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Sample records for modular neural oscillators

  1. Deinterlacing using modular neural network

    NASA Astrophysics Data System (ADS)

    Woo, Dong H.; Eom, Il K.; Kim, Yoo S.

    2004-05-01

    Deinterlacing is the conversion process from the interlaced scan to progressive one. While many previous algorithms that are based on weighted-sum cause blurring in edge region, deinterlacing using neural network can reduce the blurring through recovering of high frequency component by learning process, and is found robust to noise. In proposed algorithm, input image is divided into edge and smooth region, and then, to each region, one neural network is assigned. Through this process, each neural network learns only patterns that are similar, therefore it makes learning more effective and estimation more accurate. But even within each region, there are various patterns such as long edge and texture in edge region. To solve this problem, modular neural network is proposed. In proposed modular neural network, two modules are combined in output node. One is for low frequency feature of local area of input image, and the other is for high frequency feature. With this structure, each modular neural network can learn different patterns with compensating for drawback of counterpart. Therefore it can adapt to various patterns within each region effectively. In simulation, the proposed algorithm shows better performance compared with conventional deinterlacing methods and single neural network method.

  2. Modular, Hierarchical Learning By Artificial Neural Networks

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F.; Toomarian, Nikzad

    1996-01-01

    Modular and hierarchical approach to supervised learning by artificial neural networks leads to neural networks more structured than neural networks in which all neurons fully interconnected. These networks utilize general feedforward flow of information and sparse recurrent connections to achieve dynamical effects. The modular organization, sparsity of modular units and connections, and fact that learning is much more circumscribed are all attractive features for designing neural-network hardware. Learning streamlined by imitating some aspects of biological neural networks.

  3. A neural network with modular hierarchical learning

    NASA Technical Reports Server (NTRS)

    Baldi, Pierre F. (Inventor); Toomarian, Nikzad (Inventor)

    1994-01-01

    This invention provides a new hierarchical approach for supervised neural learning of time dependent trajectories. The modular hierarchical methodology leads to architectures which are more structured than fully interconnected networks. The networks utilize a general feedforward flow of information and sparse recurrent connections to achieve dynamic effects. The advantages include the sparsity of units and connections, the modular organization. A further advantage is that the learning is much more circumscribed learning than in fully interconnected systems. The present invention is embodied by a neural network including a plurality of neural modules each having a pre-established performance capability wherein each neural module has an output outputting present results of the performance capability and an input for changing the present results of the performance capabilitiy. For pattern recognition applications, the performance capability may be an oscillation capability producing a repeating wave pattern as the present results. In the preferred embodiment, each of the plurality of neural modules includes a pre-established capability portion and a performance adjustment portion connected to control the pre-established capability portion.

  4. Chimera States in Neural Oscillators

    NASA Astrophysics Data System (ADS)

    Bahar, Sonya; Glaze, Tera

    2014-03-01

    Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep.

  5. A modular neural model of motor synergies.

    PubMed

    Byadarhaly, Kiran V; Perdoor, Mithun C; Minai, Ali A

    2012-08-01

    Animals such as reptiles, amphibians and mammals (including humans) are mechanically extremely complex. It has been estimated that the human body has between 500 and 1400 degrees of freedom! And yet, these animals can generate an infinite variety of very precise, complicated and goal-directed movements in continuously changing and uncertain environments. Understanding how this is achieved is of great interest to both biologists and engineers. There are essentially two questions that must be addressed: (1) What type of control strategy is used to handle the large number of degrees of freedom involved? and (2) How is this strategy instantiated in the substrate of neural and musculoskeletal elements comprising the animal bodies? The first question has been studied intensively for several decades, providing strong indications that, rather than using standard feedback control based on continuous tracking of desired trajectories, animals' movements emerge from the controlled combination of pre-configured movement primitives or synergies. These synergies represent coordinated activity patterns over groups of muscles, and can be triggered as a whole with controlled amplitude and temporal offset. Complex movements can thus be constructed from the appropriate combination of a relatively small number of synergies, greatly simplifying the control problem. Although experimental studies on animal movements have confirmed the existence of motor synergies, and their utility has been demonstrated in the control of fairly complex robots, their neural basis remains poorly understood. In this paper, we introduce a simple but plausible and general neural model for motor synergies based on the principle that these functional modules reflect the structural modularity of the underlying physical system. Using this model, we show that a small set of synergies selected through a redundancy-reduction principle can generate a rich motor repertoire in a model two-jointed arm system. We

  6. Oscillation onset in neural delayed feedback

    SciTech Connect

    Longtin, A.

    1990-01-01

    This paper studies dynamical aspects of neural systems with delayed negative feedback modelled by nonlinear delay-differential equations. These systems undergo a Hopf bifurcation from a stable fixed point to a limit cycle oscillation as certain parameters are varied. We show that their frequency of oscillation is robust to parameter variations and noisy fluctuations, a property that makes these systems good candidates for pacemakers. The onset of oscillation is postponed by both additive and parametric noise in the sense that the state variable spends more time near the fixed point. Finally, we show that a distributed delay (rather than a fixed delay) also stabilizes the fixed point solution. 40 refs., 2 figs.

  7. A Flexible Behavioral Learning System with Modular Neural Networks

    NASA Astrophysics Data System (ADS)

    Takeuchi, Johane; Shouno, Osamu; Tsujino, Hiroshi

    Future robots/agents will perform situated behaviors for each user. Flexible behavioral learning is required for coping with diverse and unexpected users' situations. Unexpected situations are usually not tractable for machine learning systems that are designed for pre-defined problems. In order to realize such a flexible learning system, we were trying to create a learning model that can function in several different kinds of state transitions without specific adjustments for each transition as a first step. We constructed a modular neural network model based on reinforcement learning. We expected that combining a modular architecture with neural networks could accelerate the learning speed of neural networks. The inputs of our neural network model always include not only observed states but also memory information for any transition. In pure Markov decision processes, memory information is not necessary, rather it can lead to lower performance. On the other hand, partially observable conditions require memory information to select proper actions. We demonstrated that the new learning model could actually learn those multiple kinds of state transitions with the same architectures and parameters, and without pre-designed models of environments. This paper describes the performances of constructed models using probabilistically fluctuated Markov decision processes including partially observable conditions. In the test transitions, the observed state probabilistically fluctuated. The new learning model could function in those complex transitions. In addition, the learning speeds of our model are comparable to a reinforcement learning algorithm implemented with a pre-defined and optimized table-representation of states.

  8. A modular neural network approach to fault diagnosis.

    PubMed

    Rodriguez, C; Rementeria, S; Martin, J I; Lafuente, A; Muguerza, J; Perez, J

    1996-01-01

    Certain real-world applications present serious challenges to conventional neural-network design procedures. Blindly trying to train huge networks may lead to unsatisfactory results and wrong conclusions about the type of problems that can be tackled using that technology. In this paper a modular solution to power systems alarm handling and fault diagnosis is described that overcomes the limitations of "toy" alternatives constrained to small and fixed-topology electrical networks. In contrast to monolithic diagnosis systems, the neural-network-based approach presented here accomplishes the scalability and dynamic adaptability requirements of the application. Mapping the power grid onto a set of interconnected modules that model the functional behavior of electrical equipment provides the flexibility and speed demanded by the problem. After a preliminary generation of candidate fault locations, competition among hypotheses results in a fully justified diagnosis that may include simultaneous faults. The way in which the neural system is conceived allows for a natural parallel implementation. PMID:18255587

  9. Chimera-like States in Modular Neural Networks

    PubMed Central

    Hizanidis, Johanne; Kouvaris, Nikos E.; Gorka, Zamora-López; Díaz-Guilera, Albert; Antonopoulos, Chris G.

    2016-01-01

    Chimera states, namely the coexistence of coherent and incoherent behavior, were previously analyzed in complex networks. However, they have not been extensively studied in modular networks. Here, we consider a neural network inspired by the connectome of the C. elegans soil worm, organized into six interconnected communities, where neurons obey chaotic bursting dynamics. Neurons are assumed to be connected with electrical synapses within their communities and with chemical synapses across them. As our numerical simulations reveal, the coaction of these two types of coupling can shape the dynamics in such a way that chimera-like states can happen. They consist of a fraction of synchronized neurons which belong to the larger communities, and a fraction of desynchronized neurons which are part of smaller communities. In addition to the Kuramoto order parameter ρ, we also employ other measures of coherence, such as the chimera-like χ and metastability λ indices, which quantify the degree of synchronization among communities and along time, respectively. We perform the same analysis for networks that share common features with the C. elegans neural network. Similar results suggest that under certain assumptions, chimera-like states are prominent phenomena in modular networks, and might provide insight for the behavior of more complex modular networks. PMID:26796971

  10. Hybrid interior point training of modular neural networks.

    PubMed

    Szymanski, P T; Lemmon, M; Bett, C J

    1998-03-01

    Modular neural networks use a single gating neuron to select the outputs of a collection of agent neurons. Expectation-maximization (EM) algorithms provide one way of training modular neural networks to approximate non-linear functionals. This paper introduces a hybrid interior-point (HIP) algorithm for training modular networks. The HIP algorithm combines an interior-point linear programming (LP) algorithm with a Newton-Raphson iteration in such a way that the computational efficiency of the interior point LP methods is preserved. The algorithm is formally proven to converge asymptotically to locally optimal networks with a total computational cost that scales in a polynomial manner with problem size. Simulation experiments show that the HIP algorithm produces networks whose average approximation error is better than that of EM-trained networks. These results also demonstrate that the computational cost of the HIP algorithm scales at a slower rate than the EM-procedure and that, for small-size networks, the total computational costs of both methods are comparable. PMID:12662833

  11. Texture image classification using modular radial basis function neural networks

    NASA Astrophysics Data System (ADS)

    Chang, Chuan-Yu; Wang, Hung-Jen; Fu, Shih-Yu

    2010-01-01

    Image classification has become an important topic in multimedia processing. Recently, neural network-based methods have been proposed to solve the classification problem. Among them, the radial basis function neural network (RBFNN) is the most popular architecture, because it has good learning and approximation capabilities. However, traditional RBFNNs are sensitive to center initialization. To obtain appropriate centers, it needs to find significant features for further RBF clustering. In addition, the training procedure of a traditional RBFNN is time consuming. Therefore, in this work, a combination of a self-organizing map (SOM) and learning vector quantization (LVQ) neural networks is proposed to select more appropriate centers for an RBFNN, and a modular RBF neural network (MRBFNN) is proposed to improve the classification rate and to speed up the training time. Experimental results show that the proposed MRBFNN has better performance than those of the traditional RBFNN, the discrete wavelength transform (DWT)-based method, the tree structured wavelet (TWS), the discrete wavelet frame (DWF), the rotated wavelet filter (RWF), and the wavelet neural network based on adaptive norm entropy (WNN-ANE) methods.

  12. Complexity versus modularity and heterogeneity in oscillatory networks: Combining segregation and integration in neural systems

    NASA Astrophysics Data System (ADS)

    Zhao, Ming; Zhou, Changsong; Chen, Yuhan; Hu, Bambi; Wang, Bing-Hong

    2010-10-01

    Normal functioning in many realistic complex dynamical systems, such as neural networks, requires coherence and synchronization for collective actions of network components. However, strong synchronization of the whole network is often related to pathological situations. A regime in between enabling both segregation in subsystems and integration as a whole is thus desirable. Here, we characterize this regime by complexity of synchronization patterns and study its relationship to heterogeneous and modular architecture in complex network of oscillators. We show that these networks possess a broad range of high complexity associated with the formation of dynamical clusters and the coordination between the clusters. In realistic networks of C. elegans and cat cortex, the complexity is reduced when the original network is rewired in various ways, reflecting that the neural systems are organized to provide a combination of segregation and integration with the coexistence of various complex network features, especially modularity and heterogeneity. Our work can stimulate further studies on structure-function relationships in neural systems through the inquiry of the specific functional roles of the intermediate dynamical regime.

  13. Dynamics of modularity of neural activity in the brain during development

    NASA Astrophysics Data System (ADS)

    Deem, Michael; Chen, Man

    2014-03-01

    Theory suggests that more modular systems can have better response functions at short times. This theory suggests that greater cognitive performance may be achieved for more modular neural activity, and that modularity of neural activity may, therefore, likely increase with development in children. We study the relationship between age and modularity of brain neural activity in developing children. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. We interpret these results as evidence of selection for plasticity in the cognitive function of the human brain. We present a model to illustrate how modularity can provide greater cognitive performance at short times and enhance fast, low-level, automatic cognitive processes. Conversely, high-level, effortful, conscious cognitive processes may not benefit from modularity. We use quasispecies theory to predict how the average modularity evolves with age, given a fitness function extracted from the model. We suggest further experiments exploring the effect of modularity on cognitive performance and suggest that modularity may be a potential biomarker for injury, rehabilitation, or disease.

  14. Neural Modularity Helps Organisms Evolve to Learn New Skills without Forgetting Old Skills

    PubMed Central

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-01-01

    A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

  15. Neural modularity helps organisms evolve to learn new skills without forgetting old skills.

    PubMed

    Ellefsen, Kai Olav; Mouret, Jean-Baptiste; Clune, Jeff

    2015-04-01

    A long-standing goal in artificial intelligence is creating agents that can learn a variety of different skills for different problems. In the artificial intelligence subfield of neural networks, a barrier to that goal is that when agents learn a new skill they typically do so by losing previously acquired skills, a problem called catastrophic forgetting. That occurs because, to learn the new task, neural learning algorithms change connections that encode previously acquired skills. How networks are organized critically affects their learning dynamics. In this paper, we test whether catastrophic forgetting can be reduced by evolving modular neural networks. Modularity intuitively should reduce learning interference between tasks by separating functionality into physically distinct modules in which learning can be selectively turned on or off. Modularity can further improve learning by having a reinforcement learning module separate from sensory processing modules, allowing learning to happen only in response to a positive or negative reward. In this paper, learning takes place via neuromodulation, which allows agents to selectively change the rate of learning for each neural connection based on environmental stimuli (e.g. to alter learning in specific locations based on the task at hand). To produce modularity, we evolve neural networks with a cost for neural connections. We show that this connection cost technique causes modularity, confirming a previous result, and that such sparsely connected, modular networks have higher overall performance because they learn new skills faster while retaining old skills more and because they have a separate reinforcement learning module. Our results suggest (1) that encouraging modularity in neural networks may help us overcome the long-standing barrier of networks that cannot learn new skills without forgetting old ones, and (2) that one benefit of the modularity ubiquitous in the brains of natural animals might be to

  16. Development of modularity in the neural activity of children’s brains

    PubMed Central

    Chen, Man; Deem, Michael W.

    2015-01-01

    We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from fMRI data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e. task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease. PMID:25619207

  17. Development of modularity in the neural activity of childrenʼs brains

    NASA Astrophysics Data System (ADS)

    Chen, Man; Deem, Michael W.

    2015-02-01

    We study how modularity of the human brain changes as children develop into adults. Theory suggests that modularity can enhance the response function of a networked system subject to changing external stimuli. Thus, greater cognitive performance might be achieved for more modular neural activity, and modularity might likely increase as children develop. The value of modularity calculated from functional magnetic resonance imaging (fMRI) data is observed to increase during childhood development and peak in young adulthood. Head motion is deconvolved from the fMRI data, and it is shown that the dependence of modularity on age is independent of the magnitude of head motion. A model is presented to illustrate how modularity can provide greater cognitive performance at short times, i.e. task switching. A fitness function is extracted from the model. Quasispecies theory is used to predict how the average modularity evolves with age, illustrating the increase of modularity during development from children to adults that arises from selection for rapid cognitive function in young adults. Experiments exploring the effect of modularity on cognitive performance are suggested. Modularity may be a potential biomarker for injury, rehabilitation, or disease.

  18. Encoding of event timing in the phase of neural oscillations.

    PubMed

    Kösem, Anne; Gramfort, Alexandre; van Wassenhove, Virginie

    2014-05-15

    Time perception is a critical component of conscious experience. To be in synchrony with the environment, the brain must deal not only with differences in the speed of light and sound but also with its computational and neural transmission delays. Here, we asked whether the brain could actively compensate for temporal delays by changing its processing time. Specifically, can changes in neural timing or in the phase of neural oscillation index perceived timing? For this, a lag-adaptation paradigm was used to manipulate participants' perceived audiovisual (AV) simultaneity of events while they were recorded with magnetoencephalography (MEG). Desynchronized AV stimuli were presented rhythmically to elicit a robust 1 Hz frequency-tagging of auditory and visual cortical responses. As participants' perception of AV simultaneity shifted, systematic changes in the phase of entrained neural oscillations were observed. This suggests that neural entrainment is not a passive response and that the entrained neural oscillation shifts in time. Crucially, our results indicate that shifts in neural timing in auditory cortices linearly map participants' perceived AV simultaneity. To our knowledge, these results provide the first mechanistic evidence for active neural compensation in the encoding of sensory event timing in support of the emergence of time awareness. PMID:24531044

  19. Entrained neural oscillations in multiple frequency bands comodulate behavior

    PubMed Central

    Henry, Molly J.; Herrmann, Björn

    2014-01-01

    Our sensory environment is teeming with complex rhythmic structure, to which neural oscillations can become synchronized. Neural synchronization to environmental rhythms (entrainment) is hypothesized to shape human perception, as rhythmic structure acts to temporally organize cortical excitability. In the current human electroencephalography study, we investigated how behavior is influenced by neural oscillatory dynamics when the rhythmic fluctuations in the sensory environment take on a naturalistic degree of complexity. Listeners detected near-threshold gaps in auditory stimuli that were simultaneously modulated in frequency (frequency modulation, 3.1 Hz) and amplitude (amplitude modulation, 5.075 Hz); modulation rates and types were chosen to mimic the complex rhythmic structure of natural speech. Neural oscillations were entrained by both the frequency modulation and amplitude modulation in the stimulation. Critically, listeners’ target-detection accuracy depended on the specific phase–phase relationship between entrained neural oscillations in both the 3.1-Hz and 5.075-Hz frequency bands, with the best performance occurring when the respective troughs in both neural oscillations coincided. Neural-phase effects were specific to the frequency bands entrained by the rhythmic stimulation. Moreover, the degree of behavioral comodulation by neural phase in both frequency bands exceeded the degree of behavioral modulation by either frequency band alone. Our results elucidate how fluctuating excitability, within and across multiple entrained frequency bands, shapes the effective neural processing of environmental stimuli. More generally, the frequency-specific nature of behavioral comodulation effects suggests that environmental rhythms act to reduce the complexity of high-dimensional neural states. PMID:25267634

  20. Understanding the Emergence of Modularity in Neural Systems

    ERIC Educational Resources Information Center

    Bullinaria, John A.

    2007-01-01

    Modularity in the human brain remains a controversial issue, with disagreement over the nature of the modules that exist, and why, when, and how they emerge. It is a natural assumption that modularity offers some form of computational advantage, and hence evolution by natural selection has translated those advantages into the kind of modular…

  1. Oscillation-induced signal transmission and gating in neural circuits.

    PubMed

    Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

    2014-12-01

    Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

  2. Oscillation-Induced Signal Transmission and Gating in Neural Circuits

    PubMed Central

    Jahnke, Sven; Memmesheimer, Raoul-Martin; Timme, Marc

    2014-01-01

    Reliable signal transmission constitutes a key requirement for neural circuit function. The propagation of synchronous pulse packets through recurrent circuits is hypothesized to be one robust form of signal transmission and has been extensively studied in computational and theoretical works. Yet, although external or internally generated oscillations are ubiquitous across neural systems, their influence on such signal propagation is unclear. Here we systematically investigate the impact of oscillations on propagating synchrony. We find that for standard, additive couplings and a net excitatory effect of oscillations, robust propagation of synchrony is enabled in less prominent feed-forward structures than in systems without oscillations. In the presence of non-additive coupling (as mediated by fast dendritic spikes), even balanced oscillatory inputs may enable robust propagation. Here, emerging resonances create complex locking patterns between oscillations and spike synchrony. Interestingly, these resonances make the circuits capable of selecting specific pathways for signal transmission. Oscillations may thus promote reliable transmission and, in co-action with dendritic nonlinearities, provide a mechanism for information processing by selectively gating and routing of signals. Our results are of particular interest for the interpretation of sharp wave/ripple complexes in the hippocampus, where previously learned spike patterns are replayed in conjunction with global high-frequency oscillations. We suggest that the oscillations may serve to stabilize the replay. PMID:25503492

  3. Macroscopic Neural Oscillation during Skilled Reaching Movements in Humans

    PubMed Central

    Chung, Chun Kee

    2016-01-01

    The neural mechanism of skilled movements, such as reaching, has been considered to differ from that of rhythmic movement such as locomotion. It is generally thought that skilled movements are consciously controlled by the brain, while rhythmic movements are usually controlled autonomously by the spinal cord and brain stem. However, several studies in recent decades have suggested that neural networks in the spinal cord may also be involved in the generation of skilled movements. Moreover, a recent study revealed that neural activities in the motor cortex exhibit rhythmic oscillations corresponding to movement frequency during reaching movements as rhythmic movements. However, whether the oscillations are generated in the spinal cord or the cortical circuit in the motor cortex causes the oscillations is unclear. If the spinal cord is involved in the skilled movements, then similar rhythmic oscillations with time delays should be found in macroscopic neural activity. We measured whole-brain MEG signals during reaching. The MEG signals were analyzed using a dynamical analysis method. We found that rhythmic oscillations with time delays occur in all subjects during reaching movements. The results suggest that the corticospinal system is involved in the generation and control of the skilled movements as rhythmic movements. PMID:27524996

  4. Macroscopic Neural Oscillation during Skilled Reaching Movements in Humans.

    PubMed

    Yeom, Hong Gi; Kim, June Sic; Chung, Chun Kee

    2016-01-01

    The neural mechanism of skilled movements, such as reaching, has been considered to differ from that of rhythmic movement such as locomotion. It is generally thought that skilled movements are consciously controlled by the brain, while rhythmic movements are usually controlled autonomously by the spinal cord and brain stem. However, several studies in recent decades have suggested that neural networks in the spinal cord may also be involved in the generation of skilled movements. Moreover, a recent study revealed that neural activities in the motor cortex exhibit rhythmic oscillations corresponding to movement frequency during reaching movements as rhythmic movements. However, whether the oscillations are generated in the spinal cord or the cortical circuit in the motor cortex causes the oscillations is unclear. If the spinal cord is involved in the skilled movements, then similar rhythmic oscillations with time delays should be found in macroscopic neural activity. We measured whole-brain MEG signals during reaching. The MEG signals were analyzed using a dynamical analysis method. We found that rhythmic oscillations with time delays occur in all subjects during reaching movements. The results suggest that the corticospinal system is involved in the generation and control of the skilled movements as rhythmic movements. PMID:27524996

  5. Can computational efficiency alone drive the evolution of modularity in neural networks?

    PubMed Central

    Tosh, Colin R.

    2016-01-01

    Some biologists have abandoned the idea that computational efficiency in processing multipart tasks or input sets alone drives the evolution of modularity in biological networks. A recent study confirmed that small modular (neural) networks are relatively computationally-inefficient but large modular networks are slightly more efficient than non-modular ones. The present study determines whether these efficiency advantages with network size can drive the evolution of modularity in networks whose connective architecture can evolve. The answer is no, but the reason why is interesting. All simulations (run in a wide variety of parameter states) involving gradualistic connective evolution end in non-modular local attractors. Thus while a high performance modular attractor exists, such regions cannot be reached by gradualistic evolution. Non-gradualistic evolutionary simulations in which multi-modularity is obtained through duplication of existing architecture appear viable. Fundamentally, this study indicates that computational efficiency alone does not drive the evolution of modularity, even in large biological networks, but it may still be a viable mechanism when networks evolve by non-gradualistic means. PMID:27573614

  6. What works in auditory working memory? A neural oscillations perspective.

    PubMed

    Wilsch, Anna; Obleser, Jonas

    2016-06-01

    Working memory is a limited resource: brains can only maintain small amounts of sensory input (memory load) over a brief period of time (memory decay). The dynamics of slow neural oscillations as recorded using magneto- and electroencephalography (M/EEG) provide a window into the neural mechanics of these limitations. Especially oscillations in the alpha range (8-13Hz) are a sensitive marker for memory load. Moreover, according to current models, the resultant working memory load is determined by the relative noise in the neural representation of maintained information. The auditory domain allows memory researchers to apply and test the concept of noise quite literally: Employing degraded stimulus acoustics increases memory load and, at the same time, allows assessing the cognitive resources required to process speech in noise in an ecologically valid and clinically relevant way. The present review first summarizes recent findings on neural oscillations, especially alpha power, and how they reflect memory load and memory decay in auditory working memory. The focus is specifically on memory load resulting from acoustic degradation. These findings are then contrasted with contextual factors that benefit neural as well as behavioral markers of memory performance, by reducing representational noise. We end on discussing the functional role of alpha power in auditory working memory and suggest extensions of the current methodological toolkit. This article is part of a Special Issue entitled SI: Auditory working memory. PMID:26556773

  7. Neural circuits underlying the generation of theta oscillations.

    PubMed

    Pignatelli, Michele; Beyeler, Anna; Leinekugel, Xavier

    2012-01-01

    Theta oscillations represent the neural network configuration underlying active awake behavior and paradoxical sleep. This major EEG pattern has been extensively studied, from physiological to anatomical levels, for more than half a century. Nevertheless the cellular and network mechanisms accountable for the theta generation are still not fully understood. This review synthesizes the current knowledge on the circuitry involved in the generation of theta oscillations, from the hippocampus to extra hippocampal structures such as septal complex, entorhinal cortex and pedunculopontine tegmentum, a main trigger of theta state through direct and indirect projections to the septal complex. We conclude with a short overview of the perspectives offered by technical advances for deciphering more precisely the different neural components underlying the emergence of theta oscillations. PMID:21964249

  8. Collective oscillations in disordered neural networks

    NASA Astrophysics Data System (ADS)

    Olmi, Simona; Livi, Roberto; Politi, Antonio; Torcini, Alessandro

    2010-04-01

    We investigate the onset of collective oscillations in a excitatory pulse-coupled network of leaky integrate-and-fire neurons in the presence of quenched and annealed disorder. We find that the disorder induces a weak form of chaos that is analogous to that arising in the Kuramoto model for a finite number N of oscillators [O. V. Popovych , Phys. Rev. E 71 065201(R) (2005)]. In fact, the maximum Lyapunov exponent turns out to scale to zero for N→∞ , with an exponent that is different for the two types of disorder. In the thermodynamic limit, the random-network dynamics reduces to that of a fully homogeneous system with a suitably scaled coupling strength. Moreover, we show that the Lyapunov spectrum of the periodically collective state scales to zero as 1/N2 , analogously to the scaling found for the “splay state.”

  9. Analysis of torsional oscillations using an artificial neural network

    SciTech Connect

    Hsu, Y.Y.; Jeng, L,H. )

    1992-12-01

    In this paper, a novel approach using an artificial neural network (ANN) is proposed for the analysis of torsional oscillations in a power system. In the developed artificial neural network, those system variables such as generator loadings and capacitor compensation ratio which have major impacts on the damping characteristics of torsional oscillatio modes are employed as the inputs. The outputs of the neural net provide the desired eigenvalues for torsional modes. Once the connection weights of the neural network have been learned using a set of training data derived off-line, the neural network can be applied to torsional analysis in real-time situations. To demonstrate the effectiveness of the proposed neural net, torsional analysis is performed on the IEEE First Benchmark Model. It is concluded from the test results that accurate assessment of the torsional mode eigenvalues can be achieved by the neural network in a very efficient manner. Thereofore, the proposed neural network approach can serve as a valuable tool to system operators in conducting SSR analysis in operational planning.

  10. Frequency transitions in odor-evoked neural oscillations

    PubMed Central

    Ito, Iori; Bazhenov, Maxim; Ong, Rose Chik-ying; Raman, Baranidharan; Stopfer, Mark

    2009-01-01

    Summary In many species sensory stimuli elicit the oscillatory synchronization of groups of neurons. What determines the properties of these oscillations? In the olfactory system of the moth we found that odors elicited oscillatory synchronization through a neural mechanism like that described in locust and Drosophila. During responses to long odor pulses, oscillations suddenly slowed as net olfactory receptor neuron (ORN) output decreased; thus, stimulus intensity appeared to determine oscillation frequency. However, changing the concentration of the odor had little effect upon oscillatory frequency. Our recordings in vivo and computational models based on these results suggested the main effect of increasing odor concentration was to recruit additional, less well-tuned ORNs whose firing rates were tightly constrained by adaptation and saturation. Thus, in the periphery, concentration is encoded mainly by the size of the responsive ORN population, and oscillation frequency is set by the adaptation and saturation of this response. PMID:20005825

  11. Genesis and synchronization properties of fast neural oscillations

    NASA Astrophysics Data System (ADS)

    Bazhenov, Maxim; Rulkov, Nikolai

    2008-03-01

    Fast neural network oscillations in gamma (30-80 Hz) range are associated with attentiveness and sensory perception and have strong relation to both cognitive processing and temporal binding of sensory stimuli. These oscillations are found in different brain systems including cerebral cortex, hippocampus and olfactory bulb. Cortical gamma oscillations may become synchronized within 1-2 msec over distances up to a few millimeters. In this study we used computational network models to analyze basic synaptic mechanisms and synchronization properties of fast neural oscillations. Using the network models of synaptically coupled pyramidal neurons (up to 500,000 cells) and fast spiking interneurons (up to 125,000 cells) we found that the strength of feedback inhibition determined the network synchronization state: either global network oscillations with near zero phase lag between remote sites or waves of gamma activity propagating through the network. Long-range excitatory connections between pyramidal cells were not required for long-range synchronization. The model predicts that local inhibitory circuits can mediate global network synchronization with phase delays being much smaller than activity propagation time between remote network sites.

  12. Feature extraction and pattern classification of remote sensing data by a modular neural system

    NASA Astrophysics Data System (ADS)

    Blonda, Palma; la Forgia, Vincenza; Pasquariello, Guido; Satalino, Giuseppe

    1996-02-01

    A modular neural network architecture has been used for the classification of remote sensed data in two experiments carried out to study two different but rather usual situations in real remote sensing applications. Such situations concern the availability of high-dimensional data in the first setting and an imperfect data set with a limited number of features in the second. The learning task of the supervised multilayer perceptron classifier has been made more efficient by preprocessing the input with unsupervised neural modules for feature discovery. The linear propagation network is introduced in the first experiment to evaluate the effectiveness of the neural data compression stage before classification, whereas in the second experiment data clustering before labeling is evaluated by the Kohonen self-organizing feature map network. The results of the two experiments confirm that modular learning performs better than nonmodular learning with respect to both learning quality and speed.

  13. Land Surface Temperature Forecasting using spectral observations of MODIS and Modular Neural Networks

    NASA Astrophysics Data System (ADS)

    Taghavi, Farahnaz; Zargaran, Zahrah; Ahmadi, Abbas

    Land Surface Temperature (LST) is a significant parameter for many applications including numerical weather prediction, climate and environmental studies. The goal of this study is using a combination of Modular neural networks and satellite image as input to predict the LST in Tehran ,Iran.In this study, two MLP and RBF neural networks and an algorithm for calculating of LST based spectral observations of MODerate resolution Imaging Spectra-radiometer (MODIS) are used This algorithm include Brightness Temperature of channel 31(BT31) and 32(BT32) on thermal band of MODIS. The algorithm are written using Hierarchical Data Format (HDF) calibrated data which has the spatial resolution of 1km by ENVI (Environment for Visualizing Images) software, and output products are in HDF format. Initial results show that modular neural network helps to improve networks' generalization and learning speed and the main reason for selecting these networks is their good performance in this problem.The model has a modular learning and structure. Since the task decomposition at first and the combination of results to get the final prediction at the end are key and effective points on the performance of modular neural network, in this study we propose a new approach to this issue. This method uses the Self-Organizing Map (SOM) Neural Network and Particle Swarm Optimization(PSO) algorithm for task decomposition. The proposed model combines this neural networks and optimization algorithms. Results indicate that use of PSO algorithm has caused suitable distribution of clusters obtained from SOM algorithm. In addition to the use of satellite images has improved the performance of the proposed model. Finally, the results obtained from this model will be compared with some other methods with non-modular structure and learning and it is shown that this proposed model is able to produce accurate results. The result of this comparison show that training time of model in the forecasting of land

  14. Feature extraction and pattern classification for remotely sensed data analysis by a modular neural system

    NASA Astrophysics Data System (ADS)

    Blonda, Palma N.; la Forgia, Vincenza; Pasquariello, Guido; Satalino, Giuseppe

    1994-12-01

    In this paper a modular neural network architecture is proposed for classification of Remote Sensed data. The neural network learning task of the supervised Multi Layer Perceptron (MLP) Classifier has been made more efficient by pre-processing the input with an unsupervised feature discovery neural module. Two classification experiments have been carried for coping with two different situations, very usual in real remote sensing applications: the availability of complex data, such as high dimensional and multisourced data, and on the contrary, the case of imperfect low dimensional data set, with a limited number of samples. In the first experiment on a multitemporal data set, the Linear Propagation Network (LPN) has been introduced to evaluate the effectiveness of neural data compression stage before classification. In the second experiment on a poor data set, the Kohonen Self Organising Feature Map (SOM) Network has been introduced for clustering data before labelling. In the paper is also illustrated the criterion for the selection of an optimal number of cluster centres to be used as node number of the output SOM layer. The results of the two experiments have confirmed that modular learning performs better than the non-modular one in learning quality and speed.

  15. Synchronization and cluster sequences in modular and evolving map-based neural networks

    NASA Astrophysics Data System (ADS)

    Maslennikov, Oleg V.; Nekorkin, Vladimir I.

    2016-06-01

    The impact of modularity and time delay for spiking neural networks is considered in the first part of this report. We show for different complex topologies that time delay controls regimes of inter-module synchronization as well as the oscillatory rate of modules. In the second part of the work we study a paradigmatic model of the evolving neural network whose topology is influenced by nodal dynamics which results in generating different cluster sequences. We show the conditions under which the sequences are robust to small perturbations of initial conditions, parameter detuning, and noise, while at the same are selective to information stimuli.

  16. Patterns of interval correlations in neural oscillators with adaptation

    PubMed Central

    Schwalger, Tilo; Lindner, Benjamin

    2013-01-01

    Neural firing is often subject to negative feedback by adaptation currents. These currents can induce strong correlations among the time intervals between spikes. Here we study analytically the interval correlations of a broad class of noisy neural oscillators with spike-triggered adaptation of arbitrary strength and time scale. Our weak-noise theory provides a general relation between the correlations and the phase-response curve (PRC) of the oscillator, proves anti-correlations between neighboring intervals for adapting neurons with type I PRC and identifies a single order parameter that determines the qualitative pattern of correlations. Monotonically decaying or oscillating correlation structures can be related to qualitatively different voltage traces after spiking, which can be explained by the phase plane geometry. At high firing rates, the long-term variability of the spike train associated with the cumulative interval correlations becomes small, independent of model details. Our results are verified by comparison with stochastic simulations of the exponential, leaky, and generalized integrate-and-fire models with adaptation. PMID:24348372

  17. Higher-order spike triggered analysis of neural oscillators.

    PubMed

    Ota, Keisuke; Omori, Toshiaki; Miyakawa, Hiroyoshi; Okada, Masato; Aonishi, Toru

    2012-01-01

    For the purpose of elucidating the neural coding process based on the neural excitability mechanism, researchers have recently investigated the relationship between neural dynamics and the spike triggered stimulus ensemble (STE). Ermentrout et al. analytically derived the relational equation between the phase response curve (PRC) and the spike triggered average (STA). The STA is the first cumulant of the STE. However, in order to understand the neural function as the encoder more explicitly, it is necessary to elucidate the relationship between the PRC and higher-order cumulants of the STE. In this paper, we give a general formulation to relate the PRC and the nth moment of the STE. By using this formulation, we derive a relational equation between the PRC and the spike triggered covariance (STC), which is the covariance of the STE. We show the effectiveness of the relational equation through numerical simulations and use the equation to identify the feature space of the rat hippocampal CA1 pyramidal neurons from their PRCs. Our result suggests that the hippocampal CA1 pyramidal neurons oscillating in the theta frequency range are commonly sensitive to inputs composed of theta and gamma frequency components. PMID:23226249

  18. Nonlinear Oscillations, Noise and Chaos in Neural Delayed Feedback.

    NASA Astrophysics Data System (ADS)

    Longtin, Andre

    Bifurcations and complex oscillations in the human pupil light reflex (PLR) are studied. Autonomous pupil area oscillations are produced by substituting electronically controllable nonlinear feedback for the normal negative feedback of this reflex. A physiologically sound theoretical framework in which to study pupillary oscillations is developed. The model, framed as a delay-differential equation (DDE), agrees quantitatively with the simpler periodic behaviors and qualitatively with the complex behaviors. Much of the aperiodicity in the data can be ascribed to noise and transients rather than to chaos. The critical behavior of the PLR at oscillation onset is different with piecewise constant rather than smooth negative feedback. In the former, relative fluctuations in period are larger than those in amplitude, and vice versa in the latter. Properties of the time solutions and densities of a stochastic DDE are used to explain this experimental result. The Hopf bifurcation in this system is postponed by both additive and multiplicative colored noise. Theoretical insight into the behavior of stationary densities of DDE's and the origin of the postponement is given, and implications for analyzing bifurcations in neural delayed feedback systems are discussed.

  19. Entrainment Ranges for Chains of Forced Neural and Phase Oscillators.

    PubMed

    Massarelli, Nicole; Clapp, Geoffrey; Hoffman, Kathleen; Kiemel, Tim

    2016-12-01

    Sensory input to the lamprey central pattern generator (CPG) for locomotion is known to have a significant role in modulating lamprey swimming. Lamprey CPGs are known to have the ability to entrain to a bending stimulus, that is, in the presence of a rhythmic signal, the CPG will change its frequency to match the stimulus frequency. Bending experiments in which the lamprey spinal cord has been removed and mechanically bent back and forth at a single point have been used to determine the range of frequencies that can entrain the CPG rhythm. First, we model the lamprey locomotor CPG as a chain of neural oscillators with three classes of neurons and sinusoidal forcing representing edge cell input. We derive a phase model using the connections described in the neural model. This results in a simpler model yet maintains some properties of the neural model. For both the neural model and the derived phase model, entrainment ranges are computed for forcing at different points along the chain while varying both intersegmental coupling strength and the coupling strength between the forcer and chain. Entrainment ranges for chains with nonuniform intersegmental coupling asymmetry are larger when forcing is applied to the middle of the chain than when it is applied to either end, a result that is qualitatively similar to the experimental results. In the limit of weak coupling in the chain, the entrainment results of the neural model approach the entrainment results for the derived phase model. Both biological experiments and the robustness of non-monotonic entrainment ranges as a function of the forcing position across different classes of CPG models with nonuniform asymmetric coupling suggest that a specific property of the intersegmental coupling of the CPG is key to entrainment. PMID:27091694

  20. Establishing a Statistical Link between Network Oscillations and Neural Synchrony

    PubMed Central

    Zhou, Pengcheng; Burton, Shawn D.; Snyder, Adam C.; Smith, Matthew A.; Urban, Nathaniel N.; Kass, Robert E.

    2015-01-01

    Pairs of active neurons frequently fire action potentials or “spikes” nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons’ fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron’s firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony. PMID:26465621

  1. Establishing a Statistical Link between Network Oscillations and Neural Synchrony.

    PubMed

    Zhou, Pengcheng; Burton, Shawn D; Snyder, Adam C; Smith, Matthew A; Urban, Nathaniel N; Kass, Robert E

    2015-10-01

    Pairs of active neurons frequently fire action potentials or "spikes" nearly synchronously (i.e., within 5 ms of each other). This spike synchrony may occur by chance, based solely on the neurons' fluctuating firing patterns, or it may occur too frequently to be explicable by chance alone. When spike synchrony above chances levels is present, it may subserve computation for a specific cognitive process, or it could be an irrelevant byproduct of such computation. Either way, spike synchrony is a feature of neural data that should be explained. A point process regression framework has been developed previously for this purpose, using generalized linear models (GLMs). In this framework, the observed number of synchronous spikes is compared to the number predicted by chance under varying assumptions about the factors that affect each of the individual neuron's firing-rate functions. An important possible source of spike synchrony is network-wide oscillations, which may provide an essential mechanism of network information flow. To establish the statistical link between spike synchrony and network-wide oscillations, we have integrated oscillatory field potentials into our point process regression framework. We first extended a previously-published model of spike-field association and showed that we could recover phase relationships between oscillatory field potentials and firing rates. We then used this new framework to demonstrate the statistical relationship between oscillatory field potentials and spike synchrony in: 1) simulated neurons, 2) in vitro recordings of hippocampal CA1 pyramidal cells, and 3) in vivo recordings of neocortical V4 neurons. Our results provide a rigorous method for establishing a statistical link between network oscillations and neural synchrony. PMID:26465621

  2. Analytical Insights on Theta-Gamma Coupled Neural Oscillators

    PubMed Central

    2013-01-01

    In this paper, we study the dynamics of a quadratic integrate-and-fire neuron, spiking in the gamma (30–100 Hz) range, coupled to a delta/theta frequency (1–8 Hz) neural oscillator. Using analytical and semianalytical methods, we were able to derive characteristic spiking times for the system in two distinct regimes (depending on parameter values): one regime where the gamma neuron is intrinsically oscillating in the absence of theta input, and a second one in which gamma spiking is directly gated by theta input, i.e., windows of gamma activity alternate with silence periods depending on the underlying theta phase. In the former case, we transform the equations such that the system becomes analogous to the Mathieu differential equation. By solving this equation, we can compute numerically the time to the first gamma spike, and then use singular perturbation theory to find successive spike times. On the other hand, in the excitable condition, we make direct use of singular perturbation theory to obtain an approximation of the time to first gamma spike, and then extend the result to calculate ensuing gamma spikes in a recursive fashion. We thereby give explicit formulas for the onset and offset of gamma spike burst during a theta cycle, and provide an estimation of the total number of spikes per theta cycle both for excitable and oscillator regimes. PMID:23945442

  3. Signal/background classification in a cosmic ray space experiment by a modular neural system

    NASA Astrophysics Data System (ADS)

    Bellotti, Roberto; Castellano, Marcello; De Marzo, Carlo N.; Satalino, Giuseppe

    1995-04-01

    In the cosmic ray space experiments, the separation of the signal from background is a hard task. Due to the well-known critical conditions that characterize this class of experiments, some changes of the detector performances can be observed during the data taking. As a consequence, differences between the test and real data are found as systematic errors in the classification phase. In this paper, a modular classification system based on neural networks is proposed for the signal/background discrimination task in cosmic ray space experiments, without a priori knowledge of the discriminating feature distributions. The system is composed by two neural modules. The first one is a self organizing map (SOM) that both clusters the real data space in suitable classes of similarity and builds a prototype for each of them; a skilled inspection of the prototypes defines the signal and background. The second one, a multi layer perceptron (MLP) with a single hidden layer, adapts the classification model based on training/test data to the real experimental conditions. The MLP synaptic weights adaptive formation takes into account the labelled real data set as defined in the first system-phase. The modular neural system has been applied in the context of TRAMP-Si experiment, performed on the NASA Balloon-Borne Magnet Facility, for the positron/proton discrimination.

  4. Neural Oscillations Carry Speech Rhythm through to Comprehension

    PubMed Central

    Peelle, Jonathan E.; Davis, Matthew H.

    2012-01-01

    A key feature of speech is the quasi-regular rhythmic information contained in its slow amplitude modulations. In this article we review the information conveyed by speech rhythm, and the role of ongoing brain oscillations in listeners’ processing of this content. Our starting point is the fact that speech is inherently temporal, and that rhythmic information conveyed by the amplitude envelope contains important markers for place and manner of articulation, segmental information, and speech rate. Behavioral studies demonstrate that amplitude envelope information is relied upon by listeners and plays a key role in speech intelligibility. Extending behavioral findings, data from neuroimaging – particularly electroencephalography (EEG) and magnetoencephalography (MEG) – point to phase locking by ongoing cortical oscillations to low-frequency information (~4–8 Hz) in the speech envelope. This phase modulation effectively encodes a prediction of when important events (such as stressed syllables) are likely to occur, and acts to increase sensitivity to these relevant acoustic cues. We suggest a framework through which such neural entrainment to speech rhythm can explain effects of speech rate on word and segment perception (i.e., that the perception of phonemes and words in connected speech is influenced by preceding speech rate). Neuroanatomically, acoustic amplitude modulations are processed largely bilaterally in auditory cortex, with intelligible speech resulting in differential recruitment of left-hemisphere regions. Notable among these is lateral anterior temporal cortex, which we propose functions in a domain-general fashion to support ongoing memory and integration of meaningful input. Together, the reviewed evidence suggests that low-frequency oscillations in the acoustic speech signal form the foundation of a rhythmic hierarchy supporting spoken language, mirrored by phase-locked oscillations in the human brain. PMID:22973251

  5. Wave Generation in Unidirectional Chains of Idealized Neural Oscillators.

    PubMed

    Fernandez, Bastien; Mintchev, Stanislav M

    2016-12-01

    We investigate the dynamics of unidirectional semi-infinite chains of type-I oscillators that are periodically forced at their root node, as an archetype of wave generation in neural networks. In previous studies, numerical simulations based on uniform forcing have revealed that trajectories approach a traveling wave in the far-downstream, large time limit. While this phenomenon seems typical, it is hardly anticipated because the system does not exhibit any of the crucial properties employed in available proofs of existence of traveling waves in lattice dynamical systems. Here, we give a full mathematical proof of generation under uniform forcing in a simple piecewise affine setting for which the dynamics can be solved explicitly. In particular, our analysis proves existence, global stability, and robustness with respect to perturbations of the forcing, of families of waves with arbitrary period/wave number in some range, for every value of the parameters in the system. PMID:27059027

  6. Driving neural oscillations with correlated spatial input and topographic feedback

    NASA Astrophysics Data System (ADS)

    Hutt, Axel; Sutherland, Connie; Longtin, André

    2008-08-01

    We consider how oscillatory activity in networks of excitable systems depends on spatial correlations of random inputs and the spatial range of feedback coupling. Analysis of a neural field model with topographic delayed recurrent feedback reveals how oscillations in certain frequency bands, including the gamma band, are enhanced by increases in the input correlation length. Further, the enhancement is maximal when this length exceeds the feedback coupling range. Suppression of oscillatory power occurs concomitantly in other bands. These effects depend solely on the ratio of input and feedback length scales. The precise positions of these bands are determined by the synaptic constants and the delays. The results agree with numerical simulations of the model and of a network of stochastic spiking neurons, and are expected for any noise-driven excitable element networks.

  7. The predictive roles of neural oscillations in speech motor adaptability.

    PubMed

    Sengupta, Ranit; Nasir, Sazzad M

    2016-06-01

    The human speech system exhibits a remarkable flexibility by adapting to alterations in speaking environments. While it is believed that speech motor adaptation under altered sensory feedback involves rapid reorganization of speech motor networks, the mechanisms by which different brain regions communicate and coordinate their activity to mediate adaptation remain unknown, and explanations of outcome differences in adaption remain largely elusive. In this study, under the paradigm of altered auditory feedback with continuous EEG recordings, the differential roles of oscillatory neural processes in motor speech adaptability were investigated. The predictive capacities of different EEG frequency bands were assessed, and it was found that theta-, beta-, and gamma-band activities during speech planning and production contained significant and reliable information about motor speech adaptability. It was further observed that these bands do not work independently but interact with each other suggesting an underlying brain network operating across hierarchically organized frequency bands to support motor speech adaptation. These results provide novel insights into both learning and disorders of speech using time frequency analysis of neural oscillations. PMID:26936976

  8. Induced neural beta oscillations predict categorical speech perception abilities.

    PubMed

    Bidelman, Gavin M

    2015-02-01

    Neural oscillations have been linked to various perceptual and cognitive brain operations. Here, we examined the role of these induced brain responses in categorical speech perception (CP), a phenomenon in which similar features are mapped to discrete, common identities despite their equidistant/continuous physical spacing. We recorded neuroelectric activity while participants rapidly classified sounds along a vowel continuum (/u/ to /a/). Time-frequency analyses applied to the EEG revealed distinct temporal dynamics in induced (non-phase locked) oscillations; increased β (15-30Hz) coded prototypical vowel sounds carrying well-defined phonetic categories whereas increased γ (50-70Hz) accompanied ambiguous tokens near the categorical boundary. Notably, changes in β activity were strongly correlated with the slope of listeners' psychometric identification functions, a measure of the "steepness" of their categorical percept. Our findings demonstrate that in addition to previously observed evoked (phase-locked) correlates of CP, induced brain activity in the β-band codes the ambiguity and strength of categorical speech percepts. PMID:25540857

  9. Task induced modulation of neural oscillations in electrophysiological brain networks.

    PubMed

    Brookes, M J; Liddle, E B; Hale, J R; Woolrich, M W; Luckhoo, H; Liddle, P F; Morris, P G

    2012-12-01

    In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms. PMID:22906787

  10. Discovering Multimodal Behavior in Ms. Pac-Man through Evolution of Modular Neural Networks

    PubMed Central

    Schrum, Jacob; Miikkulainen, Risto

    2015-01-01

    Ms. Pac-Man is a challenging video game in which multiple modes of behavior are required: Ms. Pac-Man must escape ghosts when they are threats and catch them when they are edible, in addition to eating all pills in each level. Past approaches to learning behavior in Ms. Pac-Man have treated the game as a single task to be learned using monolithic policy representations. In contrast, this paper uses a framework called Modular Multi-objective NEAT (MM-NEAT) to evolve modular neural networks. Each module defines a separate behavior. The modules are used at different times according to a policy that can be human-designed (i.e. Multitask) or discovered automatically by evolution. The appropriate number of modules can be fixed or discovered using a genetic operator called Module Mutation. Several versions of Module Mutation are evaluated in this paper. Both fixed modular networks and Module Mutation networks outperform monolithic networks and Multitask networks. Interestingly, the best networks dedicate modules to critical behaviors (such as escaping when surrounded after luring ghosts near a power pill) that do not follow the customary division of the game into chasing edible and escaping threat ghosts. The results demonstrate that MM-NEAT can discover interesting and effective behavior for agents in challenging games. PMID:27030803

  11. It's All in the Rhythm: The Role of Cannabinoids in Neural Oscillations and Psychosis.

    PubMed

    Skosnik, Patrick D; Cortes-Briones, Jose A; Hajós, Mihály

    2016-04-01

    Evidence has accumulated over the past several decades suggesting that both exocannabinoids and endocannabinoids play a role in the pathophysiology of schizophrenia. The current article presents evidence suggesting that one of the mechanisms whereby cannabinoids induce psychosis is through the alteration in synchronized neural oscillations. Neural oscillations, particularly in the gamma (30-80 Hz) and theta (4-7 Hz) ranges, are disrupted in schizophrenia and are involved in various areas of perceptual and cognitive function. Regarding cannabinoids, preclinical evidence from slice and local field potential recordings has shown that central cannabinoid receptor (cannabinoid receptor type 1) agonists decrease the power of neural oscillations, particularly in the gamma and theta bands. Further, the administration of cannabinoids during critical stages of neural development has been shown to disrupt the brain's ability to generate synchronized neural oscillations in adulthood. In humans, studies examining the effects of chronic cannabis use (utilizing electroencephalography) have shown abnormalities in neural oscillations in a pattern similar to those observed in schizophrenia. Finally, recent studies in humans have also shown disruptions in neural oscillations after the acute administration of delta-9-tetrahydrocannabinol, the primary psychoactive constituent in cannabis. Taken together, these data suggest that both acute and chronic cannabinoids can disrupt the ability of the brain to generate synchronized oscillations at functionally relevant frequencies. Hence, this may represent one of the primary mechanisms whereby cannabinoids induce disruptions in attention, working memory, sensory-motor integration, and many other psychosis-related behavioral effects. PMID:26850792

  12. Category-Specificity Can Emerge from Bottom-Up Visual Characteristics: Evidence from a Modular Neural Network

    ERIC Educational Resources Information Center

    Gale, Tim M.; Laws, Keith R.

    2006-01-01

    The role of bottom-up visual processes in category-specific object recognition has been largely unexplored. We examined the role of low-level visual characteristics in category specific recognition using a modular neural network comprising both unsupervised and supervised components. One hundred standardised pictures from ten different categories…

  13. Identifying Robust and Sensitive Frequency Bands for Interrogating Neural Oscillations

    PubMed Central

    Shackman, Alexander J.; McMenamin, Brenton W.; Maxwell, Jeffrey S.; Greischar, Lawrence L.; Davidson, Richard J.

    2010-01-01

    Recent years have seen an explosion of interest in using neural oscillations to characterize the mechanisms supporting cognition and emotion. Oftentimes, oscillatory activity is indexed by mean power density in predefined frequency bands. Some investigators use broad bands originally defined by prominent surface features of the spectrum. Others rely on narrower bands originally defined by spectral factor analysis (SFA). Presently, the robustness and sensitivity of these competing band definitions remains unclear. Here, a Monte Carlo-based SFA strategy was used to decompose the tonic (“resting” or “spontaneous”) electroencephalogram (EEG) into five bands: delta (1–5Hz), alpha-low (6–9Hz), alpha-high (10–11Hz), beta (12–19Hz), and gamma (>21Hz). This pattern was consistent across SFA methods, artifact correction/rejection procedures, scalp regions, and samples. Subsequent analyses revealed that SFA failed to deliver enhanced sensitivity; narrow alpha sub-bands proved no more sensitive than the classical broadband to individual differences in temperament or mean differences in task-induced activation. Other analyses suggested that residual ocular and muscular artifact was the dominant source of activity during quiescence in the delta and gamma bands. This was observed following threshold-based artifact rejection or independent component analysis (ICA)-based artifact correction, indicating that such procedures do not necessarily confer adequate protection. Collectively, these findings highlight the limitations of several commonly used EEG procedures and underscore the necessity of routinely performing exploratory data analyses, particularly data visualization, prior to hypothesis testing. They also suggest the potential benefits of using techniques other than SFA for interrogating high-dimensional EEG datasets in the frequency or time-frequency (event-related spectral perturbation, event-related synchronization / desynchronization) domains. PMID

  14. Estimating one-dimensional models from frequency-domain electromagnetic data using modular neural networks

    SciTech Connect

    Poulton, M.M.; Birken, R.A.

    1998-03-01

    An artificial neural network interpretation system is being used to interpret data from a frequency-domain electromagnetic (EM) geophysical system in near real time. The interpretation system integrates 45 separate networks in a data visualization shell. The networks produce interpretations at three different transmitter-receiver (Tx-Rx) separations for half-space and layered-earth interpretations. Modular neural networks (MNN`s) were found to be the only paradigm that could successfully perform the layered-earth interpretations. An MNN with 16 inputs, five local experts, each with seven hidden processing elements, and three outputs was trained on 4795 patterns for 200 epochs. For two-layer models with a resistivity contrast greater than 2:1, resistivity estimates had greater than 96% accuracy for the first-layer resistivity, greater than 98% for the second-layer resistivity, and greater than 96% for the thickness of the first layer. If the contrast is less than 2:1, the resistivity accuracies are unaffected but thickness estimates for layers less than 2 m are unreliable. A Tx-Rx separation of 16 m with maximum depth of penetration of 8 m was assumed for the example cited.

  15. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques

    NASA Astrophysics Data System (ADS)

    Wu, C. L.; Chau, K. W.; Fan, C.

    2010-07-01

    SummaryThis study is an attempt to seek a relatively optimal data-driven model for rainfall forecasting from three aspects: model inputs, modeling methods, and data-preprocessing techniques. Four rain data records from different regions, namely two monthly and two daily series, are examined. A comparison of seven input techniques, either linear or nonlinear, indicates that linear correlation analysis (LCA) is capable of identifying model inputs reasonably. A proposed model, modular artificial neural network (MANN), is compared with three benchmark models, viz. artificial neural network (ANN), K-nearest-neighbors (K-NN), and linear regression (LR). Prediction is performed in the context of two modes including normal mode (viz., without data preprocessing) and data preprocessing mode. Results from the normal mode indicate that MANN performs the best among all four models, but the advantage of MANN over ANN is not significant in monthly rainfall series forecasting. Under the data preprocessing mode, each of LR, K-NN and ANN is respectively coupled with three data-preprocessing techniques including moving average (MA), principal component analysis (PCA), and singular spectrum analysis (SSA). Results indicate that the improvement of model performance generated by SSA is considerable whereas those of MA or PCA are slight. Moreover, when MANN is coupled with SSA, results show that advantages of MANN over other models are quite noticeable, particularly for daily rainfall forecasting. Therefore, the proposed optimal rainfall forecasting model can be derived from MANN coupled with SSA.

  16. Steady-state BOLD Response to Higher-order Cognition Modulates Low-Frequency Neural Oscillations.

    PubMed

    Wang, Yi-Feng; Dai, Gang-Shu; Liu, Feng; Long, Zhi-Liang; Yan, Jin H; Chen, Hua-Fu

    2015-12-01

    Steady-state responses (SSRs) reflect the synchronous neural oscillations evoked by noninvasive and consistently repeated stimuli at the fundamental or harmonic frequencies. The steady-state evoked potentials (SSEPs; the representative form of the SSRs) have been widely used in the cognitive and clinical neurosciences and brain-computer interface research. However, the steady-state evoked potentials have limitations in examining high-frequency neural oscillations and basic cognition. In addition, synchronous neural oscillations in the low frequency range (<1 Hz) and in higher-order cognition have received a little attention. Therefore, we examined the SSRs in the low frequency range using a new index, the steady-state BOLD responses (SSBRs) evoked by semantic stimuli. Our results revealed that the significant SSBRs were induced at the fundamental frequency of stimuli and the first harmonic in task-related regions, suggesting the enhanced variability of neural oscillations entrained by exogenous stimuli. The SSBRs were independent of neurovascular coupling and characterized by sensorimotor bias, an indication of regional-dependent neuroplasticity. Furthermore, the amplitude of SSBRs may predict behavioral performance and show the psychophysiological relevance. Our findings provide valuable insights into the understanding of the SSRs evoked by higher-order cognition and how the SSRs modulate low-frequency neural oscillations. PMID:26284992

  17. Neural oscillations during non-rapid eye movement sleep as biomarkers of circuit dysfunction in schizophrenia.

    PubMed

    Gardner, Richard J; Kersanté, Flavie; Jones, Matthew W; Bartsch, Ullrich

    2014-04-01

    The neurophysiology of non-rapid eye movement sleep is characterized by the occurrence of neural network oscillations with distinct origins and frequencies, which act in concert to support sleep-dependent information processing. Thalamocortical circuits generate slow (0.25-4 Hz) oscillations reflecting synchronized temporal windows of cortical activity, whereas concurrent waxing and waning spindle oscillations (8-15 Hz) act to facilitate cortical plasticity. Meanwhile, fast (140-200 Hz) and brief (< 200 ms) hippocampal ripple oscillations are associated with the reactivation of neural assemblies recruited during prior wakefulness. The extent of the forebrain areas engaged by these oscillations, and the variety of cellular and synaptic mechanisms involved, make them sensitive assays of distributed network function. Each of these three oscillations makes crucial contributions to the offline memory consolidation processes supported by non-rapid eye movement sleep. Slow, spindle and ripple oscillations are therefore potential surrogates of cognitive function and may be used as diagnostic measures in a range of brain diseases. We review the evidence for disrupted slow, spindle and ripple oscillations in schizophrenia, linking pathophysiological mechanisms to the functional impact of these neurophysiological changes and drawing links with the cognitive symptoms that accompany this condition. Finally, we discuss potential therapies that may normalize the coordinated activity of these three oscillations in order to restore healthy cognitive function. PMID:24712989

  18. New Approach for Feature Selection of Thermomechanically Processed HSLA Steel using Pruned-Modular Neural Networks

    NASA Astrophysics Data System (ADS)

    Das, Prasun; Ghosh, Avishek; Bhattacharyay, Bidyut Kr.; Datta, Shubhabrata

    2012-10-01

    A new approach has been used in modeling of strength and ductility of high strength low alloy (HSLA) steel, where a comparative study among fully-connected neural network, modular network and pruned-module architecture has been performed. The important features for modeling such a complex steel processing system have been worked out. Performance evaluation and feature selection in the soft computing domain are the two important activities for modeling input-output relationship. The need arises specially when the system is complex in terms of type of network architecture, number of features involved, number of inter-connections, application domain etc. In this paper, an attempt is made to develop a new metric of performance evaluation, using mean squared error and the total number of inter-connections of a network to improve the understanding about a complex system of thermomechanically controlled processed HSLA steels. The methodology for feature selection is developed next based on the functional form of output in terms of input variables where gradient of the function can be computed in the network.

  19. Simultaneous Neural Control of Simple Reaching and Grasping with the Modular Prosthetic Limb using Intracranial EEG

    PubMed Central

    Fifer, Matthew S.; Hotson, Guy; Wester, Brock A.; McMullen, David; Wang, Yujing; Johannes, Matthew S.; Katyal, Kapil D.; Helder, John B.; Para, Matthew P.; Vogelstein, R. Jacob; Anderson, William S.; Thakor, Nitish V.; Crone, Nathan E.

    2014-01-01

    Intracranial electroencephalographic (iEEG) signals from two human subjects were used to achieve simultaneous neural control of reaching and grasping movements with the Johns Hopkins University Applied Physics Lab (JHU/APL) Modular Prosthetic Limb (MPL), a dexterous robotic prosthetic arm. We performed functional mapping of high gamma activity while the subject made reaching and grasping movements to identify task-selective electrodes. Independent, online control of reaching and grasping was then achieved using high gamma activity from a small subset of electrodes with a model trained on short blocks of reaching and grasping with no further adaptation. Classification accuracy did not decline (p<0.05, one-way ANOVA) over three blocks of testing in either subject. Mean classification accuracy during independently executed overt reach and grasp movements for (Subject 1, Subject 2) were (0.85, 0.81) and (0.80, 0.96) respectively, and during simultaneous execution they were (0.83, 0.88) and (0.58, 0.88) respectively. Our models leveraged knowledge of the subject's individual functional neuroanatomy for reaching and grasping movements, allowing rapid acquisition of control in a time-sensitive clinical setting. We demonstrate the potential feasibility of verifying functionally meaningful iEEG-based control of the MPL prior to chronic implantation, during which additional capabilities of the MPL might be exploited with further training. PMID:24235276

  20. Noise-induced Synchronized Oscillations in an Inhibitory Neural Network

    NASA Astrophysics Data System (ADS)

    José, Jorge V.; Tiesinga, Paul

    2003-05-01

    We study the properties of synchronized states in a neuronal network model that represents circuitry of the thalamus or the locust antennal lobe. We find noise-driven and noise-sustained synchronized neural cluster states. We determine the theoretical maximum information output of these networks in terms of the Shannon entropy. Synchronized states are usually characterized by more regular spike trains and reduced information output. We find, however, that even a single neuron can still transmit a significant amount of information. The information content is contained in the changing participation of neurons in neural assemblies. Similar neural assemblies were recently observed in the locust olfactory system.

  1. Periodic oscillation for a Hopfield neural networks with neutral delays

    NASA Astrophysics Data System (ADS)

    Gui, Zhanji; Ge, Weigao; Yang, Xiao-Song

    2007-04-01

    In this Letter, a Hopfield neural networks model with neutral delay are investigated by means of an abstract continuous theorem of k-set contractive operator and some analysis technique. Sufficient conditions are obtained for the existence of periodic solutions.

  2. Towards a proper estimation of phase-amplitude coupling in neural oscillations

    PubMed Central

    Dvorak, Dino; Fenton, André A.

    2014-01-01

    Background The phase-amplitude coupling (PAC) between distinct neural oscillations is critical to brain functions that include cross-scale organization, selection of attention, routing the flow of information through neural circuits, memory processing and information coding. Several methods for PAC estimation have been proposed but the limitations of PAC estimation as well as the assumptions about the data for accurate PAC estimation are unclear. New Method We define boundary conditions for standard PAC algorithms and propose “oscillation-triggered coupling” (OTC), a parameter-free, data-driven algorithm for unbiased estimation of PAC. OTC establishes a unified framework that treats individual oscillations as discrete events for estimating PAC from a set of oscillations and for characterizing events from time windows as short as a single modulating oscillation. Results For accurate PAC estimation, standard PAC algorithms require amplitude filters with a bandwidth at least twice the modulatory frequency. The phase filters must be moderately narrow-band, especially when the modulatory rhythm is non-sinusoidal. The minimally appropriate analysis window is ~10 seconds. We then demonstrate that OTC can characterize PAC by treating neural oscillations as discrete events rather than continuous phase and amplitude time series. Comparison with existing methods These findings show that in addition to providing the same information about PAC as the standard approach, OTC facilitates characterization of single oscillations and their sequences, in addition to explaining the role of individual oscillations in generating PAC patterns. Conclusions OTC allows PAC analysis at the level of individual oscillations and therefore enables investigation of PAC at the time scales of cognitive phenomena. PMID:24447842

  3. Oscillation, Conduction Delays, and Learning Cooperate to Establish Neural Competition in Recurrent Networks

    PubMed Central

    Kato, Hideyuki; Ikeguchi, Tohru

    2016-01-01

    Specific memory might be stored in a subnetwork consisting of a small population of neurons. To select neurons involved in memory formation, neural competition might be essential. In this paper, we show that excitable neurons are competitive and organize into two assemblies in a recurrent network with spike timing-dependent synaptic plasticity (STDP) and axonal conduction delays. Neural competition is established by the cooperation of spontaneously induced neural oscillation, axonal conduction delays, and STDP. We also suggest that the competition mechanism in this paper is one of the basic functions required to organize memory-storing subnetworks into fine-scale cortical networks. PMID:26840529

  4. Synchronized Rhythmic Oscillation in a Noisy Neural Network

    NASA Astrophysics Data System (ADS)

    Yu, Yuguo; Liu, Feng; Wang, Wei

    2003-12-01

    The occurrence of synchronized oscillation and its critical behavior in a globally coupled stochastic Hodgkin-Huxley (HH) neuronal network are studied in this paper. It is found that there is a critical curve for the coupling strength versus noise intensity, which shows a V-shaped structure and divides the network behavior into an asynchronous firing state and a synchronous one. Analysis of the scaling behavior near the bifurcation point reveals that this transition is analogous to a second-order phase transition. The frequency of synchronized oscillations is within the range of 40-80 Hz, and its physical origin is explored by studying single HH neuron’s impedance. The intrinsic property of single neuron may account for the generation and the frequency characteristics of synchronized rhythmic oscillations.

  5. Patterns of theta oscillation reflect the neural basis of individual differences in epistemic motivation.

    PubMed

    Mussel, Patrick; Ulrich, Natalie; Allen, John J B; Osinsky, Roman; Hewig, Johannes

    2016-01-01

    Theta oscillations in the EEG have been shown to reflect ongoing cognitive processes related to mental effort. Here, we show that the pattern of theta oscillation in response to varying cognitive demands reflects stable individual differences in the personality trait epistemic motivation: Individuals with high levels of epistemic motivation recruit relatively more cognitive resources in response to situations possessing high, compared to low, cognitive demand; individuals with low levels do not show such a specific response. Our results provide direct evidence for the theory of the construct need for cognition and add to our understanding of the neural processes underlying theta oscillations. More generally, we provide an explanation how individual differences in personality traits might be represented on a neural level. PMID:27380648

  6. Patterns of theta oscillation reflect the neural basis of individual differences in epistemic motivation

    PubMed Central

    Mussel, Patrick; Ulrich, Natalie; Allen, John J. B.; Osinsky, Roman; Hewig, Johannes

    2016-01-01

    Theta oscillations in the EEG have been shown to reflect ongoing cognitive processes related to mental effort. Here, we show that the pattern of theta oscillation in response to varying cognitive demands reflects stable individual differences in the personality trait epistemic motivation: Individuals with high levels of epistemic motivation recruit relatively more cognitive resources in response to situations possessing high, compared to low, cognitive demand; individuals with low levels do not show such a specific response. Our results provide direct evidence for the theory of the construct need for cognition and add to our understanding of the neural processes underlying theta oscillations. More generally, we provide an explanation how individual differences in personality traits might be represented on a neural level. PMID:27380648

  7. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication

    PubMed Central

    Symons, Ashley E.; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A.

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  8. The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

    PubMed

    Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

    2016-01-01

    Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

  9. HIRREM™: a noninvasive, allostatic methodology for relaxation and auto-calibration of neural oscillations.

    PubMed

    Gerdes, Lee; Gerdes, Peter; Lee, Sung W; H Tegeler, Charles

    2013-03-01

    Disturbances of neural oscillation patterns have been reported with many disease states. We introduce methodology for HIRREM™ (high-resolution, relational, resonance-based electroencephalic mirroring), also known as Brainwave Optimization™, a noninvasive technology to facilitate relaxation and auto-calibration of neural oscillations. HIRREM is a precision-guided technology for allostatic therapeutics, intended to help the brain calibrate its own functional set points to optimize fitness. HIRREM technology collects electroencephalic data through two-channel recordings and delivers a series of audible musical tones in near real time. Choices of tone pitch and timing are made by mathematical algorithms, principally informed by the dominant frequency in successive instants of time, to permit resonance between neural oscillatory frequencies and the musical tones. Relaxation of neural oscillations through HIRREM appears to permit auto-calibration toward greater hemispheric symmetry and more optimized proportionation of regional spectral power. To illustrate an application of HIRREM, we present data from a randomized clinical trial of HIRREM as an intervention for insomnia (n = 19). On average, there was reduction of right-dominant temporal lobe high-frequency (23-36 Hz) EEG asymmetry over the course of eight successive HIRREM sessions. There was a trend for correlation between reduction of right temporal lobe dominance and magnitude of insomnia symptom reduction. Disturbances of neural oscillation have implications for both neuropsychiatric health and downstream peripheral (somatic) physiology. The possibility of noninvasive optimization for neural oscillatory set points through HIRREM suggests potentially multitudinous roles for this technology. Research is currently ongoing to further explore its potential applications and mechanisms of action. PMID:23532171

  10. HIRREM™: a noninvasive, allostatic methodology for relaxation and auto-calibration of neural oscillations

    PubMed Central

    Gerdes, Lee; Gerdes, Peter; Lee, Sung W; H Tegeler, Charles

    2013-01-01

    Disturbances of neural oscillation patterns have been reported with many disease states. We introduce methodology for HIRREM™ (high-resolution, relational, resonance-based electroencephalic mirroring), also known as Brainwave Optimization™, a noninvasive technology to facilitate relaxation and auto-calibration of neural oscillations. HIRREM is a precision-guided technology for allostatic therapeutics, intended to help the brain calibrate its own functional set points to optimize fitness. HIRREM technology collects electroencephalic data through two-channel recordings and delivers a series of audible musical tones in near real time. Choices of tone pitch and timing are made by mathematical algorithms, principally informed by the dominant frequency in successive instants of time, to permit resonance between neural oscillatory frequencies and the musical tones. Relaxation of neural oscillations through HIRREM appears to permit auto-calibration toward greater hemispheric symmetry and more optimized proportionation of regional spectral power. To illustrate an application of HIRREM, we present data from a randomized clinical trial of HIRREM as an intervention for insomnia (n = 19). On average, there was reduction of right-dominant temporal lobe high-frequency (23–36 Hz) EEG asymmetry over the course of eight successive HIRREM sessions. There was a trend for correlation between reduction of right temporal lobe dominance and magnitude of insomnia symptom reduction. Disturbances of neural oscillation have implications for both neuropsychiatric health and downstream peripheral (somatic) physiology. The possibility of noninvasive optimization for neural oscillatory set points through HIRREM suggests potentially multitudinous roles for this technology. Research is currently ongoing to further explore its potential applications and mechanisms of action. PMID:23532171

  11. Managing heterogeneity in the study of neural oscillator dynamics

    PubMed Central

    2012-01-01

    We consider a coupled, heterogeneous population of relaxation oscillators used to model rhythmic oscillations in the pre-Bötzinger complex. By choosing specific values of the parameter used to describe the heterogeneity, sampled from the probability distribution of the values of that parameter, we show how the effects of heterogeneity can be studied in a computationally efficient manner. When more than one parameter is heterogeneous, full or sparse tensor product grids are used to select appropriate parameter values. The method allows us to effectively reduce the dimensionality of the model, and it provides a means for systematically investigating the effects of heterogeneity in coupled systems, linking ideas from uncertainty quantification to those for the study of network dynamics. PMID:22658163

  12. Singular Hopf bifurcations and mixed-mode oscillations in a two-cell inhibitory neural network

    NASA Astrophysics Data System (ADS)

    Curtu, Rodica

    2010-05-01

    Recent studies of a firing rate model for neural competition as observed in binocular rivalry and central pattern generators [R. Curtu, A. Shpiro, N. Rubin, J. Rinzel, Mechanisms for frequency control in neuronal competition models, SIAM J. Appl. Dyn. Syst. 7 (2) (2008) 609-649] showed that the variation of the stimulus strength parameter can lead to rich and interesting dynamics. Several types of behavior were identified such as: fusion, equivalent to a steady state of identical activity levels for both neural units; oscillations due to either an escape or a release mechanism; and a winner-take-all state of bistability. The model consists of two neural populations interacting through reciprocal inhibition, each endowed with a slow negative-feedback process in the form of spike frequency adaptation. In this paper we report the occurrence of another complex oscillatory pattern, the mixed-mode oscillations (MMOs). They exist in the model at the transition between the relaxation oscillator dynamical regime and the winner-take-all regime. The system distinguishes itself from other neuronal models where MMOs were found by the following interesting feature: there is no autocatalysis involved (as in the examples of voltage-gated persistent inward currents and/or intrapopulation recurrent excitation) and therefore the two cells in the network are not intrinsic oscillators; the oscillations are instead a combined result of the mutual inhibition and the adaptation. We prove that the MMOs are due to a singular Hopf bifurcation point situated in close distance to the transition point to the winner-take-all case. We also show that in the vicinity of the singular Hopf other types of bifurcations exist and we construct numerically the corresponding diagrams.

  13. Attractors and Spectral Characteristics of Neural Structures Based on the Model of the Quantum Harmonic Oscillator

    SciTech Connect

    Rigatos, Gerasimos G.

    2007-09-06

    Neural computation based on principles of quantum mechanics can provide improved models of memory processes and brain functioning and is of importance for the realization of quantum computing machines. To this end, this paper studies neural structures with weights that follow the model of the quantum harmonic oscillator. These weights correspond to diffusing particles, which interact to each other as the theory of Brownian motion predicts. The learning of the stochastic weights (convergence of the diffusing particles to an equilibrium) is analyzed. In the case of associative memories the proposed neural model results in an exponential increase of the number of attractors. Spectral analysis shows that the stochastic weights satisfy an equation which is analogous to the principle of uncertainty.

  14. Neural networks with dynamical synapses: From mixed-mode oscillations and spindles to chaos

    NASA Astrophysics Data System (ADS)

    Lee, K.; Goltsev, A. V.; Lopes, M. A.; Mendes, J. F. F.

    2013-01-01

    Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behavior. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.

  15. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems

    NASA Astrophysics Data System (ADS)

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K. Y. Michael; Zhou, Changsong

    2016-01-01

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli.

  16. Stochastic Oscillation in Self-Organized Critical States of Small Systems: Sensitive Resting State in Neural Systems.

    PubMed

    Wang, Sheng-Jun; Ouyang, Guang; Guang, Jing; Zhang, Mingsha; Wong, K Y Michael; Zhou, Changsong

    2016-01-01

    Self-organized critical states (SOCs) and stochastic oscillations (SOs) are simultaneously observed in neural systems, which appears to be theoretically contradictory since SOCs are characterized by scale-free avalanche sizes but oscillations indicate typical scales. Here, we show that SOs can emerge in SOCs of small size systems due to temporal correlation between large avalanches at the finite-size cutoff, resulting from the accumulation-release process in SOCs. In contrast, the critical branching process without accumulation-release dynamics cannot exhibit oscillations. The reconciliation of SOCs and SOs is demonstrated both in the sandpile model and robustly in biologically plausible neuronal networks. The oscillations can be suppressed if external inputs eliminate the prominent slow accumulation process, providing a potential explanation of the widely studied Berger effect or event-related desynchronization in neural response. The features of neural oscillations and suppression are confirmed during task processing in monkey eye-movement experiments. Our results suggest that finite-size, columnar neural circuits may play an important role in generating neural oscillations around the critical states, potentially enabling functional advantages of both SOCs and oscillations for sensitive response to transient stimuli. PMID:26799044

  17. A Modular Approach to Model Oscillating Control Surfaces Using Navier Stokes Equations

    NASA Technical Reports Server (NTRS)

    Guruswamy, Guru P.; Lee, Henry

    2014-01-01

    The use of active controls for rotorcraft is becoming more important for modern aerospace configurations. Efforts to reduce the vibrations of helicopter blades with use of active-controls are in progress. Modeling oscillating control surfaces using the linear aerodynamics theory is well established. However, higher-fidelity methods are needed to account for nonlinear effects, such as those that occur in transonic flow. The aeroelastic responses of a wing with an oscillating control surface, computed using the transonic small perturbation (TSP) theory, have been shown to cause important transonic flow effects such as a reversal of control surface effectiveness that occurs as the shock wave crosses the hinge line. In order to account for flow complexities such as blade-vortex interactions of rotor blades higher-fidelity methods based on the Navier-Stokes equations are used. Reference 6 presents a procedure that uses the Navier-Stokes equations with moving-sheared grids and demonstrates up to 8 degrees of control-surface amplitude, using a single grid. Later, this procedure was extended to accommodate larger amplitudes, based on sliding grid zones. The sheared grid method implemented in EulerlNavier-Stokes-based aeroelastic code ENS AERO was successfully applied to active control design by industry. Recently there are several papers that present results for oscillating control surface using Reynolds Averaged Navier-Stokes (RANS) equations. References 9 and 10 report 2-D cases by filling gaps with overset grids. Reference 9 compares integrated forces with the experiment at low oscillating frequencies whereas Ref. 10 reports parametric studies but with no validation. Reference II reports results for a 3D case by modeling the gap region with a deformed grid and compares force results with the experiment only at the mid-span of flap. In Ref. II grid is deformed to match the control surface deflections at the section where the measurements are made. However, there is no

  18. Multidimensional analysis of the abnormal neural oscillations associated with lexical processing in schizophrenia.

    PubMed

    Xu, Tingting; Stephane, Massoud; Parhi, Keshab K

    2013-04-01

    The neural mechanisms of language abnormalities, the core symptoms in schizophrenia, remain unclear. In this study, a new experimental paradigm, combining magnetoencephalography (MEG) techniques and machine intelligence methodologies, was designed to gain knowledge about the frequency, brain location, and time of occurrence of the neural oscillations that are associated with lexical processing in schizophrenia. The 248-channel MEG recordings were obtained from 12 patients with schizophrenia and 10 healthy controls, during a lexical processing task, where the patients discriminated correct from incorrect lexical stimuli that were visually presented. Event-related desynchronization/synchronization (ERD/ERS) was computed along the frequency, time, and space dimensions combined, that resulted in a large spectral-spatial-temporal ERD/ERS feature set. Machine intelligence techniques were then applied to select a small subset of oscillation patterns that are abnormal in patients with schizophrenia, according to their discriminating power in patient and control classification. Patients with schizophrenia showed abnormal ERD/ERS patterns during both lexical encoding and post-encoding periods. The top-ranked features were located at the occipital and left frontal-temporal areas, and covered a wide frequency range, including δ (1-4 Hz), α (8-12 Hz), β (12-32 Hz), and γ (32-48 Hz) bands. These top features could discriminate the patient group from the control group with 90.91% high accuracy, which demonstrates significant brain oscillation abnormalities in patients with schizophrenia at the specific frequency, time, and brain location indicated by these top features. As neural oscillation abnormality may be due to the mechanisms of the disease, the spectral, spatial, and temporal content of the discriminating features can offer useful information for helping understand the physiological basis of the language disorder in schizophrenia, as well as the pathology of the

  19. Experimental evaluation of a neural-oscillator-driven active mass damper system

    NASA Astrophysics Data System (ADS)

    Iba, Daisuke; Hongu, Junichi

    2014-03-01

    This paper proposes a new active dynamic absorber control system for high-rise buildings using a neural oscillator and a map, which estimates the amplitude level of the oscillator, and shows some experimental results by using an apparatus, which realizes the proposed control algorithm. The proposed system decides the travel distance and direction of the auxiliary mass of the dynamic absorber using the output of oscillator, which is the filtering result of structure acceleration responses by the property of the oscillator, and Amplitude-Phase map (AP-map) for estimation of the structural response in specific frequency between synchronization region, and then, transfer the auxiliary mass to the predetermined location by using a position controller. In addition, the developed active dynamic absorber system is mounted on the top of the experimental single degree of freedom structure, which represents high-rise buildings, and consists of the auxiliary mass, a DC motor, a ball screw, a microcomputer, a laser displacement sensor, and an acceleration sensor. The proposed AP-map and the algorithm to determine the travel direction of the mass using the oscillator output are embedded in the microcomputer. This paper starts by illuminating the relation among subsystems of the proposed system with reference to a block diagram, and then, shows experimental responses of the whole system excited by earthquakes to confirm the validity of the proposed system.

  20. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks

    PubMed Central

    Ertosun, Mehmet Günhan; Rubin, Daniel L.

    2015-01-01

    Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

  1. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.

    PubMed

    Ertosun, Mehmet Günhan; Rubin, Daniel L

    2015-01-01

    Brain glioma is the most common primary malignant brain tumors in adults with different pathologic subtypes: Lower Grade Glioma (LGG) Grade II, Lower Grade Glioma (LGG) Grade III, and Glioblastoma Multiforme (GBM) Grade IV. The survival and treatment options are highly dependent of this glioma grade. We propose a deep learning-based, modular classification pipeline for automated grading of gliomas using digital pathology images. Whole tissue digitized images of pathology slides obtained from The Cancer Genome Atlas (TCGA) were used to train our deep learning modules. Our modular pipeline provides diagnostic quality statistics, such as precision, sensitivity and specificity, of the individual deep learning modules, and (1) facilitates training given the limited data in this domain, (2) enables exploration of different deep learning structures for each module, (3) leads to developing less complex modules that are simpler to analyze, and (4) provides flexibility, permitting use of single modules within the framework or use of other modeling or machine learning applications, such as probabilistic graphical models or support vector machines. Our modular approach helps us meet the requirements of minimum accuracy levels that are demanded by the context of different decision points within a multi-class classification scheme. Convolutional Neural Networks are trained for each module for each sub-task with more than 90% classification accuracies on validation data set, and achieved classification accuracy of 96% for the task of GBM vs LGG classification, 71% for further identifying the grade of LGG into Grade II or Grade III on independent data set coming from new patients from the multi-institutional repository. PMID:26958289

  2. A Modular Neural Network Scheme Applied to Fault Diagnosis in Electric Power Systems

    PubMed Central

    Flores, Agustín; Morant, Francisco

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  3. A modular neural network scheme applied to fault diagnosis in electric power systems.

    PubMed

    Flores, Agustín; Quiles, Eduardo; García, Emilio; Morant, Francisco; Correcher, Antonio

    2014-01-01

    This work proposes a new method for fault diagnosis in electric power systems based on neural modules. With this method the diagnosis is performed by assigning a neural module for each type of component comprising the electric power system, whether it is a transmission line, bus or transformer. The neural modules for buses and transformers comprise two diagnostic levels which take into consideration the logic states of switches and relays, both internal and back-up, with the exception of the neural module for transmission lines which also has a third diagnostic level which takes into account the oscillograms of fault voltages and currents as well as the frequency spectrums of these oscillograms, in order to verify if the transmission line had in fact been subjected to a fault. One important advantage of the diagnostic system proposed is that its implementation does not require the use of a network configurator for the system; it does not depend on the size of the power network nor does it require retraining of the neural modules if the power network increases in size, making its application possible to only one component, a specific area, or the whole context of the power system. PMID:25610897

  4. Stability switches and multistability coexistence in a delay-coupled neural oscillators system.

    PubMed

    Song, Zigen; Xu, Jian

    2012-11-21

    In this paper, we present a neural network system composed of two delay-coupled neural oscillators, where each of these can be regarded as the dynamical system describing the average activity of neural population. Analyzing the corresponding characteristic equation, the local stability of rest state is studied. The system exhibits the switch phenomenon between the rest state and periodic activity. Furthermore, the Hopf bifurcation is analyzed and the bifurcation curve is given in the parameters plane. The stability of the bifurcating periodic solutions and direction of the Hopf bifurcation are exhibited. Regarding time delay and coupled weight as the bifurcation parameters, the Fold-Hopf bifurcation is investigated in detail in terms of the central manifold reduction and normal form method. The neural system demonstrates the coexistence of the rest states and periodic activities in the different parameter regions. Employing the normal form of the original system, the coexistence regions are illustrated approximately near the Fold-Hopf singularity point. Finally, numerical simulations are performed to display more complex dynamics. The results illustrate that system may exhibit the rich coexistence of the different neuro-computational properties, such as the rest states, periodic activities, and quasi-periodic behavior. In particular, some periodic activities can evolve into the bursting-type behaviors with the varying time delay. It implies that the coexistence of the quasi-periodic activity and bursting-type behavior can be obtained if the suitable value of system parameter is chosen. PMID:22921877

  5. Disrupted gamma-band neural oscillations during coherent motion perception in heavy cannabis users.

    PubMed

    Skosnik, Patrick D; Krishnan, Giri P; D'Souza, Deepak C; Hetrick, William P; O'Donnell, Brian F

    2014-12-01

    Previous work in animals and humans has shown that exogenous cannabinoids disrupt time-locked, evoked gamma oscillations (30-80 Hz). However, no studies to date have examined the effect of cannabis on non-time-locked, induced gamma oscillations during more complex Gestalt perception. The current study therefore utilized electroencephalography (EEG) to examine gamma oscillations during coherent motion perception in heavy cannabis users and controls. Chronic cannabis users (n = 24; 12 h abstinence before study; positive 11-nor-9-carboxy-delta-9-tetrahydrocannabinol urine levels) and cannabis-naive controls (n = 23) were evaluated. Stimuli consisted of random dot kinetograms (RDKs) that subjects passively viewed during three different conditions: coherent motion, incoherent motion, and static. Time × frequency analysis on EEG data was performed using Fourier-based mean trial power (MTP). Transient event-related potentials (ERPs) to stimulus onset (visual N100 and P200 components) were also evaluated. The results showed that the coherent motion condition produced a robust increase in neural activity in the gamma range (induced power from 40 to 59 Hz) as compared with the incoherent motion and static conditions. As predicted, the cannabis group showed significant reductions in induced gamma power in the coherent condition relative to healthy controls. No differences were observed between the groups in the N100 or P200 components, indicating intact primary sensory processing. Finally, cannabis users showed a trend toward increased scores on the Chapman Perceptual Aberration Scale (PAS) that was positively correlated with total years of active cannabis use. These data suggest that cannabis use may interfere with the generation of induced gamma-band neural oscillations that could in part mediate the perceptual-altering effects of exogenous cannabinoids. PMID:24990428

  6. Disrupted Gamma-Band Neural Oscillations During Coherent Motion Perception in Heavy Cannabis Users

    PubMed Central

    Skosnik, Patrick D; Krishnan, Giri P; D'Souza, Deepak C; Hetrick, William P; O'Donnell, Brian F

    2014-01-01

    Previous work in animals and humans has shown that exogenous cannabinoids disrupt time-locked, evoked gamma oscillations (30–80 Hz). However, no studies to date have examined the effect of cannabis on non-time-locked, induced gamma oscillations during more complex Gestalt perception. The current study therefore utilized electroencephalography (EEG) to examine gamma oscillations during coherent motion perception in heavy cannabis users and controls. Chronic cannabis users (n=24; 12 h abstinence before study; positive 11-nor-9-carboxy-delta-9-tetrahydrocannabinol urine levels) and cannabis-naive controls (n=23) were evaluated. Stimuli consisted of random dot kinetograms (RDKs) that subjects passively viewed during three different conditions: coherent motion, incoherent motion, and static. Time × frequency analysis on EEG data was performed using Fourier-based mean trial power (MTP). Transient event-related potentials (ERPs) to stimulus onset (visual N100 and P200 components) were also evaluated. The results showed that the coherent motion condition produced a robust increase in neural activity in the gamma range (induced power from 40 to 59 Hz) as compared with the incoherent motion and static conditions. As predicted, the cannabis group showed significant reductions in induced gamma power in the coherent condition relative to healthy controls. No differences were observed between the groups in the N100 or P200 components, indicating intact primary sensory processing. Finally, cannabis users showed a trend toward increased scores on the Chapman Perceptual Aberration Scale (PAS) that was positively correlated with total years of active cannabis use. These data suggest that cannabis use may interfere with the generation of induced gamma-band neural oscillations that could in part mediate the perceptual-altering effects of exogenous cannabinoids. PMID:24990428

  7. Robust synchronization of coupled neural oscillators using the derivative-free nonlinear Kalman Filter.

    PubMed

    Rigatos, Gerasimos

    2014-12-01

    A synchronizing control scheme for coupled neural oscillators of the FitzHugh-Nagumo type is proposed. Using differential flatness theory the dynamical model of two coupled neural oscillators is transformed into an equivalent model in the linear canonical (Brunovsky) form. A similar linearized description is succeeded using differential geometry methods and the computation of Lie derivatives. For such a model it becomes possible to design a state feedback controller that assures the synchronization of the membrane's voltage variations for the two neurons. To compensate for disturbances that affect the neurons' model as well as for parametric uncertainties and variations a disturbance observer is designed based on Kalman Filtering. This consists of implementation of the standard Kalman Filter recursion on the linearized equivalent model of the coupled neurons and computation of state and disturbance estimates using the diffeomorphism (relations about state variables transformation) provided by differential flatness theory. After estimating the disturbance terms in the neurons' model their compensation becomes possible. The performance of the synchronization control loop is tested through simulation experiments. PMID:26396646

  8. Speaker's hand gestures modulate speech perception through phase resetting of ongoing neural oscillations.

    PubMed

    Biau, Emmanuel; Torralba, Mireia; Fuentemilla, Lluis; de Diego Balaguer, Ruth; Soto-Faraco, Salvador

    2015-07-01

    Speakers often accompany speech with spontaneous beat gestures in natural spoken communication. These gestures are usually aligned with lexical stress and can modulate the saliency of their affiliate words. Here we addressed the consequences of beat gestures on the neural correlates of speech perception. Previous studies have highlighted the role played by theta oscillations in temporal prediction of speech. We hypothesized that the sight of beat gestures may influence ongoing low-frequency neural oscillations around the onset of the corresponding words. Electroencephalographic (EEG) recordings were acquired while participants watched a continuous, naturally recorded discourse. The phase-locking value (PLV) at word onset was calculated from the EEG from pairs of identical words that had been pronounced with and without a concurrent beat gesture in the discourse. We observed an increase in PLV in the 5-6 Hz theta range as well as a desynchronization in the 8-10 Hz alpha band around the onset of words preceded by a beat gesture. These findings suggest that beats help tune low-frequency oscillatory activity at relevant moments during natural speech perception, providing a new insight of how speech and paralinguistic information are integrated. PMID:25595613

  9. Coherent neural oscillations predict future motor and language improvement after stroke.

    PubMed

    Nicolo, Pierre; Rizk, Sviatlana; Magnin, Cécile; Pietro, Marie Di; Schnider, Armin; Guggisberg, Adrian G

    2015-10-01

    Recent findings have demonstrated that stroke lesions affect neural communication in the entire brain. However, it is less clear whether network interactions are also relevant for plasticity and repair. This study investigated whether the coherence of neural oscillations at language or motor nodes is associated with future clinical improvement. Twenty-four stroke patients underwent high-density EEG recordings and standardized motor and language tests at 2-3 weeks (T0) and 3 months (T1) after stroke onset. In addition, EEG and motor assessments were obtained from a second population of 18 stroke patients. The graph theoretical measure of weighted node degree at language and motor areas was computed as the sum of absolute imaginary coherence with all other brain regions and compared to the amount of clinical improvement from T0 to T1. At T0, beta-band weighted node degree at the ipsilesional motor cortex was linearly correlated with better subsequent motor improvement, while beta-band weighted node degree at Broca's area was correlated with better language improvement. Clinical recovery was further associated with contralesional theta-band weighted node degree. These correlations were each specific to the corresponding brain area and independent of initial clinical severity, age, and lesion size. Findings were reproduced in the second stroke group. Conversely, later coherence increases occurring between T0 and T1 were associated with less clinical improvement. Improvement of language and motor functions after stroke is therefore associated with inter-regional synchronization of neural oscillations in the first weeks after stroke. A better understanding of network mechanisms of plasticity may lead to new prognostic biomarkers and therapeutic targets.See Ward (doi:10.1093/brain/awv265) for a scientific commentary on this article. PMID:26163304

  10. Neuronal current magnetic resonance imaging of evoked potentials and neural oscillations

    NASA Astrophysics Data System (ADS)

    Jiang, Xia

    Despite its great success, the current functional magnetic resonance imaging (MRI) technique relies on changes in cerebral hemodynamic parameters to infer the underlying neural activities, and as a result is limited in its spatial and temporal resolutions. In this dissertation, we discuss the feasibility of neuronal current MRI (nc-MRI), a novel technique in which the small magnetic field changes caused by neuronal electrical activities are directly measured by MRI. Two studies are described. In the first study, we investigated the feasibility of detecting the magnetic field produced by sensory evoked potentials. To eliminate the blood-oxygen-level-dependent (BOLD) effect on the MRI signal, which confounded most previous studies, an octopus visual system model was developed, which, for the first time, allowed for an in vivo investigation of nc-MRI in a BOLD-free environment. Electrophysiological responses were measured in the octopus retina and optical lobe to guide the nc-MRI acquisition. Our results indicated that no nc-MRI signal change related to neuronal activation could be detected at 0.2°/0.2% threshold for signal phase/magnitude respectively, while robust electrophysiological responses were recorded. In the second study, we discuss the feasibility of detecting neural oscillations with MRI, Based on previous studies, a novel approach was proposed in which an external oscillatory field was exploited as the excitation pulse under a spin-locked condition. This approach has the advantages of increased sensitivity and lowered physiological noise. Successful detection of sub-nanotesla field was demonstrated in phantom. Our results suggest that evoked potentials are too weak for nc-MRI detection with the current hardware, and that previous positive findings were likely due to hemodynamic confounders. On the other hand, oscillatory magnetic field can be efficiently detected in phantom. Given the stronger equivalent current dipoles produced by neural oscillations

  11. Neural oscillations as a signature of efficient coding in the presence of synaptic delays

    PubMed Central

    Chalk, Matthew; Gutkin, Boris; Denève, Sophie

    2016-01-01

    Cortical networks exhibit 'global oscillations', in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a 'prediction error' while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code. DOI: http://dx.doi.org/10.7554/eLife.13824.001 PMID:27383272

  12. Neural oscillations as a signature of efficient coding in the presence of synaptic delays.

    PubMed

    Chalk, Matthew; Gutkin, Boris; Denève, Sophie

    2016-01-01

    Cortical networks exhibit 'global oscillations', in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a 'prediction error' while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code. PMID:27383272

  13. Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network

    PubMed Central

    Finger, Holger; König, Peter

    2014-01-01

    Synchronization has been suggested as a mechanism of binding distributed feature representations facilitating segmentation of visual stimuli. Here we investigate this concept based on unsupervised learning using natural visual stimuli. We simulate dual-variable neural oscillators with separate activation and phase variables. The binding of a set of neurons is coded by synchronized phase variables. The network of tangential synchronizing connections learned from the induced activations exhibits small-world properties and allows binding even over larger distances. We evaluate the resulting dynamic phase maps using segmentation masks labeled by human experts. Our simulation results show a continuously increasing phase synchrony between neurons within the labeled segmentation masks. The evaluation of the network dynamics shows that the synchrony between network nodes establishes a relational coding of the natural image inputs. This demonstrates that the concept of binding by synchrony is applicable in the context of unsupervised learning using natural visual stimuli. PMID:24478685

  14. Phase synchrony facilitates binding and segmentation of natural images in a coupled neural oscillator network.

    PubMed

    Finger, Holger; König, Peter

    2013-01-01

    Synchronization has been suggested as a mechanism of binding distributed feature representations facilitating segmentation of visual stimuli. Here we investigate this concept based on unsupervised learning using natural visual stimuli. We simulate dual-variable neural oscillators with separate activation and phase variables. The binding of a set of neurons is coded by synchronized phase variables. The network of tangential synchronizing connections learned from the induced activations exhibits small-world properties and allows binding even over larger distances. We evaluate the resulting dynamic phase maps using segmentation masks labeled by human experts. Our simulation results show a continuously increasing phase synchrony between neurons within the labeled segmentation masks. The evaluation of the network dynamics shows that the synchrony between network nodes establishes a relational coding of the natural image inputs. This demonstrates that the concept of binding by synchrony is applicable in the context of unsupervised learning using natural visual stimuli. PMID:24478685

  15. Forecasting the Indian summer monsoon intraseasonal oscillations using genetic algorithm and neural network

    NASA Astrophysics Data System (ADS)

    Dwivedi, Suneet; Pandey, Avinash C.

    2011-08-01

    The correct and timely forecast of the Indian summer monsoon Intraseasonal Oscillations (ISOs) is very important. It has great impact on the agriculture and economy of the Indian subcontinent region. The applicability of Genetic Algorithm (GA) is demonstrated for nonlinear curve fitting of the inherently chaotic and noisy Lorenz time series and the ISO data. A robust method is developed for the very long-range prediction of the ISO using a feed-forward time delay backpropagation Artificial Neural Network (ANN). Using an iterative one-step-ahead prediction strategy, five years (120 pentads) of advanced prediction is made for the ISO data with good forecast skill. It is shown that a hybrid GA-ANN model may be used as an early forecast model followed by ANN only model as a more reliable model.

  16. Age-Related Neural Oscillation Patterns During the Processing of Temporally Manipulated Speech.

    PubMed

    Rufener, Katharina S; Oechslin, Mathias S; Wöstmann, Malte; Dellwo, Volker; Meyer, Martin

    2016-05-01

    This EEG-study aims to investigate age-related differences in the neural oscillation patterns during the processing of temporally modulated speech. Viewing from a lifespan perspective, we recorded the electroencephalogram (EEG) data of three age samples: young adults, middle-aged adults and older adults. Stimuli consisted of temporally degraded sentences in Swedish-a language unfamiliar to all participants. We found age-related differences in phonetic pattern matching when participants were presented with envelope-degraded sentences, whereas no such age-effect was observed in the processing of fine-structure-degraded sentences. Irrespective of age, during speech processing the EEG data revealed a relationship between envelope information and the theta band (4-8 Hz) activity. Additionally, an association between fine-structure information and the gamma band (30-48 Hz) activity was found. No interaction, however, was found between acoustic manipulation of stimuli and age. Importantly, our main finding was paralleled by an overall enhanced power in older adults in high frequencies (gamma: 30-48 Hz). This occurred irrespective of condition. For the most part, this result is in line with the Asymmetric Sampling in Time framework (Poeppel in Speech Commun 41:245-255, 2003), which assumes an isomorphic correspondence between frequency modulations in neurophysiological patterns and acoustic oscillations in spoken language. We conclude that speech-specific neural networks show strong stability over adulthood, despite initial processes of cortical degeneration indicated by enhanced gamma power. The results of our study therefore confirm the concept that sensory and cognitive processes undergo multidirectional trajectories within the context of healthy aging. PMID:26613726

  17. Synaptic organizations and dynamical properties of weakly connected neural oscillators. II. Learning phase information.

    PubMed

    Hoppensteadt, F C; Izhikevich, E M

    1996-08-01

    This is the second of two articles devoted to analyzing the relationship between synaptic organizations (anatomy) and dynamical properties (function) of networks of neural oscillators near multiple supercritical Andronov-Hopf bifurcation points. Here we analyze learning processes in such networks. Regarding learning dynamics, we assume (1) learning is local (i.e. synaptic modification depends on pre- and postsynaptic neurons but not on others), (2) synapses modify slowly relative to characteristic neuron response times, (3) in the absence of either pre- or postsynaptic activity, the synapse weakens (forgets). Our major goal is to analyze all synaptic organizations of oscillatory neural networks that can memorize and retrieve phase information or time delays. We show that such network have the following attributes: (1) the rate of synaptic plasticity connected with learning is determined locally by the presynaptic neurons, (2) the excitatory neurons must be long-axon relay neurons capable of forming distant connections with other excitatory and inhibitory neurons, (3) if inhibitory neurons have long axons, then the network can learn, passively forget and actively unlearn information by adjusting synaptic plasticity rates. PMID:8855351

  18. Frontal preparatory neural oscillations associated with cognitive control: A developmental study comparing young adults and adolescents.

    PubMed

    Hwang, Kai; Ghuman, Avniel S; Manoach, Dara S; Jones, Stephanie R; Luna, Beatriz

    2016-08-01

    Functional magnetic resonance imaging (fMRI) studies suggest that age-related changes in the frontal cortex may underlie developmental improvements in cognitive control. In the present study we used magnetoencephalography (MEG) to identify frontal oscillatory neurodynamics that support age-related improvements in cognitive control during adolescence. We characterized the differences in neural oscillations in adolescents and adults during the preparation to suppress a prepotent saccade (antisaccade trials-AS) compared to preparing to generate a more automatic saccade (prosaccade trials-PS). We found that for adults, AS were associated with increased beta-band (16-38Hz) power in the dorsal lateral prefrontal cortex (DLPFC), enhanced alpha- to low beta-band (10-18Hz) power in the frontal eye field (FEF) that predicted performance, and increased cross-frequency alpha-beta (10-26Hz) amplitude coupling between the DLPFC and the FEF. Developmental comparisons between adults and adolescents revealed similar engagement of DLPFC beta-band power but weaker FEF alpha-band power, and lower cross-frequency coupling between the DLPFC and the FEF in adolescents. These results suggest that lateral prefrontal neural activity associated with cognitive control is adult-like by adolescence; the development of cognitive control from adolescence to adulthood is instead associated with increases in frontal connectivity and strengthening of inhibition signaling for suppressing task-incompatible processes. PMID:27173759

  19. Prediction of the Wrist Joint Position During a Postural Tremor Using Neural Oscillators and an Adaptive Controller

    PubMed Central

    Kobravi, Hamid Reza; Ali, Sara Hemmati; Vatandoust, Masood; Marvi, Rasoul

    2016-01-01

    The prediction of the joint angle position, especially during tremor bursts, can be useful for detecting, tracking, and forecasting tremors. Thus, this research proposes a new model for predicting the wrist joint position during rhythmic bursts and inter-burst intervals. Since a tremor is an approximately rhythmic and roughly sinusoidal movement, neural oscillators have been selected to underlie the proposed model. Two neural oscillators were adopted. Electromyogram (EMG) signals were recorded from the extensor carpi radialis and flexor carpi radialis muscles concurrent with the joint angle signals of a stroke subject in an arm constant-posture. The output frequency of each oscillator was equal to the frequency corresponding to the maximum value of power spectrum related to the rhythmic wrist joint angle signals which had been recorded during a postural tremor. The phase shift between the outputs of the two oscillators was equal to the phase shift between the muscle activation of the wrist flexor and extensor muscles. The difference between the two oscillators’ output signals was considered the main pattern. Along with a proportional compensator, an adaptive neural controller has adjusted the amplitude of the main pattern in such a way so as to minimize the wrist joint prediction error during a stroke patient's tremor burst and a healthy subject's generated artificial tremor. In regard to the range of wrist joint movement during the observed rhythmic motions, a calculated prediction error is deemed acceptable. PMID:27186540

  20. Gamma Oscillations as a Neural Signature of Shifting Times in Narrative Language

    PubMed Central

    Brederoo, Sanne Gøren; Bos, Laura Simone; Dragoy, Olga; Bastiaanse, Roelien; Baggio, Giosuè

    2015-01-01

    Verbs and other temporal expressions allow speakers to specify the location of events in time, as well as to move back and forth in time, shifting in a narrative between past, present and future. The referential flexibility of temporal expressions is well understood in linguistics but its neurocognitive bases remain unknown. We aimed at obtaining a neural signature of shifting times in narrative language. We recorded and analyzed event-related brain potentials (ERPs) and oscillatory responses to the adverb ‘now’ and to the second main verb in Punctual (‘An hour ago the boy stole a candy and now he peeled the fruit’) and Iterative (‘The entire afternoon the boy stole candy and now he peeled the fruit’) contexts. ‘An hour ago’ introduces a time frame that lies entirely in the past, ‘now’ shifts the narrative to the present, and ‘peeled’ shifts it back to the past. These two referential shifts in Punctual contexts are expected to leave very similar traces on neural responses. In contrast, ‘The entire afternoon’ specifies a time frame that may encompass past, present and future, such that both ‘now’ and ‘peeled’ are consistent with it. Here, no time shift is required. We found no difference in ERPs between Punctual and Iterative contexts either at ‘now’ or at the second verb. However, reference shifts modulated oscillatory signals. ‘Now’ and the second verb in Punctual contexts resulted in similar responses: an increase in gamma power with a left-anterior distribution. Gamma bursts were absent in Iterative contexts. We propose that gamma oscillations here reflect the binding of temporal variables to the values allowed by constraints introduced by temporal expressions in discourse. PMID:25874576

  1. Speed of synchronization in complex networks of neural oscillators: Analytic results based on Random Matrix Theory

    NASA Astrophysics Data System (ADS)

    Timme, Marc; Geisel, Theo; Wolf, Fred

    2006-03-01

    We analyze the dynamics of networks of spiking neural oscillators. First, we present an exact linear stability theory of the synchronous state for networks of arbitrary connectivity. For general neuron rise functions, stability is determined by multiple operators, for which standard analysis is not suitable. We describe a general nonstandard solution to the multioperator problem. Subsequently, we derive a class of neuronal rise functions for which all stability operators become degenerate and standard eigenvalue analysis becomes a suitable tool. Interestingly, this class is found to consist of networks of leaky integrate-and-fire neurons. For random networks of inhibitory integrate-and-fire neurons, we then develop an analytical approach, based on the theory of random matrices, to precisely determine the eigenvalue distributions of the stability operators. This yields the asymptotic relaxation time for perturbations to the synchronous state which provides the characteristic time scale on which neurons can coordinate their activity in such networks. For networks with finite in-degree, i.e., finite number of presynaptic inputs per neuron, we find a speed limit to coordinating spiking activity. Even with arbitrarily strong interaction strengths neurons cannot synchronize faster than at a certain maximal speed determined by the typical in-degree.

  2. When problem size matters: differential effects of brain stimulation on arithmetic problem solving and neural oscillations.

    PubMed

    Rütsche, Bruno; Hauser, Tobias U; Jäncke, Lutz; Grabner, Roland H

    2015-01-01

    The problem size effect is a well-established finding in arithmetic problem solving and is characterized by worse performance in problems with larger compared to smaller operand size. Solving small and large arithmetic problems has also been shown to involve different cognitive processes and distinct electroencephalography (EEG) oscillations over the left posterior parietal cortex (LPPC). In this study, we aimed to provide further evidence for these dissociations by using transcranial direct current stimulation (tDCS). Participants underwent anodal (30min, 1.5 mA, LPPC) and sham tDCS. After the stimulation, we recorded their neural activity using EEG while the participants solved small and large arithmetic problems. We found that the tDCS effects on performance and oscillatory activity critically depended on the problem size. While anodal tDCS improved response latencies in large arithmetic problems, it decreased solution rates in small arithmetic problems. Likewise, the lower-alpha desynchronization in large problems increased, whereas the theta synchronization in small problems decreased. These findings reveal that the LPPC is differentially involved in solving small and large arithmetic problems and demonstrate that the effects of brain stimulation strikingly differ depending on the involved neuro-cognitive processes. PMID:25789486

  3. When Problem Size Matters: Differential Effects of Brain Stimulation on Arithmetic Problem Solving and Neural Oscillations

    PubMed Central

    Rütsche, Bruno; Hauser, Tobias U.; Jäncke, Lutz; Grabner, Roland H.

    2015-01-01

    The problem size effect is a well-established finding in arithmetic problem solving and is characterized by worse performance in problems with larger compared to smaller operand size. Solving small and large arithmetic problems has also been shown to involve different cognitive processes and distinct electroencephalography (EEG) oscillations over the left posterior parietal cortex (LPPC). In this study, we aimed to provide further evidence for these dissociations by using transcranial direct current stimulation (tDCS). Participants underwent anodal (30min, 1.5 mA, LPPC) and sham tDCS. After the stimulation, we recorded their neural activity using EEG while the participants solved small and large arithmetic problems. We found that the tDCS effects on performance and oscillatory activity critically depended on the problem size. While anodal tDCS improved response latencies in large arithmetic problems, it decreased solution rates in small arithmetic problems. Likewise, the lower-alpha desynchronization in large problems increased, whereas the theta synchronization in small problems decreased. These findings reveal that the LPPC is differentially involved in solving small and large arithmetic problems and demonstrate that the effects of brain stimulation strikingly differ depending on the involved neuro-cognitive processes. PMID:25789486

  4. Neural network solution of the Schrödinger equation for a two-dimensional harmonic oscillator

    NASA Astrophysics Data System (ADS)

    Androsiuk, J.; Kułak, L.; Sienicki, K.

    1993-07-01

    We present computer simulations of a neural network capable of learning to perform transformations generated by the Schrödinger equation required to find eigenenergies of a two-dimensional harmonic oscillator. We show that this task can be achieved by a not fully connected back-propagation neural network containing 49 input neurons, 3 hidden layer neurons and 1 output neuron. The investigated neural network turns out to be capable of predicting eigenenergies with an average error of less than one percent. We demonstrate that the CPU time required to teach a neural network of performing the transformation produced by the Schrödinger equation is about 2 min to reach 41000 learning iterations, thus making foreseeable a direct application of a neural network in this and other more complex physical and chemical problems. A discussion of the errors due to the generalization of acquired knowledge is presented and related to a limited number of examples in learning mode and the number of neurons in the hidden layer. Decreasing the number of neurons in the hidden layer increases the apparent ability of the neural network for generalization.

  5. Interactive effect of light colours and temporal synergism of circadian neural oscillations in reproductive regulation of Japanese quail.

    PubMed

    Yadav, Suneeta; Chaturvedi, Chandra Mohini

    2016-09-01

    Avian literature reports the modulation of 'photoperiodic gonadal responses' by the temporal phase relation of serotonergic and dopaminergic oscillations in Japanese quail. But, the modulation of 'light colour responses' by the temporal synergism of neural oscillations is not yet known. Hence the present study was designed to investigate the interaction of the light colour (blue, red) and the phase relation of neural oscillations in the reproductive regulation of Japanese quail. Three week old male Japanese quail were divided into two groups and maintained under a long day length condition (16L:8D) and were exposed to a 30 lux intensity of blue LED (light emitting diode) (B LED) and a red LED light (R LED). At the age of 15.5weeks, quail of one subgroup of B LED were injected with serotonin precursor (5-HTP) and dopamine precursor (l-DOPA) 12hrs apart (B LED+12-hr) and those of the R LED group were injected with the same drugs (5mg/100g body weight over a period of thirteen days) but 8hrs apart (R LED+8-hr). The remaining subgroups of both the light colour groups (B LED & R LED) received normal saline twice daily and served as controls. Cloacal gland volume was recorded weekly until 35.5weeks of age when the study was terminated and reproductive parameters (testicular volume, GSI, seminiferous tubule diameter and plasma testosterone) were assessed. Results indicate that the 8-hr temporal phase relation of neural oscillations suppresses reproductive activity even during the photosensitive phase of the red light exposed quail (R LED+8-hr) compare to the R LED controls. On the other hand, the 12-hr temporal phase relation stimulates the gonadal development of the B LED+12-hr quail compared to the B LED controls which after completing one cycle entered into a regressive phase and remained sexually quiescent. These experiments suggest that the temporal phase relations of circadian neural oscillations, in addition to modulating the classical photoperiodic responses, may

  6. Mouse neuroblastoma cell based model and the effect of epileptic events on calcium oscillations and neural spikes

    NASA Astrophysics Data System (ADS)

    Kim, Suhwan; Baek, Juyeong; Jung, Unsang; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

    2013-05-01

    Recently, Mouse neuroblastoma cells are considered as an attractive model for the study of human neurological and prion diseases, and intensively used as a model system in different areas. Among those areas, differentiation of neuro2a (N2A) cells, receptor mediated ion current, and glutamate induced physiological response are actively investigated. The reason for the interest to mouse neuroblastoma N2A cells is that they have a fast growing rate than other cells in neural origin with a few another advantages. This study evaluated the calcium oscillations and neural spikes recording of mouse neuroblastoma N2A cells in an epileptic condition. Based on our observation of neural spikes in mouse N2A cell with our proposed imaging modality, we report that mouse neuroblastoma N2A cells can be an important model related to epileptic activity studies. It is concluded that the mouse neuroblastoma N2A cells produce the epileptic spikes in vitro in the same way as produced by the neurons or the astrocytes. This evidence advocates the increased and strong level of neurotransmitters release by enhancement in free calcium using the 4-aminopyridine which causes the mouse neuroblastoma N2A cells to produce the epileptic spikes and calcium oscillation.

  7. Mouse neuroblastoma cell-based model and the effect of epileptic events on calcium oscillations and neural spikes

    NASA Astrophysics Data System (ADS)

    Kim, Suhwan; Jung, Unsang; Baek, Juyoung; Lee, Sangwon; Jung, Woonggyu; Kim, Jeehyun; Kang, Shinwon

    2013-01-01

    Recently, mouse neuroblastoma cells have been considered as an attractive model for the study of human neurological and prion diseases, and they have been intensively used as a model system in different areas. For example, the differentiation of neuro2a (N2A) cells, receptor-mediated ion current, and glutamate-induced physiological responses have been actively investigated with these cells. These mouse neuroblastoma N2A cells are of interest because they grow faster than other cells of neural origin and have a number of other advantages. The calcium oscillations and neural spikes of mouse neuroblastoma N2A cells in epileptic conditions are evaluated. Based on our observations of neural spikes in these cells with our proposed imaging modality, we reported that they can be an important model in epileptic activity studies. We concluded that mouse neuroblastoma N2A cells produce epileptic spikes in vitro in the same way as those produced by neurons or astrocytes. This evidence suggests that increased levels of neurotransmitter release due to the enhancement of free calcium from 4-aminopyridine causes the mouse neuroblastoma N2A cells to produce epileptic spikes and calcium oscillations.

  8. Neurodynamic oscillators

    NASA Technical Reports Server (NTRS)

    Espinosa, Ismael; Gonzalez, Hortensia; Quiza, Jorge; Gonazalez, J. Jesus; Arroyo, Ruben; Lara, Ritaluz

    1995-01-01

    Oscillation of electrical activity has been found in many nervous systems, from invertebrates to vertebrates including man. There exists experimental evidence of very simple circuits with the capability of oscillation. Neurons with intrinsic oscillation have been found and also neural circuits where oscillation is a property of the network. These two types of oscillations coexist in many instances. It is nowadays hypothesized that behind synchronization and oscillation there is a system of coupled oscillators responsible for activities that range from locomotion and feature binding in vision to control of sleep and circadian rhythms. The huge knowledge that has been acquired on oscillators from the times of Lord Rayleigh has made the simulation of neural oscillators a very active endeavor. This has been enhanced with more recent physiological findings about small neural circuits by means of intracellular and extracellular recordings as well as imaging methods. The future of this interdisciplinary field looks very promising; some researchers are going into quantum mechanics with the idea of trying to provide a quantum description of the brain. In this work we describe some simulations using neuron models by means of which we form simple neural networks that have the capability of oscillation. We analyze the oscillatory activity with root locus method, cross-correlation histograms, and phase planes. In the more complicated neural network models there is the possibility of chaotic oscillatory activity and we study that by means of Lyapunov exponents. The companion paper shows an example of that kind.

  9. Olfactory system gamma oscillations: the physiological dissection of a cognitive neural system

    PubMed Central

    Rojas-Líbano, Daniel

    2008-01-01

    Oscillatory phenomena have been a focus of dynamical systems research since the time of the classical studies on the pendulum by Galileo. Fast cortical oscillations also have a long and storied history in neurophysiology, and olfactory oscillations have led the way with a depth of explanation not present in the literature of most other cortical systems. From the earliest studies of odor-evoked oscillations by Adrian, many reports have focused on mechanisms and functional associations of these oscillations, in particular for the so-called gamma oscillations. As a result, much information is now available regarding the biophysical mechanisms that underlie the oscillations in the mammalian olfactory system. Recent studies have expanded on these and addressed functionality directly in mammals and in the analogous insect system. Sub-bands within the rodent gamma oscillatory band associated with specific behavioral and cognitive states have also been identified. All this makes oscillatory neuronal networks a unique interdisciplinary platform from which to study neurocognitive and dynamical phenomena in intact, freely behaving animals. We present here a summary of what has been learned about the functional role and mechanisms of gamma oscillations in the olfactory system as a guide for similar studies in other cortical systems. PMID:19003484

  10. Patterned porous silicon photonic crystals with modular surface chemistry for spatial control of neural stem cell differentiation.

    PubMed

    Huang, Tiffany H; Pei, Yi; Zhang, Douglas; Li, Yanfen; Kilian, Kristopher A

    2016-06-01

    We present a strategy to spatially define regions of gold and nanostructured silicon photonics, each with materials-specific surface chemistry, for azide-alkyne cycloaddition of different bioactive peptides. Neural stem cells are spatially directed to undergo neurogenesis and astrogenesis as a function of both surface properties and peptide identity. PMID:27173986

  11. Bifurcation and oscillation in a time-delay neural mass model.

    PubMed

    Geng, Shujuan; Zhou, Weidong; Zhao, Xiuhe; Yuan, Qi; Ma, Zhen; Wang, Jiwen

    2014-12-01

    The neural mass model developed by Lopes da Silva et al. simulates complex dynamics between cortical areas and is able to describe a limit cycle behavior for alpha rhythms in electroencephalography (EEG). In this work, we propose a modified neural mass model that incorporates a time delay. This time-delay model can be used to simulate several different types of EEG activity including alpha wave, interictal EEG, and ictal EEG. We present a detailed description of the model's behavior with bifurcation diagrams. Through simulation and an analysis of the influence of the time delay on the model's oscillatory behavior, we demonstrate that a time delay in neuronal signal transmission could cause seizure-like activity in the brain. Further study of the bifurcations in this new neural mass model could provide a theoretical reference for the understanding of the neurodynamics in epileptic seizures. PMID:25048203

  12. High-frequency neural oscillations and visual processing deficits in schizophrenia

    PubMed Central

    Tan, Heng-Ru May; Lana, Luiz; Uhlhaas, Peter J.

    2013-01-01

    Visual information is fundamental to how we understand our environment, make predictions, and interact with others. Recent research has underscored the importance of visuo-perceptual dysfunctions for cognitive deficits and pathophysiological processes in schizophrenia. In the current paper, we review evidence for the relevance of high frequency (beta/gamma) oscillations towards visuo-perceptual dysfunctions in schizophrenia. In the first part of the paper, we examine the relationship between beta/gamma band oscillations and visual processing during normal brain functioning. We then summarize EEG/MEG-studies which demonstrate reduced amplitude and synchrony of high-frequency activity during visual stimulation in schizophrenia. In the final part of the paper, we identify neurobiological correlates as well as offer perspectives for future research to stimulate further inquiry into the role of high-frequency oscillations in visual processing impairments in the disorder. PMID:24130535

  13. Neural mechanisms of evoked oscillations: stability and interaction with transient events.

    PubMed

    Moratti, Stephan; Clementz, Brett A; Gao, Yuan; Ortiz, Tomás; Keil, Andreas

    2007-12-01

    There is increasing evidence that early event-related potentials are a result of phase alignment of ongoing background oscillations of the electroencephalogram rather than additive amplitude modulation. Steady state visual-evoked potentials (ssVEPs) can be recorded using an intensity modulated stimulus, resulting in an evoked brain response at a known frequency, i.e. the stimulation frequency. Given this property, the ssVEP is ideally suited for examining the relationship between single-trial fluctuations in phase/amplitude and the evoked brain potential resulting from averaging across trials. To address this issue, the current study investigated the contribution of single trial power and intertrial phase locking to ssVEP generation by presenting a peripheral flicker. Further, transient stimuli were presented during flicker and at three increasing latency lags following flicker offset to examine (1) to what extent a stimulus can disturb the ssVEP oscillation and (2) how phase alignment during P1-N1-P2 time windows is affected during presence of evoked oscillations. The former assessment evaluates the stability of ssVEPs and the latter the phase alignment processes to transient stimuli under experimentally induced background oscillations. We observed that ssVEPs are a result of phase alignment rather than single trial amplitude modulation. In addition, ssVEP oscillations were not disturbed by transient stimuli. Finally, phase alignment in P1-N1-P2 time windows was distorted during and shortly after steady state stimulation. We conclude that ssVEPs represent strongly phase locked oscillations sharing the same generation mechanisms as early evoked potentials. PMID:17274017

  14. Patterned porous silicon photonic crystals with modular surface chemistry for spatial control of neural stem cell differentiation

    NASA Astrophysics Data System (ADS)

    Huang, Tiffany H.; Pei, Yi; Zhang, Douglas; Li, Yanfen; Kilian, Kristopher A.

    2016-05-01

    We present a strategy to spatially define regions of gold and nanostructured silicon photonics, each with materials-specific surface chemistry, for azide-alkyne cycloaddition of different bioactive peptides. Neural stem cells are spatially directed to undergo neurogenesis and astrogenesis as a function of both surface properties and peptide identity.We present a strategy to spatially define regions of gold and nanostructured silicon photonics, each with materials-specific surface chemistry, for azide-alkyne cycloaddition of different bioactive peptides. Neural stem cells are spatially directed to undergo neurogenesis and astrogenesis as a function of both surface properties and peptide identity. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr08327c

  15. Hemispheric Asymmetry of Endogenous Neural Oscillations in Young Children: Implications for Hearing Speech In Noise

    PubMed Central

    Thompson, Elaine C.; Woodruff Carr, Kali; White-Schwoch, Travis; Tierney, Adam; Nicol, Trent; Kraus, Nina

    2016-01-01

    Speech signals contain information in hierarchical time scales, ranging from short-duration (e.g., phonemes) to long-duration cues (e.g., syllables, prosody). A theoretical framework to understand how the brain processes this hierarchy suggests that hemispheric lateralization enables specialized tracking of acoustic cues at different time scales, with the left and right hemispheres sampling at short (25 ms; 40 Hz) and long (200 ms; 5 Hz) periods, respectively. In adults, both speech-evoked and endogenous cortical rhythms are asymmetrical: low-frequency rhythms predominate in right auditory cortex, and high-frequency rhythms in left auditory cortex. It is unknown, however, whether endogenous resting state oscillations are similarly lateralized in children. We investigated cortical oscillations in children (3–5 years; N = 65) at rest and tested our hypotheses that this temporal asymmetry is evident early in life and facilitates recognition of speech in noise. We found a systematic pattern of increasing leftward asymmetry for higher frequency oscillations; this pattern was more pronounced in children who better perceived words in noise. The observed connection between left-biased cortical oscillations in phoneme-relevant frequencies and speech-in-noise perception suggests hemispheric specialization of endogenous oscillatory activity may support speech processing in challenging listening environments, and that this infrastructure is present during early childhood. PMID:26804355

  16. Hemispheric Asymmetry of Endogenous Neural Oscillations in Young Children: Implications for Hearing Speech In Noise.

    PubMed

    Thompson, Elaine C; Woodruff Carr, Kali; White-Schwoch, Travis; Tierney, Adam; Nicol, Trent; Kraus, Nina

    2016-01-01

    Speech signals contain information in hierarchical time scales, ranging from short-duration (e.g., phonemes) to long-duration cues (e.g., syllables, prosody). A theoretical framework to understand how the brain processes this hierarchy suggests that hemispheric lateralization enables specialized tracking of acoustic cues at different time scales, with the left and right hemispheres sampling at short (25 ms; 40 Hz) and long (200 ms; 5 Hz) periods, respectively. In adults, both speech-evoked and endogenous cortical rhythms are asymmetrical: low-frequency rhythms predominate in right auditory cortex, and high-frequency rhythms in left auditory cortex. It is unknown, however, whether endogenous resting state oscillations are similarly lateralized in children. We investigated cortical oscillations in children (3-5 years; N = 65) at rest and tested our hypotheses that this temporal asymmetry is evident early in life and facilitates recognition of speech in noise. We found a systematic pattern of increasing leftward asymmetry for higher frequency oscillations; this pattern was more pronounced in children who better perceived words in noise. The observed connection between left-biased cortical oscillations in phoneme-relevant frequencies and speech-in-noise perception suggests hemispheric specialization of endogenous oscillatory activity may support speech processing in challenging listening environments, and that this infrastructure is present during early childhood. PMID:26804355

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-01-01

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

  19. What changes in neural oscillations can reveal about developmental cognitive neuroscience: Language development as a case in point

    PubMed Central

    Maguire, Mandy J.; Abel, Alyson D.

    2013-01-01

    EEG is a primary method for studying temporally precise neuronal processes across the lifespan. Most of this work focuses on Event Related Potentials (ERPs); however, using time-locked time frequency analysis to decompose the EEG signal can identify and distinguish multiple changes in brain oscillations underlying cognition (Bastiaansen et al., 2010). Further this measure is thought to reflect changes in inter-neuronal communication more directly than ERPs (Nunez & Srinivasan, 2006). Although time frequency has elucidated cognitive processes in adults, applying it to cognitive development is still rare. Here, we review the basics of neuronal oscillations, some of what they reveal about adult cognitive function, and what little is known relating to children. We focus on language because it develops early and engages complex cortical networks. Additionally, because time frequency analysis of the EEG related to adult language comprehension has been incredibly informative, using similar methods with children will shed new light on current theories of language development and increase our understanding of how neural processes change over the lifespan. Our goal is to emphasize the power of this methodology and encourage its use throughout developmental cognitive neuroscience. PMID:24060670

  20. Self-Sustained Relaxation Oscillations in Time-Delay Neural Systems

    NASA Astrophysics Data System (ADS)

    Glyzin, S. D.; Kolesov, A. Yu; Rozov, N. Kh

    2016-06-01

    A new method to model the phenomena ’bursting’ and ’buffering’ in neural systems is represented. Namely, a singularly perturbed nonlinear scalar differential difference equation with two delays is introduced, which is a mathematical model of a single neuron. It is shown that for suitably chosen parameters this equation has a stable periodic solution with an arbitrary prescribed number of asymptotically high impulses (spikes) on a period interval. It is also shown that the buffering phenomenon occurs in a one-dimensional chain of diffusively coupled neurons of this type: as the number of components in the chain grows in a way compatible with a decrease of the diffusion coefficient, the number of co-existing stable periodic motions increases indefinitely.

  1. Complex patterns arise through spontaneous symmetry breaking in dense homogeneous networks of neural oscillators

    PubMed Central

    Singh, Rajeev; Menon, Shakti N.; Sinha, Sitabhra

    2016-01-01

    There has been much interest in understanding collective dynamics in networks of brain regions due to their role in behavior and cognitive function. Here we show that a simple, homogeneous system of densely connected oscillators, representing the aggregate activity of local brain regions, can exhibit a rich variety of dynamical patterns emerging via spontaneous breaking of permutation or translational symmetries. Upon removing just a few connections, we observe a striking departure from the mean-field limit in terms of the collective dynamics, which implies that the sparsity of these networks may have very important consequences. Our results suggest that the origins of some of the complicated activity patterns seen in the brain may be understood even with simple connection topologies. PMID:26916700

  2. Decreased thalamo-cortical connectivity by alteration of neural information flow in theta oscillation in depression-model rats.

    PubMed

    Zheng, Chenguang; Quan, Meina; Zhang, Tao

    2012-12-01

    Alterations in oscillatory brain activity are strongly correlated with cognitive performance in various physiological rhythms. The present study investigated whether the directionality of neural information flow (NIF) could be used to characterize the synaptic plasticity in thalamocortical (TC) pathway, and examined which frequency field oscillations were mostly related to the cognitive deficiency in depression. Two novel algorithms were employed to determine the coupling interaction between the LD thalamus and medial prefrontal cortex (mPFC) in five frequency bands, using the phase signals of local field potentials (LFP) in these two regions. The results showed that the power of neural activity in mPFC was increased in delta, theta and beta frequency bands in depression. However, the nonlinear characteristics of LFP activity were weakened in depression by means of sample entropy measurements. In the analysis of phase dynamics, the phase synchronization values were reduced in theta rhythm in stressed rats. Importantly, the coupling direction index d and the unidirectional influence from LD thalamus to mPFC were significantly reduced at the theta rhythm in rats in depression, and increased after memantine treatment, which were associated with the LTP alterations and cognitive impairment in our previous report. Moreover, the fact that the reduced entropy value was only found in mPFC might implicate postsynaptic effect involved in synaptic plasticity alteration in the depression model. The results suggest that the effects of depression on cognitive deficits are mediated via profound alterations in information flow in the TC pathway, and the directional index at theta rhythm could be used as a measurement of synaptic plasticity. PMID:22648379

  3. Neural alpha oscillations index the balance between self-other integration and segregation in real-time joint action.

    PubMed

    Novembre, Giacomo; Sammler, Daniela; Keller, Peter E

    2016-08-01

    Shared knowledge and interpersonal coordination are prerequisites for most forms of social behavior. Influential approaches to joint action have conceptualized these capacities in relation to the separate constructs of co-representation (knowledge) and self-other entrainment (coordination). Here we investigated how brain mechanisms involved in co-representation and entrainment interact to support joint action. To do so, we used a musical joint action paradigm to show that the neural mechanisms underlying co-representation and self-other entrainment are linked via a process - indexed by EEG alpha oscillations - regulating the balance between self-other integration and segregation in real time. Pairs of pianists performed short musical items while action familiarity and interpersonal (behavioral) synchronization accuracy were manipulated in a factorial design. Action familiarity referred to whether or not pianists had rehearsed the musical material performed by the other beforehand. Interpersonal synchronization was manipulated via congruent or incongruent tempo change instructions that biased performance timing towards the impending, new tempo. It was observed that, when pianists were familiar with each other's parts, millisecond variations in interpersonal synchronized behavior were associated with a modulation of alpha power over right centro-parietal scalp regions. Specifically, high behavioral entrainment was associated with self-other integration, as indexed by alpha suppression. Conversely, low behavioral entrainment encouraged reliance on internal knowledge and thus led to self-other segregation, indexed by alpha enhancement. These findings suggest that alpha oscillations index the processing of information about self and other depending on the compatibility of internal knowledge and external (environmental) events at finely resolved timescales. PMID:27449708

  4. Effects of Exercise in Immersive Virtual Environments on Cortical Neural Oscillations and Mental State.

    PubMed

    Vogt, Tobias; Herpers, Rainer; Askew, Christopher D; Scherfgen, David; Strüder, Heiko K; Schneider, Stefan

    2015-01-01

    Virtual reality environments are increasingly being used to encourage individuals to exercise more regularly, including as part of treatment those with mental health or neurological disorders. The success of virtual environments likely depends on whether a sense of presence can be established, where participants become fully immersed in the virtual environment. Exposure to virtual environments is associated with physiological responses, including cortical activation changes. Whether the addition of a real exercise within a virtual environment alters sense of presence perception, or the accompanying physiological changes, is not known. In a randomized and controlled study design, moderate-intensity Exercise (i.e., self-paced cycling) and No-Exercise (i.e., automatic propulsion) trials were performed within three levels of virtual environment exposure. Each trial was 5 minutes in duration and was followed by posttrial assessments of heart rate, perceived sense of presence, EEG, and mental state. Changes in psychological strain and physical state were generally mirrored by neural activation patterns. Furthermore, these changes indicated that exercise augments the demands of virtual environment exposures and this likely contributed to an enhanced sense of presence. PMID:26366305

  5. Vulnerability to paroxysmal oscillations in delayed neural networks: A basis for nocturnal frontal lobe epilepsy?

    NASA Astrophysics Data System (ADS)

    Quan, Austin; Osorio, Ivan; Ohira, Toru; Milton, John

    2011-12-01

    Resonance can occur in bistable dynamical systems due to the interplay between noise and delay (τ) in the absence of a periodic input. We investigate resonance in a two-neuron model with mutual time-delayed inhibitory feedback. For appropriate choices of the parameters and inputs three fixed-point attractors co-exist: two are stable and one is unstable. In the absence of noise, delay-induced transient oscillations (referred to herein as DITOs) arise whenever the initial function is tuned sufficiently close to the unstable fixed-point. In the presence of noisy perturbations, DITOs arise spontaneously. Since the correlation time for the stationary dynamics is ˜τ, we approximated a higher order Markov process by a three-state Markov chain model by rescaling time as t → 2sτ, identifying the states based on whether the sub-intervals were completely confined to one basin of attraction (the two stable attractors) or straddled the separatrix, and then determining the transition probability matrix empirically. The resultant Markov chain model captured the switching behaviors including the statistical properties of the DITOs. Our observations indicate that time-delayed and noisy bistable dynamical systems are prone to generate DITOs as switches between the two attractors occur. Bistable systems arise transiently in situations when one attractor is gradually replaced by another. This may explain, for example, why seizures in certain epileptic syndromes tend to occur as sleep stages change.

  6. Self-Sustained Irregular Activity in an Ensemble of Neural Oscillators

    NASA Astrophysics Data System (ADS)

    Ullner, Ekkehard; Politi, Antonio

    2016-01-01

    An ensemble of pulse-coupled phase oscillators is thoroughly analyzed in the presence of a mean-field coupling and a dispersion of their natural frequencies. In spite of the analogies with the Kuramoto setup, a much richer scenario is observed. The "synchronized" phase, which emerges upon increasing the coupling strength, is characterized by highly irregular fluctuations: A time-series analysis reveals that the dynamics of the order parameter is indeed high dimensional. The complex dynamics appears to be the result of the nonperturbative action of a suitably shaped phase-response curve. Such a mechanism differs from the often-invoked balance between excitation and inhibition and might provide an alternative basis to account for the self-sustained brain activity in the resting state. The potential interest of this dynamical regime is further strengthened by its (microscopic) linear stability, which makes it quite suited for computational tasks. The overall study has been performed by combining analytical and numerical studies, starting from the linear stability analysis of the asynchronous regime, to include the Fourier analysis of the Kuramoto order parameter, the computation of various types of Lyapunov exponents, and a microscopic study of the interspike intervals.

  7. Oxytocin affects spontaneous neural oscillations in trauma-exposed war veterans.

    PubMed

    Eidelman-Rothman, Moranne; Goldstein, Abraham; Levy, Jonathan; Weisman, Omri; Schneiderman, Inna; Mankuta, David; Zagoory-Sharon, Orna; Feldman, Ruth

    2015-01-01

    Exposure to combat-related trauma often leads to lifetime functional impairments. Previous research demonstrated the effects of oxytocin (OT) administration on brain regions implicated in post-traumatic stress disorder (PTSD); yet OT's effects on brain patterns in trauma-exposed veterans have not been studied. In the current study the effects of OT on spontaneous brain oscillatory activity were measured in 43 veterans using magnetoencephalography (MEG): 28 veterans who were exposed to a combat-related trauma and 15 trauma-unexposed controls. Participants participated in two experimental sessions and were administered OT or placebo (PBO) in a double-blind, placebo-control, within-subject design. Following OT/PBO administration, participants underwent a whole-head MEG scan. Plasma and salivary OT levels were assessed each session. Spontaneous brain activity measured during a 2-min resting period was subjected to source-localization analysis. Trauma-exposed veterans showed higher resting-state alpha (8-13 Hz) activity compared to controls in the left dorsolateral prefrontal cortex (dlPFC), specifically in the superior frontal gyrus (SFG) and the middle frontal gyrus (MFG), indicating decreased neural activity in these regions. The higher alpha activity was "normalized" following OT administration and under OT, group differences were no longer found. Increased resting-state alpha was associated with lower baseline plasma OT, reduced salivary OT reactivity, and more re-experiencing symptoms. These findings demonstrate effects of OT on resting-state brain functioning in prefrontal regions subserving working memory and cognitive control, which are disrupted in PTSD. Results raise the possibility that OT, traditionally studied in social contexts, may also enhance performance in cognitive tasks associated with working memory and cognitive control following trauma exposure. PMID:26175673

  8. Oxytocin affects spontaneous neural oscillations in trauma-exposed war veterans

    PubMed Central

    Eidelman-Rothman, Moranne; Goldstein, Abraham; Levy, Jonathan; Weisman, Omri; Schneiderman, Inna; Mankuta, David; Zagoory-Sharon, Orna; Feldman, Ruth

    2015-01-01

    Exposure to combat-related trauma often leads to lifetime functional impairments. Previous research demonstrated the effects of oxytocin (OT) administration on brain regions implicated in post-traumatic stress disorder (PTSD); yet OT’s effects on brain patterns in trauma-exposed veterans have not been studied. In the current study the effects of OT on spontaneous brain oscillatory activity were measured in 43 veterans using magnetoencephalography (MEG): 28 veterans who were exposed to a combat-related trauma and 15 trauma-unexposed controls. Participants participated in two experimental sessions and were administered OT or placebo (PBO) in a double-blind, placebo-control, within-subject design. Following OT/PBO administration, participants underwent a whole-head MEG scan. Plasma and salivary OT levels were assessed each session. Spontaneous brain activity measured during a 2-min resting period was subjected to source-localization analysis. Trauma-exposed veterans showed higher resting-state alpha (8–13 Hz) activity compared to controls in the left dorsolateral prefrontal cortex (dlPFC), specifically in the superior frontal gyrus (SFG) and the middle frontal gyrus (MFG), indicating decreased neural activity in these regions. The higher alpha activity was “normalized” following OT administration and under OT, group differences were no longer found. Increased resting-state alpha was associated with lower baseline plasma OT, reduced salivary OT reactivity, and more re-experiencing symptoms. These findings demonstrate effects of OT on resting-state brain functioning in prefrontal regions subserving working memory and cognitive control, which are disrupted in PTSD. Results raise the possibility that OT, traditionally studied in social contexts, may also enhance performance in cognitive tasks associated with working memory and cognitive control following trauma exposure. PMID:26175673

  9. Intelligent CAD approach for modular design

    NASA Astrophysics Data System (ADS)

    Ouyang, Miao-an; Li, Chenggang; Zhong, Yifang; Yu, Jun; Zhou, Ji

    1996-03-01

    In this paper, the technology of Artificial Intelligence is introduced into a modular design and manufacturing for machine tools. The authors present a methodology to realize the modular conceptual design combined with traditional CAD, and develop an intelligent machine tools modular conceptual system. The problem-solving strategies are described in detail. The design model and system architecture are set up. Techniques and their incorporation of expert system, case-based reasoning and artificial neural networks are clarified.

  10. Modular entanglement.

    PubMed

    Gualdi, Giulia; Giampaolo, Salvatore M; Illuminati, Fabrizio

    2011-02-01

    We introduce and discuss the concept of modular entanglement. This is the entanglement that is established between the end points of modular systems composed by sets of interacting moduli of arbitrarily fixed size. We show that end-to-end modular entanglement scales in the thermodynamic limit and rapidly saturates with the number of constituent moduli. We clarify the mechanisms underlying the onset of entanglement between distant and noninteracting quantum systems and its optimization for applications to quantum repeaters and entanglement distribution and sharing. PMID:21405382

  11. Can modular psychological concepts like affect and emotion be assigned to a distinct subset of regional neural circuits?. Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al.

    NASA Astrophysics Data System (ADS)

    Fehr, Thorsten; Herrmann, Manfred

    2015-06-01

    regional brain systems or neural modules, but rather suggest highly complex and cross-linked neural networks individually shaped by livelong learning and experience [e.g., 6,7,10,13]. This holds in particular true for complex emotional phenomena such as aggression or empathy in social interaction [8,13]. It thus remains questionable, whether - beyond primary sensory and motor-processing - a small number of modular sub-systems sufficiently cover the organisation of specific phenomenological and social features of perception and behaviour [7,10].

  12. Use of pruned computational neural networks for processing the response of oscillating chemical reactions with a view to analyzing nonlinear multicomponent mixtures.

    PubMed

    Hervás, C; Toledo, R; Silva, M

    2001-01-01

    The suitability of pruned computational neural networks (CNNs) for resolving nonlinear multicomponent systems involving synergistic effects by use of oscillating chemical reaction-based methods implemented using the analyte pulse perturbation technique is demonstrated. The CNN input data used for this purpose are estimates provided by the Levenberg-Marquardt method in the form of a three-parameter Gaussian curve associated with the singular profile obtained when the oscillating system is perturbed by an analyte mixture. The performance of the proposed method was assessed by applying it to the resolution of mixtures of pyrogallol and gallic acid based on their perturbating effect on a classical oscillating chemical system, viz. the Belousov-Zhabotinskyi reaction. A straightforward network topology (3:3:2, with 18 connections after pruning) allowed the resolution of mixtures of the two analytes in concentration ratios from 1:7 to 6:2 with a standard error of prediction for the testing set of 4.01 and 8.98% for pyrogallol and gallic acid, respectively. The reduced dimensions of the selected CNN architecture allowed a mathematical transformation of the input vector into the output one that can be easily implemented via software. Finally, the suitability of response surface analysis as an alternative to CNNs was also tested. The results were poor (relative errors were high), which confirms that properly selected pruned CNNs are effective tools for solving the analytical problem addressed in this work. PMID:11500128

  13. [Alteration of neural oscillations in hippocampal CA3 area in the fast avoidance response rat before and after electric shock avoidance training].

    PubMed

    Wang, Wei-Wei; Wang, Dan-Dan; Wang, Dan; Guan, Yan; Tang, Ying-Ying; Ye, Zheng; Li, Jing; Li, Min; Zhu, Zai-Man; Pan, Qun-Wan

    2015-10-25

    The purpose of the present study is to explore the relationship of spatial learning ability and specific electrical activities of neural oscillations in the rat. The fast and general avoidance response groups were selected on the basis of the animals' responses to the electric shock in Y type maze, and their local field potentials (LFPs) of hippocampal CA3 area were recorded by wireless telemetry before and after shock avoidance training, respectively. The components of neural oscillations related to spatial identifying and learning ability were analyzed. The results showed that, compared with the general avoidance response group, the fast avoidance response group did not show any differences of LFPs in hippocampal CA3 area before electric shock avoidance trial, but showed significantly increased percentages of 0-10 Hz and 30-40 Hz rhythm in right hippocampal CA3 area after the shock avoidance training (P < 0.01 or P < 0.05). Fast Fourier transform showed that percentage increase of 0-10 Hz band occurred mainly in θ (3-7 Hz) frequency, and 30-40 Hz frequency change was equivalent to the γ1 band. Furthermore, compared with those before training, only the percentages of β, β2 (20-30 Hz) and γ1 rhythm increased (P < 0.01 or P < 0.05) in fast avoidance response rats after training, while the θ rhythm percentage remained unchanged. In contrast, θ rhythm percentage and the large amplitude (intensity: +2.5 - -2.5 db) θ waves in right CA3 area of general avoidance response rats were significantly reduced after training (P < 0.01). These results suggest that the increased percentages of β2 and γ1 rhythm and high-level (unchanged) percentage of θ rhythm in the right hippocampus CA3 area might be related to strong spatial cognition ability of fast avoidance response rats. PMID:26490066

  14. Active mass damper system for high-rise buildings using neural oscillator and position controller considering stroke limitation of the auxiliary mass

    NASA Astrophysics Data System (ADS)

    Hongu, J.; Iba, D.; Nakamura, M.; Moriwaki, I.

    2016-04-01

    This paper proposes a problem-solving method for the stroke limitation problem, which is related to auxiliary masses of active mass damper systems for high-rise buildings. The proposed method is used in a new simple control system for the active mass dampers mimicking the motion of bipedal mammals, which has a neural oscillator synchronizing with the acceleration response of structures and a position controller. In the system, the travel distance and direction of the auxiliary mass of the active mass damper is determined by reference to the output of the neural oscillator, and then, the auxiliary mass is transferred to the decided location by using a PID controller. The one of the purpose of the previouslyproposed system is stroke restriction problem avoidance of the auxiliary mass during large earthquakes by the determination of the desired value within the stroke limitation of the auxiliary mass. However, only applying the limited desired value could not rigorously restrict the auxiliary mass within the limitation, because the excessive inertia force except for the control force produced by the position controller affected on the motion of the auxiliary mass. In order to eliminate the effect on the auxiliary mass by the structural absolute acceleration, a cancellation method is introduced by adding a term to the control force of the position controller. We first develop the previously-proposed system for the active mass damper and the additional term for cancellation, and verity through numerical experiments that the new system is able to operate the auxiliary mass within the restriction during large earthquakes. Based on the comparison of the proposed system with the LQ system, a conclusion was drawn regarding which the proposed neuronal system with the additional term appears to be able to limit the stroke of the auxiliary mass of the AMD.

  15. A modular 256-channel micro electrode array platform for in vitro and in vivo neural stimulation and recording: BioMEA.

    PubMed

    Charvet, G; Billoint, O; Gharbi, S; Heuschkel, M; Georges, C; Kauffmann, T; Pellissier, A; Yvert, B; Guillemaud, R

    2010-01-01

    In order to understand the dynamics of large neural networks, where information is widely distributed over thousands of cells, one of today's challenges is to successfully monitor the simultaneous activity of as many neurons as possible. This is made possible by using the Micro-Electrode Array (MEA) technology allowing neural cell culture and/or tissue slice experimentation in vitro. Thanks to development of microelectronics' technologies, a novel data acquisition system based on MEA technology has been developed, the BioMEA™. It combines the most advanced MEA biochips with integrated electronics, and a novel user-friendly software interface. To move from prototype (result of the RMNT research project NEUROCOM) to manufactured product, a number of changes have been made. Here, we present a 256-channel MEA data acquisition system with integrated electronics (BioMEA™) allowing simultaneous recording and stimulation of neural networks for in vitro and in vivo applications. This integration is a first step towards an implantable device for BCI (Brain Computer Interface) studies and neural prosthesis. PMID:21095937

  16. Early gamma oscillations during rapid auditory processing in children with a language-learning impairment: Changes in neural mass activity after training

    PubMed Central

    Heim, Sabine; Keil, Andreas; Choudhury, Naseem; Friedman, Jennifer Thomas; Benasich, April A.

    2013-01-01

    Children with language-learning impairment (LLI) have consistently shown difficulty with tasks requiring precise, rapid auditory processing. Remediation based on neural plasticity assumes that the temporal precision of neural coding can be improved by intensive training protocols. Here, we examined the extent to which early oscillatory responses in auditory cortex change after audio-visual training, using combined source modeling and time-frequency analysis of the human electroencephalogram (EEG). Twenty-one elementary school students diagnosed with LLI underwent the intervention for an average of 32 days. Pre- and post-training assessments included standardized language/literacy tests and EEG recordings in response to fast-rate tone doublets. Twelve children with typical language development were also tested twice, with no intervention given. Behaviorally, improvements on measures of language were observed in the LLI group following completion of training. During the first EEG assessment, we found reduced amplitude and phase-locking of early (45–75 ms) oscillations in the gamma-band range (29–52 Hz), specifically in the LLI group, for the second stimulus of the tone doublet. Amplitude reduction for the second tone was no longer evident for the LLI children post-intervention, although these children still exhibited attenuated phase-locking. Our findings suggest that specific aspects of inefficient sensory cortical processing in LLI are ameliorated after training. PMID:23352997

  17. Early gamma oscillations during rapid auditory processing in children with a language-learning impairment: changes in neural mass activity after training.

    PubMed

    Heim, Sabine; Keil, Andreas; Choudhury, Naseem; Thomas Friedman, Jennifer; Benasich, April A

    2013-04-01

    Children with language-learning impairment (LLI) have consistently shown difficulty with tasks requiring precise, rapid auditory processing. Remediation based on neural plasticity assumes that the temporal precision of neural coding can be improved by intensive training protocols. Here, we examined the extent to which early oscillatory responses in auditory cortex change after audio-visual training, using combined source modeling and time-frequency analysis of the human electroencephalogram (EEG). Twenty-one elementary school students diagnosed with LLI underwent the intervention for an average of 32 days. Pre- and post-training assessments included standardized language/literacy tests and EEG recordings in response to fast-rate tone doublets. Twelve children with typical language development were also tested twice, with no intervention given. Behaviorally, improvements on measures of language were observed in the LLI group following completion of training. During the first EEG assessment, we found reduced amplitude and phase-locking of early (45-75 ms) oscillations in the gamma-band range (29-52 Hz), specifically in the LLI group, for the second stimulus of the tone doublet. Amplitude reduction for the second tone was no longer evident for the LLI children post-intervention, although these children still exhibited attenuated phase-locking. Our findings suggest that specific aspects of inefficient sensory cortical processing in LLI are ameliorated after training. PMID:23352997

  18. The brain's resting-state activity is shaped by synchronized cross-frequency coupling of neural oscillations

    PubMed Central

    Florin, Esther; Baillet, Sylvain

    2015-01-01

    Functional imaging of the resting brain consistently reveals broad motifs of correlated blood oxygen level dependent (BOLD) activity that engage cerebral regions from distinct functional systems. Yet, the neurophysiological processes underlying these organized, large-scale fluctuations remain to be uncovered. Using magnetoencephalography (MEG) imaging during rest in 12 healthy subjects we analyse the resting state networks and their underlying neurophysiology. We first demonstrate non-invasively that cortical occurrences of high-frequency oscillatory activity are conditioned to the phase of slower spontaneous fluctuations in neural ensembles. We further show that resting-state networks emerge from synchronized phase-amplitude coupling across the brain. Overall, these findings suggest a unified principle of local-to-global neural signaling for long-range brain communication. PMID:25680519

  19. Modular shield

    DOEpatents

    Snyder, Keith W.

    2002-01-01

    A modular system for containing projectiles has a sheet of material including at least a polycarbonate layer held by a metal frame having a straight frame member corresponding to each straight edge of the sheet. Each frame member has a U-shaped shield channel covering and holding a straight edge of the sheet and an adjacent U-shaped clamp channel rigidly held against the shield channel. A flexible gasket separates each sheet edge from its respective shield channel; and each frame member is fastened to each adjacent frame member only by clamps extending between adjacent clamp channels.

  20. Experience Drives Synchronization: The phase and Amplitude Dynamics of Neural Oscillations to Musical Chords Are Differentially Modulated by Musical Expertise

    PubMed Central

    Pallesen, Karen Johanne; Bailey, Christopher J.; Brattico, Elvira; Gjedde, Albert; Palva, J. Matias; Palva, Satu

    2015-01-01

    Musical expertise is associated with structural and functional changes in the brain that underlie facilitated auditory perception. We investigated whether the phase locking (PL) and amplitude modulations (AM) of neuronal oscillations in response to musical chords are correlated with musical expertise and whether they reflect the prototypicality of chords in Western tonal music. To this aim, we recorded magnetoencephalography (MEG) while musicians and non-musicians were presented with common prototypical major and minor chords, and with uncommon, non-prototypical dissonant and mistuned chords, while watching a silenced movie. We then analyzed the PL and AM of ongoing oscillations in the theta (4–8 Hz) alpha (8–14 Hz), beta- (14–30 Hz) and gamma- (30–80 Hz) bands to these chords. We found that musical expertise was associated with strengthened PL of ongoing oscillations to chords over a wide frequency range during the first 300 ms from stimulus onset, as opposed to increased alpha-band AM to chords over temporal MEG channels. In musicians, the gamma-band PL was strongest to non-prototypical compared to other chords, while in non-musicians PL was strongest to minor chords. In both musicians and non-musicians the long-latency (> 200 ms) gamma-band PL was also sensitive to chord identity, and particularly to the amplitude modulations (beats) of the dissonant chord. These findings suggest that musical expertise modulates oscillation PL to musical chords and that the strength of these modulations is dependent on chord prototypicality. PMID:26291324

  1. Effects of Dopamine and Serotonin Systems on Modulating Neural Oscillations in Hippocampus-Prefrontal Cortex Pathway in Rats.

    PubMed

    Xu, Xiaxia; Zheng, Chenguang; An, Lei; Wang, Rubin; Zhang, Tao

    2016-07-01

    Theta and gamma oscillations are believed to play an important role in cognition and memory, and their phase coupling facilitates the information transmission in hippocampal-cortex network. In a rat model of chronic stress, the phase coupling of both theta and gamma oscillations between ventral hippocampal CA1 (vCA1) and medial prefrontal cortex (mPFC) was found to be disrupted, which was associated with the impaired synaptic plasticity in the pathway. However, little was known about the mechanisms underlying the process. In order to address this issue, both dopamine and serotonin as monoaminergic neurotransmitters were involved in this study, since they were crucial factors in pathological basis of depressive disorder. Local field potentials (LFPs) were recorded simultaneously at both vCA1 and mPFC regions under anesthesia, before and after the injection of dopamine D1 receptor antagonist and 5-HT1A receptor agonist, respectively. The results showed that the blockage of D1 receptor could lead to depression-like decrement on theta phase coupling. In addition, the activation of 5-HT1A receptor enhanced vCA1-mPFC coupling on gamma oscillations, and attenuated CA1 theta-fast gamma cross frequency coupling. These data suggest that the theta phase coupling between vCA1 and mPFC may be modulated by dopamine system that is an underlying mechanism of the cognitive dysfunction in depression. Besides, the serotonergic system is probably involved in the regulation of gamma oscillations coupling in vCA1-mPFC network. PMID:26969669

  2. Experience Drives Synchronization: The phase and Amplitude Dynamics of Neural Oscillations to Musical Chords Are Differentially Modulated by Musical Expertise.

    PubMed

    Pallesen, Karen Johanne; Bailey, Christopher J; Brattico, Elvira; Gjedde, Albert; Palva, J Matias; Palva, Satu

    2015-01-01

    Musical expertise is associated with structural and functional changes in the brain that underlie facilitated auditory perception. We investigated whether the phase locking (PL) and amplitude modulations (AM) of neuronal oscillations in response to musical chords are correlated with musical expertise and whether they reflect the prototypicality of chords in Western tonal music. To this aim, we recorded magnetoencephalography (MEG) while musicians and non-musicians were presented with common prototypical major and minor chords, and with uncommon, non-prototypical dissonant and mistuned chords, while watching a silenced movie. We then analyzed the PL and AM of ongoing oscillations in the theta (4-8 Hz) alpha (8-14 Hz), beta- (14-30 Hz) and gamma- (30-80 Hz) bands to these chords. We found that musical expertise was associated with strengthened PL of ongoing oscillations to chords over a wide frequency range during the first 300 ms from stimulus onset, as opposed to increased alpha-band AM to chords over temporal MEG channels. In musicians, the gamma-band PL was strongest to non-prototypical compared to other chords, while in non-musicians PL was strongest to minor chords. In both musicians and non-musicians the long-latency (> 200 ms) gamma-band PL was also sensitive to chord identity, and particularly to the amplitude modulations (beats) of the dissonant chord. These findings suggest that musical expertise modulates oscillation PL to musical chords and that the strength of these modulations is dependent on chord prototypicality. PMID:26291324

  3. Organization of Anti-Phase Synchronization Pattern in Neural Networks: What are the Key Factors?

    PubMed Central

    Li, Dong; Zhou, Changsong

    2011-01-01

    Anti-phase oscillation has been widely observed in cortical neural network. Elucidating the mechanism underlying the organization of anti-phase pattern is of significance for better understanding more complicated pattern formations in brain networks. In dynamical systems theory, the organization of anti-phase oscillation pattern has usually been considered to relate to time delay in coupling. This is consistent to conduction delays in real neural networks in the brain due to finite propagation velocity of action potentials. However, other structural factors in cortical neural network, such as modular organization (connection density) and the coupling types (excitatory or inhibitory), could also play an important role. In this work, we investigate the anti-phase oscillation pattern organized on a two-module network of either neuronal cell model or neural mass model, and analyze the impact of the conduction delay times, the connection densities, and coupling types. Our results show that delay times and coupling types can play key roles in this organization. The connection densities may have an influence on the stability if an anti-phase pattern exists due to the other factors. Furthermore, we show that anti-phase synchronization of slow oscillations can be achieved with small delay times if there is interaction between slow and fast oscillations. These results are significant for further understanding more realistic spatiotemporal dynamics of cortico-cortical communications. PMID:22232576

  4. Modular Certification

    NASA Technical Reports Server (NTRS)

    Rushby, John; Miner, Paul S. (Technical Monitor)

    2002-01-01

    Airplanes are certified as a whole: there is no established basis for separately certifying some components, particularly software-intensive ones, independently of their specific application in a given airplane. The absence of separate certification inhibits the development of modular components that could be largely "precertified" and used in several different contexts within a single airplane, or across many different airplanes. In this report, we examine the issues in modular certification of software components and propose an approach based on assume-guarantee reasoning. We extend the method from verification to certification by considering behavior in the presence of failures. This exposes the need for partitioning, and separation of assumptions and guarantees into normal and abnormal cases. We then identify three classes of property that must be verified within this framework: safe function, true guarantees, and controlled failure. We identify a particular assume-guarantee proof rule (due to McMillan) that is appropriate to the applications considered, and formally verify its soundness in PVS.

  5. microRNA input into a neural ultradian oscillator controls emergence and timing of alternative cell states

    PubMed Central

    Goodfellow, Marc; Phillips, Nicholas E.; Manning, Cerys; Galla, Tobias; Papalopulu, Nancy

    2014-01-01

    Progenitor maintenance, timed differentiation and the potential to enter quiescence are three fundamental processes that underlie the development of any organ system. In the nervous system, progenitor cells show short-period oscillations in the expression of the transcriptional repressor Hes1, while neurons and quiescent progenitors show stable low and high levels of Hes1, respectively. Here we use experimental data to develop a mathematical model of the double-negative interaction between Hes1 and a microRNA, miR-9, with the aim of understanding how cells transition from one state to another. We show that the input of miR-9 into the Hes1 oscillator tunes its oscillatory dynamics, and endows the system with bistability and the ability to measure time to differentiation. Our results suggest that a relatively simple and widespread network of cross-repressive interactions provides a unifying framework for progenitor maintenance, the timing of differentiation and the emergence of alternative cell states. PMID:24595054

  6. Slow noise in the period of a biological oscillator underlies gradual trends and abrupt transitions in phasic relationships in hybrid neural networks.

    PubMed

    Thounaojam, Umeshkanta S; Cui, Jianxia; Norman, Sharon E; Butera, Robert J; Canavier, Carmen C

    2014-05-01

    In order to study the ability of coupled neural oscillators to synchronize in the presence of intrinsic as opposed to synaptic noise, we constructed hybrid circuits consisting of one biological and one computational model neuron with reciprocal synaptic inhibition using the dynamic clamp. Uncoupled, both neurons fired periodic trains of action potentials. Most coupled circuits exhibited qualitative changes between one-to-one phase-locking with fairly constant phasic relationships and phase slipping with a constant progression in the phasic relationships across cycles. The phase resetting curve (PRC) and intrinsic periods were measured for both neurons, and used to construct a map of the firing intervals for both the coupled and externally forced (PRC measurement) conditions. For the coupled network, a stable fixed point of the map predicted phase locking, and its absence produced phase slipping. Repetitive application of the map was used to calibrate different noise models to simultaneously fit the noise level in the measurement of the PRC and the dynamics of the hybrid circuit experiments. Only a noise model that added history-dependent variability to the intrinsic period could fit both data sets with the same parameter values, as well as capture bifurcations in the fixed points of the map that cause switching between slipping and locking. We conclude that the biological neurons in our study have slowly-fluctuating stochastic dynamics that confer history dependence on the period. Theoretical results to date on the behavior of ensembles of noisy biological oscillators may require re-evaluation to account for transitions induced by slow noise dynamics. PMID:24830924

  7. Gamma Oscillation in Schizophrenia

    PubMed Central

    O'Donnell, Brian F.; Youn, Soyoung; Kwon, Jun Soo

    2011-01-01

    Dysfunctional neural circuitry has been found to be involved in abnormalities of perception and cognition in patients with schizophrenia. Gamma oscillations are essential for integrating information within neural circuits and have therefore been associated with many perceptual and cognitive processes in healthy human subjects and animals. This review presents an overview of the neural basis of gamma oscillations and the abnormalities in the GABAergic interneuronal system thought to be responsible for gamma-range deficits in schizophrenia. We also review studies of gamma activity in sensory and cognitive processes, including auditory steady state response, attention, object representation, and working memory, in animals, healthy humans and patients with schizophrenia. PMID:22216037

  8. Modular robot

    DOEpatents

    Ferrante, T.A.

    1997-11-11

    A modular robot may comprise a main body having a structure defined by a plurality of stackable modules. The stackable modules may comprise a manifold, a valve module, and a control module. The manifold may comprise a top surface and a bottom surface having a plurality of fluid passages contained therein, at least one of the plurality of fluid passages terminating in a valve port located on the bottom surface of the manifold. The valve module is removably connected to the manifold and selectively fluidically connects the plurality of fluid passages contained in the manifold to a supply of pressurized fluid and to a vent. The control module is removably connected to the valve module and actuates the valve module to selectively control a flow of pressurized fluid through different ones of the plurality of fluid passages in the manifold. The manifold, valve module, and control module are mounted together in a sandwich-like manner and comprise a main body. A plurality of leg assemblies are removably connected to the main body and are removably fluidically connected to the fluid passages in the manifold so that each of the leg assemblies can be selectively actuated by the flow of pressurized fluid in different ones of the plurality of fluid passages in the manifold. 12 figs.

  9. Modular robot

    DOEpatents

    Ferrante, Todd A.

    1997-01-01

    A modular robot may comprise a main body having a structure defined by a plurality of stackable modules. The stackable modules may comprise a manifold, a valve module, and a control module. The manifold may comprise a top surface and a bottom surface having a plurality of fluid passages contained therein, at least one of the plurality of fluid passages terminating in a valve port located on the bottom surface of the manifold. The valve module is removably connected to the manifold and selectively fluidically connects the plurality of fluid passages contained in the manifold to a supply of pressurized fluid and to a vent. The control module is removably connected to the valve module and actuates the valve module to selectively control a flow of pressurized fluid through different ones of the plurality of fluid passages in the manifold. The manifold, valve module, and control module are mounted together in a sandwich-like manner and comprise a main body. A plurality of leg assemblies are removably connected to the main body and are removably fluidically connected to the fluid passages in the manifold so that each of the leg assemblies can be selectively actuated by the flow of pressurized fluid in different ones of the plurality of fluid passages in the manifold.

  10. Neural Consequences of Increasing Body Weight: Evidence from Somatosensory Evoked Potentials and the Frequency-Specificity of Brain Oscillations

    PubMed Central

    Lhomond, Olivia; Teasdale, Normand; Simoneau, Martin; Mouchnino, Laurence

    2016-01-01

    Previous studies on the control of human balance suggested that increased pressure under the feet, leading to reduced plantar sole mechanoreceptors sensitivity, increases body sway. Although this suggestion is attracting, it is unclear whether increased plantar sole pressure simply reduces the transmission of plantar sole afferent to the cortex or also alters the sensorimotor integrative mechanisms. Here we used electrical stimulation applied under the sole of the foot to probe the sensorimotor mechanisms processing foot mechanoreceptors. Balance control of healthy individuals was assessed either when wearing a loaded vest or in normal-weight condition. In the Loaded condition, we observed decreased cortical activity over the primary somatosensory cortex (SI) for both an early P50-N90 somatosensory evoked potential (SEP) and for oscillatory brain activity within the gamma band (30–80 Hz). These reductions were interpreted as a disrupted early sensory transmission (i.e., decreased early SEP) leading to a decreased perception of plantar sole sensory information (i.e., decreased gamma band power). These early sensory mechanisms for the Loaded condition were associated with an increase in the late P170-N210 SEP and oscillatory brain activity within the beta band (19–24 Hz). These neural signatures involved areas which are engaged in sensorimotor integrative processes (secondary somatosensory cortex (SII) and right temporoparietal junction). Altered early and late sensory processes may result from the increase pressure on the mechanoreceptors of the foot sole and not from postural instability per se. Indeed, postural instability with normal weight condition did not lead to SEP changes. PMID:27445758

  11. Modularity Induced Gating and Delays in Neuronal Networks.

    PubMed

    Shein-Idelson, Mark; Cohen, Gilad; Ben-Jacob, Eshel; Hanein, Yael

    2016-04-01

    Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. PMID:27104350

  12. Modularity Induced Gating and Delays in Neuronal Networks

    PubMed Central

    Shein-Idelson, Mark; Cohen, Gilad; Hanein, Yael

    2016-01-01

    Neural networks, despite their highly interconnected nature, exhibit distinctly localized and gated activation. Modularity, a distinctive feature of neural networks, has been recently proposed as an important parameter determining the manner by which networks support activity propagation. Here we use an engineered biological model, consisting of engineered rat cortical neurons, to study the role of modular topology in gating the activity between cell populations. We show that pairs of connected modules support conditional propagation (transmitting stronger bursts with higher probability), long delays and propagation asymmetry. Moreover, large modular networks manifest diverse patterns of both local and global activation. Blocking inhibition decreased activity diversity and replaced it with highly consistent transmission patterns. By independently controlling modularity and disinhibition, experimentally and in a model, we pose that modular topology is an important parameter affecting activation localization and is instrumental for population-level gating by disinhibition. PMID:27104350

  13. Reduction in menopause-related symptoms associated with use of a noninvasive neurotechnology for autocalibration of neural oscillations

    PubMed Central

    Tegeler, Charles H.; Tegeler, Catherine L.; Cook, Jared F.; Lee, Sung W.; Pajewski, Nicholas M.

    2015-01-01

    Abstract Objective Increased amplitudes in high-frequency brain electrical activity are reported with menopausal hot flashes. We report outcomes associated with the use of High-resolution, relational, resonance-based, electroencephalic mirroring—a noninvasive neurotechnology for autocalibration of neural oscillations—by women with perimenopausal and postmenopausal hot flashes. Methods Twelve women with hot flashes (median age, 56 y; range, 46-69 y) underwent a median of 13 (range, 8-23) intervention sessions for a median of 9.5 days (range, 4-32). This intervention uses algorithmic analysis of brain electrical activity and near real-time translation of brain frequencies into variable tones for acoustic stimulation. Hot flash frequency and severity were recorded by daily diary. Primary outcomes included hot flash severity score, sleep, and depressive symptoms. High-frequency amplitudes (23-36 Hz) from bilateral temporal scalp recordings were measured at baseline and during serial sessions. Self-reported symptom inventories for sleep and depressive symptoms were collected. Results The median change in hot flash severity score was −0.97 (range, −3.00 to 1.00; P = 0.015). Sleep and depression scores decreased by −8.5 points (range, −20 to −1; P = 0.022) and −5.5 points (range, −32 to 8; P = 0.015), respectively. The median sum of amplitudes for the right and left temporal high-frequency brain electrical activity was 8.44 μV (range, 6.27-16.66) at baseline and decreased by a median of −2.96 μV (range, −11.05 to −0.65; P = 0.0005) by the final session. Conclusions Hot flash frequency and severity, symptoms of insomnia and depression, and temporal high-frequency brain electrical activity decrease after High-resolution, relational, resonance-based, electroencephalic mirroring. Larger controlled trials with longer follow-up are warranted. PMID:25668305

  14. The effects of high-frequency oscillations in hippocampal electrical activities on the classification of epileptiform events using artificial neural networks

    NASA Astrophysics Data System (ADS)

    Chiu, Alan W. L.; Jahromi, Shokrollah S.; Khosravani, Houman; Carlen, Peter L.; Bardakjian, Berj L.

    2006-03-01

    The existence of hippocampal high-frequency electrical activities (greater than 100 Hz) during the progression of seizure episodes in both human and animal experimental models of epilepsy has been well documented (Bragin A, Engel J, Wilson C L, Fried I and Buzsáki G 1999 Hippocampus 9 137-42 Khosravani H, Pinnegar C R, Mitchell J R, Bardakjian B L, Federico P and Carlen P L 2005 Epilepsia 46 1-10). However, this information has not been studied between successive seizure episodes or utilized in the application of seizure classification. In this study, we examine the dynamical changes of an in vitro low Mg2+ rat hippocampal slice model of epilepsy at different frequency bands using wavelet transforms and artificial neural networks. By dividing the time-frequency spectrum of each seizure-like event (SLE) into frequency bins, we can analyze their burst-to-burst variations within individual SLEs as well as between successive SLE episodes. Wavelet energy and wavelet entropy are estimated for intracellular and extracellular electrical recordings using sufficiently high sampling rates (10 kHz). We demonstrate that the activities of high-frequency oscillations in the 100-400 Hz range increase as the slice approaches SLE onsets and in later episodes of SLEs. Utilizing the time-dependent relationship between different frequency bands, we can achieve frequency-dependent state classification. We demonstrate that activities in the frequency range 100-400 Hz are critical for the accurate classification of the different states of electrographic seizure-like episodes (containing interictal, preictal and ictal states) in brain slices undergoing recurrent spontaneous SLEs. While preictal activities can be classified with an average accuracy of 77.4 ± 6.7% utilizing the frequency spectrum in the range 0-400 Hz, we can also achieve a similar level of accuracy by using a nonlinear relationship between 100-400 Hz and <4 Hz frequency bands only.

  15. Modern Schools? Think Modular!

    ERIC Educational Resources Information Center

    Jackson, Lisa M.

    1998-01-01

    Examines how modular educational facilities can provide a viable alternative in building construction when speed and safety are key construction issues. Explains the durability of modular structures, their adherence to building codes, and the flexibility that they provide in design and appearance. The advantages to permanent modular construction…

  16. Portable modular detection system

    DOEpatents

    Brennan, James S.; Singh, Anup; Throckmorton, Daniel J.; Stamps, James F.

    2009-10-13

    Disclosed herein are portable and modular detection devices and systems for detecting electromagnetic radiation, such as fluorescence, from an analyte which comprises at least one optical element removably attached to at least one alignment rail. Also disclosed are modular detection devices and systems having an integrated lock-in amplifier and spatial filter and assay methods using the portable and modular detection devices.

  17. Dynamics of overlapping structures in modular networks.

    PubMed

    Almendral, J A; Leyva, I; Li, D; Sendiña-Nadal, I; Havlin, S; Boccaletti, S

    2010-07-01

    Modularity is a fundamental feature of real networks, being intimately bounded to their functionality, i.e., to their capability of performing parallel tasks in a coordinated way. Although the modular structure of real graphs has been intensively studied, very little is known on the interactions between functional modules of a graph. Here, we present a general method based on synchronization of networking oscillators, that is able to detect overlapping structures in multimodular environments. We furthermore report the full analytical and theoretical description on the relationship between the overlapping dynamics and the underlying network topology. The method is illustrated by means of a series of applications. PMID:20866697

  18. Detecting complex network modularity by dynamical clustering

    NASA Astrophysics Data System (ADS)

    Boccaletti, S.; Ivanchenko, M.; Latora, V.; Pluchino, A.; Rapisarda, A.

    2007-04-01

    Based on cluster desynchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort O(KN) on a generic graph with N nodes and K links.

  19. Nature's Autonomous Oscillators

    NASA Technical Reports Server (NTRS)

    Mayr, H. G.; Yee, J.-H.; Mayr, M.; Schnetzler, R.

    2012-01-01

    Nonlinearity is required to produce autonomous oscillations without external time dependent source, and an example is the pendulum clock. The escapement mechanism of the clock imparts an impulse for each swing direction, which keeps the pendulum oscillating at the resonance frequency. Among nature's observed autonomous oscillators, examples are the quasi-biennial oscillation and bimonthly oscillation of the Earth atmosphere, and the 22-year solar oscillation. The oscillations have been simulated in numerical models without external time dependent source, and in Section 2 we summarize the results. Specifically, we shall discuss the nonlinearities that are involved in generating the oscillations, and the processes that produce the periodicities. In biology, insects have flight muscles, which function autonomously with wing frequencies that far exceed the animals' neural capacity; Stretch-activation of muscle contraction is the mechanism that produces the high frequency oscillation of insect flight, discussed in Section 3. The same mechanism is also invoked to explain the functioning of the cardiac muscle. In Section 4, we present a tutorial review of the cardio-vascular system, heart anatomy, and muscle cell physiology, leading up to Starling's Law of the Heart, which supports our notion that the human heart is also a nonlinear oscillator. In Section 5, we offer a broad perspective of the tenuous links between the fluid dynamical oscillators and the human heart physiology.

  20. Hebbian learning in parallel and modular memories.

    PubMed

    Poon, C S; Shah, J V

    1998-02-01

    Many cognitive and sensorimotor functions in the brain involve parallel and modular memory subsystems that are adapted by activity-dependent Hebbian synaptic plasticity. This is in contrast to the multilayer perceptron model of supervised learning where sensory information is presumed to be integrated by a common pool of hidden units through backpropagation learning. Here we show that Hebbian learning in parallel and modular memories is more advantageous than backpropagation learning in lumped memories in two respects: it is computationally much more efficient and structurally much simpler to implement with biological neurons. Accordingly, we propose a more biologically relevant neural network model, called a tree-like perceptron, which is a simple modification of the multilayer perceptron model to account for the general neural architecture, neuronal specificity, and synaptic learning rule in the brain. The model features a parallel and modular architecture in which adaptation of the input-to-hidden connection follows either a Hebbian or anti-Hebbian rule depending on whether the hidden units are excitatory or inhibitory, respectively. The proposed parallel and modular architecture and implicit interplay between the types of synaptic plasticity and neuronal specificity are exhibited by some neocortical and cerebellar systems. PMID:9525034

  1. Modularity of Prosthetic Implants.

    PubMed

    Barrack

    1994-01-01

    The vast majority of total-joint-replacement components currently utilized are modular to some degree. Modularity reduces inventory and increases the surgeon's options in both primary and revision total-joint arthroplasty. Use of a modular interface, however, increases the risk of fretting, wear debris, and dissociation and mismatching of components. The use of modular heads in total hip replacement is firmly established. The occurrence of corrosion and fretting has been recognized, and most manufacturers have improved the quality of the interface to minimize these problems. Modular polyethylene liners also offer advantages, particularly in revision procedures, where the option of additional screw fixation remains important. Many uncemented acetabular components are inserted without screws, which may generate renewed interest in one-piece factory-preassembled components. The conformity, locking mechanism, and nonarticular interface of modular acetabular components have all been studied and improved. Modular tibial components offer additional flexibility in the performance of total knee replacement but introduce the risk of dissociation and increased polyethylene wear; in revision procedures, modularity provides a valuable option for dealing with bone loss and an additional method of fixation by means of press-fit stems. Modular humeral components offer a significant advantage with limited apparent risk; however, longer clinical experience is required to assess potential problems. PMID:10708990

  2. Small Modular Biomass Systems

    SciTech Connect

    2002-12-01

    This fact sheet provides information about modular biomass systems. Small modular biomass systems can help supply electricity to rural areas, businesses, and the billions of people who live without power worldwide. These systems use locally available biomass fuels such as wood, crop waste, animal manures, and landfill gas.

  3. Modular Buildings Buying Guide.

    ERIC Educational Resources Information Center

    Morris, Susan

    1991-01-01

    Suggests that child care program directors who are expanding their programs or opening new child care centers investigate the possibility of renting, leasing, or purchasing a modular building. Discusses the advantages of modular buildings over conventional building construction or rented space in an occupied building. Provides information about…

  4. Modular avionic architectures

    NASA Astrophysics Data System (ADS)

    Trujillo, Edward

    The author presents an analysis revealing some of the salient features of modular avionics. A decomposition of the modular avionics concept is performed, highlighting some of the key features of such architectures. Several layers of architecture can be found in such concepts, including those relating to software structure, communication, and supportability. Particular emphasis is placed on the layer relating to partitioning, which gives rise to those features of integration, modularity, and commonality. Where integration is the sharing of common tasks or items to gain efficiency and flexibility, modularity is the partitioning of a system into reconfigurable and maintainable items, and commonality is partitioning to maximize the use of identical items across the range of applications. Two architectures, MASA (Modular Avionics System Architecture) and Pave Pillar, are considered in particular.

  5. Induced oscillations and the distributed cortical sources during the Wisconsin card sorting test performance in schizophrenic patients: new clues to neural connectivity.

    PubMed

    González-Hernández, J A; Cedeño, I; Pita-Alcorta, C; Galán, L; Aubert, E; Figueredo-Rodríguez, P

    2003-04-01

    Prefrontal dysfunction has been associated with schizophrenia. Activation during Wisconsin card sorting test (WCST) is a common approach used in functional neuroimaging to address this failure. Equally, current knowledge states that oscillations are basic forms of cells-assembly communications during mental activity. Promising results were revealed in a previous study assessing healthy subjects, WCST and oscillations. However, those previous studies failed to meet the functional integration of the network during the WCST in schizophrenics, based on the induced oscillations and their distributed cortical sources. In this research, we utilized the brain electrical tomography (variable-resolution brain electromagnetic tomography) technique to accomplish this goal. Task specific delta, theta, alpha and beta-2 oscillations were induced and simultaneously synchronized over large extensions of cortex, encompassing prefrontal, temporal and posterior regions as in healthy subjects. Every frequency had a well-defined network involving a variable number of areas and sharing some of them. Oscillations at 11.5, 5.0 and 30 Hz seem to reflect an abnormal increase or decrease, being located at supplementary motor area (SMA), left occipitotemporal region (OT), and right frontotemporal subregions (RFT), respectively. Three cortical areas appeared to be critical, that may lead to difficulties either in coordinating/sequencing the input/output of the prefrontal networks-SMA, and retention of information in memory-RFT, both preceded or paralleled by a deficient visual information processing-OT. PMID:12694897

  6. Diversity and Unity of Modularity

    ERIC Educational Resources Information Center

    Seok, Bongrae

    2006-01-01

    Since the publication of Fodor's (1983) The Modularity of Mind, there have been quite a few discussions of cognitive modularity among cognitive scientists. Generally, in those discussions, modularity means a property of specialized cognitive processes or a domain-specific body of information. In actuality, scholars understand modularity in many…

  7. Modular tokamak magnetic system

    DOEpatents

    Yang, Tien-Fang

    1988-01-01

    A modular tokamak system comprised of a plurality of interlocking moldules. Each module is comprised of a vacuum vessel section, a toroidal field coil, moldular saddle coils which generate a poloidal magnetic field and ohmic heating coils.

  8. Modularity in signaling systems

    NASA Astrophysics Data System (ADS)

    Del Vecchio, Domitilla

    2012-08-01

    Modularity is a property by which the behavior of a system does not change upon interconnection. It is crucial for understanding the behavior of a complex system from the behavior of the composing subsystems. Whether modularity holds in biology is an intriguing and largely debated question. In this paper, we discuss this question taking a control system theory view and focusing on signaling systems. In particular, we argue that, despite signaling systems being constituted of structural modules, such as covalent modification cycles, modularity does not hold in general. As in any engineering system, impedance-like effects, called retroactivity, appear at interconnections and alter the behavior of connected modules. We further argue that while signaling systems have evolved sophisticated ways to counter-act retroactivity and enforce modularity, retroactivity may also be exploited to finely control the information processing of signaling pathways. Testable predictions and experimental evidence are discussed with their implications.

  9. Modular avionics packaging standardization

    NASA Astrophysics Data System (ADS)

    Austin, M.; McNichols, J. K.

    The Modular Avionics Packaging (MAP) Program for packaging future military avionics systems with the objective of improving reliability, maintainability, and supportability, and reducing equipment life cycle costs is addressed. The basic MAP packaging concepts called the Standard Avionics Module, the Standard Enclosure, and the Integrated Rack are summarized, and the benefits of modular avionics packaging, including low risk design, technology independence with common functions, improved maintainability and life cycle costs are discussed. Progress made in MAP is briefly reviewed.

  10. Modularity and mental architecture.

    PubMed

    Robbins, Philip

    2013-11-01

    Debates about the modularity of cognitive architecture have been ongoing for at least the past three decades, since the publication of Fodor's landmark book The Modularity of Mind. According to Fodor, modularity is essentially tied to informational encapsulation, and as such is only found in the relatively low-level cognitive systems responsible for perception and language. According to Fodor's critics in the evolutionary psychology camp, modularity simply reflects the fine-grained functional specialization dictated by natural selection, and it characterizes virtually all aspects of cognitive architecture, including high-level systems for judgment, decision making, and reasoning. Though both of these perspectives on modularity have garnered support, the current state of evidence and argument suggests that a broader skepticism about modularity may be warranted. WIREs Cogn Sci 2013, 4:641-649. doi: 10.1002/wcs.1255 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. PMID:26304269

  11. Neural Networks Of VLSI Components

    NASA Technical Reports Server (NTRS)

    Eberhardt, Silvio P.

    1991-01-01

    Concept for design of electronic neural network calls for assembly of very-large-scale integrated (VLSI) circuits of few standard types. Each VLSI chip, which contains both analog and digital circuitry, used in modular or "building-block" fashion by interconnecting it in any of variety of ways with other chips. Feedforward neural network in typical situation operates under control of host computer and receives inputs from, and sends outputs to, other equipment.

  12. Brain oscillations in neuropsychiatric disease.

    PubMed

    Başar, Erol

    2013-09-01

    The term "brain (or neural) oscillations" refers to the rhythmic and/or repetitive electrical activity generated spontaneously and in response to stimuli by neural tissue in the central nervous system. The importance of brain oscillations in sensory-cognitive processes has become increasingly evident. It has also become clear that event-related oscillations are modified in many types of neuropathology, in particular in cognitive impairment. This review discusses methods such as evoked/event-related oscillations and spectra, coherence analysis, and phase locking. It gives examples of applications of essential methods and concepts in bipolar disorder that provide a basis for fundamental notions regarding neurophysiologic biomarkers in cognitive impairment. The take-home message is that in the development of diagnostic and pharmacotherapeutic strategies, neurophysiologic data should be analyzed in a framework that uses a multiplicity of methods and frequency bands. PMID:24174901

  13. Synthetic in vitro transcriptional oscillators.

    PubMed

    Kim, Jongmin; Winfree, Erik

    2011-02-01

    The construction of synthetic biochemical circuits from simple components illuminates how complex behaviors can arise in chemistry and builds a foundation for future biological technologies. A simplified analog of genetic regulatory networks, in vitro transcriptional circuits, provides a modular platform for the systematic construction of arbitrary circuits and requires only two essential enzymes, bacteriophage T7 RNA polymerase and Escherichia coli ribonuclease H, to produce and degrade RNA signals. In this study, we design and experimentally demonstrate three transcriptional oscillators in vitro. First, a negative feedback oscillator comprising two switches, regulated by excitatory and inhibitory RNA signals, showed up to five complete cycles. To demonstrate modularity and to explore the design space further, a positive-feedback loop was added that modulates and extends the oscillatory regime. Finally, a three-switch ring oscillator was constructed and analyzed. Mathematical modeling guided the design process, identified experimental conditions likely to yield oscillations, and explained the system's robust response to interference by short degradation products. Synthetic transcriptional oscillators could prove valuable for systematic exploration of biochemical circuit design principles and for controlling nanoscale devices and orchestrating processes within artificial cells. PMID:21283141

  14. Fractional oscillator.

    PubMed

    Stanislavsky, A A

    2004-11-01

    We consider a fractional oscillator which is a generalization of the conventional linear oscillator in the framework of fractional calculus. It is interpreted as an ensemble average of ordinary harmonic oscillators governed by a stochastic time arrow. The intrinsic absorption of the fractional oscillator results from the full contribution of the harmonic oscillator ensemble: these oscillators differ a little from each other in frequency so that each response is compensated by an antiphase response of another harmonic oscillator. This allows one to draw a parallel in the dispersion analysis for media described by a fractional oscillator and an ensemble of ordinary harmonic oscillators with damping. The features of this analysis are discussed. PMID:15600586

  15. Self Evolving Modular Network

    NASA Astrophysics Data System (ADS)

    Tokunaga, Kazuhiro; Kawabata, Nobuyuki; Furukawa, Tetsuo

    We propose a novel modular network called the Self-Evolving Modular Network (SEEM). The SEEM has a modular network architecture with a graph structure and these following advantages: (1) new modules are added incrementally to allow the network to adapt in a self-organizing manner, and (2) graph's paths are formed based on the relationships between the models represented by modules. The SEEM is expected to be applicable to evolving functions of an autonomous robot in a self-organizing manner through interaction with the robot's environment and categorizing large-scale information. This paper presents the architecture and an algorithm for the SEEM. Moreover, performance characteristic and effectiveness of the network are shown by simulations using cubic functions and a set of 3D-objects.

  16. Symmetric modular torsatron

    DOEpatents

    Rome, J.A.; Harris, J.H.

    1984-01-01

    A fusion reactor device is provided in which the magnetic fields for plasma confinement in a toroidal configuration is produced by a plurality of symmetrical modular coils arranged to form a symmetric modular torsatron referred to as a symmotron. Each of the identical modular coils is helically deformed and comprise one field period of the torsatron. Helical segments of each coil are connected by means of toroidally directed windbacks which may also provide part of the vertical field required for positioning the plasma. The stray fields of the windback segments may be compensated by toroidal coils. A variety of magnetic confinement flux surface configurations may be produced by proper modulation of the winding pitch of the helical segments of the coils, as in a conventional torsatron, winding the helix on a noncircular cross section and varying the poloidal and radial location of the windbacks and the compensating toroidal ring coils.

  17. Modular optical detector system

    DOEpatents

    Horn, Brent A.; Renzi, Ronald F.

    2006-02-14

    A modular optical detector system. The detector system is designed to detect the presence of molecules or molecular species by inducing fluorescence with exciting radiation and detecting the emitted fluorescence. Because the system is capable of accurately detecting and measuring picomolar concentrations it is ideally suited for use with microchemical analysis systems generally and capillary chromatographic systems in particular. By employing a modular design, the detector system provides both the ability to replace various elements of the detector system without requiring extensive realignment or recalibration of the components as well as minimal user interaction with the system. In addition, the modular concept provides for the use and addition of a wide variety of components, including optical elements (lenses and filters), light sources, and detection means, to fit particular needs.

  18. Dynamics of a neural system with a multiscale architecture

    PubMed Central

    Breakspear, Michael; Stam, Cornelis J

    2005-01-01

    The architecture of the brain is characterized by a modular organization repeated across a hierarchy of spatial scales—neurons, minicolumns, cortical columns, functional brain regions, and so on. It is important to consider that the processes governing neural dynamics at any given scale are not only determined by the behaviour of other neural structures at that scale, but also by the emergent behaviour of smaller scales, and the constraining influence of activity at larger scales. In this paper, we introduce a theoretical framework for neural systems in which the dynamics are nested within a multiscale architecture. In essence, the dynamics at each scale are determined by a coupled ensemble of nonlinear oscillators, which embody the principle scale-specific neurobiological processes. The dynamics at larger scales are ‘slaved’ to the emergent behaviour of smaller scales through a coupling function that depends on a multiscale wavelet decomposition. The approach is first explicated mathematically. Numerical examples are then given to illustrate phenomena such as between-scale bifurcations, and how synchronization in small-scale structures influences the dynamics in larger structures in an intuitive manner that cannot be captured by existing modelling approaches. A framework for relating the dynamical behaviour of the system to measured observables is presented and further extensions to capture wave phenomena and mode coupling are suggested. PMID:16087448

  19. Mathematical Analysis and Simulations of the Neural Circuit for Locomotion in Lampreys

    NASA Astrophysics Data System (ADS)

    Zhaoping, Li; Lewis, Alex; Scarpetta, Silvia

    2004-05-01

    We analyze the dynamics of the neural circuit of the lamprey central pattern generator. This analysis provides insight into how neural interactions form oscillators and enable spontaneous oscillations in a network of damped oscillators, which were not apparent in previous simulations or abstract phase oscillator models. We also show how the different behavior regimes (characterized by phase and amplitude relationships between oscillators) of forward or backward swimming, and turning, can be controlled using the neural connection strengths and external inputs.

  20. Modular biowaste monitoring system

    NASA Technical Reports Server (NTRS)

    Fogal, G. L.

    1975-01-01

    The objective of the Modular Biowaste Monitoring System Program was to generate and evaluate hardware for supporting shuttle life science experimental and diagnostic programs. An initial conceptual design effort established requirements and defined an overall modular system for the collection, measurement, sampling and storage of urine and feces biowastes. This conceptual design effort was followed by the design, fabrication and performance evaluation of a flight prototype model urine collection, volume measurement and sampling capability. No operational or performance deficiencies were uncovered as a result of the performance evaluation tests.

  1. Criteria for software modularization

    NASA Technical Reports Server (NTRS)

    Card, David N.; Page, Gerald T.; Mcgarry, Frank E.

    1985-01-01

    A central issue in programming practice involves determining the appropriate size and information content of a software module. This study attempted to determine the effectiveness of two widely used criteria for software modularization, strength and size, in reducing fault rate and development cost. Data from 453 FORTRAN modules developed by professional programmers were analyzed. The results indicated that module strength is a good criterion with respect to fault rate, whereas arbitrary module size limitations inhibit programmer productivity. This analysis is a first step toward defining empirically based standards for software modularization.

  2. Neural rhythmic symphony of human walking observation: Upside-down and Uncoordinated condition on cortical theta, alpha, beta and gamma oscillations

    PubMed Central

    Zarka, David; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Dan, Bernard; Cheron, Guy

    2014-01-01

    Biological motion observation has been recognized to produce dynamic change in sensorimotor activation according to the observed kinematics. Physical plausibility of the spatial-kinematic relationship of human movement may play a major role in the top-down processing of human motion recognition. Here, we investigated the time course of scalp activation during observation of human gait in order to extract and use it on future integrated brain-computer interface using virtual reality (VR). We analyzed event related potentials (ERP), the event related spectral perturbation (ERSP) and the inter-trial coherence (ITC) from high-density EEG recording during video display onset (−200–600 ms) and the steady state visual evoked potentials (SSVEP) inside the video of human walking 3D-animation in three conditions: Normal; Upside-down (inverted images); and Uncoordinated (pseudo-randomly mixed images). We found that early visual evoked response P120 was decreased in Upside-down condition. The N170 and P300b amplitudes were decreased in Uncoordinated condition. In Upside-down and Uncoordinated conditions, we found decreased alpha power and theta phase-locking. As regards gamma oscillation, power was increased during the Upside-down animation and decreased during the Uncoordinated animation. An SSVEP-like response oscillating at about 10 Hz was also described showing that the oscillating pattern is enhanced 300 ms after the heel strike event only in the Normal but not in the Upside-down condition. Our results are consistent with most of previous point-light display studies, further supporting possible use of virtual reality for neurofeedback applications. PMID:25278847

  3. A neural jet charge tagger for the measurement of the B/s0 anti-B/s0 oscillation frequency at CDF

    SciTech Connect

    Lecci, Claudia; /Karlsruhe U., EKP

    2005-07-01

    A Jet Charge Tagger algorithm for b-flavour tagging for the measurement of {Delta}m{sub s} at CDF has been presented. The tagger is based on a b-track probability variable and a b-jet probability variable, both obtained by combining the information available in b{bar b} events with a Neural Network. The tagging power measured on data is 0.917 {+-} 0.031% e+SVT sample; 0.938 {+-} 0.029% {mu}+SVT sample which is {approx}30% larger than the cut based Jet Charge Tagger employed for the B{sub s}{sup 0} mixing analysis presented by CDF at the Winter Conferences 2005. The improved power of the tagger is due to the selection of the b-jet with a Neural Network variable, which uses correlated jet variables in an optimal way. The development of the track and jet probability has profited from studies performed on simulated events, which allowed to understand better the features of b{bar b} events. For the first time in the CDF B group a Monte Carlo sample comprising flavour creation and additional b{bar b} production processes has been examined and compared to Run II data. It has been demonstrated that a Monte Carlo sample with only flavour creation b{bar b} production processes is not sufficient to describe b{bar b} data collected at CDF. The sample with additional processes introduced in this thesis is thus essential for tagging studies. Although the event description is satisfactory, the flavour information in the Monte Carlo sample differs with respect to data. This difference needs to be clarified by further studies. In addition, the track and the jet probabilities are the first official tools based on Neural Networks for B-Physics at CDF. They have proven that the simulation is understood to such an advanced level that Neural Networks can be employed. Further work is going on in this direction: a Soft Electron and a Soft Muon Tagger based on Neural Networks are under development as of now. Several possible tagger setups have been studied and the Jet Charge Tagger reached

  4. The Evolution of Modular Construction.

    ERIC Educational Resources Information Center

    American School & University, 1993

    1993-01-01

    Explores how the myths of modular construction for schools began; also discusses the advances made in steel and modular construction. The major advantages of using permanent modular construction for schools are highlighted, including its rapid construction, use of standard building materials, financial flexibility, and durability. (GR)

  5. Modular Perspectives on Bilingualism.

    ERIC Educational Resources Information Center

    Francis, Norbert

    2002-01-01

    This research review traces the current discussion on models of bilingualism to the contributions of Vygotsky and Luria. Proposes that a modular approach to studying the different aspects of bilingual development promises to chart a course toward finding a broader common ground around research findings and interpretations that appear to be…

  6. State Librarianship: Modular Curriculum.

    ERIC Educational Resources Information Center

    Robbins, Jane; Powell, Anne

    This modular curriculum on state librarianship is designed to be used as a basis for a full-length library science course, instructional segments of several courses, continuing education courses, or workshops. The 20 curriculum modules cover the many facets of state libraries and their activities--history, functions, social and political…

  7. Modular invariant inflation

    NASA Astrophysics Data System (ADS)

    Kobayashi, Tatsuo; Nitta, Daisuke; Urakawa, Yuko

    2016-08-01

    Modular invariance is a striking symmetry in string theory, which may keep stringy corrections under control. In this paper, we investigate a phenomenological consequence of the modular invariance, assuming that this symmetry is preserved as well as in a four dimensional (4D) low energy effective field theory. As a concrete setup, we consider a modulus field T whose contribution in the 4D effective field theory remains invariant under the modular transformation and study inflation drived by T. The modular invariance restricts a possible form of the scalar potenntial. As a result, large field models of inflation are hardly realized. Meanwhile, a small field model of inflation can be still accomodated in this restricted setup. The scalar potential traced during the slow-roll inflation mimics the hilltop potential Vht, but it also has a non-negligible deviation from Vht. Detecting the primordial gravitational waves predicted in this model is rather challenging. Yet, we argue that it may be still possible to falsify this model by combining the information in the reheating process which can be determined self-completely in this setup.

  8. Modularity in robotic systems

    NASA Technical Reports Server (NTRS)

    Tesar, Delbert; Butler, Michael S.

    1989-01-01

    Most robotic systems today are designed one at a time, at a high cost of time and money. This wasteful approach has been necessary because the industry has not established a foundation for the continued evolution of intelligent machines. The next generation of robots will have to be generic, versatile machines capable of absorbing new technology rapidly and economically. This approach is demonstrated in the success of the personal computer, which can be upgraded or expanded with new software and hardware at virtually every level. Modularity is perceived as a major opportunity to reduce the 6 to 7 year design cycle time now required for new robotic manipulators, greatly increasing the breadth and speed of diffusion of robotic systems in manufacturing. Modularity and its crucial role in the next generation of intelligent machines are the focus of interest. The main advantages that modularity provides are examined; types of modules needed to create a generic robot are discussed. Structural modules designed by the robotics group at the University of Texas at Austin are examined to demonstrate the advantages of modular design.

  9. MRV - Modular Robotic Vehicle

    NASA Technical Reports Server (NTRS)

    Ridley, Justin; Bluethmann, Bill

    2015-01-01

    The Modular Robotic Vehicle, or MRV, completed in 2013, was developed at the Johnson Space Center in order to advance technologies which have applications for future vehicles both in space and on Earth. With seating for two people, MRV is a fully electric vehicle modeled as a "city car", suited for busy urban environments.

  10. From neural oscillations to reasoning ability: Simulating the effect of the theta-to-gamma cycle length ratio on individual scores in a figural analogy test.

    PubMed

    Chuderski, Adam; Andrelczyk, Krzysztof

    2015-02-01

    Several existing computational models of working memory (WM) have predicted a positive relationship (later confirmed empirically) between WM capacity and the individual ratio of theta to gamma oscillatory band lengths. These models assume that each gamma cycle represents one WM object (e.g., a binding of its features), whereas the theta cycle integrates such objects into the maintained list. As WM capacity strongly predicts reasoning, it might be expected that this ratio also predicts performance in reasoning tasks. However, no computational model has yet explained how the differences in the theta-to-gamma ratio found among adult individuals might contribute to their scores on a reasoning test. Here, we propose a novel model of how WM capacity constraints figural analogical reasoning, aimed at explaining inter-individual differences in reasoning scores in terms of the characteristics of oscillatory patterns in the brain. In the model, the gamma cycle encodes the bindings between objects/features and the roles they play in the relations processed. Asynchrony between consecutive gamma cycles results from lateral inhibition between oscillating bindings. Computer simulations showed that achieving the highest WM capacity required reaching the optimal level of inhibition. When too strong, this inhibition eliminated some bindings from WM, whereas, when inhibition was too weak, the bindings became unstable and fell apart or became improperly grouped. The model aptly replicated several empirical effects and the distribution of individual scores, as well as the patterns of correlations found in the 100-people sample attempting the same reasoning task. Most importantly, the model's reasoning performance strongly depended on its theta-to-gamma ratio in same way as the performance of human participants depended on their WM capacity. The data suggest that proper regulation of oscillations in the theta and gamma bands may be crucial for both high WM capacity and effective complex

  11. Modular integrated video system

    SciTech Connect

    Gaertner, K.J.; Heaysman, B.; Holt, R.; Sonnier, C.

    1986-01-01

    The Modular Integrated Video System (MIVS) is intended to provide a simple, highly reliable closed circuit television (CCTV) system capable of replacing the IAEA Twin Minolta Film Camera Systems in those safeguards facilities where mains power is readily available, and situations where it is desired to have the CCTV camera separated from the CCTV recording console. This paper describes the MIVS and the Program Plan which is presently being followed for the development, testing, and implementation of the system.

  12. Neuromechanical principles underlying movement modularity and their implications for rehabilitation

    PubMed Central

    Ting, Lena H.; Chiel, Hillel J.; Trumbower, Randy D.; Allen, Jessica L.; McKay, J. Lucas; Hackney, Madeleine E.; Kesar, Trisha M.

    2015-01-01

    Summary Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e. motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual, and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson’s disease. Recent studies provide insights into the yet unknown underlying neural mechanisms of motor modules, motor impairment and motor learning, and may lead to better understanding of the causal nature of modularity and its underlying neural substrates. PMID:25856485

  13. Neuromechanical principles underlying movement modularity and their implications for rehabilitation.

    PubMed

    Ting, Lena H; Chiel, Hillel J; Trumbower, Randy D; Allen, Jessica L; McKay, J Lucas; Hackney, Madeleine E; Kesar, Trisha M

    2015-04-01

    Neuromechanical principles define the properties and problems that shape neural solutions for movement. Although the theoretical and experimental evidence is debated, we present arguments for consistent structures in motor patterns, i.e., motor modules, that are neuromechanical solutions for movement particular to an individual and shaped by evolutionary, developmental, and learning processes. As a consequence, motor modules may be useful in assessing sensorimotor deficits specific to an individual and define targets for the rational development of novel rehabilitation therapies that enhance neural plasticity and sculpt motor recovery. We propose that motor module organization is disrupted and may be improved by therapy in spinal cord injury, stroke, and Parkinson's disease. Recent studies provide insights into the yet-unknown underlying neural mechanisms of motor modules, motor impairment, and motor learning and may lead to better understanding of the causal nature of modularity and its underlying neural substrates. PMID:25856485

  14. Galactic oscillations

    NASA Technical Reports Server (NTRS)

    Miller, R. H.

    1991-01-01

    Long-lived oscillations that act like normal modes are described. The total kinetic energy is found to vary with time by amounts far in excess of the fluctuations expected from the virial theorem, and the variation shows periodic patterns that suggest oscillations. Experimental results indicate that oscillation amplitudes depend on the nature of the model. It is noted that it is difficult to answer questions about likely amplitudes in real galaxies with any confidence at the present time.

  15. Investigation of efficient features for image recognition by neural networks.

    PubMed

    Goltsev, Alexander; Gritsenko, Vladimir

    2012-04-01

    In the paper, effective and simple features for image recognition (named LiRA-features) are investigated in the task of handwritten digit recognition. Two neural network classifiers are considered-a modified 3-layer perceptron LiRA and a modular assembly neural network. A method of feature selection is proposed that analyses connection weights formed in the preliminary learning process of a neural network classifier. In the experiments using the MNIST database of handwritten digits, the feature selection procedure allows reduction of feature number (from 60 000 to 7000) preserving comparable recognition capability while accelerating computations. Experimental comparison between the LiRA perceptron and the modular assembly neural network is accomplished, which shows that recognition capability of the modular assembly neural network is somewhat better. PMID:22391231

  16. Calcium Oscillations

    PubMed Central

    Dupont, Geneviève; Combettes, Laurent; Bird, Gary S.; Putney, James W.

    2011-01-01

    Calcium signaling results from a complex interplay between activation and inactivation of intracellular and extracellular calcium permeable channels. This complexity is obvious from the pattern of calcium signals observed with modest, physiological concentrations of calcium-mobilizing agonists, which typically present as sequential regenerative discharges of stored calcium, a process referred to as calcium oscillations. In this review, we discuss recent advances in understanding the underlying mechanism of calcium oscillations through the power of mathematical modeling. We also summarize recent findings on the role of calcium entry through store-operated channels in sustaining calcium oscillations and in the mechanism by which calcium oscillations couple to downstream effectors. PMID:21421924

  17. New realities of modular construction

    SciTech Connect

    Duty, J.M. Jr. ); Fisher, D. ); Lewis, W.W. )

    1993-12-01

    Modular construction has both advantages and disadvantages. Advantages are safety, reduction of construction time and faster plant startup time, reduced labor cost, weather friendliness, increased quality and efficiency, simultaneous production capability, testing ease and fewer interruptions to an operating plant. Disadvantages are transportation costs, module size limitations, transportation-accessibility needs, increased engineering effort, and offloading and setting needs. These pros and cons were identified by a Construction Industry Institute (C2) task force established in 1989 to assess modular construction strengths and weaknesses. Objective: develop a decision-support tool to evaluate a project's suitability for modularization. The task force first had to learn what drivers influence modularization and then develop a set of characteristics of the ideal project for modularization. To help in this research, academics from the University of Houston and Purdue University developed MODEX, an expert system which became the decision-support tool. The paper first discusses the myths of modularization and then describes MODEX.

  18. Stochastic cellular automata model of neural networks.

    PubMed

    Goltsev, A V; de Abreu, F V; Dorogovtsev, S N; Mendes, J F F

    2010-06-01

    We propose a stochastic dynamical model of noisy neural networks with complex architectures and discuss activation of neural networks by a stimulus, pacemakers, and spontaneous activity. This model has a complex phase diagram with self-organized active neural states, hybrid phase transitions, and a rich array of behaviors. We show that if spontaneous activity (noise) reaches a threshold level then global neural oscillations emerge. Stochastic resonance is a precursor of this dynamical phase transition. These oscillations are an intrinsic property of even small groups of 50 neurons. PMID:20866454

  19. Robotic hand with modular extensions

    DOEpatents

    Salisbury, Curt Michael; Quigley, Morgan

    2015-01-20

    A robotic device is described herein. The robotic device includes a frame that comprises a plurality of receiving regions that are configured to receive a respective plurality of modular robotic extensions. The modular robotic extensions are removably attachable to the frame at the respective receiving regions by way of respective mechanical fuses. Each mechanical fuse is configured to trip when a respective modular robotic extension experiences a predefined load condition, such that the respective modular robotic extension detaches from the frame when the load condition is met.

  20. Modular biometric system

    NASA Astrophysics Data System (ADS)

    Hsu, Charles; Viazanko, Michael; O'Looney, Jimmy; Szu, Harold

    2009-04-01

    Modularity Biometric System (MBS) is an approach to support AiTR of the cooperated and/or non-cooperated standoff biometric in an area persistent surveillance. Advanced active and passive EOIR and RF sensor suite is not considered here. Neither will we consider the ROC, PD vs. FAR, versus the standoff POT in this paper. Our goal is to catch the "most wanted (MW)" two dozens, separately furthermore ad hoc woman MW class from man MW class, given their archrivals sparse front face data basis, by means of various new instantaneous input called probing faces. We present an advanced algorithm: mini-Max classifier, a sparse sample realization of Cramer-Rao Fisher bound of the Maximum Likelihood classifier that minimize the dispersions among the same woman classes and maximize the separation among different man-woman classes, based on the simple feature space of MIT Petland eigen-faces. The original aspect consists of a modular structured design approach at the system-level with multi-level architectures, multiple computing paradigms, and adaptable/evolvable techniques to allow for achieving a scalable structure in terms of biometric algorithms, identification quality, sensors, database complexity, database integration, and component heterogenity. MBS consist of a number of biometric technologies including fingerprints, vein maps, voice and face recognitions with innovative DSP algorithm, and their hardware implementations such as using Field Programmable Gate arrays (FPGAs). Biometric technologies and the composed modularity biometric system are significant for governmental agencies, enterprises, banks and all other organizations to protect people or control access to critical resources.

  1. Modular gear bearings

    NASA Technical Reports Server (NTRS)

    Vranish, John M. (Inventor)

    2009-01-01

    A gearing system using modular gear bearing components. Each component is composed of a core, one or more modules attached to the core and two or more fastening modules rigidly attaching the modules to the core. The modules, which are attached to the core, may consist of gears, rollers or gear bearing components. The core orientation affects the orientation of the modules attached to the core. This is achieved via the keying arrangement of the core and the component modules that attach to the core. Such an arrangement will also facilitate the phase tuning of gear modules with respect to the core and other gear modules attached to the core.

  2. Modular space station facilities.

    NASA Technical Reports Server (NTRS)

    Parker, P. J.

    1973-01-01

    The modular space station will operate as a general purpose laboratory (GPL). In addition, the space station will be able to support many attached or free-flying research and application modules that would be dedicated to specific projects like astronomy or earth observations. The GPL primary functions have been organized into functional laboratories including an electrical/electronics laboratory, a mechanical sciences laboratory, an experiment and test isolation laboratory, a hard data process facility, a data evaluation facility, an optical sciences laboratory, a biomedical and biosciences laboratory, and an experiment/secondary command and control center.

  3. From Caenorhabditis elegans to the human connectome: a specific modular organization increases metabolic, functional and developmental efficiency

    PubMed Central

    Kim, Jinseop S.; Kaiser, Marcus

    2014-01-01

    The connectome, or the entire connectivity of a neural system represented by a network, ranges across various scales from synaptic connections between individual neurons to fibre tract connections between brain regions. Although the modularity they commonly show has been extensively studied, it is unclear whether the connection specificity of such networks can already be fully explained by the modularity alone. To answer this question, we study two networks, the neuronal network of Caenorhabditis elegans and the fibre tract network of human brains obtained through diffusion spectrum imaging. We compare them to their respective benchmark networks with varying modularities, which are generated by link swapping to have desired modularity values. We find several network properties that are specific to the neural networks and cannot be fully explained by the modularity alone. First, the clustering coefficient and the characteristic path length of both C. elegans and human connectomes are higher than those of the benchmark networks with similar modularity. High clustering coefficient indicates efficient local information distribution, and high characteristic path length suggests reduced global integration. Second, the total wiring length is smaller than for the alternative configurations with similar modularity. This is due to lower dispersion of connections, which means each neuron in the C. elegans connectome or each region of interest in the human connectome reaches fewer ganglia or cortical areas, respectively. Third, both neural networks show lower algorithmic entropy compared with the alternative arrangements. This implies that fewer genes are needed to encode for the organization of neural systems. While the first two findings show that the neural topologies are efficient in information processing, this suggests that they are also efficient from a developmental point of view. Together, these results show that neural systems are organized in such a way as to yield

  4. Diversity and time delays induce resonance in a modular neuronal network

    NASA Astrophysics Data System (ADS)

    Jia, Y. B.; Yang, X. L.; Kurths, J.

    2014-12-01

    This paper focuses on the resonance dynamics of a modular neuronal network consisting of several small-world subnetworks. The considered network is composed of delay-coupled FitzHugh-Nagumo (FHN) neurons, whose characteristic parameters present diversity in the form of quenched noise. Our numerical results indicate that when such a network is subjected to an external subthreshold periodic signal, its collective response is optimized for an intermediate level of diversity, namely, a resonant behavior can be induced by an appropriate level of diversity. How the probabilities of intramodule and intermodule connections, as well as the number of subnetworks influence the diversity-induced resonance are also discussed. Further, conclusive evidences demonstrate the nontrivial role of time-delayed coupling on the diversity-induced resonance properties. Especially, multiple resonance is obviously detected when time delays are located at integer multiples of the oscillation period of the signal. Moreover, the phenomenon of fine-tuned delays in inducing multiple resonance remains when diversity is within an intermediate range. Our findings have implications that neural systems may profit from their generic diversity and delayed coupling to optimize the response to external stimulus.

  5. Modular antenna design study

    NASA Technical Reports Server (NTRS)

    Ribble, J. W.

    1981-01-01

    The mechanical design of a modular antenna concept was developed sufficiently to allow manufacture of a working demonstration model of a module, to predict mass properties, and to make performance estimates for antenna reflectors composed of these modules. The primary features of this concept are: (1) each module is an autonomous structural element which can be attached to adjacent modules through a three point connection; (2) the upper surface is a folding hexagonal truss plate mechanism which serves as the supporting structure for a reflective surface; and (3) the entire truss and surface can be folded into a cylindrical envelope in which all truss elements are essentially parallel. The kinematic studies and engineering demonstration model fully verified the deployment kinematics, stowing philosophy, and deployment sequencing for large antenna modules. It was established that such modules can be stowed in packages as small as 25 cm in diameter, using 1.27 cm diameter structural tubes. The development activity indicates that this deployable modular approach towards building large structures in space will support erection of 450 m apertures for operation up to 3 GHz with a single space shuttle flight.

  6. Terpene Biosynthesis: Modularity Rules

    PubMed Central

    Oldfield, Eric; Lin, Fu-Yang

    2013-01-01

    Terpenes are the largest class of small molecule natural products on Earth, and the most abundant by mass. Here, we summarize recent developments in elucidating the structure and function of the proteins involved in their biosynthesis. There are 6 main building blocks or modules (α,β,γ,δ,ε and ζ) that make up the structures of these enzymes: the αα and αδ head-to-tail trans-prenyl transferases that produce trans-isoprenoid diphosphates from C5 precursors; the ε head-to-head prenyl transferases that convert these diphosphates into the tri-and tetra-terpene precursors of sterols, hopanoids and carotenoids; the βγ di- and tri-terpene synthases; the ζ head-to-tail cis-prenyl transferases that produce the cis-isoprenoid diphosphates involved in bacterial cell wall biosynthesis, and finally the α, αβ and αβγ terpene synthases that produce plant terpenes, with many of these modular enzymes having originated from ancestral α and β domain proteins. We also review progress in determining the structure and function of the two 4Fe-4S reductases involved in formation of the C5 diphosphates in many bacteria, where again, highly modular structures are found. PMID:22105807

  7. Modular reflector concept study

    NASA Technical Reports Server (NTRS)

    Vaughan, D. H.

    1981-01-01

    A study was conducted to evaluate the feasibility of space erecting a 100 meter paraboloidal radio frequency reflector by joining a number of individually deployed structural modules. Three module design concepts were considered: (1) the deployable cell module (DCM); (2) the modular paraboloidal erectable truss antenna (Mod-PETA); and (3) the modular erectable truss antenna (META). With the space shuttle (STS) as the launch system, the methodology of packaging and stowing in the orbiter, and of dispensing, deploying and joining, in orbit, were studied and the necessary support equipment identified. The structural performance of the completed reflectors was evaluated and their overall operational capability and feasibility were evaluated and compared. The potential of the three concepts to maintain stable shape in the space environment was determined. Their ability to operate at radio frequencies of 1 GHz and higher was assessed assuming the reflector surface to consist of a number of flat, hexagonal facets. A parametric study was performed to determine figure degradation as a function of reflector size, flat facet size, and f/D ratio.

  8. Modular robotic architecture

    NASA Astrophysics Data System (ADS)

    Smurlo, Richard P.; Laird, Robin T.

    1991-03-01

    The development of control architectures for mobile systems is typically a task undertaken with each new application. These architectures address different operational needs and tend to be difficult to adapt to more than the problem at hand. The development of a flexible and extendible control system with evolutionary growth potential for use on mobile robots will help alleviate these problems and if made widely available will promote standardization and cornpatibility among systems throughout the industry. The Modular Robotic Architecture (MRA) is a generic control systern that meets the above needs by providing developers with a standard set of software hardware tools that can be used to design modular robots (MODBOTs) with nearly unlimited growth potential. The MODBOT itself is a generic creature that must be customized by the developer for a particular application. The MRA facilitates customization of the MODBOT by providing sensor actuator and processing modules that can be configured in almost any manner as demanded by the application. The Mobile Security Robot (MOSER) is an instance of a MODBOT that is being developed using the MRA. Navigational Sonar Module RF Link Control Station Module hR Link Detection Module Near hR Proximi Sensor Module Fluxgate Compass and Rate Gyro Collision Avoidance Sonar Module Figure 1. Remote platform module configuration of the Mobile Security Robot (MOSER). Acoustical Detection Array Stereoscopic Pan and Tilt Module High Level Processing Module Mobile Base 566

  9. Modular radiochemistry synthesis system

    SciTech Connect

    Satyamurthy, Nagichettiar; Barrio, Jorge R; Amarasekera, Bernard; Van Dam, R. Michael; Olma, Sebastian; Williams, Dirk; Eddings, Mark A; Shen, Clifton Kwang-Fu

    2015-02-10

    A modular chemical production system includes multiple modules for performing a chemical reaction, particularly of radiochemical compounds, from a remote location. One embodiment comprises a reaction vessel including a moveable heat source with the position thereof relative to the reaction vessel being controllable from a remote position. Alternatively the heat source may be fixed in location and the reaction vial is moveable into and out of the heat source. The reaction vessel has one or more sealing plugs, the positioning of which in relationship to the reaction vessel is controllable from a remote position. Also the one or more reaction vessel sealing plugs can include one or more conduits there through for delivery of reactants, gases at atmospheric or an elevated pressure, inert gases, drawing a vacuum and removal of reaction end products to and from the reaction vial, the reaction vial with sealing plug in position being operable at elevated pressures. The modular chemical production system is assembled from modules which can each include operating condition sensors and controllers configured for monitoring and controlling the individual modules and the assembled system from a remote position. Other modules include, but are not limited to a Reagent Storage and Delivery Module, a Cartridge Purification Module, a Microwave Reaction Module, an External QC/Analysis/Purification Interface Module, an Aliquotting Module, an F-18 Drying Module, a Concentration Module, a Radiation Counting Module, and a Capillary Reactor Module.

  10. Modular radiochemistry synthesis system

    SciTech Connect

    Satyamurthy, Nagichettiar; Barrio, Jorge R.; Amarasekera, Bernard; Van Dam, R. Michael; Olma, Sebastian; Williams, Dirk; Eddings, Mark; Shen, Clifton Kwang-Fu

    2015-12-15

    A modular chemical production system includes multiple modules for performing a chemical reaction, particularly of radiochemical compounds, from a remote location. One embodiment comprises a reaction vessel including a moveable heat source with the position thereof relative to the reaction vessel being controllable from a remote position. Alternatively the heat source may be fixed in location and the reaction vial is moveable into and out of the heat source. The reaction vessel has one or more sealing plugs, the positioning of which in relationship to the reaction vessel is controllable from a remote position. Also the one or more reaction vessel sealing plugs can include one or more conduits there through for delivery of reactants, gases at atmospheric or an elevated pressure, inert gases, drawing a vacuum and removal of reaction end products to and from the reaction vial, the reaction vial with sealing plug in position being operable at elevated pressures. The modular chemical production system is assembled from modules which can each include operating condition sensors and controllers configured for monitoring and controlling the individual modules and the assembled system from a remote position. Other modules include, but are not limited to a Reagent Storage and Delivery Module, a Cartridge Purification Module, a Microwave Reaction Module, an External QC/Analysis/Purification Interface Module, an Aliquotting Module, an F-18 Drying Module, a Concentration Module, a Radiation Counting Module, and a Capillary Reactor Module.

  11. Modular Robotic Vehicle

    NASA Technical Reports Server (NTRS)

    Borroni-Bird, Christopher E. (Inventor); Vitale, Robert L. (Inventor); Lee, Chunhao J. (Inventor); Ambrose, Robert O. (Inventor); Bluethmann, William J. (Inventor); Junkin, Lucien Q. (Inventor); Lutz, Jonathan J. (Inventor); Guo, Raymond (Inventor); Lapp, Anthony Joseph (Inventor); Ridley, Justin S. (Inventor)

    2015-01-01

    A modular robotic vehicle includes a chassis, driver input devices, an energy storage system (ESS), a power electronics module (PEM), modular electronic assemblies (eModules) connected to the ESS via the PEM, one or more master controllers, and various embedded controllers. Each eModule includes a drive wheel containing a propulsion-braking module, and a housing containing propulsion and braking control assemblies with respective embedded propulsion and brake controllers, and a mounting bracket covering a steering control assembly with embedded steering controllers. The master controller, which is in communication with each eModule and with the driver input devices, communicates with and independently controls each eModule, by-wire, via the embedded controllers to establish a desired operating mode. Modes may include a two-wheel, four-wheel, diamond, and omni-directional steering modes as well as a park mode. A bumper may enable docking with another vehicle, with shared control over the eModules of the vehicles.

  12. Quantum modular forms, mock modular forms, and partial theta functions

    NASA Astrophysics Data System (ADS)

    Kimport, Susanna

    Defined by Zagier in 2010, quantum modular forms have been the subject of an explosion of recent research. Many of these results are aimed at discovering examples of these functions, which are defined on the rational numbers and have "nice" modularity properties. Though the subject is in its early stages, numerous results (including Zagier's original examples) show these objects naturally arising from many areas of mathematics as limits of other modular-like functions. One such family of examples is due to Folsom, Ono, and Rhoades, who connected these new objects to partial theta functions (introduced by Rogers in 1917) and mock modular forms (about which there is a rich theory, whose origins date back to Ramanujan in 1920). In this thesis, we build off of the work of Folsom, Ono, and Rhoades by providing an infinite family of quantum modular forms of arbitrary positive half-integral weight. Further, this family of quantum modular forms "glues" mock modular forms to partial theta functions and is constructed from a so-called "universal" mock theta function by extending a method of Eichler and Zagier (originally defined for holomorphic Jacobi forms) into a non-holomorphic setting. In addition to the infinite family, we explore the weight 1/2 and 3/2 functions in more depth. For both of these weights, we are able to explicitly write down the quantum modular form, as well as the corresponding "errors to modularity," which can be shown to be Mordell integrals of specific theta functions and, as a consequence, are real-analytic functions. Finally, we turn our attention to the partial theta functions associated with these low weight examples. Berndt and Kim provide asymptotic expansions for a certain class of partial theta functions as q approaches 1 radially within the unit disk. Here, we extend this work to not only obtain asymptotic expansions for this class of functions as q approaches any root of unity, but also for a certain class of derivatives of these functions

  13. Spacecraft Modularity for Serviceable Satellites

    NASA Technical Reports Server (NTRS)

    Rossetti, Dino; Keer, Beth; Panek, John; Ritter, Bob; Reed, Benjamin; Cepollina, Frank

    2015-01-01

    Spacecraft modularity has been a topic of interest at NASA since the 1970s, when the Multi-­-Mission Modular Spacecraft (MMS) was developed at the Goddard Space Flight Center. Since then, modular concepts have been employed for a variety of spacecraft and, as in the case of the Hubble Space Telescope (HST) and the International Space Station (ISS), have been critical to the success of on-­- orbit servicing. Modularity is even more important for future robotic servicing. Robotic satellite servicing technologies under development by NASA can extend mission life and reduce lifecycle cost and risk. These are optimized when the target spacecraft is designed for servicing, including advanced modularity. This paper will explore how spacecraft design, as demonstrated by the Reconfigurable Operational spacecraft for Science and Exploration (ROSE) spacecraft architecture, and servicing technologies can be developed in parallel to fully take advantage of the promise of both.

  14. Spacecraft Modularity for Serviceable Satellites

    NASA Technical Reports Server (NTRS)

    Reed, Benjamin B.; Rossetti, Dino; Keer, Beth; Panek, John; Cepollina, Frank; Ritter, Robert

    2015-01-01

    Spacecraft modularity has been a topic of interest at NASA since the 1970s, when the Multi-Mission Modular Spacecraft (MMS) was developed at the Goddard Space Flight Center. Since then, modular concepts have been employed for a variety of spacecraft and, as in the case of the Hubble Space Telescope (HST) and the International Space Station (ISS), have been critical to the success of on-orbit servicing. Modularity is even more important for future robotic servicing. Robotic satellite servicing technologies under development by NASA can extend mission life and reduce life-cycle cost and risk. These are optimized when the target spacecraft is designed for servicing, including advanced modularity. This paper will explore how spacecraft design, as demonstrated by the Reconfigurable Operational spacecraft for Science and Exploration (ROSE) spacecraft architecture, and servicing technologies can be developed in parallel to fully take advantage of the promise of both.

  15. Microelectronic oscillator

    NASA Technical Reports Server (NTRS)

    Kleinberg, L. L.

    1969-01-01

    Bipolar transistor operated in a grounded base configuration is used as the inductor in a microelectronic oscillator. This configuration is employed using thin-film hybrid technology and is also applicable to monolithic technology.

  16. Raindrop oscillations

    NASA Technical Reports Server (NTRS)

    Beard, K. V.

    1982-01-01

    A model of the change in shape of a raindrop is presented. Raindrops measured by two orthogonal cameras were classified by shape and orientation to determine the nature of the oscillation. A physical model based on potential energy was then developed to study the amplitude variation of oscillating drops. The model results show that oscillations occur about the equilibrium axis ratio, but the time average axis ratio if significantly more spherical for large amplitudes because of asymmetry in the surface potential energy. A generalization of the model to oscillations produced by turbulence yields average axis ratios that are consistent with the camera measurements. The model results for average axis ratios were applied to rainfall studies with a dual polarized radar.

  17. Power oscillator

    DOEpatents

    Gitsevich, Aleksandr

    2001-01-01

    An oscillator includes an amplifier having an input and an output, and an impedance transformation network connected between the input of the amplifier and the output of the amplifier, wherein the impedance transformation network is configured to provide suitable positive feedback from the output of the amplifier to the input of the amplifier to initiate and sustain an oscillating condition, and wherein the impedance transformation network is configured to protect the input of the amplifier from a destructive feedback signal. One example of the oscillator is a single active element device capable of providing over 70 watts of power at over 70% efficiency. Various control circuits may be employed to match the driving frequency of the oscillator to a plurality of tuning states of the lamp.

  18. Modular Flooring System

    NASA Technical Reports Server (NTRS)

    Thate, Robert

    2012-01-01

    The modular flooring system (MFS) was developed to provide a portable, modular, durable carpeting solution for NASA fs Robotics Alliance Project fs (RAP) outreach efforts. It was also designed to improve and replace a modular flooring system that was too heavy for safe use and transportation. The MFS was developed for use as the flooring for various robotics competitions that RAP utilizes to meet its mission goals. One of these competitions, the FIRST Robotics Competition (FRC), currently uses two massive rolls of broadloom carpet for the foundation of the arena in which the robots are contained during the competition. The area of the arena is approximately 30 by 72 ft (approximately 9 by 22 m). This carpet is very cumbersome and requires large-capacity vehicles, and handling equipment and personnel to transport and deploy. The broadloom carpet sustains severe abuse from the robots during a regular three-day competition, and as a result, the carpet is not used again for competition. Similarly, broadloom carpets used for trade shows at convention centers around the world are typically discarded after only one use. This innovation provides a green solution to this wasteful practice. Each of the flooring modules in the previous system weighed 44 lb (.20 kg). The improvements in the overall design of the system reduce the weight of each module by approximately 22 lb (.10 kg) (50 %), and utilize an improved "module-to-module" connection method that is superior to the previous system. The MFS comprises 4-by-4-ft (.1.2-by- 1.2-m) carpet module assemblies that utilize commercially available carpet tiles that are bonded to a lightweight substrate. The substrate surface opposite from the carpeted surface has a module-to-module connecting interface that allows for the modules to be connected, one to the other, as the modules are constructed. This connection is hidden underneath the modules, creating a smooth, co-planar flooring surface. The modules are stacked and strapped

  19. Hemodynamic responses can modulate the brain oscillations in low frequency

    NASA Astrophysics Data System (ADS)

    Lu, Feng-Mei; Wang, Yi-Feng; Yuan, Zhen

    2016-03-01

    Previous studies have showed that the steady-state responses were able to be used as an effective index for modulating the neural oscillations in the high frequency ranges (> 1 Hz). However, the neural oscillations in low frequency ranges (<1 Hz) remain unknown. In this study, a series of fNIRS experimental tests were conducted to validate if the low frequency bands (0.1 Hz - 0.8 Hz) steady-state hemoglobin responses (SSHbRs) could be evoked and modulate the neural oscillation during a serial reaction time (SRT) task.

  20. Age-related changes in modular organization of human brain functional networks.

    PubMed

    Meunier, David; Achard, Sophie; Morcom, Alexa; Bullmore, Ed

    2009-02-01

    Graph theory allows us to quantify any complex system, e.g., in social sciences, biology or technology, that can be abstractly described as a set of nodes and links. Here we derived human brain functional networks from fMRI measurements of endogenous, low frequency, correlated oscillations in 90 cortical and subcortical regions for two groups of healthy (young and older) participants. We investigated the modular structure of these networks and tested the hypothesis that normal brain aging might be associated with changes in modularity of sparse networks. Newman's modularity metric was maximised and topological roles were assigned to brain regions depending on their specific contributions to intra- and inter-modular connectivity. Both young and older brain networks demonstrated significantly non-random modularity. The young brain network was decomposed into 3 major modules: central and posterior modules, which comprised mainly nodes with few inter-modular connections, and a dorsal fronto-cingulo-parietal module, which comprised mainly nodes with extensive inter-modular connections. The mean network in the older group also included posterior, superior central and dorsal fronto-striato-thalamic modules but the number of intermodular connections to frontal modular regions was significantly reduced, whereas the number of connector nodes in posterior and central modules was increased. PMID:19027073

  1. The modular power subsystem for the multimission modular spacecraft

    NASA Technical Reports Server (NTRS)

    Harris, D. W.

    1978-01-01

    The block diagram, subsystems, and components of the modular power subsystem for the multimission modular spacecraft (MMS) are described. The basic design studies were guided by considerations of cost, efficiency, simplicity, and flexibility to serve a variety of missions. Components discussed are the power regulator unit, the power control unit, the signal conditioning assembly, bus protection assembly, and the 20 Ah and 50 Ah batteries. The plan for the modular power subsystem protoflight module tests is shown. The testing has four phases: (1) component level tests, (2) subsystem integration and initial performance test, (3) subsystem protoflight environmental tests, and (4) subsystem final performance tests, qualification/acceptance review and delivery.

  2. Modular electronics packaging system

    NASA Technical Reports Server (NTRS)

    Hunter, Don J. (Inventor)

    2001-01-01

    A modular electronics packaging system includes multiple packaging slices that are mounted horizontally to a base structure. The slices interlock to provide added structural support. Each packaging slice includes a rigid and thermally conductive housing having four side walls that together form a cavity to house an electronic circuit. The chamber is enclosed on one end by an end wall, or web, that isolates the electronic circuit from a circuit in an adjacent packaging slice. The web also provides a thermal path between the electronic circuit and the base structure. Each slice also includes a mounting bracket that connects the packaging slice to the base structure. Four guide pins protrude from the slice into four corresponding receptacles in an adjacent slice. A locking element, such as a set screw, protrudes into each receptacle and interlocks with the corresponding guide pin. A conduit is formed in the slice to allow electrical connection to the electronic circuit.

  3. Modular error embedding

    DOEpatents

    Sandford, II, Maxwell T.; Handel, Theodore G.; Ettinger, J. Mark

    1999-01-01

    A method of embedding auxiliary information into the digital representation of host data containing noise in the low-order bits. The method applies to digital data representing analog signals, for example digital images. The method reduces the error introduced by other methods that replace the low-order bits with auxiliary information. By a substantially reverse process, the embedded auxiliary data can be retrieved easily by an authorized user through use of a digital key. The modular error embedding method includes a process to permute the order in which the host data values are processed. The method doubles the amount of auxiliary information that can be added to host data values, in comparison with bit-replacement methods for high bit-rate coding. The invention preserves human perception of the meaning and content of the host data, permitting the addition of auxiliary data in the amount of 50% or greater of the original host data.

  4. Modular Optical PDV System

    SciTech Connect

    Araceli Rutkowski, David Esquibel

    2008-12-11

    A modular optical photon Doppler velocimetry (PDV) detector system has been developed by using readily available optical components with a 20-GHz Miteq optical detector into eight channels of single-wide modules integrated into a 3U rack unit (1U = 1.75 inches) with a common power supply. Optical fibers were precisely trimmed, welded, and timed within each unit. This system has been used to collect dynamic velocity data on various physics experiments. An optical power meter displays the laser input power to the module and optical power at the detector. An adjustable micro-electromechanical system (MEMS) optical attenuator is used to adjust the amount of unshifted light entering the detector. Front panel LEDs show the presence of power to the module. A fully loaded chassis with eight channels consumes 45 watts of power. Each chassis requires 1U spacing above and below for heat management. Modules can be easily replaced.

  5. Modular arctic structures system

    SciTech Connect

    Reusswig, G. H.

    1984-12-04

    A modular and floatable offshore exploration and production platform system for use in shallow arctic waters is disclosed. A concrete base member is floated to the exploration or production site, and ballated into a predredged cavity. The cavity and base are sized to provide a stable horizontal base 30 feet below the mean water/ice plane. An exploration or production platform having a massive steel base is floated to the site and ballasted into position on the base. Together, the platform, base and ballast provide a massive gravity structure that is capable of resisting large ice and wave forces that impinge on the structure. The steel platform has a sloping hourglass profile to deflect horizontal ice loads vertically, and convert the horizontal load to a vertical tensile stress, which assists in breaking the ice as it advances toward the structure.

  6. Modular small hydro configuration

    NASA Astrophysics Data System (ADS)

    1981-09-01

    Smaller sites (those under 750 kilowatts) which previously were not attractive to develop using equipment intended for application at larger scale sites, were the focal point in the conception of a system which utilizes standard industrial components which are generally available within short procurement times. Such components were integrated into a development scheme for sites having 20 feet to 150 feet of head. The modular small hydro configuration maximizes the use of available components and minimizes modification of existing civil works. A key aspect of the development concept is the use of a vertical turbine multistage pump, used in the reverse mode as a hydraulic turbine. The configuration allows for automated operation and control of the hydroelectric facilities with sufficient flexibility for inclusion of potential hydroelectric sites into dispersed storage and generation (DSG) utility grid systems.

  7. Modular weapon control unit

    SciTech Connect

    Boccabella, M.F.; McGovney, G.N.

    1997-01-01

    The goal of the Modular Weapon Control Unit (MWCU) program was to design and develop a reconfigurable weapon controller (programmer/sequencer) that can be adapted to different weapon systems based on the particular requirements for that system. Programmers from previous systems are conceptually the same and perform similar tasks. Because of this commonality and the amount of re-engineering necessary with the advent of every new design, the idea of a modular, adaptable system has emerged. Also, the controller can be used in more than one application for a specific weapon system. Functionality has been divided into a Processor Module (PM) and an Input/Output Module (IOM). The PM will handle all operations that require calculations, memory, and timing. The IOM will handle interfaces to the rest of the system, input level shifting, output drive capability, and detection of interrupt conditions. Configuration flexibility is achieved in two ways. First, the operation of the PM is determined by a surface mount Read-Only Memory (ROM). Other surface-mount components can be added or neglected as necessary for functionality. Second, IOMs consist of configurable input buffers, configurable output drivers, and configurable interrupt generation. Further, these modules can be added singly or in groups to a Processor Module to achieve the required I/O configuration. The culmination of this LDRD was the building of both Processor Module and Input/Output Module. The MWCU was chosen as a test system to evaluate Low-Temperature Co-fired Ceramic (LTCC) technology, desirable for high component density and good thermal characteristics.

  8. Nonequilibrium landscape theory of neural networks

    PubMed Central

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-01-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  9. Some new modular equations and their applications

    NASA Astrophysics Data System (ADS)

    Yi, Jinhee; Sim, Hyo Seob

    2006-07-01

    Ramanujan derived 23 beautiful eta-function identities, which are certain types of modular equations. We found more than 70 of certain types of modular equations by using Garvan's Maple q-series package. In this paper, we prove some new modular equations which we found by employing the theory of modular form and we give some applications for them.

  10. Spatiotemporal dynamics of continuum neural fields

    NASA Astrophysics Data System (ADS)

    Bressloff, Paul C.

    2012-01-01

    We survey recent analytical approaches to studying the spatiotemporal dynamics of continuum neural fields. Neural fields model the large-scale dynamics of spatially structured biological neural networks in terms of nonlinear integrodifferential equations whose associated integral kernels represent the spatial distribution of neuronal synaptic connections. They provide an important example of spatially extended excitable systems with nonlocal interactions and exhibit a wide range of spatially coherent dynamics including traveling waves oscillations and Turing-like patterns.

  11. Programmable Oscillator

    NASA Technical Reports Server (NTRS)

    Quirk, Kevin J.; Patawaran, Ferze D.; Nguyen, Danh H.; Lee, Clement G.; Nguyen, Huy

    2011-01-01

    A programmable oscillator is a frequency synthesizer with an output phase that tracks an arbitrary function. An offset, phase-locked loop circuit is used in combination with an error control feedback loop to precisely control the output phase of the oscillator. To down-convert the received signal, several stages of mixing may be employed with the compensation for the time-base distortion of the carrier occurring at any one of those stages. In the Goldstone Solar System Radar (GSSR), the compensation occurs in the mixing from an intermediate frequency (IF), whose value is dependent on the station and band, to a common IF used in the final stage of down-conversion to baseband. The programmable oscillator (PO) is used in the final stage of down-conversion to generate the IF, along with a time-varying phase component that matches the time-base distortion of the carrier, thus removing it from the final down-converted signal.

  12. Modular Arithmetic in the Marketplace.

    ERIC Educational Resources Information Center

    Gallian, Joseph A.; Winters, Steven

    1988-01-01

    Several schemes use modular arithmetic to append a check digit to product identification numbers for error detection. Some schemes are discussed, including ones for money orders and library books. Then a foolproof method is presented. (MNS)

  13. The evolutionary origins of modularity.

    PubMed

    Clune, Jeff; Mouret, Jean-Baptiste; Lipson, Hod

    2013-03-22

    A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks--their organization as functional, sparsely connected subunits--but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes. PMID:23363632

  14. The evolutionary origins of modularity

    PubMed Central

    Clune, Jeff; Mouret, Jean-Baptiste; Lipson, Hod

    2013-01-01

    A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks—their organization as functional, sparsely connected subunits—but there is no consensus regarding why modularity itself evolved. Although most hypotheses assume indirect selection for evolvability, here we demonstrate that the ubiquitous, direct selection pressure to reduce the cost of connections between network nodes causes the emergence of modular networks. Computational evolution experiments with selection pressures to maximize network performance and minimize connection costs yield networks that are significantly more modular and more evolvable than control experiments that only select for performance. These results will catalyse research in numerous disciplines, such as neuroscience and genetics, and enhance our ability to harness evolution for engineering purposes. PMID:23363632

  15. Spacecraft Modularity for Serviceable Satellites

    NASA Technical Reports Server (NTRS)

    Rossetti, Dino; Keer, Beth; Panek, John; Reed, Benjamin; Cepollina, Frank; Ritter, Robert

    2015-01-01

    Satellite servicing has been a proven capability of NASA since the first servicing missions in the 1980s with astronauts on the space shuttle. This capability enabled the on-orbit assembly of the International Space Station (ISS) and saved the Hubble Space Telescope (HST) mission following the discovery of the flawed primary mirror. The effectiveness and scope of servicing opportunities, especially using robotic servicers, is a function of how cooperative a spacecraft is. In this paper, modularity will be presented as a critical design aspect for a spacecraft that is cooperative from a servicing perspective. Different features of modularity are discussed using examples from HST and the Multimission Modular Spacecraft (MMS) program from the 1980s and 1990s. The benefits of modularity will be presented including those directly related to servicing and those outside of servicing including reduced costs and increased flexibility. The new Reconfigurable Operational spacecraft for Science and Exploration (ROSE) concept is introduced as an affordable implementation of modularity that provides cost savings and flexibility. Key aspects of the ROSE architecture are discussed such as the module design and the distributed avionics architecture. The ROSE concept builds on the experience from MMS and due to its modularity, would be highly suitable as a future client for on-orbit servicing.

  16. The modularity of pollination networks

    PubMed Central

    Olesen, Jens M.; Bascompte, Jordi; Dupont, Yoko L.; Jordano, Pedro

    2007-01-01

    In natural communities, species and their interactions are often organized as nonrandom networks, showing distinct and repeated complex patterns. A prevalent, but poorly explored pattern is ecological modularity, with weakly interlinked subsets of species (modules), which, however, internally consist of strongly connected species. The importance of modularity has been discussed for a long time, but no consensus on its prevalence in ecological networks has yet been reached. Progress is hampered by inadequate methods and a lack of large datasets. We analyzed 51 pollination networks including almost 10,000 species and 20,000 links and tested for modularity by using a recently developed simulated annealing algorithm. All networks with >150 plant and pollinator species were modular, whereas networks with <50 species were never modular. Both module number and size increased with species number. Each module includes one or a few species groups with convergent trait sets that may be considered as coevolutionary units. Species played different roles with respect to modularity. However, only 15% of all species were structurally important to their network. They were either hubs (i.e., highly linked species within their own module), connectors linking different modules, or both. If these key species go extinct, modules and networks may break apart and initiate cascades of extinction. Thus, species serving as hubs and connectors should receive high conservation priorities. PMID:18056808

  17. Modular Approach to Spintronics

    PubMed Central

    Camsari, Kerem Yunus; Ganguly, Samiran; Datta, Supriyo

    2015-01-01

    There has been enormous progress in the last two decades, effectively combining spintronics and magnetics into a powerful force that is shaping the field of memory devices. New materials and phenomena continue to be discovered at an impressive rate, providing an ever-increasing set of building blocks that could be exploited in designing transistor-like functional devices of the future. The objective of this paper is to provide a quantitative foundation for this building block approach, so that new discoveries can be integrated into functional device concepts, quickly analyzed and critically evaluated. Through careful benchmarking against available theory and experiment we establish a set of elemental modules representing diverse materials and phenomena. These elemental modules can be integrated seamlessly to model composite devices involving both spintronic and nanomagnetic phenomena. We envision the library of modules to evolve both by incorporating new modules and by improving existing modules as the field progresses. The primary contribution of this paper is to establish the ground rules or protocols for a modular approach that can build a lasting bridge between materials scientists and circuit designers in the field of spintronics and nanomagnetics. PMID:26066079

  18. Modular Isotopic Thermoelectric Generator

    SciTech Connect

    Schock, Alfred

    1981-04-03

    Advanced RTG concepts utilizing improved thermoelectric materials and converter concepts are under study at Fairchild for DOE. The design described here is based on DOE's newly developed radioisotope heat source, and on an improved silicon-germanium material and a multicouple converter module under development at Syncal. Fairchild's assignment was to combine the above into an attractive power system for use in space, and to assess the specific power and other attributes of that design. The resultant design is highly modular, consisting of standard RTG slices, each producing ~24 watts at the desired output voltage of 28 volt. Thus, the design could be adapted to various space missions over a wide range of power levels, with little or no redesign. Each RTG slice consists of a 250-watt heat source module, eight multicouple thermoelectric modules, and standard sections of insulator, housing, radiator fins, and electrical circuit. The design makes it possible to check each thermoelectric module for electrical performance, thermal contact, leaktightness, and performance stability, after the generator is fully assembled; and to replace any deficient modules without disassembling the generator or perturbing the others. The RTG end sections provide the spring-loaded supports required to hold the free-standing heat source stack together during launch vibration. Details analysis indicates that the design offers a substantial improvement in specific power over the present generator of RTGs, using the same heat source modules. There are three copies in the file.

  19. Comparing artificial and biological dynamical neural networks

    NASA Astrophysics Data System (ADS)

    McAulay, Alastair D.

    2006-05-01

    Modern computers can be made more friendly and otherwise improved by making them behave more like humans. Perhaps we can learn how to do this from biology in which human brains evolved over a long period of time. Therefore, we first explain a commonly used biological neural network (BNN) model, the Wilson-Cowan neural oscillator, that has cross-coupled excitatory (positive) and inhibitory (negative) neurons. The two types of neurons are used for frequency modulation communication between neurons which provides immunity to electromagnetic interference. We then evolve, for the first time, an artificial neural network (ANN) to perform the same task. Two dynamical feed-forward artificial neural networks use cross-coupling feedback (like that in a flip-flop) to form an ANN nonlinear dynamic neural oscillator with the same equations as the Wilson-Cowan neural oscillator. Finally we show, through simulation, that the equations perform the basic neural threshold function, switching between stable zero output and a stable oscillation, that is a stable limit cycle. Optical implementation with an injected laser diode and future research are discussed.

  20. Modular Stirling Radioisotope Generator

    NASA Technical Reports Server (NTRS)

    Schmitz, Paul C.; Mason, Lee S.; Schifer, Nicholas A.

    2016-01-01

    High-efficiency radioisotope power generators will play an important role in future NASA space exploration missions. Stirling Radioisotope Generators (SRGs) have been identified as a candidate generator technology capable of providing mission designers with an efficient, high-specific-power electrical generator. SRGs high conversion efficiency has the potential to extend the limited Pu-238 supply when compared with current Radioisotope Thermoelectric Generators (RTGs). Due to budgetary constraints, the Advanced Stirling Radioisotope Generator (ASRG) was canceled in the fall of 2013. Over the past year a joint study by NASA and the Department of Energy (DOE) called the Nuclear Power Assessment Study (NPAS) recommended that Stirling technologies continue to be explored. During the mission studies of the NPAS, spare SRGs were sometimes required to meet mission power system reliability requirements. This led to an additional mass penalty and increased isotope consumption levied on certain SRG-based missions. In an attempt to remove the spare power system, a new generator architecture is considered, which could increase the reliability of a Stirling generator and provide a more fault-tolerant power system. This new generator called the Modular Stirling Radioisotope Generator (MSRG) employs multiple parallel Stirling convertor/controller strings, all of which share the heat from the General Purpose Heat Source (GPHS) modules. For this design, generators utilizing one to eight GPHS modules were analyzed, which provided about 50 to 450 W of direct current (DC) to the spacecraft, respectively. Four Stirling convertors are arranged around each GPHS module resulting in from 4 to 32 Stirling/controller strings. The convertors are balanced either individually or in pairs, and are radiatively coupled to the GPHS modules. Heat is rejected through the housing/radiator, which is similar in construction to the ASRG. Mass and power analysis for these systems indicate that specific

  1. Modular Stirling Radioisotope Generator

    NASA Technical Reports Server (NTRS)

    Schmitz, Paul C.; Mason, Lee S.; Schifer, Nicholas A.

    2015-01-01

    High efficiency radioisotope power generators will play an important role in future NASA space exploration missions. Stirling Radioisotope Generators (SRG) have been identified as a candidate generator technology capable of providing mission designers with an efficient, high specific power electrical generator. SRGs high conversion efficiency has the potential to extend the limited Pu-238 supply when compared with current Radioisotope Thermoelectric Generators (RTG). Due to budgetary constraints, the Advanced Stirling Radioisotope Generator (ASRG) was canceled in the fall of 2013. Over the past year a joint study by NASA and DOE called the Nuclear Power Assessment Study (NPAS) recommended that Stirling technologies continue to be explored. During the mission studies of the NPAS, spare SRGs were sometimes required to meet mission power system reliability requirements. This led to an additional mass penalty and increased isotope consumption levied on certain SRG-based missions. In an attempt to remove the spare power system, a new generator architecture is considered which could increase the reliability of a Stirling generator and provide a more fault-tolerant power system. This new generator called the Modular Stirling Radioisotope Generator (MSRG) employs multiple parallel Stirling convertor/controller strings, all of which share the heat from the General Purpose Heat Source (GPHS) modules. For this design, generators utilizing one to eight GPHS modules were analyzed, which provide about 50 to 450 watts DC to the spacecraft, respectively. Four Stirling convertors are arranged around each GPHS module resulting in from 4 to 32 Stirling/controller strings. The convertors are balanced either individually or in pairs, and are radiatively coupled to the GPHS modules. Heat is rejected through the housing/radiator which is similar in construction to the ASRG. Mass and power analysis for these systems indicate that specific power may be slightly lower than the ASRG and

  2. Annotation: Tourette Syndrome--A Relentless Drumbeat--Driven by Misguided Brain Oscillations

    ERIC Educational Resources Information Center

    Leckman, James F.; Vaccarino, Flora M.; Kalanithi, Paul S. A.; Rothenberger, Aribert

    2006-01-01

    Objective: This annotation reviews recent evidence that points to the likely role of aberrant neural oscillations in the pathogenesis of Tourette syndrome (TS). Methods: The available anatomic and electrophysiological findings in TS are reviewed in the context of an emerging picture of the crucial role that neural oscillations play in maintaining…

  3. STABILIZED OSCILLATOR

    DOEpatents

    Jessen, P.L.; Price, H.J.

    1958-03-18

    This patent relates to sine-wave generators and in particular describes a generator with a novel feedback circuit resulting in improved frequency stability. The generator comprises two triodes having a common cathode circuit connected to oscillate at a frequency and amplitude at which the loop galn of the circutt ls unity, and another pair of triodes having a common cathode circuit arranged as a conventional amplifier. A signal is conducted from the osciliator through a frequency selective network to the amplifier and fed back to the osciliator. The unique feature of the feedback circuit is the amplifier operates in the nonlinear portion of its tube characteristics thereby providing a relatively constant feedback voltage to the oscillator irrespective of the amplitude of its input signal.

  4. Solar Oscillations

    NASA Technical Reports Server (NTRS)

    Duvall, Thomas

    2004-01-01

    Oscillations were first detected in the solar photosphere in 1962 by Leighton and students. In 1970 it was calculated that these oscillations, with a period near five minutes, were the manifestations of acoustic waves trapped in the interior. The subsequent measurements of the frequencies of global oscillation modes from the spatio-temporal power spectrum of the waves made possible the refinement of solar interior models. Over the years, increased understanding of the nuclear reaction rates, the opacity, the equation of state, convection, and gravitational settling have resulted. Mass flows shift the frequencies of modes leading to very accurate measurements of the interior rotation as a function of radius and latitude. In recent years, analogues of terrestrial seismology have led to a tomography of the interior, including measurements of global north-south flows and flow and wave speed measurements below features such as sunspots. The future of helioseismology seems bright with the approval of NASA's Solar Dynamics Observatory mission, to be launched in 2008.

  5. Product modular design incorporating preventive maintenance issues

    NASA Astrophysics Data System (ADS)

    Gao, Yicong; Feng, Yixiong; Tan, Jianrong

    2016-03-01

    Traditional modular design methods lead to product maintenance problems, because the module form of a system is created according to either the function requirements or the manufacturing considerations. For solving these problems, a new modular design method is proposed with the considerations of not only the traditional function related attributes, but also the maintenance related ones. First, modularity parameters and modularity scenarios for product modularity are defined. Then the reliability and economic assessment models of product modularity strategies are formulated with the introduction of the effective working age of modules. A mathematical model used to evaluate the difference among the modules of the product so that the optimal module of the product can be established. After that, a multi-objective optimization problem based on metrics for preventive maintenance interval different degrees and preventive maintenance economics is formulated for modular optimization. Multi-objective GA is utilized to rapidly approximate the Pareto set of optimal modularity strategy trade-offs between preventive maintenance cost and preventive maintenance interval difference degree. Finally, a coordinate CNC boring machine is adopted to depict the process of product modularity. In addition, two factorial design experiments based on the modularity parameters are constructed and analyzed. These experiments investigate the impacts of these parameters on the optimal modularity strategies and the structure of module. The research proposes a new modular design method, which may help to improve the maintainability of product in modular design.

  6. Neural Synchrony in Schizophrenia: From Networks to New Treatments

    PubMed Central

    Ford, Judith M.; Krystal, John H.; Mathalon, Daniel H.

    2007-01-01

    Evidence is accumulating that brain regions communicate with each other in the temporal domain, relying on coincidence of neural activity to detect phasic relationships among neurons and neural assemblies. This coordination between neural populations has been described as “self-organizing,” an “emergent property” of neural networks arising from the temporal synchrony between synaptic transmission and firing of distinct neuronal populations. Evidence is also accumulating that communication and coordination failures between different brain regions may account for a wide range of problems in schizophrenia, from psychosis to cognitive dysfunction. We review the knowledge about the functional neuroanatomy and neurochemistry of neural oscillations and oscillation abnormalities in schizophrenia. Based on this, we argue that we can begin to use oscillations, across frequencies, to do translational studies to understand the neural basis of schizophrenia. PMID:17567628

  7. A modular network for legged locomotion

    NASA Astrophysics Data System (ADS)

    Golubitsky, Martin; Stewart, Ian; Buono, Pietro-Luciano; Collins, J. J.

    1998-04-01

    In this paper we use symmetry methods to study networks of coupled cells, which are models for central pattern generators (CPGs). In these models the cells obey identical systems of differential equations and the network specifies how cells are coupled. Previously, Collins and Stewart showed that the phase relations of many of the standard gaits of quadrupeds and hexapods can be obtained naturally via Hopf bifurcation in small networks. For example, the networks they used to study quadrupeds all had four cells, with the understanding that each cell determined the phase of the motion of one leg. However, in their work it seemed necessary to employ several different four-oscillator networks to obtain all of the standard quadrupedal gaits. We show that this difficulty with four-oscillator networks is unavoidable, but that the problems can be overcome by using a larger network. Specifically, we show that the standard gaits of a quadruped, including walk, trot and pace, cannot all be realized by a single four-cell network without introducing unwanted conjugacies between trot and pace - conjugacies that imply a dynamic equivalence between these gaits that seems inconsistent with observations. In this sense a single network with four cells cannot model the CPG of a quadruped. We also introduce a single eight-cell network that can model all of the primary gaits of quadrupeds without these unwanted conjugacies. Moreover, this network is modular in that it naturally generalizes to provide models of gaits in hexapods, centipedes, and millipedes. The analysis of models for many-legged animals shows that wave-like motions, similar to those obtained by Kopell and Ermentrout, can be expected. However, our network leads to a prediction that the wavelength of the wave motion will divide twice the length of the animal. Indeed, we reproduce illustrations of wave-like motions in centipedes where the animal is approximately one-and-a-half wavelength long - motions that are consistent

  8. Coevolution, modularity and human disease.

    PubMed

    Fraser, Hunter B

    2006-12-01

    The concepts of coevolution and modularity have been studied separately for decades. Recent advances in genomics have led to the first systematic studies in each of these fields at the molecular level, resulting in several important discoveries. Both coevolution and modularity appear to be pervasive features of genomic data from all species studied to date, and their presence can be detected in many types of datasets, including genome sequences, gene expression data, and protein-protein interaction data. Moreover, the combination of these two ideas might have implications for our understanding of many aspects of biology, ranging from the general architecture of living systems to the causes of various human diseases. PMID:17005391

  9. Portable or Modular? There Is a Difference....

    ERIC Educational Resources Information Center

    Morton, Mike

    2002-01-01

    Describes differences between two types of school facilities: portable (prebuilt, temporary wood structure installed on site) and modular (method of construction for permanent buildings). Provides details of modular construction. (PKP)

  10. Modular synthetic inverters from zinc finger proteins and small RNAs

    DOE PAGESBeta

    Hsia, Justin; Holtz, William J.; Maharbiz, Michel M.; Arcak, Murat; Keasling, Jay D.; Rao, Christopher V.

    2016-02-17

    Synthetic zinc finger proteins (ZFPs) can be created to target promoter DNA sequences, repressing transcription. The binding of small RNA (sRNA) to ZFP mRNA creates an ultrasensitive response to generate higher effective Hill coefficients. Here we combined three “off the shelf” ZFPs and three sRNAs to create new modular inverters in E. coli and quantify their behavior using induction fold. We found a general ordering of the effects of the ZFPs and sRNAs on induction fold that mostly held true when combining these parts. We then attempted to construct a ring oscillator using our new inverters. In conclusion, our chosenmore » parts performed insufficiently to create oscillations, but we include future directions for improvement upon our work presented here.« less