Huerta, Ramón

    2003-07-01

    Spreading of certain infections in complex networks is effectively suppressed by using intelligent strategies for epidemic control. One such standard epidemiological strategy consists in tracing contacts of infected individuals. In this paper, we use a recently introduced generalization of the standard susceptible-infectious-removed stochastic model for epidemics in sparse random networks which incorporates an additional (traced) state. We describe a deterministic mean-field description which yields quantitative agreement with stochastic simulations on random graphs. We also discuss the role of contact tracing in epidemics control in small-world and scale-free networks. Effectiveness of contact tracing grows as the rewiring probability is reduced.

  16. A Network Model of Credit Risk Contagion

    Directory of Open Access Journals (Sweden)

    Ting-Qiang Chen

    2012-01-01

    Full Text Available A network model of credit risk contagion is presented, in which the effect of behaviors of credit risk holders and the financial market regulators and the network structure are considered. By introducing the stochastic dominance theory, we discussed, respectively, the effect mechanisms of the degree of individual relationship, individual attitude to credit risk contagion, the individual ability to resist credit risk contagion, the monitoring strength of the financial market regulators, and the network structure on credit risk contagion. Then some derived and proofed propositions were verified through numerical simulations.

  17. The International Trade Network: weighted network analysis and modelling

    International Nuclear Information System (INIS)

    Bhattacharya, K; Mukherjee, G; Manna, S S; Saramäki, J; Kaski, K

    2008-01-01

    Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN

  18. Keystone Business Models for Network Security Processors

    OpenAIRE

    Arthur Low; Steven Muegge

    2013-01-01

    Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor...

  19. Stochastic modeling and analysis of telecoms networks

    CERN Document Server

    Decreusefond, Laurent

    2012-01-01

    This book addresses the stochastic modeling of telecommunication networks, introducing the main mathematical tools for that purpose, such as Markov processes, real and spatial point processes and stochastic recursions, and presenting a wide list of results on stability, performances and comparison of systems.The authors propose a comprehensive mathematical construction of the foundations of stochastic network theory: Markov chains, continuous time Markov chains are extensively studied using an original martingale-based approach. A complete presentation of stochastic recursions from an

  20. Decomposed Implicit Models of Piecewise - Linear Networks

    Directory of Open Access Journals (Sweden)

    J. Brzobohaty

    1992-05-01

    Full Text Available The general matrix form of the implicit description of a piecewise-linear (PWL network and the symbolic block diagram of the corresponding circuit model are proposed. Their decomposed forms enable us to determine quite separately the existence of the individual breakpoints of the resultant PWL characteristic and their coordinates using independent network parameters. For the two-diode and three-diode cases all the attainable types of the PWL characteristic are introduced.

  1. Artificial Immune Networks: Models and Applications

    Directory of Open Access Journals (Sweden)

    Xian Shen

    2008-06-01

    Full Text Available Artificial Immune Systems (AIS, which is inspired by the nature immune system, has been applied for solving complex computational problems in classification, pattern rec- ognition, and optimization. In this paper, the theory of the natural immune system is first briefly introduced. Next, we compare some well-known AIS and their applications. Several representative artificial immune networks models are also dis- cussed. Moreover, we demonstrate the applications of artificial immune networks in various engineering fields.

  2. Continuum Modeling of Biological Network Formation

    KAUST Repository

    Albi, Giacomo

    2017-04-10

    We present an overview of recent analytical and numerical results for the elliptic–parabolic system of partial differential equations proposed by Hu and Cai, which models the formation of biological transportation networks. The model describes the pressure field using a Darcy type equation and the dynamics of the conductance network under pressure force effects. Randomness in the material structure is represented by a linear diffusion term and conductance relaxation by an algebraic decay term. We first introduce micro- and mesoscopic models and show how they are connected to the macroscopic PDE system. Then, we provide an overview of analytical results for the PDE model, focusing mainly on the existence of weak and mild solutions and analysis of the steady states. The analytical part is complemented by extensive numerical simulations. We propose a discretization based on finite elements and study the qualitative properties of network structures for various parameter values.

  3. Adaptive-network models of collective dynamics

    Science.gov (United States)

    Zschaler, G.

    2012-09-01

    Complex systems can often be modelled as networks, in which their basic units are represented by abstract nodes and the interactions among them by abstract links. This network of interactions is the key to understanding emergent collective phenomena in such systems. In most cases, it is an adaptive network, which is defined by a feedback loop between the local dynamics of the individual units and the dynamical changes of the network structure itself. This feedback loop gives rise to many novel phenomena. Adaptive networks are a promising concept for the investigation of collective phenomena in different systems. However, they also present a challenge to existing modelling approaches and analytical descriptions due to the tight coupling between local and topological degrees of freedom. In this work, which is essentially my PhD thesis, I present a simple rule-based framework for the investigation of adaptive networks, using which a wide range of collective phenomena can be modelled and analysed from a common perspective. In this framework, a microscopic model is defined by the local interaction rules of small network motifs, which can be implemented in stochastic simulations straightforwardly. Moreover, an approximate emergent-level description in terms of macroscopic variables can be derived from the microscopic rules, which we use to analyse the system's collective and long-term behaviour by applying tools from dynamical systems theory. We discuss three adaptive-network models for different collective phenomena within our common framework. First, we propose a novel approach to collective motion in insect swarms, in which we consider the insects' adaptive interaction network instead of explicitly tracking their positions and velocities. We capture the experimentally observed onset of collective motion qualitatively in terms of a bifurcation in this non-spatial model. We find that three-body interactions are an essential ingredient for collective motion to emerge

  4. Network Design Models for Container Shipping

    DEFF Research Database (Denmark)

    Reinhardt, Line Blander; Kallehauge, Brian; Nielsen, Anders Nørrelund

    This paper presents a study of the network design problem in container shipping. The paper combines the network design and fleet assignment problem into a mixed integer linear programming model minimizing the overall cost. The major contributions of this paper is that the time of a vessel route...... is included in the calculation of the capacity and that a inhomogeneous fleet is modeled. The model also includes the cost of transshipment which is one of the major cost for the shipping companies. The concept of pseudo simple routes is introduced to expand the set of feasible routes. The linearization...

  5. The cortical signature of amyotrophic lateral sclerosis.

    Directory of Open Access Journals (Sweden)

    Federica Agosta

    Full Text Available The aim of this study was to explore the pattern of regional cortical thickness in patients with non-familial amyotrophic lateral sclerosis (ALS and to investigate whether cortical thinning is associated with disease progression rate. Cortical thickness analysis was performed in 44 ALS patients and 26 healthy controls. Group differences in cortical thickness and the age-by-group effects were assessed using vertex-by-vertex and multivariate linear models. The discriminatory ability of MRI variables in distinguishing patients from controls was estimated using the Concordance Statistics (C-statistic within logistic regression analyses. Correlations between cortical thickness measures and disease progression rate were tested using the Pearson coefficient. Relative to controls, ALS patients showed a bilateral cortical thinning of the primary motor, prefrontal and ventral frontal cortices, cingulate gyrus, insula, superior and inferior temporal and parietal regions, and medial and lateral occipital areas. There was a significant age-by-group effect in the sensorimotor cortices bilaterally, suggesting a stronger association between age and cortical thinning in ALS patients compared to controls. The mean cortical thickness of the sensorimotor cortices distinguished patients with ALS from controls (C-statistic ≥ 0.74. Cortical thinning of the left sensorimotor cortices was related to a faster clinical progression (r = -0.33, p = 0.03. Cortical thickness measurements allowed the detection and quantification of motor and extramotor involvement in patients with ALS. Cortical thinning of the precentral gyrus might offer a marker of upper motor neuron involvement and disease progression.

  6. The cortical signature of amyotrophic lateral sclerosis.

    Science.gov (United States)

    Agosta, Federica; Valsasina, Paola; Riva, Nilo; Copetti, Massimiliano; Messina, Maria Josè; Prelle, Alessandro; Comi, Giancarlo; Filippi, Massimo

    2012-01-01

    The aim of this study was to explore the pattern of regional cortical thickness in patients with non-familial amyotrophic lateral sclerosis (ALS) and to investigate whether cortical thinning is associated with disease progression rate. Cortical thickness analysis was performed in 44 ALS patients and 26 healthy controls. Group differences in cortical thickness and the age-by-group effects were assessed using vertex-by-vertex and multivariate linear models. The discriminatory ability of MRI variables in distinguishing patients from controls was estimated using the Concordance Statistics (C-statistic) within logistic regression analyses. Correlations between cortical thickness measures and disease progression rate were tested using the Pearson coefficient. Relative to controls, ALS patients showed a bilateral cortical thinning of the primary motor, prefrontal and ventral frontal cortices, cingulate gyrus, insula, superior and inferior temporal and parietal regions, and medial and lateral occipital areas. There was a significant age-by-group effect in the sensorimotor cortices bilaterally, suggesting a stronger association between age and cortical thinning in ALS patients compared to controls. The mean cortical thickness of the sensorimotor cortices distinguished patients with ALS from controls (C-statistic ≥ 0.74). Cortical thinning of the left sensorimotor cortices was related to a faster clinical progression (r = -0.33, p = 0.03). Cortical thickness measurements allowed the detection and quantification of motor and extramotor involvement in patients with ALS. Cortical thinning of the precentral gyrus might offer a marker of upper motor neuron involvement and disease progression.

  7. Characterization and Modeling of Network Traffic

    DEFF Research Database (Denmark)

    Shawky, Ahmed; Bergheim, Hans; Ragnarsson, Olafur

    2011-01-01

    -arrival time, IP addresses, port numbers and transport protocol are the only necessary parameters to model network traffic behaviour. In order to recreate this behaviour, a complex model is needed which is able to recreate traffic behaviour based on a set of statistics calculated from the parameters values...

  8. Recurrent network models for perfect temporal integration of fluctuating correlated inputs.

    Directory of Open Access Journals (Sweden)

    Hiroshi Okamoto

    2009-06-01

    Full Text Available Temporal integration of input is essential to the accumulation of information in various cognitive and behavioral processes, and gradually increasing neuronal activity, typically occurring within a range of seconds, is considered to reflect such computation by the brain. Some psychological evidence suggests that temporal integration by the brain is nearly perfect, that is, the integration is non-leaky, and the output of a neural integrator is accurately proportional to the strength of input. Neural mechanisms of perfect temporal integration, however, remain largely unknown. Here, we propose a recurrent network model of cortical neurons that perfectly integrates partially correlated, irregular input spike trains. We demonstrate that the rate of this temporal integration changes proportionately to the probability of spike coincidences in synaptic inputs. We analytically prove that this highly accurate integration of synaptic inputs emerges from integration of the variance of the fluctuating synaptic inputs, when their mean component is kept constant. Highly irregular neuronal firing and spike coincidences are the major features of cortical activity, but they have been separately addressed so far. Our results suggest that the efficient protocol of information integration by cortical networks essentially requires both features and hence is heterotic.

  9. Phenomenological network models: Lessons for epilepsy surgery.

    Science.gov (United States)

    Hebbink, Jurgen; Meijer, Hil; Huiskamp, Geertjan; van Gils, Stephan; Leijten, Frans

    2017-10-01

    The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient. © 2017 The Authors. Epilepsia published by Wiley Periodicals, Inc. on behalf of International League Against Epilepsy.

  10. Agent based modeling of energy networks

    International Nuclear Information System (INIS)

    Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

    2014-01-01

    Highlights: • A new approach for energy network modeling is designed and tested. • The agent-based approach is general and no technology dependent. • The models can be easily extended. • The range of applications encompasses from small to large energy infrastructures. - Abstract: Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

  11. Delay and Disruption Tolerant Networking MACHETE Model

    Science.gov (United States)

    Segui, John S.; Jennings, Esther H.; Gao, Jay L.

    2011-01-01

    To verify satisfaction of communication requirements imposed by unique missions, as early as 2000, the Communications Networking Group at the Jet Propulsion Laboratory (JPL) saw the need for an environment to support interplanetary communication protocol design, validation, and characterization. JPL's Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE), described in Simulator of Space Communication Networks (NPO-41373) NASA Tech Briefs, Vol. 29, No. 8 (August 2005), p. 44, combines various commercial, non-commercial, and in-house custom tools for simulation and performance analysis of space networks. The MACHETE environment supports orbital analysis, link budget analysis, communications network simulations, and hardware-in-the-loop testing. As NASA is expanding its Space Communications and Navigation (SCaN) capabilities to support planned and future missions, building infrastructure to maintain services and developing enabling technologies, an important and broader role is seen for MACHETE in design-phase evaluation of future SCaN architectures. To support evaluation of the developing Delay Tolerant Networking (DTN) field and its applicability for space networks, JPL developed MACHETE models for DTN Bundle Protocol (BP) and Licklider/Long-haul Transmission Protocol (LTP). DTN is an Internet Research Task Force (IRTF) architecture providing communication in and/or through highly stressed networking environments such as space exploration and battlefield networks. Stressed networking environments include those with intermittent (predictable and unknown) connectivity, large and/or variable delays, and high bit error rates. To provide its services over existing domain specific protocols, the DTN protocols reside at the application layer of the TCP/IP stack, forming a store-and-forward overlay network. The key capabilities of the Bundle Protocol include custody-based reliability, the ability to cope with intermittent connectivity

  12. A comprehensive Network Security Risk Model for process control networks.

    Science.gov (United States)

    Henry, Matthew H; Haimes, Yacov Y

    2009-02-01

    The risk of cyber attacks on process control networks (PCN) is receiving significant attention due to the potentially catastrophic extent to which PCN failures can damage the infrastructures and commodity flows that they support. Risk management addresses the coupled problems of (1) reducing the likelihood that cyber attacks would succeed in disrupting PCN operation and (2) reducing the severity of consequences in the event of PCN failure or manipulation. The Network Security Risk Model (NSRM) developed in this article provides a means of evaluating the efficacy of candidate risk management policies by modeling the baseline risk and assessing expectations of risk after the implementation of candidate measures. Where existing risk models fall short of providing adequate insight into the efficacy of candidate risk management policies due to shortcomings in their structure or formulation, the NSRM provides model structure and an associated modeling methodology that captures the relevant dynamics of cyber attacks on PCN for risk analysis. This article develops the NSRM in detail in the context of an illustrative example.

  13. Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions.

    Science.gov (United States)

    Wagatsuma, Nobuhiko; Sakai, Ko

    2016-01-01

    Border ownership (BO) indicates which side of a contour owns a border, and it plays a fundamental role in figure-ground segregation. The majority of neurons in V2 and V4 areas of monkeys exhibit BO selectivity. A physiological work reported that the responses of BO-selective cells show a rapid transition when a presented square is flipped along its classical receptive field (CRF) so that the opposite BO is presented, whereas the transition is significantly slower when a square with a clear BO is replaced by an ambiguous edge, e.g., when the square is enlarged greatly. The rapid transition seemed to reflect the influence of feedforward processing on BO selectivity. Herein, we investigated the role of feedforward signals and cortical interactions for time-courses in BO-selective cells by modeling a visual cortical network comprising V1, V2, and posterior parietal (PP) modules. In our computational model, the recurrent pathways among these modules gradually established the visual progress and the BO assignments. Feedforward inputs mainly determined the activities of these modules. Surrounding suppression/facilitation of early-level areas modulates the activities of V2 cells to provide BO signals. Weak feedback signals from the PP module enhanced the contrast gain extracted in V1, which underlies the attentional modulation of BO signals. Model simulations exhibited time-courses depending on the BO ambiguity, which were caused by the integration delay of V1 and V2 cells and the local inhibition therein given the difference in input stimulus. However, our model did not fully explain the characteristics of crucially slow transition: the responses of BO-selective physiological cells indicated the persistent activation several times longer than that of our model after the replacement with the ambiguous edge. Furthermore, the time-course of BO-selective model cells replicated the attentional modulation of response time in human psychophysical experiments. These attentional

  14. Modeling the Time-Course of Responses for the Border Ownership Selectivity Based on the Integration of Feedforward Signals and Visual Cortical Interactions

    Science.gov (United States)

    Wagatsuma, Nobuhiko; Sakai, Ko

    2017-01-01

    Border ownership (BO) indicates which side of a contour owns a border, and it plays a fundamental role in figure-ground segregation. The majority of neurons in V2 and V4 areas of monkeys exhibit BO selectivity. A physiological work reported that the responses of BO-selective cells show a rapid transition when a presented square is flipped along its classical receptive field (CRF) so that the opposite BO is presented, whereas the transition is significantly slower when a square with a clear BO is replaced by an ambiguous edge, e.g., when the square is enlarged greatly. The rapid transition seemed to reflect the influence of feedforward processing on BO selectivity. Herein, we investigated the role of feedforward signals and cortical interactions for time-courses in BO-selective cells by modeling a visual cortical network comprising V1, V2, and posterior parietal (PP) modules. In our computational model, the recurrent pathways among these modules gradually established the visual progress and the BO assignments. Feedforward inputs mainly determined the activities of these modules. Surrounding suppression/facilitation of early-level areas modulates the activities of V2 cells to provide BO signals. Weak feedback signals from the PP module enhanced the contrast gain extracted in V1, which underlies the attentional modulation of BO signals. Model simulations exhibited time-courses depending on the BO ambiguity, which were caused by the integration delay of V1 and V2 cells and the local inhibition therein given the difference in input stimulus. However, our model did not fully explain the characteristics of crucially slow transition: the responses of BO-selective physiological cells indicated the persistent activation several times longer than that of our model after the replacement with the ambiguous edge. Furthermore, the time-course of BO-selective model cells replicated the attentional modulation of response time in human psychophysical experiments. These attentional

  15. Discrete dynamic modeling of cellular signaling networks.

    Science.gov (United States)

    Albert, Réka; Wang, Rui-Sheng

    2009-01-01

    Understanding signal transduction in cellular systems is a central issue in systems biology. Numerous experiments from different laboratories generate an abundance of individual components and causal interactions mediating environmental and developmental signals. However, for many signal transduction systems there is insufficient information on the overall structure and the molecular mechanisms involved in the signaling network. Moreover, lack of kinetic and temporal information makes it difficult to construct quantitative models of signal transduction pathways. Discrete dynamic modeling, combined with network analysis, provides an effective way to integrate fragmentary knowledge of regulatory interactions into a predictive mathematical model which is able to describe the time evolution of the system without the requirement for kinetic parameters. This chapter introduces the fundamental concepts of discrete dynamic modeling, particularly focusing on Boolean dynamic models. We describe this method step-by-step in the context of cellular signaling networks. Several variants of Boolean dynamic models including threshold Boolean networks and piecewise linear systems are also covered, followed by two examples of successful application of discrete dynamic modeling in cell biology.

  16. Effects of an Environmentally-relevant Mixture of Pyrethroid Insecticides on Spontaneous Activity in Primary Cortical Networks on Microelectrode Arrays

    Data.gov (United States)

    U.S. Environmental Protection Agency — This manuscript tests the hypothesis of dose additivity of an environmental mixture of pyrethriod insecticides at the level of network function, in vitro. The...

  17. Stochastic synchronization in finite size spiking networks

    Science.gov (United States)

    Doiron, Brent; Rinzel, John; Reyes, Alex

    2006-09-01

    We study a stochastic synchronization of spiking activity in feedforward networks of integrate-and-fire model neurons. A stochastic mean field analysis shows that synchronization occurs only when the network size is sufficiently small. This gives evidence that the dynamics, and hence processing, of finite size populations can be drastically different from that observed in the infinite size limit. Our results agree with experimentally observed synchrony in cortical networks, and further strengthen the link between synchrony and propagation in cortical systems.

  18. Neural network modeling of associative memory: Beyond the Hopfield model

    Science.gov (United States)

    Dasgupta, Chandan

    1992-07-01

    A number of neural network models, in which fixed-point and limit-cycle attractors of the underlying dynamics are used to store and associatively recall information, are described. In the first class of models, a hierarchical structure is used to store an exponentially large number of strongly correlated memories. The second class of models uses limit cycles to store and retrieve individual memories. A neurobiologically plausible network that generates low-amplitude periodic variations of activity, similar to the oscillations observed in electroencephalographic recordings, is also described. Results obtained from analytic and numerical studies of the properties of these networks are discussed.

  19. Constitutive modelling of composite biopolymer networks.

    Science.gov (United States)

    Fallqvist, B; Kroon, M

    2016-04-21

    The mechanical behaviour of biopolymer networks is to a large extent determined at a microstructural level where the characteristics of individual filaments and the interactions between them determine the response at a macroscopic level. Phenomena such as viscoelasticity and strain-hardening followed by strain-softening are observed experimentally in these networks, often due to microstructural changes (such as filament sliding, rupture and cross-link debonding). Further, composite structures can also be formed with vastly different mechanical properties as compared to the individual networks. In this present paper, we present a constitutive model presented in a continuum framework aimed at capturing these effects. Special care is taken to formulate thermodynamically consistent evolution laws for dissipative effects. This model, incorporating possible anisotropic network properties, is based on a strain energy function, split into an isochoric and a volumetric part. Generalisation to three dimensions is performed by numerical integration over the unit sphere. Model predictions indicate that the constitutive model is well able to predict the elastic and viscoelastic response of biological networks, and to an extent also composite structures. Copyright © 2016 Elsevier Ltd. All rights reserved.

  20. Modelling students' knowledge organisation: Genealogical conceptual networks

    Science.gov (United States)

    Koponen, Ismo T.; Nousiainen, Maija

    2018-04-01

    Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

  1. Modelling Users` Trust in Online Social Networks

    Directory of Open Access Journals (Sweden)

    Iacob Cătoiu

    2014-02-01

    Full Text Available Previous studies (McKnight, Lankton and Tripp, 2011; Liao, Lui and Chen, 2011 have shown the crucial role of trust when choosing to disclose sensitive information online. This is the case of online social networks users, who must disclose a certain amount of personal data in order to gain access to these online services. Taking into account privacy calculus model and the risk/benefit ratio, we propose a model of users’ trust in online social networks with four variables. We have adapted metrics for the purpose of our study and we have assessed their reliability and validity. We use a Partial Least Squares (PLS based structural equation modelling analysis, which validated all our initial assumptions, indicating that our three predictors (privacy concerns, perceived benefits and perceived risks explain 48% of the variation of users’ trust in online social networks, the resulting variable of our study. We also discuss the implications and further research opportunities of our study.

  2. Bayesian network modelling of upper gastrointestinal bleeding

    Science.gov (United States)

    Aisha, Nazziwa; Shohaimi, Shamarina; Adam, Mohd Bakri

    2013-09-01

    Bayesian networks are graphical probabilistic models that represent causal and other relationships between domain variables. In the context of medical decision making, these models have been explored to help in medical diagnosis and prognosis. In this paper, we discuss the Bayesian network formalism in building medical support systems and we learn a tree augmented naive Bayes Network (TAN) from gastrointestinal bleeding data. The accuracy of the TAN in classifying the source of gastrointestinal bleeding into upper or lower source is obtained. The TAN achieves a high classification accuracy of 86% and an area under curve of 92%. A sensitivity analysis of the model shows relatively high levels of entropy reduction for color of the stool, history of gastrointestinal bleeding, consistency and the ratio of blood urea nitrogen to creatinine. The TAN facilitates the identification of the source of GIB and requires further validation.

  3. A Model of Network Porosity

    Science.gov (United States)

    2016-02-04

    of complex systems [1]. Although the ODD protocol was originally intended for individual-based or agent-based models ( ABM ), we adopt this protocol for...applies to information transfer between air-gapped systems . Trust relationships between devices (e.g. a trust relationship created by a domain controller...prevention systems , and data leakage protection systems . 2.2 ATTACKER The model specifies an attacker who gains access to internal enclaves by

  4. Modeling and optimization of potable water network

    Energy Technology Data Exchange (ETDEWEB)

    Djebedjian, B.; Rayan, M.A. [Mansoura Univ., El-Mansoura (Egypt); Herrick, A. [Suez Canal Authority, Ismailia (Egypt)

    2000-07-01

    Software was developed in order to optimize the design of water distribution systems and pipe networks. While satisfying all the constraints imposed such as pipe diameter and nodal pressure, it was based on a mathematical model treating looped networks. The optimum network configuration and cost are determined considering parameters like pipe diameter, flow rate, corresponding pressure and hydraulic losses. It must be understood that minimum cost is relative to the different objective functions selected. The determination of the proper objective function often depends on the operating policies of a particular company. The solution for the optimization technique was obtained by using a non-linear technique. To solve the optimal design of network, the model was derived using the sequential unconstrained minimization technique (SUMT) of Fiacco and McCormick, which decreased the number of iterations required. The pipe diameters initially assumed were successively adjusted to correspond to the existing commercial pipe diameters. The technique was then applied to a two-loop network without pumps or valves. Fed by gravity, it comprised eight pipes, 1000 m long each. The first evaluation of the method proved satisfactory. As with other methods, it failed to find the global optimum. In the future, research efforts will be directed to the optimization of networks with pumps and reservoirs. 24 refs., 3 tabs., 1 fig.

  5. Modelling dendritic ecological networks in space: An integrated network perspective

    Science.gov (United States)

    Erin E. Peterson; Jay M. Ver Hoef; Dan J. Isaak; Jeffrey A. Falke; Marie-Josee Fortin; Chris E. Jordan; Kristina McNyset; Pascal Monestiez; Aaron S. Ruesch; Aritra Sengupta; Nicholas Som; E. Ashley Steel; David M. Theobald; Christian E. Torgersen; Seth J. Wenger

    2013-01-01

    Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of...

  6. PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2015-11-01

    Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.

  7. Artificial neural network cardiopulmonary modeling and diagnosis

    Science.gov (United States)

    Kangas, Lars J.; Keller, Paul E.

    1997-01-01

    The present invention is a method of diagnosing a cardiopulmonary condition in an individual by comparing data from a progressive multi-stage test for the individual to a non-linear multi-variate model, preferably a recurrent artificial neural network having sensor fusion. The present invention relies on a cardiovascular model developed from physiological measurements of an individual. Any differences between the modeled parameters and the parameters of an individual at a given time are used for diagnosis.

  8. Green Network Planning Model for Optical Backbones

    DEFF Research Database (Denmark)

    Gutierrez Lopez, Jose Manuel; Riaz, M. Tahir; Jensen, Michael

    2010-01-01

    on the environment in general. In network planning there are existing planning models focused on QoS provisioning, investment minimization or combinations of both and other parameters. But there is a lack of a model for designing green optical backbones. This paper presents novel ideas to be able to define......Communication networks are becoming more essential for our daily lives and critically important for industry and governments. The intense growth in the backbone traffic implies an increment of the power demands of the transmission systems. This power usage might have a significant negative effect...

  9. A Model for Telestrok Network Evaluation

    DEFF Research Database (Denmark)

    Storm, Anna; Günzel, Franziska; Theiss, Stephan

    2011-01-01

    analysis lacking, current telestroke reimbursement by third-party payers is limited to special contracts and not included in the regular billing system. Based on a systematic literature review and expert interviews with health care economists, third-party payers and neurologists, a Markov model...... was developed from the third-party payer perspective. In principle, it enables telestroke networks to conduct cost-effectiveness studies, because the majority of the required data can be extracted from health insurance companies’ databases and the telestroke network itself. The model presents a basis...

  10. Large-scale, high-resolution multielectrode-array recording depicts functional network differences of cortical and hippocampal cultures.

    Directory of Open Access Journals (Sweden)

    Shinya Ito

    Full Text Available 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.

  11. Lifespan anxiety is reflected in human amygdala cortical connectivity

    Science.gov (United States)

    He, Ye; Xu, Ting; Zhang, Wei

    2016-01-01

    Abstract The amygdala plays a pivotal role in processing anxiety and connects to large‐scale brain networks. However, intrinsic functional connectivity (iFC) between amygdala and these networks has rarely been examined in relation to anxiety, especially across the lifespan. We employed resting‐state functional MRI data from 280 healthy adults (18–83.5 yrs) to elucidate the relationship between anxiety and amygdala iFC with common cortical networks including the visual network, somatomotor network, dorsal attention network, ventral attention network, limbic network, frontoparietal network, and default network. Global and network‐specific iFC were separately computed as mean iFC of amygdala with the entire cerebral cortex and each cortical network. We detected negative correlation between global positive amygdala iFC and trait anxiety. Network‐specific associations between amygdala iFC and anxiety were also detectable. Specifically, the higher iFC strength between the left amygdala and the limbic network predicted lower state anxiety. For the trait anxiety, left amygdala anxiety–connectivity correlation was observed in both somatomotor and dorsal attention networks, whereas the right amygdala anxiety–connectivity correlation was primarily distributed in the frontoparietal and ventral attention networks. Ventral attention network exhibited significant anxiety–gender interactions on its iFC with amygdala. Together with findings from additional vertex‐wise analysis, these data clearly indicated that both low‐level sensory networks and high‐level associative networks could contribute to detectable predictions of anxiety behaviors by their iFC profiles with the amygdala. This set of systems neuroscience findings could lead to novel functional network models on neural correlates of human anxiety and provide targets for novel treatment strategies on anxiety disorders. Hum Brain Mapp 37:1178–1193, 2016. © 2015 The Authors Human Brain Mapping

  12. PROJECT ACTIVITY ANALYSIS WITHOUT THE NETWORK MODEL

    Directory of Open Access Journals (Sweden)

    S. Munapo

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This paper presents a new procedure for analysing and managing activity sequences in projects. The new procedure determines critical activities, critical path, start times, free floats, crash limits, and other useful information without the use of the network model. Even though network models have been successfully used in project management so far, there are weaknesses associated with the use. A network is not easy to generate, and dummies that are usually associated with it make the network diagram complex – and dummy activities have no meaning in the original project management problem. The network model for projects can be avoided while still obtaining all the useful information that is required for project management. What are required are the activities, their accurate durations, and their predecessors.

    AFRIKAANSE OPSOMMING: Die navorsing beskryf ’n nuwerwetse metode vir die ontleding en bestuur van die sekwensiële aktiwiteite van projekte. Die voorgestelde metode bepaal kritiese aktiwiteite, die kritieke pad, aanvangstye, speling, verhasing, en ander groothede sonder die gebruik van ’n netwerkmodel. Die metode funksioneer bevredigend in die praktyk, en omseil die administratiewe rompslomp van die tradisionele netwerkmodelle.

  13. Mobility Models for Next Generation Wireless Networks Ad Hoc, Vehicular and Mesh Networks

    CERN Document Server

    Santi, Paolo

    2012-01-01

    Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks provides the reader with an overview of mobility modelling, encompassing both theoretical and practical aspects related to the challenging mobility modelling task. It also: Provides up-to-date coverage of mobility models for next generation wireless networksOffers an in-depth discussion of the most representative mobility models for major next generation wireless network application scenarios, including WLAN/mesh networks, vehicular networks, wireless sensor networks, and

  14. Modeling Renewable Penertration Using a Network Economic Model

    Science.gov (United States)

    Lamont, A.

    2001-03-01

    This paper evaluates the accuracy of a network economic modeling approach in designing energy systems having renewable and conventional generators. The network approach models the system as a network of processes such as demands, generators, markets, and resources. The model reaches a solution by exchanging prices and quantity information between the nodes of the system. This formulation is very flexible and takes very little time to build and modify models. This paper reports an experiment designing a system with photovoltaic and base and peak fossil generators. The level of PV penetration as a function of its price and the capacities of the fossil generators were determined using the network approach and using an exact, analytic approach. It is found that the two methods agree very closely in terms of the optimal capacities and are nearly identical in terms of annual system costs.

  15. Security Modeling on the Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Marn-Ling Shing

    2007-10-01

    Full Text Available In order to keep the price down, a purchaser sends out the request for quotation to a group of suppliers in a supply chain network. The purchaser will then choose a supplier with the best combination of price and quality. A potential supplier will try to collect the related information about other suppliers so he/she can offer the best bid to the purchaser. Therefore, confidentiality becomes an important consideration for the design of a supply chain network. Chen et al. have proposed the application of the Bell-LaPadula model in the design of a secured supply chain network. In the Bell-LaPadula model, a subject can be in one of different security clearances and an object can be in one of various security classifications. All the possible combinations of (Security Clearance, Classification pair in the Bell-LaPadula model can be thought as different states in the Markov Chain model. This paper extends the work done by Chen et al., provides more details on the Markov Chain model and illustrates how to use it to monitor the security state transition in the supply chain network.

  16. An evolving model of online bipartite networks

    Science.gov (United States)

    Zhang, Chu-Xu; Zhang, Zi-Ke; Liu, Chuang

    2013-12-01

    Understanding the structure and evolution of online bipartite networks is a significant task since they play a crucial role in various e-commerce services nowadays. Recently, various attempts have been tried to propose different models, resulting in either power-law or exponential degree distributions. However, many empirical results show that the user degree distribution actually follows a shifted power-law distribution, the so-called Mandelbrot’s law, which cannot be fully described by previous models. In this paper, we propose an evolving model, considering two different user behaviors: random and preferential attachment. Extensive empirical results on two real bipartite networks, Delicious and CiteULike, show that the theoretical model can well characterize the structure of real networks for both user and object degree distributions. In addition, we introduce a structural parameter p, to demonstrate that the hybrid user behavior leads to the shifted power-law degree distribution, and the region of power-law tail will increase with the increment of p. The proposed model might shed some lights in understanding the underlying laws governing the structure of real online bipartite networks.

  17. An autocatalytic network model for stock markets

    Science.gov (United States)

    Caetano, Marco Antonio Leonel; Yoneyama, Takashi

    2015-02-01

    The stock prices of companies with businesses that are closely related within a specific sector of economy might exhibit movement patterns and correlations in their dynamics. The idea in this work is to use the concept of autocatalytic network to model such correlations and patterns in the trends exhibited by the expected returns. The trends are expressed in terms of positive or negative returns within each fixed time interval. The time series derived from these trends is then used to represent the movement patterns by a probabilistic boolean network with transitions modeled as an autocatalytic network. The proposed method might be of value in short term forecasting and identification of dependencies. The method is illustrated with a case study based on four stocks of companies in the field of natural resource and technology.

  18. Recently learned foreign abstract and concrete nouns are represented in distinct cortical networks similar to the native language.

    Science.gov (United States)

    Mayer, Katja M; Macedonia, Manuela; von Kriegstein, Katharina

    2017-09-01

    In the native language, abstract and concrete nouns are represented in distinct areas of the cerebral cortex. Currently, it is unknown whether this is also the case for abstract and concrete nouns of a foreign language. Here, we taught adult native speakers of German 45 abstract and 45 concrete nouns of a foreign language. After learning the nouns for 5 days, participants performed a vocabulary translation task during functional magnetic resonance imaging. Translating abstract nouns in contrast to concrete nouns elicited responses in regions that are also responsive to abstract nouns in the native language: the left inferior frontal gyrus and the left middle and superior temporal gyri. Concrete nouns elicited larger responses in the angular gyri bilaterally and the left parahippocampal gyrus than abstract nouns. The cluster in the left angular gyrus showed psychophysiological interaction (PPI) with the left lingual gyrus. The left parahippocampal gyrus showed PPI with the posterior cingulate cortex. Similar regions have been previously found for concrete nouns in the native language. The results reveal similarities in the cortical representation of foreign language nouns with the representation of native language nouns that already occur after 5 days of vocabulary learning. Furthermore, we showed that verbal and enriched learning methods were equally suitable to teach foreign abstract and concrete nouns. Hum Brain Mapp 38:4398-4412, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  19. Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity.

    Science.gov (United States)

    Diaz-Pier, Sandra; Naveau, Mikaël; Butz-Ostendorf, Markus; Morrison, Abigail

    2016-01-01

    With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.

  20. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

    Science.gov (United States)

    Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

    2011-11-01

    The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

  1. Transcranial Direct Current Stimulation Targeting Primary Motor Versus Dorsolateral Prefrontal Cortices: Proof-of-Concept Study Investigating Functional Connectivity of Thalamocortical Networks Specific to Sensory-Affective Information Processing.

    Science.gov (United States)

    Sankarasubramanian, Vishwanath; Cunningham, David A; Potter-Baker, Kelsey A; Beall, Erik B; Roelle, Sarah M; Varnerin, Nicole M; Machado, Andre G; Jones, Stephen E; Lowe, Mark J; Plow, Ela B

    2017-04-01

    The pain matrix is comprised of an extensive network of brain structures involved in sensory and/or affective information processing. The thalamus is a key structure constituting the pain matrix. The thalamus serves as a relay center receiving information from multiple ascending pathways and relating information to and from multiple cortical areas. However, it is unknown how thalamocortical networks specific to sensory-affective information processing are functionally integrated. Here, in a proof-of-concept study in healthy humans, we aimed to understand this connectivity using transcranial direct current stimulation (tDCS) targeting primary motor (M1) or dorsolateral prefrontal cortices (DLPFC). We compared changes in functional connectivity (FC) with DLPFC tDCS to changes in FC with M1 tDCS. FC changes were also compared to further investigate its relation with individual's baseline experience of pain. We hypothesized that resting-state FC would change based on tDCS location and would represent known thalamocortical networks. Ten right-handed individuals received a single application of anodal tDCS (1 mA, 20 min) to right M1 and DLPFC in a single-blind, sham-controlled crossover study. FC changes were studied between ventroposterolateral (VPL), the sensory nucleus of thalamus, and cortical areas involved in sensory information processing and between medial dorsal (MD), the affective nucleus, and cortical areas involved in affective information processing. Individual's perception of pain at baseline was assessed using cutaneous heat pain stimuli. We found that anodal M1 tDCS and anodal DLPFC tDCS both increased FC between VPL and sensorimotor cortices, although FC effects were greater with M1 tDCS. Similarly, anodal M1 tDCS and anodal DLPFC tDCS both increased FC between MD and motor cortices, but only DLPFC tDCS modulated FC between MD and affective cortices, like DLPFC. Our findings suggest that M1 stimulation primarily modulates FC of sensory networks

  2. Reduced topological efficiency in cortical-basal Ganglia motor network of Parkinson's disease: a resting state fMRI study.

    Science.gov (United States)

    Wei, Luqing; Zhang, Jiuquan; Long, Zhiliang; Wu, Guo-Rong; Hu, Xiaofei; Zhang, Yanling; Wang, Jian

    2014-01-01

    Parkinson's disease (PD) is mainly characterized by dopamine depletion of the cortico-basal ganglia (CBG) motor circuit. Given that dopamine dysfunction could affect functional brain network efficiency, the present study utilized resting-state fMRI (rs-fMRI) and graph theoretical approach to investigate the topological efficiency changes of the CBG motor network in patients with PD during a relatively hypodopaminergic state (12 hours after a last dose of dopamimetic treatment). We found that PD compared with controls had remarkable decreased efficiency in the CBG motor network, with the most pronounced changes observed in rostral supplementary motor area (pre-SMA), caudal SMA (SMA-proper), primary motor cortex (M1), primary somatosensory cortex (S1), thalamus (THA), globus pallidus (GP), and putamen (PUT). Furthermore, reduced efficiency in pre-SMA, M1, THA and GP was significantly correlated with Unified Parkinson's Disease Rating Scale (UPDRS) motor scores in PD patients. Together, our results demonstrate that individuals with PD appear to be less effective at information transfer within the CBG motor pathway, which provides a novel perspective on neurobiological explanation for the motor symptoms in patients. These findings are in line with the pathophysiology of PD, suggesting that network efficiency metrics may be used to identify and track the pathology of PD.

  3. Stereopsis and 3D surface perception by spiking neurons in laminar cortical circuits: a method for converting neural rate models into spiking models.

    Science.gov (United States)

    Cao, Yongqiang; Grossberg, Stephen

    2012-02-01

    A laminar cortical model of stereopsis and 3D surface perception is developed and simulated. The model shows how spiking neurons that interact in hierarchically organized laminar circuits of the visual cortex can generate analog properties of 3D visual percepts. The model describes how monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the LGN and cortical areas V1, V2, and V4. It proposes how interactions between layers 4, 3B, and 2/3 in V1 and V2 contribute to stereopsis, and how binocular and monocular information combine to form 3D boundary and surface representations. The model suggests how surface-to-boundary feedback from V2 thin stripes to pale stripes helps to explain how computationally complementary boundary and surface formation properties lead to a single consistent percept, eliminate redundant 3D boundaries, and trigger figure-ground perception. The model also shows how false binocular boundary matches may be eliminated by Gestalt grouping properties. In particular, the disparity filter, which helps to solve the correspondence problem by eliminating false matches, is realized using inhibitory interneurons as part of the perceptual grouping process by horizontal connections in layer 2/3 of cortical area V2. The 3D sLAMINART model simulates 3D surface percepts that are consciously seen in 18 psychophysical experiments. These percepts include contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, da Vinci stereopsis, and perceptual closure. The model hereby illustrates a general method of unlumping rate-based models that use the membrane equations of neurophysiology into models that use spiking neurons, and which may be embodied in VLSI chips that use spiking neurons to minimize heat production. Copyright

  4. Keystone Business Models for Network Security Processors

    Directory of Open Access Journals (Sweden)

    Arthur Low

    2013-07-01

    Full Text Available Network security processors are critical components of high-performance systems built for cybersecurity. Development of a network security processor requires multi-domain experience in semiconductors and complex software security applications, and multiple iterations of both software and hardware implementations. Limited by the business models in use today, such an arduous task can be undertaken only by large incumbent companies and government organizations. Neither the “fabless semiconductor” models nor the silicon intellectual-property licensing (“IP-licensing” models allow small technology companies to successfully compete. This article describes an alternative approach that produces an ongoing stream of novel network security processors for niche markets through continuous innovation by both large and small companies. This approach, referred to here as the "business ecosystem model for network security processors", includes a flexible and reconfigurable technology platform, a “keystone” business model for the company that maintains the platform architecture, and an extended ecosystem of companies that both contribute and share in the value created by innovation. New opportunities for business model innovation by participating companies are made possible by the ecosystem model. This ecosystem model builds on: i the lessons learned from the experience of the first author as a senior integrated circuit architect for providers of public-key cryptography solutions and as the owner of a semiconductor startup, and ii the latest scholarly research on technology entrepreneurship, business models, platforms, and business ecosystems. This article will be of interest to all technology entrepreneurs, but it will be of particular interest to owners of small companies that provide security solutions and to specialized security professionals seeking to launch their own companies.

  5. Horizontal integration and cortical dynamics.

    Science.gov (United States)

    Gilbert, C D

    1992-07-01

    We have discussed several results that lead to a view that cells in the visual system are endowed with dynamic properties, influenced by context, expectation, and long-term modifications of the cortical network. These observations will be important for understanding how neuronal ensembles produce a system that perceives, remembers, and adapts to injury. The advantage to being able to observe changes at early stages in a sensory pathway is that one may be able to understand the way in which neuronal ensembles encode and represent images at the level of their receptive field properties, of cortical topographies, and of the patterns of connections between cells participating in a network.

  6. Modeling and Simulation Network Data Standards

    Science.gov (United States)

    2011-09-30

    approaches . 2.3. JNAT. JNAT is a Web application that provides connectivity and network analysis capability. JNAT uses propagation models and low-fidelity...COMBATXXI Movement Logger Data Output Dictionary. Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal...B-8 Field # Geocentric Coordinates (GCC) Heading Geodetic Coordinates (GDC) Heading Universal Transverse Mercator (UTM) Heading

  7. The Kuramoto model in complex networks

    Science.gov (United States)

    Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen

    2016-01-01

    Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.

  8. An architectural model for network interconnection

    NARCIS (Netherlands)

    van Sinderen, Marten J.; Vissers, C.A.; Kalin, T.

    1983-01-01

    This paper presents a technique of successive decomposition of a common users' activity to illustrate the problems of network interconnection. The criteria derived from this approach offer a structuring principle which is used to develop an architectural model that embeds heterogeneous subnetworks

  9. Computational Modeling of Complex Protein Activity Networks

    NARCIS (Netherlands)

    Schivo, Stefano; Leijten, Jeroen; Karperien, Marcel; Post, Janine N.; Prignet, Claude

    2017-01-01

    Because of the numerous entities interacting, the complexity of the networks that regulate cell fate makes it impossible to analyze and understand them using the human brain alone. Computational modeling is a powerful method to unravel complex systems. We recently described the development of a

  10. A Model of Mental State Transition Network

    Science.gov (United States)

    Xiang, Hua; Jiang, Peilin; Xiao, Shuang; Ren, Fuji; Kuroiwa, Shingo

    Emotion is one of the most essential and basic attributes of human intelligence. Current AI (Artificial Intelligence) research is concentrating on physical components of emotion, rarely is it carried out from the view of psychology directly(1). Study on the model of artificial psychology is the first step in the development of human-computer interaction. As affective computing remains unpredictable, creating a reasonable mental model becomes the primary task for building a hybrid system. A pragmatic mental model is also the fundament of some key topics such as recognition and synthesis of emotions. In this paper a Mental State Transition Network Model(2) is proposed to detect human emotions. By a series of psychological experiments, we present a new way to predict coming human's emotions depending on the various current emotional states under various stimuli. Besides, people in different genders and characters are taken into consideration in our investigation. According to the psychological experiments data derived from 200 questionnaires, a Mental State Transition Network Model for describing the transitions in distribution among the emotions and relationships between internal mental situations and external are concluded. Further more the coefficients of the mental transition network model were achieved. Comparing seven relative evaluating experiments, an average precision rate of 0.843 is achieved using a set of samples for the proposed model.

  11. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    Massive small unmanned aerial vehicles are envisioned to operate in the near future. While there are lots of research problems need to be addressed before dense operations can happen, trajectory modeling remains as one of the keys to understand and develop policies, regulations, and requirements for safe and efficient unmanned aerial vehicle operations. The fidelity requirement of a small unmanned vehicle trajectory model is high because these vehicles are sensitive to winds due to their small size and low operational altitude. Both vehicle control systems and dynamic models are needed for trajectory modeling, which makes the modeling a great challenge, especially considering the fact that manufactures are not willing to share their control systems. This work proposed to use a neural network approach for modelling small unmanned vehicle's trajectory without knowing its control system and bypassing exhaustive efforts for aerodynamic parameter identification. As a proof of concept, instead of collecting data from flight tests, this work used the trajectory data generated by a mathematical vehicle model for training and testing the neural network. The results showed great promise because the trained neural network can predict 4D trajectories accurately, and prediction errors were less than 2:0 meters in both temporal and spatial dimensions.

  12. Modeling Insurgent Network Structure and Dynamics

    Science.gov (United States)

    Gabbay, Michael; Thirkill-Mackelprang, Ashley

    2010-03-01

    We present a methodology for mapping insurgent network structure based on their public rhetoric. Indicators of cooperative links between insurgent groups at both the leadership and rank-and-file levels are used, such as joint policy statements or joint operations claims. In addition, a targeting policy measure is constructed on the basis of insurgent targeting claims. Network diagrams which integrate these measures of insurgent cooperation and ideology are generated for different periods of the Iraqi and Afghan insurgencies. The network diagrams exhibit meaningful changes which track the evolution of the strategic environment faced by insurgent groups. Correlations between targeting policy and network structure indicate that insurgent targeting claims are aimed at establishing a group identity among the spectrum of rank-and-file insurgency supporters. A dynamical systems model of insurgent alliance formation and factionalism is presented which evolves the relationship between insurgent group dyads as a function of their ideological differences and their current relationships. The ability of the model to qualitatively and quantitatively capture insurgent network dynamics observed in the data is discussed.

  13. Hybrid simulation models of production networks

    CERN Document Server

    Kouikoglou, Vassilis S

    2001-01-01

    This book is concerned with a most important area of industrial production, that of analysis and optimization of production lines and networks using discrete-event models and simulation. The book introduces a novel approach that combines analytic models and discrete-event simulation. Unlike conventional piece-by-piece simulation, this method observes a reduced number of events between which the evolution of the system is tracked analytically. Using this hybrid approach, several models are developed for the analysis of production lines and networks. The hybrid approach combines speed and accuracy for exceptional analysis of most practical situations. A number of optimization problems, involving buffer design, workforce planning, and production control, are solved through the use of hybrid models.

  14. Propagating semantic information in biochemical network models

    Directory of Open Access Journals (Sweden)

    Schulz Marvin

    2012-01-01

    Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.

  15. Motor Cortex and Motor Cortical Interhemispheric Communication in Walking After Stroke: The Roles of Transcranial Magnetic Stimulation and Animal Models in Our Current and Future Understanding.

    Science.gov (United States)

    Charalambous, Charalambos C; Bowden, Mark G; Adkins, DeAnna L

    2016-01-01

    Despite the plethora of human neurophysiological research, the bilateral involvement of the leg motor cortical areas and their interhemispheric interaction during both normal and impaired human walking is poorly understood. Using transcranial magnetic stimulation (TMS), we have expanded our understanding of the role upper-extremity motor cortical areas play in normal movements and how stroke alters this role, and probed the efficacy of interventions to improve post-stroke arm function. However, similar investigations of the legs have lagged behind, in part, due to the anatomical difficulty in using TMS to stimulate the leg motor cortical areas. Additionally, leg movements are predominately bilaterally controlled and require interlimb coordination that may involve both hemispheres. The sensitive, but invasive, tools used in animal models of locomotion hold great potential for increasing our understanding of the bihemispheric motor cortical control of walking. In this review, we discuss 3 themes associated with the bihemispheric motor cortical control of walking after stroke: (a) what is known about the role of the bihemispheric motor cortical control in healthy and poststroke leg movements, (b) how the neural remodeling of the contralesional hemisphere can affect walking recovery after a stroke, and (c) what is the effect of behavioral rehabilitation training of walking on the neural remodeling of the motor cortical areas bilaterally. For each theme, we discuss how rodent models can enhance the present knowledge on human walking by testing hypotheses that cannot be investigated in humans, and how these findings can then be back-translated into the neurorehabilitation of poststroke walking. © The Author(s) 2015.

  16. Model Predictive Control of Sewer Networks

    DEFF Research Database (Denmark)

    Pedersen, Einar B.; Herbertsson, Hannes R.; Niemann, Henrik

    2016-01-01

    The developments in solutions for management of urban drainage are of vital importance, as the amount of sewer water from urban areas continues to increase due to the increase of the world’s population and the change in the climate conditions. How a sewer network is structured, monitored and cont...... benchmark model. Due to the inherent constraints the applied approach is based on Model Predictive Control....

  17. Modeling Multistandard Wireless Networks in OPNET

    DEFF Research Database (Denmark)

    Zakrzewska, Anna; Berger, Michael Stübert; Ruepp, Sarah Renée

    2011-01-01

    Future wireless communication is emerging towards one heterogeneous platform. In this new environment wireless access will be provided by multiple radio technologies that are cooperating and complementing one another. The paper investigates the possibilities of developing such a multistandard sys...... system using OPNET Modeler. A network model consisting of LTE interworking with WLAN and WiMAX is considered from the radio resource management perspective. In particular, implementing a joint packet scheduler across multiple systems is discussed more in detail....

  18. Modelling dendritic ecological networks in space: anintegrated network perspective

    Science.gov (United States)

    Peterson, Erin E.; Ver Hoef, Jay M.; Isaak, Dan J.; Falke, Jeffrey A.; Fortin, Marie-Josée; Jordon, Chris E.; McNyset, Kristina; Monestiez, Pascal; Ruesch, Aaron S.; Sengupta, Aritra; Som, Nicholas; Steel, E. Ashley; Theobald, David M.; Torgersen, Christian E.; Wenger, Seth J.

    2013-01-01

    Dendritic ecological networks (DENs) are a unique form of ecological networks that exhibit a dendritic network topology (e.g. stream and cave networks or plant architecture). DENs have a dual spatial representation; as points within the network and as points in geographical space. Consequently, some analytical methods used to quantify relationships in other types of ecological networks, or in 2-D space, may be inadequate for studying the influence of structure and connectivity on ecological processes within DENs. We propose a conceptual taxonomy of network analysis methods that account for DEN characteristics to varying degrees and provide a synthesis of the different approaches within

  19. Unified Model for Generation Complex Networks with Utility Preferential Attachment

    International Nuclear Information System (INIS)

    Wu Jianjun; Gao Ziyou; Sun Huijun

    2006-01-01

    In this paper, based on the utility preferential attachment, we propose a new unified model to generate different network topologies such as scale-free, small-world and random networks. Moreover, a new network structure named super scale network is found, which has monopoly characteristic in our simulation experiments. Finally, the characteristics of this new network are given.

  20. Cortical networks for vision and language in dyslexic and normal children of variable socio-economic status.

    Science.gov (United States)

    Monzalvo, Karla; Fluss, Joel; Billard, Catherine; Dehaene, Stanislas; Dehaene-Lambertz, Ghislaine

    2012-05-15

    In dyslexia, anomalous activations have been described in both left temporo-parietal language cortices and in left ventral visual occipito-temporal cortex. However, the reproducibility, task-dependency, and presence of these brain anomalies in childhood rather than adulthood remain debated. We probed the large-scale organization of ventral visual and spoken language areas in dyslexic children using minimal target-detection tasks that were performed equally well by all groups. In 23 normal and 23 dyslexic 10-year-old children from two different socio-economic status (SES) backgrounds, we compared fMRI activity to visually presented houses, faces, and written strings, and to spoken sentences in the native or in a foreign language. Our results confirm a disorganization of both ventral visual and spoken language areas in dyslexic children. Visually, dyslexic children showed a normal lateral-to-medial mosaic of preferences, as well as normal responses to houses and checkerboards, but a reduced activation to words in the visual word form area (VWFA) and to faces in the right fusiform face area (FFA). Auditorily, dyslexic children exhibited reduced responses to speech in posterior temporal cortex, left insula and supplementary motor area, as well as reduced responses to maternal language in subparts of the planum temporale, left basal language area and VWFA. By correlating these two findings, we identify spoken-language predictors of VWFA activation to written words, which differ for dyslexic and normal readers. Similarities in fMRI deficits in both SES groups emphasize the existence of a core set of brain activation anomalies in dyslexia, regardless of culture, language and SES, without however resolving whether these anomalies are a cause or a consequence of impaired reading. Copyright © 2012 Elsevier Inc. All rights reserved.

  1. Functional model of biological neural networks.

    Science.gov (United States)

    Lo, James Ting-Ho

    2010-12-01

    A functional model of biological neural networks, called temporal hierarchical probabilistic associative memory (THPAM), is proposed in this paper. THPAM comprises functional models of dendritic trees for encoding inputs to neurons, a first type of neuron for generating spike trains, a second type of neuron for generating graded signals to modulate neurons of the first type, supervised and unsupervised Hebbian learning mechanisms for easy learning and retrieving, an arrangement of dendritic trees for maximizing generalization, hardwiring for rotation-translation-scaling invariance, and feedback connections with different delay durations for neurons to make full use of present and past informations generated by neurons in the same and higher layers. These functional models and their processing operations have many functions of biological neural networks that have not been achieved by other models in the open literature and provide logically coherent answers to many long-standing neuroscientific questions. However, biological justifications of these functional models and their processing operations are required for THPAM to qualify as a macroscopic model (or low-order approximate) of biological neural networks.

  2. On traffic modelling in GPRS networks

    DEFF Research Database (Denmark)

    Madsen, Tatiana Kozlova; Schwefel, Hans-Peter; Prasad, Ramjee

    2005-01-01

    Optimal design and dimensioning of wireless data networks, such as GPRS, requires the knowledge of traffic characteristics of different data services. This paper presents an in-detail analysis of an IP-level traffic measurements taken in an operational GPRS network. The data measurements reported...... here are done at the Gi interface. The aim of this paper is to reveal some key statistics of GPRS data applications and to validate if the existing traffic models can adequately describe traffic volume and inter-arrival time distribution for different services. Additionally, we present a method of user...

  3. Not only … but also: REM sleep creates and NREM Stage 2 instantiates landmark junctions in cortical memory networks.

    Science.gov (United States)

    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. Copyright © 2015 Elsevier Inc. All rights reserved.

  4. Practice and Learning: Spatiotemporal Differences in Thalamo-Cortical-Cerebellar Networks Engagement across Learning Phases in Schizophrenia.

    Science.gov (United States)

    Korostil, Michele; Remington, Gary; McIntosh, Anthony Randal

    2016-01-01

    Understanding how practice mediates the transition of brain-behavior networks between early and later stages of learning is constrained by the common approach to analysis of fMRI data. Prior imaging studies have mostly relied on a single scan, and parametric, task-related analyses. Our experiment incorporates a multisession fMRI lexicon-learning experiment with multivariate, whole-brain analysis to further knowledge of the distributed networks supporting practice-related learning in schizophrenia (SZ). Participants with SZ were compared with healthy control (HC) participants as they learned a novel lexicon during two fMRI scans over a several day period. All participants were trained to equal task proficiency prior to scanning. Behavioral-Partial Least Squares, a multivariate analytic approach, was used to analyze the imaging data. Permutation testing was used to determine statistical significance and bootstrap resampling to determine the reliability of the findings. With practice, HC participants transitioned to a brain-accuracy network incorporating dorsostriatal regions in late-learning stages. The SZ participants did not transition to this pattern despite comparable behavioral results. Instead, successful learners with SZ were differentiated primarily on the basis of greater engagement of perceptual and perceptual-integration brain regions. There is a different spatiotemporal unfolding of brain-learning relationships in SZ. In SZ, given the same amount of practice, the movement from networks suggestive of effortful learning toward subcortically driven procedural one differs from HC participants. Learning performance in SZ is driven by varying levels of engagement in perceptual regions, which suggests perception itself is impaired and may impact downstream, "higher level" cognition.

  5. Modeling and Theories of Pathophysiology and Physiology of the Basal Ganglia–Thalamic–Cortical System: Critical Analysis

    Science.gov (United States)

    Montgomery Jr., Erwin B.

    2016-01-01

    Theories impact the movement disorders clinic, not only affecting the development of new therapies but determining how current therapies are used. Models are theories that are procedural rather than declarative. Theories and models are important because, as argued by Kant, one cannot know the thing-in-itself (das Ding an sich) and only a model is knowable. Further, biological variability forces higher level abstraction relevant for all variants. It is that abstraction that is raison d’être of theories and models. Theories “connect the dots” to move from correlation to causation. The necessity of theory makes theories helpful or counterproductive. Theories and models of the pathophysiology and physiology of the basal ganglia–thalamic–cortical system do not spontaneously arise but have a history and consequently are legacies. Over the last 40 years, numerous theories and models of the basal ganglia have been proposed only to be forgotten or dismissed, rarely critiqued. It is not harsh to say that current popular theories positing increased neuronal activities in the Globus Pallidus Interna (GPi), excessive beta oscillations and increased synchronization not only fail to provide an adequate explication but are inconsistent with many observations. It is likely that their shared intellectual and epistemic inheritance plays a factor in their shared failures. These issues are critically examined. How one is to derive theories and models and have hope these will be better is explored as well. PMID:27708569

  6. A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

    Science.gov (United States)

    Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

    2016-03-01

    Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

  7. A Networks Approach to Modeling Enzymatic Reactions.

    Science.gov (United States)

    Imhof, P

    2016-01-01

    Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.

  8. A improved Network Security Situation Awareness Model

    Directory of Open Access Journals (Sweden)

    Li Fangwei

    2015-08-01

    Full Text Available In order to reflect the situation of network security assessment performance fully and accurately, a new network security situation awareness model based on information fusion was proposed. Network security situation is the result of fusion three aspects evaluation. In terms of attack, to improve the accuracy of evaluation, a situation assessment method of DDoS attack based on the information of data packet was proposed. In terms of vulnerability, a improved Common Vulnerability Scoring System (CVSS was raised and maked the assessment more comprehensive. In terms of node weights, the method of calculating the combined weights and optimizing the result by Sequence Quadratic Program (SQP algorithm which reduced the uncertainty of fusion was raised. To verify the validity and necessity of the method, a testing platform was built and used to test through evaluating 2000 DAPRA data sets. Experiments show that the method can improve the accuracy of evaluation results.

  9. Spatial Models and Networks of Living Systems

    DEFF Research Database (Denmark)

    Juul, Jeppe Søgaard

    When studying the dynamics of living systems, insight can often be gained by developing a mathematical model that can predict future behaviour of the system or help classify system characteristics. However, in living cells, organisms, and especially groups of interacting individuals, a large number...... variables of the system. However, this approach disregards any spatial structure of the system, which may potentially change the behaviour drastically. An alternative approach is to construct a cellular automaton with nearest neighbour interactions, or even to model the system as a complex network...... with interactions defined by network topology. In this thesis I first describe three different biological models of ageing and cancer, in which spatial structure is important for the system dynamics. I then turn to describe characteristics of ecosystems consisting of three cyclically interacting species...

  10. Fractional virus epidemic model on financial networks

    Directory of Open Access Journals (Sweden)

    Balci Mehmet Ali

    2016-01-01

    Full Text Available In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.

  11. Canonical cortical circuits: current evidence and theoretical implications

    Directory of Open Access Journals (Sweden)

    Capone F

    2016-04-01

    Full Text Available Fioravante Capone,1,2 Matteo Paolucci,1,2 Federica Assenza,1,2 Nicoletta Brunelli,1,2 Lorenzo Ricci,1,2 Lucia Florio,1,2 Vincenzo Di Lazzaro1,2 1Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome, Italy; 2Fondazione Alberto Sordi – Research Institute for Aging, Rome, ItalyAbstract: Neurophysiological and neuroanatomical studies have found that the same basic structural and functional organization of neuronal circuits exists throughout the cortex. This kind of cortical organization, termed canonical circuit, has been functionally demonstrated primarily by studies involving visual striate cortex, and then, the concept has been extended to different cortical areas. In brief, the canonical circuit is composed of superficial pyramidal neurons of layers II/III receiving different inputs and deep pyramidal neurons of layer V that are responsible for cortex output. Superficial and deep pyramidal neurons are reciprocally connected, and inhibitory interneurons participate in modulating the activity of the circuit. The main intuition of this model is that the entire cortical network could be modeled as the repetition of relatively simple modules composed of relatively few types of excitatory and inhibitory, highly interconnected neurons. We will review the origin and the application of the canonical cortical circuit model in the six sections of this paper. The first section (The origins of the concept of canonical circuit: the cat visual cortex reviews the experiments performed in the cat visual cortex, from the origin of the concept of canonical circuit to the most recent developments in the modelization of cortex. The second (The canonical circuit in neocortex and third (Toward a canonical circuit in agranular cortex sections try to extend the concept of canonical circuit to other cortical areas, providing some significant examples of circuit functioning in different cytoarchitectonic

  12. Entanglement effects in model polymer networks

    Science.gov (United States)

    Everaers, R.; Kremer, K.

    The influence of topological constraints on the local dynamics in cross-linked polymer melts and their contribution to the elastic properties of rubber elastic systems are a long standing problem in statistical mechanics. Polymer networks with diamond lattice connectivity (Everaers and Kremer 1995, Everaers and Kremer 1996a) are idealized model systems which isolate the effect of topology conservation from other sources of quenched disorder. We study their behavior in molecular dynamics simulations under elongational strain. In our analysis we compare the measured, purely entropic shear moduli G to the predictions of statistical mechanical models of rubber elasticity, making extensive use of the microscopic structural and topological information available in computer simulations. We find (Everaers and Kremer 1995) that the classical models of rubber elasticity underestimate the true change in entropy in a deformed network significantly, because they neglect the tension along the contour of the strands which cannot relax due to entanglements (Everaers and Kremer (in preparation)). This contribution and the fluctuations in strained systems seem to be well described by the constrained mode model (Everaers 1998) which allows to treat the crossover from classical rubber elasticity to the tube model for polymer networks with increasing strand length within one transparant formalism. While this is important for the description of the effects we try to do a first quantitative step towards their explanation by topological considerations. We show (Everaers and Kremer 1996a) that for the comparatively short strand lengths of our diamond networks the topology contribution to the shear modulus is proportional to the density of entangled mesh pairs with non-zero Gauss linking number. Moreover, the prefactor can be estimated consistently within a rather simple model developed by Vologodskii et al. and by Graessley and Pearson, which is based on the definition of an entropic

  13. Cortico-cortical communication dynamics

    Directory of Open Access Journals (Sweden)

    Per E Roland

    2014-05-01

    Full Text Available IIn principle, cortico-cortical communication dynamics is simple: neurons in one cortical area communicate by sending action potentials that release glutamate and excite their target neurons in other cortical areas. In practice, knowledge about cortico-cortical communication dynamics is minute. One reason is that no current technique can capture the fast spatio-temporal cortico-cortical evolution of action potential transmission and membrane conductances with sufficient spatial resolution. A combination of optogenetics and monosynaptic tracing with virus can reveal the spatio-temporal cortico-cortical dynamics of specific neurons and their targets, but does not reveal how the dynamics evolves under natural conditions. Spontaneous ongoing action potentials also spread across cortical areas and are difficult to separate from structured evoked and intrinsic brain activity such as thinking. At a certain state of evolution, the dynamics may engage larger populations of neurons to drive the brain to decisions, percepts and behaviors. For example, successfully evolving dynamics to sensory transients can appear at the mesoscopic scale revealing how the transient is perceived. As a consequence of these methodological and conceptual difficulties, studies in this field comprise a wide range of computational models, large-scale measurements (e.g., by MEG, EEG, and a combination of invasive measurements in animal experiments. Further obstacles and challenges of studying cortico-cortical communication dynamics are outlined in this critical review.

  14. Northern emporia and maritime networks. Modelling past communication using archaeological network analysis

    DEFF Research Database (Denmark)

    Sindbæk, Søren Michael

    2015-01-01

    preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...

  15. Performance modeling, loss networks, and statistical multiplexing

    CERN Document Server

    Mazumdar, Ravi

    2009-01-01

    This monograph presents a concise mathematical approach for modeling and analyzing the performance of communication networks with the aim of understanding the phenomenon of statistical multiplexing. The novelty of the monograph is the fresh approach and insights provided by a sample-path methodology for queueing models that highlights the important ideas of Palm distributions associated with traffic models and their role in performance measures. Also presented are recent ideas of large buffer, and many sources asymptotics that play an important role in understanding statistical multiplexing. I

  16. Network interactions underlying mirror feedback in stroke: A dynamic causal modeling study

    Directory of Open Access Journals (Sweden)

    Soha Saleh

    2017-01-01

    Full Text Available Mirror visual feedback (MVF is potentially a powerful tool to facilitate recovery of disordered movement and stimulate activation of under-active brain areas due to stroke. The neural mechanisms underlying MVF have therefore been a focus of recent inquiry. Although it is known that sensorimotor areas can be activated via mirror feedback, the network interactions driving this effect remain unknown. The aim of the current study was to fill this gap by using dynamic causal modeling to test the interactions between regions in the frontal and parietal lobes that may be important for modulating the activation of the ipsilesional motor cortex during mirror visual feedback of unaffected hand movement in stroke patients. Our intent was to distinguish between two theoretical neural mechanisms that might mediate ipsilateral activation in response to mirror-feedback: transfer of information between bilateral motor cortices versus recruitment of regions comprising an action observation network which in turn modulate the motor cortex. In an event-related fMRI design, fourteen chronic stroke subjects performed goal-directed finger flexion movements with their unaffected hand while observing real-time visual feedback of the corresponding (veridical or opposite (mirror hand in virtual reality. Among 30 plausible network models that were tested, the winning model revealed significant mirror feedback-based modulation of the ipsilesional motor cortex arising from the contralesional parietal cortex, in a region along the rostral extent of the intraparietal sulcus. No winning model was identified for the veridical feedback condition. We discuss our findings in the context of supporting the latter hypothesis, that mirror feedback-based activation of motor cortex may be attributed to engagement of a contralateral (contralesional action observation network. These findings may have important implications for identifying putative cortical areas, which may be targeted with

  17. Artificial Neural Network Model for Predicting Compressive

    Directory of Open Access Journals (Sweden)

    Salim T. Yousif

    2013-05-01

    Full Text Available   Compressive strength of concrete is a commonly used criterion in evaluating concrete. Although testing of the compressive strength of concrete specimens is done routinely, it is performed on the 28th day after concrete placement. Therefore, strength estimation of concrete at early time is highly desirable. This study presents the effort in applying neural network-based system identification techniques to predict the compressive strength of concrete based on concrete mix proportions, maximum aggregate size (MAS, and slump of fresh concrete. Back-propagation neural networks model is successively developed, trained, and tested using actual data sets of concrete mix proportions gathered from literature.    The test of the model by un-used data within the range of input parameters shows that the maximum absolute error for model is about 20% and 88% of the output results has absolute errors less than 10%. The parametric study shows that water/cement ratio (w/c is the most significant factor  affecting the output of the model.     The results showed that neural networks has strong potential as a feasible tool for predicting compressive strength of concrete.

  18. UAV Trajectory Modeling Using Neural Networks

    Science.gov (United States)

    Xue, Min

    2017-01-01

    Large amount of small Unmanned Aerial Vehicles (sUAVs) are projected to operate in the near future. Potential sUAV applications include, but not limited to, search and rescue, inspection and surveillance, aerial photography and video, precision agriculture, and parcel delivery. sUAVs are expected to operate in the uncontrolled Class G airspace, which is at or below 500 feet above ground level (AGL), where many static and dynamic constraints exist, such as ground properties and terrains, restricted areas, various winds, manned helicopters, and conflict avoidance among sUAVs. How to enable safe, efficient, and massive sUAV operations at the low altitude airspace remains a great challenge. NASA's Unmanned aircraft system Traffic Management (UTM) research initiative works on establishing infrastructure and developing policies, requirement, and rules to enable safe and efficient sUAVs' operations. To achieve this goal, it is important to gain insights of future UTM traffic operations through simulations, where the accurate trajectory model plays an extremely important role. On the other hand, like what happens in current aviation development, trajectory modeling should also serve as the foundation for any advanced concepts and tools in UTM. Accurate models of sUAV dynamics and control systems are very important considering the requirement of the meter level precision in UTM operations. The vehicle dynamics are relatively easy to derive and model, however, vehicle control systems remain unknown as they are usually kept by manufactures as a part of intellectual properties. That brings challenges to trajectory modeling for sUAVs. How to model the vehicle's trajectories with unknown control system? This work proposes to use a neural network to model a vehicle's trajectory. The neural network is first trained to learn the vehicle's responses at numerous conditions. Once being fully trained, given current vehicle states, winds, and desired future trajectory, the neural

  19. Motor cortical plasticity in Parkinson's disease.

    Science.gov (United States)

    Udupa, Kaviraja; Chen, Robert

    2013-09-04

    In Parkinson's disease (PD), there are alterations of the basal ganglia (BG) thalamocortical networks, primarily due to degeneration of nigrostriatal dopaminergic neurons. These changes in subcortical networks lead to plastic changes in primary motor cortex (M1), which mediates cortical motor output and is a potential target for treatment of PD. Studies investigating the motor cortical plasticity using non-invasive transcranial magnetic stimulation (TMS) have found altered plasticity in PD, but there are inconsistencies among these studies. This is likely because plasticity depends on many factors such as the extent of dopaminergic loss and disease severity, response to dopaminergic replacement therapies, development of l-DOPA-induced dyskinesias (LID), the plasticity protocol used, medication, and stimulation status in patients treated with deep brain stimulation (DBS). The influences of LID and DBS on BG and M1 plasticity have been explored in animal models and in PD patients. In addition, many other factors such age, genetic factors (e.g., brain derived neurotropic factor and other neurotransmitters or receptors polymorphism), emotional state, time of the day, physical fitness have been documented to play role in the extent of plasticity induced by TMS in human studies. In this review, we summarize the studies that investigated M1 plasticity in PD and demonstrate how these afore-mentioned factors affect motor cortical plasticity in PD. We conclude that it is important to consider the clinical, demographic, and technical factors that influence various plasticity protocols while developing these protocols as diagnostic or prognostic tools in PD. We also discuss how the modulation of cortical excitability and the plasticity with these non-invasive brain stimulation techniques facilitate the understanding of the pathophysiology of PD and help design potential therapeutic possibilities in this disorder.

  20. Mapping and modeling of physician collaboration network.

    Science.gov (United States)

    Uddin, Shahadat; Hamra, Jafar; Hossain, Liaquat

    2013-09-10

    Effective provisioning of healthcare services during patient hospitalization requires collaboration involving a set of interdependent complex tasks, which needs to be carried out in a synergistic manner. Improved patients' outcome during and after hospitalization has been attributed to how effective different health services provisioning groups carry out their tasks in a coordinated manner. Previous studies have documented the underlying relationships between collaboration among physicians on the effective outcome in delivering health services for improved patient outcomes. However, there are very few systematic empirical studies with a focus on the effect of collaboration networks among healthcare professionals and patients' medical condition. On the basis of the fact that collaboration evolves among physicians when they visit a common hospitalized patient, in this study, we first propose an approach to map collaboration network among physicians from their visiting information to patients. We termed this network as physician collaboration network (PCN). Then, we use exponential random graph (ERG) models to explore the microlevel network structures of PCNs and their impact on hospitalization cost and hospital readmission rate. ERG models are probabilistic models that are presented by locally determined explanatory variables and can effectively identify structural properties of networks such as PCN. It simplifies a complex structure down to a combination of basic parameters such as 2-star, 3-star, and triangle. By applying our proposed mapping approach and ERG modeling technique to the electronic health insurance claims dataset of a very large Australian health insurance organization, we construct and model PCNs. We notice that the 2-star (subset of 3 nodes in which 1 node is connected to each of the other 2 nodes) parameter of ERG has significant impact on hospitalization cost. Further, we identify that triangle (subset of 3 nodes in which each node is connected to

  1. How the human brain goes virtual: distinct cortical regions of the person-processing network are involved in self-identification with virtual agents.

    Science.gov (United States)

    Ganesh, Shanti; van Schie, Hein T; de Lange, Floris P; Thompson, Evan; Wigboldus, Daniël H J

    2012-07-01

    Millions of people worldwide engage in online role-playing with their avatar, a virtual agent that represents the self. Previous behavioral studies have indicated that many gamers identify more strongly with their avatar than with their biological self. Through their avatar, gamers develop social networks and learn new social-cognitive skills. The cognitive neurosciences have yet to identify the neural processes that underlie self-identification with these virtual agents. We applied functional neuroimaging to 22 long-term online gamers and 21 nongaming controls, while they rated personality traits of self, avatar, and familiar others. Strikingly, neuroimaging data revealed greater avatar-referential cortical activity in the left inferior parietal lobe, a region associated with self-identification from a third-person perspective. The magnitude of this brain activity correlated positively with the propensity to incorporate external body enhancements into one's bodily identity. Avatar-referencing furthermore recruited greater activity in the rostral anterior cingulate gyrus, suggesting relatively greater emotional self-involvement with one's avatar. Post-scanning behavioral data revealed superior recognition memory for avatar relative to others. Interestingly, memory for avatar positively covaried with play duration. These findings significantly advance our knowledge about the brain's plasticity to self-identify with virtual agents and the human cognitive-affective potential to live and learn in virtual worlds.

  2. Modeling In-Network Aggregation in VANETs

    NARCIS (Netherlands)

    Dietzel, Stefan; Kargl, Frank; Heijenk, Geert; Schaub, Florian

    2011-01-01

    The multitude of applications envisioned for vehicular ad hoc networks requires efficient communication and dissemination mechanisms to prevent network congestion. In-network data aggregation promises to reduce bandwidth requirements and enable scalability in large vehicular networks. However, most

  3. Different Epidemic Models on Complex Networks

    International Nuclear Information System (INIS)

    Zhang Haifeng; Small, Michael; Fu Xinchu

    2009-01-01

    Models for diseases spreading are not just limited to SIS or SIR. For instance, for the spreading of AIDS/HIV, the susceptible individuals can be classified into different cases according to their immunity, and similarly, the infected individuals can be sorted into different classes according to their infectivity. Moreover, some diseases may develop through several stages. Many authors have shown that the individuals' relation can be viewed as a complex network. So in this paper, in order to better explain the dynamical behavior of epidemics, we consider different epidemic models on complex networks, and obtain the epidemic threshold for each case. Finally, we present numerical simulations for each case to verify our results.