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

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

PubMed Central

2007-01-01

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

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

PubMed

Wang, Lipo

2007-06-01

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

Beta band oscillations in motor cortex reflect neural population signals that delay movement onset

PubMed Central

Khanna, Preeya; Carmena, Jose M

2017-01-01

Motor cortical beta oscillations have been reported for decades, yet their behavioral correlates remain unresolved. Some studies link beta oscillations to changes in underlying neural activity, but the specific behavioral manifestations of these reported changes remain elusive. To investigate how changes in population neural activity, beta oscillations, and behavior are linked, we recorded multi-scale neural activity from motor cortex while three macaques performed a novel neurofeedback task. Subjects volitionally brought their beta oscillatory power to an instructed state and subsequently executed an arm reach. Reaches preceded by a reduction in beta power exhibited significantly faster movement onset times than reaches preceded by an increase in beta power. Further, population neural activity was found to shift farther from a movement onset state during beta oscillations that were neurofeedback-induced or naturally occurring during reaching tasks. This finding establishes a population neural basis for slowed movement onset following periods of beta oscillatory activity. DOI: http://dx.doi.org/10.7554/eLife.24573.001 PMID:28467303

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

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

Frequency transitions in odor-evoked neural oscillations.

PubMed

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

2009-12-10

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 that 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.

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.

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

Oscillator Neural Network Retrieving Sparsely Coded Phase Patterns

NASA Astrophysics Data System (ADS)

Aoyagi, Toshio; Nomura, Masaki

1999-08-01

Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case of sparsely coded patterns, it is biologically important to consider the timings of firings and to study how such consideration influences storage capacities and quality of recalled patterns. For this purpose, we propose a simple extended model of oscillator neural networks to allow for expression of a nonfiring state. Analyzing both equilibrium states and dynamical properties in recalling processes, we find that the system possesses good associative memory.

The neural oscillations of conflict adaptation in the human frontal region.

PubMed

Tang, Dandan; Hu, Li; Chen, Antao

2013-07-01

Incongruency between print color and the semantic meaning of a word in a classical Stroop task activates the human conflict monitoring system and triggers a behavioral conflict. Conflict adaptation has been suggested to mediate the cortical processing of neural oscillations in such a conflict situation. However, the basic mechanisms that underlie the influence of conflict adaptation on the changes of neural oscillations are not clear. In the present study, electroencephalography (EEG) data were recorded from sixteen healthy human participants while they were performing a color-word Stroop task within a novel look-to-do transition design that included two response modalities. In the 'look' condition, participants were informed to look at the color of presented words but no responses were required; in the 'do' condition, they were informed to make arranged responses to the color of presented words. Behaviorally, a reliable conflict adaptation was observed. Time-frequency analysis revealed that (1) in the 'look' condition, theta-band activity in the left- and right-frontal regions reflected a conflict-related process at a response inhibition level; and (2) in the 'do' condition, both theta-band activity in the left-frontal region and alpha-band activity in the left-, right-, and centro-frontal regions reflected a process of conflict control, which triggered neural and behavioral adaptation. Taken together, these results suggest that there are frontal mechanisms involving neural oscillations that can mediate response inhibition processes and control behavioral conflict. Copyright © 2013 Elsevier B.V. All rights reserved.

Classification capacity of a modular neural network implementing neurally inspired architecture and training rules.

PubMed

Poirazi, Panayiota; Neocleous, Costas; Pattichis, Costantinos S; Schizas, Christos N

2004-05-01

A three-layer neural network (NN) with novel adaptive architecture has been developed. The hidden layer of the network consists of slabs of single neuron models, where neurons within a slab--but not between slabs--have the same type of activation function. The network activation functions in all three layers have adaptable parameters. The network was trained using a biologically inspired, guided-annealing learning rule on a variety of medical data. Good training/testing classification performance was obtained on all data sets tested. The performance achieved was comparable to that of SVM classifiers. It was shown that the adaptive network architecture, inspired from the modular organization often encountered in the mammalian cerebral cortex, can benefit classification performance.

Chaotic itinerancy in the oscillator neural network without Lyapunov functions.

PubMed

Uchiyama, Satoki; Fujisaka, Hirokazu

2004-09-01

Chaotic itinerancy (CI), which is defined as an incessant spontaneous switching phenomenon among attractor ruins in deterministic dynamical systems without Lyapunov functions, is numerically studied in the case of an oscillator neural network model. The model is the pseudoinverse-matrix version of the previous model [S. Uchiyama and H. Fujisaka, Phys. Rev. E 65, 061912 (2002)] that was studied theoretically with the aid of statistical neurodynamics. It is found that CI in neural nets can be understood as the intermittent dynamics of weakly destabilized chaotic retrieval solutions. Copyright 2004 American Institute of Physics

Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior.

PubMed

Cohen, Michael X; Donner, Tobias H

2013-12-01

Action monitoring and conflict resolution require the rapid and flexible coordination of activity in multiple brain regions. Oscillatory neural population activity may be a key physiological mechanism underlying such rapid and flexible network coordination. EEG power modulations of theta-band (4-8 Hz) activity over the human midfrontal cortex during response conflict have been proposed to reflect neural oscillations that support conflict detection and resolution processes. However, it has remained unclear whether this frequency-band-specific activity reflects neural oscillations or nonoscillatory responses (i.e., event-related potentials). Here, we show that removing the phase-locked component of the EEG did not reduce the strength of the conflict-related modulation of the residual (i.e., non-phase-locked) theta power over midfrontal cortex. Furthermore, within-subject regression analyses revealed that the non-phase-locked theta power was a significantly better predictor of the conflict condition than was the time-domain phase-locked EEG component. Finally, non-phase-locked theta power showed robust and condition-specific (high- vs. low-conflict) cross-trial correlations with reaction time, whereas the phase-locked component did not. Taken together, our results indicate that most of the conflict-related and behaviorally relevant midfrontal EEG signal reflects a modulation of ongoing theta-band oscillations that occurs during the decision process but is not phase-locked to the stimulus or to the response.

Odor Evoked Neural Oscillations in Drosophila Are Mediated by Widely Branching Interneurons

PubMed Central

Tanaka, Nobuaki K.; Ito, Kei; Stopfer, Mark

2009-01-01

Stimulus-evoked oscillatory synchronization of neurons has been observed in a wide range of species. Here, we combined genetic strategies with paired intracellular and local field potential (LFP) recordings from the intact brain of Drosophila to study mechanisms of odor-evoked neural oscillations. We found common food odors at natural concentrations elicited oscillations in LFP recordings made from the mushroom body (MB), a site of sensory integration and analogous to the vertebrate pyriform cortex. The oscillations were reversibly abolished by application of the GABAa blocker picrotoxin. Intracellular recordings from local and projection neurons within the antennal lobe (AL, analogous to the olfactory bulb) revealed odor-elicited spikes and sub-threshold membrane potential oscillations that were tightly phase-locked to LFP oscillations recorded downstream in the MBs. These results suggested that, as in locusts, odors may elicit the oscillatory synchronization of AL neurons by means of GABAergic inhibition from local neurons (LNs). An analysis of the morphologies of genetically distinguished LNs revealed two populations of GABAergic neurons in the AL. One population of LNs innervated parts of glomeruli lacking terminals of receptor neurons, whereas the other branched more widely, innervating throughout the glomeruli, suggesting the two populations might participate in different neural circuits. To test the functional roles of these LNs, we used the temperature-sensitive dynamin mutant gene, shibire, to conditionally and reversibly block chemical transmission from each or both of these populations of LNs. We found only the more widely branching population of LNs is necessary for generating odor-elicited oscillations. PMID:19571150

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.

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

Emergence of Slow Collective Oscillations in Neural Networks with Spike-Timing Dependent Plasticity

NASA Astrophysics Data System (ADS)

Mikkelsen, Kaare; Imparato, Alberto; Torcini, Alessandro

2013-05-01

The collective dynamics of excitatory pulse coupled neurons with spike-timing dependent plasticity is studied. The introduction of spike-timing dependent plasticity induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.

Different patterns of puberty effect in neural oscillation to negative stimuli: sex differences.

PubMed

Yuan, Jiajin; Ju, Enxia; Yang, Jiemin; Chen, Xuhai; Li, Hong

2014-12-01

The present study investigated the impact of puberty on sex differences in neural sensitivity to negative stimuli. Event-related oscillation technique was used. Because girls are more vulnerable to affective disturbances than boys during adolescence, it was hypothesized that puberty exerts different influences on neural sensitivity to negative stimuli in boys and girls. EEGs were recorded for highly negative (HN), mildly negative (MN) and neutral pictures, when boys and girls distinct in pubertal status performed a non-emotional distracting task. No emotion effect and its interaction with sex and puberty were observed in response latencies. However, puberty influenced the gamma-band oscillation effect for negative stimuli differently for boys and girls: Pre-pubertal boys showed a significant emotion effect for HN stimuli, whose size was decreased in pubertal boys. By contrast, there was a significant emotion effect for HN stimuli in pubertal girls but not in pre-pubertal girls. On the other hand, the size of the emotion effect for HN stimuli was similar for pre-pubertal boys and girls; while this effect was significantly more pronounced in pubertal girls compared to pubertal boys. Additionally, the size of the emotion effect in gamma oscillations decreased as a function of pubertal development during both HN and MN stimulation in boys. For girls, the emotion effect in gamma oscillations increased with pubertal development during HN stimulation. Thus, puberty is associated with reduced neural sensitivity in boys but increased sensitivity in girls, in reaction to negative stimuli. The implications of these results for the psychopathology during adolescence were discussed.

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

PubMed

Schrum, Jacob; Miikkulainen, Risto

2016-03-12

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.

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

The Effect of Inhibitory Neuron on the Evolution Model of Higher-Order Coupling Neural Oscillator Population

PubMed Central

Qi, Yi; Wang, Rubin; Jiao, Xianfa; Du, Ying

2014-01-01

We proposed a higher-order coupling neural network model including the inhibitory neurons and examined the dynamical evolution of average number density and phase-neural coding under the spontaneous activity and external stimulating condition. The results indicated that increase of inhibitory coupling strength will cause decrease of average number density, whereas increase of excitatory coupling strength will cause increase of stable amplitude of average number density. Whether the neural oscillator population is able to enter the new synchronous oscillation or not is determined by excitatory and inhibitory coupling strength. In the presence of external stimulation, the evolution of the average number density is dependent upon the external stimulation and the coupling term in which the dominator will determine the final evolution. PMID:24516505

The relative efficiency of modular and non-modular networks of different size

PubMed Central

Tosh, Colin R.; McNally, Luke

2015-01-01

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity. PMID:25631996

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

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

A modular architecture for transparent computation in recurrent neural networks.

PubMed

Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

2017-01-01

Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

PubMed

Mall, Susmita; Chakraverty, S

2016-08-01

Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

Highly-Efficient and Modular Medium-Voltage Converters

DTIC Science & Technology

2015-09-28

HVDC modular multilevel converter in decoupled double synchronous reference frame for voltage oscillation reduction," IEEE Trans. Ind...Electron., vol. 29, pp. 77-88, Jan 2014. [10] M. Guan and Z. Xu, "Modeling and control of a modular multilevel converter -based HVDC system under...34 Modular multilevel converter design for VSC HVDC applications," IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 3, pp.

Synchrony-induced modes of oscillation of a neural field model

NASA Astrophysics Data System (ADS)

Esnaola-Acebes, Jose M.; Roxin, Alex; Avitabile, Daniele; Montbrió, Ernest

2017-11-01

We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially homogeneous state and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states and are maintained away from onset.

Synchrony-induced modes of oscillation of a neural field model.

PubMed

Esnaola-Acebes, Jose M; Roxin, Alex; Avitabile, Daniele; Montbrió, Ernest

2017-11-01

We investigate the modes of oscillation of heterogeneous ring networks of quadratic integrate-and-fire (QIF) neurons with nonlocal, space-dependent coupling. Perturbations of the equilibrium state with a particular wave number produce transient standing waves with a specific temporal frequency, analogously to those in a tense string. In the neuronal network, the equilibrium corresponds to a spatially homogeneous, asynchronous state. Perturbations of this state excite the network's oscillatory modes, which reflect the interplay of episodes of synchronous spiking with the excitatory-inhibitory spatial interactions. In the thermodynamic limit, an exact low-dimensional neural field model describing the macroscopic dynamics of the network is derived. This allows us to obtain formulas for the Turing eigenvalues of the spatially homogeneous state and hence to obtain its stability boundary. We find that the frequency of each Turing mode depends on the corresponding Fourier coefficient of the synaptic pattern of connectivity. The decay rate instead is identical for all oscillation modes as a consequence of the heterogeneity-induced desynchronization of the neurons. Finally, we numerically compute the spectrum of spatially inhomogeneous solutions branching from the Turing bifurcation, showing that similar oscillatory modes operate in neural bump states and are maintained away from onset.

Neural mechanisms of mental schema: a triplet of delta, low beta/spindle and ripple oscillations.

PubMed

Ohki, Takefumi; Takei, Yuichi

2018-02-06

Schemas are higher-level knowledge structures that integrate and organise lower-level representations. As internal templates, schemas are formed according to how events are perceived, interpreted and remembered. Although these higher-level units are assumed to play a fundamental role in our daily life from an early age, the neuronal basis and mechanisms of schema formation and use remain largely unknown. It is important to elucidate how the brain constructs and maintains these higher-level units. In order to examine the possible neural underpinnings of schema, we recapitulate previous work and discuss their findings related to schemas as the brain template. We specifically focused on low beta/spindle oscillations, which are assumed to be the key components of schemas, and propose that the brain template is implemented with a triplet of neural oscillations, that is delta, low beta/spindle and ripple oscillations. © 2018 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Cholinergic enhancement of visual attention and neural oscillations in the human brain.

PubMed

Bauer, Markus; Kluge, Christian; Bach, Dominik; Bradbury, David; Heinze, Hans Jochen; Dolan, Raymond J; Driver, Jon

2012-03-06

Cognitive processes such as visual perception and selective attention induce specific patterns of brain oscillations. The neurochemical bases of these spectral changes in neural activity are largely unknown, but neuromodulators are thought to regulate processing. The cholinergic system is linked to attentional function in vivo, whereas separate in vitro studies show that cholinergic agonists induce high-frequency oscillations in slice preparations. This has led to theoretical proposals that cholinergic enhancement of visual attention might operate via gamma oscillations in visual cortex, although low-frequency alpha/beta modulation may also play a key role. Here we used MEG to record cortical oscillations in the context of administration of a cholinergic agonist (physostigmine) during a spatial visual attention task in humans. This cholinergic agonist enhanced spatial attention effects on low-frequency alpha/beta oscillations in visual cortex, an effect correlating with a drug-induced speeding of performance. By contrast, the cholinergic agonist did not alter high-frequency gamma oscillations in visual cortex. Thus, our findings show that cholinergic neuromodulation enhances attentional selection via an impact on oscillatory synchrony in visual cortex, for low rather than high frequencies. We discuss this dissociation between high- and low-frequency oscillations in relation to proposals that lower-frequency oscillations are generated by feedback pathways within visual cortex. Copyright © 2012 Elsevier Ltd. All rights reserved.

Δ9-THC Disrupts Gamma (γ)-Band Neural Oscillations in Humans.

PubMed

Cortes-Briones, Jose; Skosnik, Patrick D; Mathalon, Daniel; Cahill, John; Pittman, Brian; Williams, Ashley; Sewell, R Andrew; Ranganathan, Mohini; Roach, Brian; Ford, Judith; D'Souza, Deepak Cyril

2015-08-01

Gamma (γ)-band oscillations play a key role in perception, associative learning, and conscious awareness and have been shown to be disrupted by cannabinoids in animal studies. The goal of this study was to determine whether cannabinoids disrupt γ-oscillations in humans and whether these effects relate to their psychosis-relevant behavioral effects. The acute, dose-related effects of Δ-9-tetrahydrocannabinol (Δ(9)-THC) on the auditory steady-state response (ASSR) were studied in humans (n=20) who completed 3 test days during which they received intravenous Δ(9)-THC (placebo, 0.015, and 0.03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design. Electroencephalography (EEG) was recorded while subjects listened to auditory click trains presented at 20, 30, and 40 Hz. Psychosis-relevant effects were measured with the Positive and Negative Syndrome scale (PANSS). Δ(9)-THC (0.03 mg/kg) reduced intertrial coherence (ITC) in the 40 Hz condition compared with 0.015 mg/kg and placebo. No significant effects were detected for 30 and 20 Hz stimulation. Furthermore, there was a negative correlation between 40 Hz ITC and PANSS subscales and total scores under the influence of Δ(9)-THC. Δ(9)-THC (0.03 mg/kg) reduced evoked power during 40 Hz stimulation at a trend level. Recent users of cannabis showed blunted Δ(9)-THC effects on ITC and evoked power. We show for the first time in humans that cannabinoids disrupt γ-band neural oscillations. Furthermore, there is a relationship between disruption of γ-band neural oscillations and psychosis-relevant phenomena induced by cannabinoids. These findings add to a growing literature suggesting some overlap between the acute effects of cannabinoids and the behavioral and psychophysiological alterations observed in psychotic disorders.

Δ9-THC Disrupts Gamma (γ)-Band Neural Oscillations in Humans

PubMed Central

Cortes-Briones, Jose; Skosnik, Patrick D; Mathalon, Daniel; Cahill, John; Pittman, Brian; Williams, Ashley; Sewell, R Andrew; Ranganathan, Mohini; Roach, Brian; Ford, Judith; D'Souza, Deepak Cyril

2015-01-01

Gamma (γ)-band oscillations play a key role in perception, associative learning, and conscious awareness and have been shown to be disrupted by cannabinoids in animal studies. The goal of this study was to determine whether cannabinoids disrupt γ-oscillations in humans and whether these effects relate to their psychosis-relevant behavioral effects. The acute, dose-related effects of Δ-9-tetrahydrocannabinol (Δ9-THC) on the auditory steady-state response (ASSR) were studied in humans (n=20) who completed 3 test days during which they received intravenous Δ9-THC (placebo, 0.015, and 0.03 mg/kg) in a double-blind, randomized, crossover, and counterbalanced design. Electroencephalography (EEG) was recorded while subjects listened to auditory click trains presented at 20, 30, and 40 Hz. Psychosis-relevant effects were measured with the Positive and Negative Syndrome scale (PANSS). Δ9-THC (0.03 mg/kg) reduced intertrial coherence (ITC) in the 40 Hz condition compared with 0.015 mg/kg and placebo. No significant effects were detected for 30 and 20 Hz stimulation. Furthermore, there was a negative correlation between 40 Hz ITC and PANSS subscales and total scores under the influence of Δ9-THC. Δ9-THC (0.03 mg/kg) reduced evoked power during 40 Hz stimulation at a trend level. Recent users of cannabis showed blunted Δ9-THC effects on ITC and evoked power. We show for the first time in humans that cannabinoids disrupt γ-band neural oscillations. Furthermore, there is a relationship between disruption of γ-band neural oscillations and psychosis-relevant phenomena induced by cannabinoids. These findings add to a growing literature suggesting some overlap between the acute effects of cannabinoids and the behavioral and psychophysiological alterations observed in psychotic disorders. PMID:25709097

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

Gamma Oscillations of Spiking Neural Populations Enhance Signal Discrimination

PubMed Central

Masuda, Naoki; Doiron, Brent

2007-01-01

Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood. We propose that gamma oscillations induce binomial-like spike-count statistics across noisy neural populations. Using simplified models of spiking neurons, we show how the discrimination of static signals based on the population spike-count response is improved with gamma induced binomial statistics. These results give an important mechanistic link between the neural correlates of attention and the discrimination tasks where attention is known to enhance performance. Further, they show how a rhythmicity of spike responses can enhance coding schemes that are not temporally sensitive. PMID:18052541

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

Frequency modulation of neural oscillations according to visual task demands.

PubMed

Wutz, Andreas; Melcher, David; Samaha, Jason

2018-02-06

Temporal integration in visual perception is thought to occur within cycles of occipital alpha-band (8-12 Hz) oscillations. Successive stimuli may be integrated when they fall within the same alpha cycle and segregated for different alpha cycles. Consequently, the speed of alpha oscillations correlates with the temporal resolution of perception, such that lower alpha frequencies provide longer time windows for perceptual integration and higher alpha frequencies correspond to faster sampling and segregation. Can the brain's rhythmic activity be dynamically controlled to adjust its processing speed according to different visual task demands? We recorded magnetoencephalography (MEG) while participants switched between task instructions for temporal integration and segregation, holding stimuli and task difficulty constant. We found that the peak frequency of alpha oscillations decreased when visual task demands required temporal integration compared with segregation. Alpha frequency was strategically modulated immediately before and during stimulus processing, suggesting a preparatory top-down source of modulation. Its neural generators were located in occipital and inferotemporal cortex. The frequency modulation was specific to alpha oscillations and did not occur in the delta (1-3 Hz), theta (3-7 Hz), beta (15-30 Hz), or gamma (30-50 Hz) frequency range. These results show that alpha frequency is under top-down control to increase or decrease the temporal resolution of visual perception.

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.

The Involvement of Endogenous Neural Oscillations in the Processing of Rhythmic Input: More Than a Regular Repetition of Evoked Neural Responses

PubMed Central

Zoefel, Benedikt; ten Oever, Sanne; Sack, Alexander T.

2018-01-01

It is undisputed that presenting a rhythmic stimulus leads to a measurable brain response that follows the rhythmic structure of this stimulus. What is still debated, however, is the question whether this brain response exclusively reflects a regular repetition of evoked responses, or whether it also includes entrained oscillatory activity. Here we systematically present evidence in favor of an involvement of entrained neural oscillations in the processing of rhythmic input while critically pointing out which questions still need to be addressed before this evidence could be considered conclusive. In this context, we also explicitly discuss the potential functional role of such entrained oscillations, suggesting that these stimulus-aligned oscillations reflect, and serve as, predictive processes, an idea often only implicitly assumed in the literature. PMID:29563860

Prediction of general mental ability based on neural oscillation measures of sleep.

PubMed

Bódizs, Róbert; Kis, Tamás; Lázár, Alpár Sándor; Havrán, Linda; Rigó, Péter; Clemens, Zsófia; Halász, Péter

2005-09-01

The usual assessment of general mental ability (or intelligence) is based on performance attained in reasoning and problem-solving tasks. Differences in general mental ability have been associated with event-related neural activity patterns of the wakeful working brain or physical, chemical and electrical brain features measured during wakeful resting conditions. Recent evidences suggest that specific sleep electroencephalogram oscillations are related to wakeful cognitive performances. Our aim is to reveal the relationship between non-rapid eye movement sleep-specific oscillations (the slow oscillation, delta activity, slow and fast sleep spindle density, the grouping of slow and fast sleep spindles) and general mental ability assessed by the Raven Progressive Matrices Test (RPMT). The grouping of fast sleep spindles by the cortical slow oscillation in the left frontopolar derivation (Fp1) as well as the density of fast sleep spindles over the right frontal area (Fp2, F4), correlated positively with general mental ability. Data from those selected electrodes that showed the high correlations with general mental ability explained almost 70% of interindividual variance in RPMT scores. Results suggest that individual differences in general mental ability are reflected in fast sleep spindle-related oscillatory activity measured over the frontal cortex.

When modularization fails to occur: a developmental perspective.

PubMed

D'Souza, Dean; Karmiloff-Smith, Annette

2011-05-01

We argue that models of adult cognition defined in terms of independently functioning modules cannot be applied to development, whether typical or atypical. The infant brain starts out highly interconnected, and it is only over developmental time that neural networks become increasingly specialized-that is, relatively modularized. In the case of atypical development, even when behavioural scores fall within the normal range, they are frequently underpinned by different cognitive and neural processes. In other words, in neurodevelopmental disorders the gradual process of relative modularization may fail to occur.

Model of rhythmic ball bouncing using a visually controlled neural oscillator.

PubMed

Avrin, Guillaume; Siegler, Isabelle A; Makarov, Maria; Rodriguez-Ayerbe, Pedro

2017-10-01

The present paper investigates the sensory-driven modulations of central pattern generator dynamics that can be expected to reproduce human behavior during rhythmic hybrid tasks. We propose a theoretical model of human sensorimotor behavior able to account for the observed data from the ball-bouncing task. The novel control architecture is composed of a Matsuoka neural oscillator coupled with the environment through visual sensory feedback. The architecture's ability to reproduce human-like performance during the ball-bouncing task in the presence of perturbations is quantified by comparison of simulated and recorded trials. The results suggest that human visual control of the task is achieved online. The adaptive behavior is made possible by a parametric and state control of the limit cycle emerging from the interaction of the rhythmic pattern generator, the musculoskeletal system, and the environment. NEW & NOTEWORTHY The study demonstrates that a behavioral model based on a neural oscillator controlled by visual information is able to accurately reproduce human modulations in a motor action with respect to sensory information during the rhythmic ball-bouncing task. The model attractor dynamics emerging from the interaction between the neuromusculoskeletal system and the environment met task requirements, environmental constraints, and human behavioral choices without relying on movement planning and explicit internal models of the environment. Copyright © 2017 the American Physiological Society.

Altered Neural Oscillations During Multisensory Integration in Adolescents with Fetal Alcohol Spectrum Disorder.

PubMed

Bolaños, Alfredo D; Coffman, Brian A; Candelaria-Cook, Felicha T; Kodituwakku, Piyadasa; Stephen, Julia M

2017-12-01

Children with fetal alcohol spectrum disorder (FASD), who were exposed to alcohol in utero, display a broad range of sensory, cognitive, and behavioral deficits, which are broadly theorized to be rooted in altered brain function and structure. Based on the role of neural oscillations in multisensory integration from past studies, we hypothesized that adolescents with FASD would show a decrease in oscillatory power during event-related gamma oscillatory activity (30 to 100 Hz), when compared to typically developing healthy controls (HC), and that such decrease in oscillatory power would predict behavioral performance. We measured sensory neurophysiology using magnetoencephalography (MEG) during passive auditory, somatosensory, and multisensory (synchronous) stimulation in 19 adolescents (12 to 21 years) with FASD and 23 age- and gender-matched HC. We employed a cross-hemisphere multisensory paradigm to assess interhemispheric connectivity deficits in children with FASD. Time-frequency analysis of MEG data revealed a significant decrease in gamma oscillatory power for both unisensory and multisensory conditions in the FASD group relative to HC, based on permutation testing of significant group differences. Greater beta oscillatory power (15 to 30 Hz) was also noted in the FASD group compared to HC in both unisensory and multisensory conditions. Regression analysis revealed greater predictive power of multisensory oscillations from unisensory oscillations in the FASD group compared to the HC group. Furthermore, multisensory oscillatory power, for both groups, predicted performance on the Intra-Extradimensional Set Shift Task and the Cambridge Gambling Task. Altered oscillatory power in the FASD group may reflect a restricted ability to process somatosensory and multisensory stimuli during day-to-day interactions. These alterations in neural oscillations may be associated with the neurobehavioral deficits experienced by adolescents with FASD and may carry over to

Synthetic in vitro transcriptional oscillators

PubMed Central

Kim, Jongmin; Winfree, Erik

2011-01-01

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

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

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. Copyright © 2016 the American Physiological Society.

Oscillations, neural computations and learning during wake and sleep.

PubMed

Penagos, Hector; Varela, Carmen; Wilson, Matthew A

2017-06-01

Learning and memory theories consider sleep and the reactivation of waking hippocampal neural patterns to be crucial for the long-term consolidation of memories. Here we propose that precisely coordinated representations across brain regions allow the inference and evaluation of causal relationships to train an internal generative model of the world. This training starts during wakefulness and strongly benefits from sleep because its recurring nested oscillations may reflect compositional operations that facilitate a hierarchical processing of information, potentially including behavioral policy evaluations. This suggests that an important function of sleep activity is to provide conditions conducive to general inference, prediction and insight, which contribute to a more robust internal model that underlies generalization and adaptive behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Coordinated infraslow neural and cardiac oscillations mark fragility and offline periods in mammalian sleep

PubMed Central

Lecci, Sandro; Fernandez, Laura M. J.; Weber, Frederik D.; Cardis, Romain; Chatton, Jean-Yves; Born, Jan; Lüthi, Anita

2017-01-01

Rodents sleep in bouts lasting minutes; humans sleep for hours. What are the universal needs served by sleep given such variability? In sleeping mice and humans, through monitoring neural and cardiac activity (combined with assessment of arousability and overnight memory consolidation, respectively), we find a previously unrecognized hallmark of sleep that balances two fundamental yet opposing needs: to maintain sensory reactivity to the environment while promoting recovery and memory consolidation. Coordinated 0.02-Hz oscillations of the sleep spindle band, hippocampal ripple activity, and heart rate sequentially divide non–rapid eye movement (non-REM) sleep into offline phases and phases of high susceptibility to external stimulation. A noise stimulus chosen such that sleeping mice woke up or slept through at comparable rates revealed that offline periods correspond to raising, whereas fragility periods correspond to declining portions of the 0.02-Hz oscillation in spindle activity. Oscillations were present throughout non-REM sleep in mice, yet confined to light non-REM sleep (stage 2) in humans. In both species, the 0.02-Hz oscillation predominated over posterior cortex. The strength of the 0.02-Hz oscillation predicted superior memory recall after sleep in a declarative memory task in humans. These oscillations point to a conserved function of mammalian non-REM sleep that cycles between environmental alertness and internal memory processing in 20- to 25-s intervals. Perturbed 0.02-Hz oscillations may cause memory impairment and ill-timed arousals in sleep disorders. PMID:28246641

Recognition of abstract objects via neural oscillators: interaction among topological organization, associative memory and gamma band synchronization.

PubMed

Ursino, Mauro; Magosso, Elisa; Cuppini, Cristiano

2009-02-01

Synchronization of neural activity in the gamma band is assumed to play a significant role not only in perceptual processing, but also in higher cognitive functions. Here, we propose a neural network of Wilson-Cowan oscillators to simulate recognition of abstract objects, each represented as a collection of four features. Features are ordered in topological maps of oscillators connected via excitatory lateral synapses, to implement a similarity principle. Experience on previous objects is stored in long-range synapses connecting the different topological maps, and trained via timing dependent Hebbian learning (previous knowledge principle). Finally, a downstream decision network detects the presence of a reliable object representation, when all features are oscillating in synchrony. Simulations performed giving various simultaneous objects to the network (from 1 to 4), with some missing and/or modified properties suggest that the network can reconstruct objects, and segment them from the other simultaneously present objects, even in case of deteriorated information, noise, and moderate correlation among the inputs (one common feature). The balance between sensitivity and specificity depends on the strength of the Hebbian learning. Achieving a correct reconstruction in all cases, however, requires ad hoc selection of the oscillation frequency. The model represents an attempt to investigate the interactions among topological maps, autoassociative memory, and gamma-band synchronization, for recognition of abstract objects.

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

Impulsivity and the Modular Organization of Resting-State Neural Networks

PubMed Central

Davis, F. Caroline; Knodt, Annchen R.; Sporns, Olaf; Lahey, Benjamin B.; Zald, David H.; Brigidi, Bart D.; Hariri, Ahmad R.

2013-01-01

Impulsivity is a complex trait associated with a range of maladaptive behaviors, including many forms of psychopathology. Previous research has implicated multiple neural circuits and neurotransmitter systems in impulsive behavior, but the relationship between impulsivity and organization of whole-brain networks has not yet been explored. Using graph theory analyses, we characterized the relationship between impulsivity and the functional segregation (“modularity”) of the whole-brain network architecture derived from resting-state functional magnetic resonance imaging (fMRI) data. These analyses revealed remarkable differences in network organization across the impulsivity spectrum. Specifically, in highly impulsive individuals, regulatory structures including medial and lateral regions of the prefrontal cortex were isolated from subcortical structures associated with appetitive drive, whereas these brain areas clustered together within the same module in less impulsive individuals. Further exploration of the modular organization of whole-brain networks revealed novel shifts in the functional connectivity between visual, sensorimotor, cortical, and subcortical structures across the impulsivity spectrum. The current findings highlight the utility of graph theory analyses of resting-state fMRI data in furthering our understanding of the neurobiological architecture of complex behaviors. PMID:22645253

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.

Using Neural Pattern Classifiers to Quantify the Modularity of Conflict–Control Mechanisms in the Human Brain

PubMed Central

Jiang, Jiefeng; Egner, Tobias

2014-01-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict–control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of “searchlight” classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict–control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict–control were not. Overall, these findings suggest a hybrid neural architecture of conflict–control that entails both modular (domain specific) and global (domain general) components. PMID:23402762

Xenon-induced power oscillations in a generic small modular reactor

NASA Astrophysics Data System (ADS)

Kitcher, Evans Damenortey

As world demand for energy continues to grow at unprecedented rates, the world energy portfolio of the future will inevitably include a nuclear energy contribution. It has been suggested that the Small Modular Reactor (SMR) could play a significant role in the spread of civilian nuclear technology to nations previously without nuclear energy. As part of the design process, the SMR design must be assessed for the threat to operations posed by xenon-induced power oscillations. In this research, a generic SMR design was analyzed with respect to just such a threat. In order to do so, a multi-physics coupling routine was developed with MCNP/MCNPX as the neutronics solver. Thermal hydraulic assessments were performed using a single channel analysis tool developed in Python. Fuel and coolant temperature profiles were implemented in the form of temperature dependent fuel cross sections generated using the SIGACE code and reactor core coolant densities. The Power Axial Offset (PAO) and Xenon Axial Offset (XAO) parameters were chosen to quantify any oscillatory behavior observed. The methodology was benchmarked against results from literature of startup tests performed at a four-loop PWR in Korea. The developed benchmark model replicated the pertinent features of the reactor within ten percent of the literature values. The results of the benchmark demonstrated that the developed methodology captured the desired phenomena accurately. Subsequently, a high fidelity SMR core model was developed and assessed. Results of the analysis revealed an inherently stable SMR design at beginning of core life and end of core life under full-power and half-power conditions. The effect of axial discretization, stochastic noise and convergence of the Monte Carlo tallies in the calculations of the PAO and XAO parameters was investigated. All were found to be quite small and the inherently stable nature of the core design with respect to xenon-induced power oscillations was confirmed. Finally, a

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.

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.

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. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using neural pattern classifiers to quantify the modularity of conflict-control mechanisms in the human brain.

PubMed

Jiang, Jiefeng; Egner, Tobias

2014-07-01

Resolving conflicting sensory and motor representations is a core function of cognitive control, but it remains uncertain to what degree control over different sources of conflict is implemented by shared (domain general) or distinct (domain specific) neural resources. Behavioral data suggest conflict-control to be domain specific, but results from neuroimaging studies have been ambivalent. Here, we employed multivoxel pattern analyses that can decode a brain region's informational content, allowing us to distinguish incidental activation overlap from actual shared information processing. We trained independent sets of "searchlight" classifiers on functional magnetic resonance imaging data to decode control processes associated with stimulus-conflict (Stroop task) and ideomotor-conflict (Simon task). Quantifying the proportion of domain-specific searchlights (capable of decoding only one type of conflict) and domain-general searchlights (capable of decoding both conflict types) in each subject, we found both domain-specific and domain-general searchlights, though the former were more common. When mapping anatomical loci of these searchlights across subjects, neural substrates of stimulus- and ideomotor-specific conflict-control were found to be anatomically consistent across subjects, whereas the substrates of domain-general conflict-control were not. Overall, these findings suggest a hybrid neural architecture of conflict-control that entails both modular (domain specific) and global (domain general) components. © The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Imaging of neural oscillations with embedded inferential and group prevalence statistics.

PubMed

Donhauser, Peter W; Florin, Esther; Baillet, Sylvain

2018-02-01

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be

Imaging of neural oscillations with embedded inferential and group prevalence statistics

PubMed Central

2018-01-01

Magnetoencephalography and electroencephalography (MEG, EEG) are essential techniques for studying distributed signal dynamics in the human brain. In particular, the functional role of neural oscillations remains to be clarified. For that reason, imaging methods need to identify distinct brain regions that concurrently generate oscillatory activity, with adequate separation in space and time. Yet, spatial smearing and inhomogeneous signal-to-noise are challenging factors to source reconstruction from external sensor data. The detection of weak sources in the presence of stronger regional activity nearby is a typical complication of MEG/EEG source imaging. We propose a novel, hypothesis-driven source reconstruction approach to address these methodological challenges. The imaging with embedded statistics (iES) method is a subspace scanning technique that constrains the mapping problem to the actual experimental design. A major benefit is that, regardless of signal strength, the contributions from all oscillatory sources, which activity is consistent with the tested hypothesis, are equalized in the statistical maps produced. We present extensive evaluations of iES on group MEG data, for mapping 1) induced oscillations using experimental contrasts, 2) ongoing narrow-band oscillations in the resting-state, 3) co-modulation of brain-wide oscillatory power with a seed region, and 4) co-modulation of oscillatory power with peripheral signals (pupil dilation). Along the way, we demonstrate several advantages of iES over standard source imaging approaches. These include the detection of oscillatory coupling without rejection of zero-phase coupling, and detection of ongoing oscillations in deeper brain regions, where signal-to-noise conditions are unfavorable. We also show that iES provides a separate evaluation of oscillatory synchronization and desynchronization in experimental contrasts, which has important statistical advantages. The flexibility of iES allows it to be

Sustained vs. oscillating expressions of Ngn2, Dll1 and Hes1: a model of neural differentiation of embryonic telencephalon.

PubMed

Barton, A; Fendrik, A J

2013-07-07

Neural progenitor cells show oscillatory expression of the Notch ligand Delta-like1 (Dll1), the Notch target Hes1 and the proneural gene Neurogenin 2 (Ngn2) during embryonic development of the mammalian telencephalon. On the other hand, expression of these genes is sustained in postmitotic neurons (upregulated for Ngn2 and Dll1, down regulated for Hes1). These facts suggest that a switch from oscillatory to sustained expression of proneural and other genes is critical in neural fate decisions. Moreover, despite controversies over the role of Numb in determining the neural fate in mammals, there is evidence that inheritance of Numb during neurogenic cell division is involved in neural differentiation. It is also known that mNumb activates Notch1 receptor degradation. The arrest of oscillations in a given cell may be due to increasing degradation of Notch1 brought about by mNumb during neurogenic division. We introduce a modification in a previous model of the gene network for two cells coupled by the Delta-Notch pathway (Wang et al., 2011). We analyze the consequences of an asymmetry between two neighbor cells in the rate of degradation of Notch (mimicking the effect of asymmetric inheritance of mNumb during the neurogenic division). The results show that a slight difference in Notch degradation between the two cells keeps oscillation going in one of them while oscillation stops in the other. Moreover, when Delta-Notch coupling is canceled, both cells show sustained expression (upregulated levels for Ngn2 and Dll1, downregulated for Hes1). We show that the model is stable against parameter variations. Moreover, to take into account the possible influence of the environment on both cells, neighboring cells are included in a mean field approximation. Both, parameter fluctuations and effects of the environment lead to asynchronous oscillations of Hes1/Ngn2 in different progenitor cells. Copyright © 2013 Elsevier Ltd. All rights reserved.

Oscillations and chaos in neural networks: an exactly solvable model.

PubMed Central

Wang, L P; Pichler, E E; Ross, J

1990-01-01

We consider a randomly diluted higher-order network with noise, consisting of McCulloch-Pitts neurons that interact by Hebbian-type connections. For this model, exact dynamical equations are derived and solved for both parallel and random sequential updating algorithms. For parallel dynamics, we find a rich spectrum of different behaviors including static retrieving and oscillatory and chaotic phenomena in different parts of the parameter space. The bifurcation parameters include first- and second-order neuronal interaction coefficients and a rescaled noise level, which represents the combined effects of the random synaptic dilution, interference between stored patterns, and additional background noise. We show that a marked difference in terms of the occurrence of oscillations or chaos exists between neural networks with parallel and random sequential dynamics. Images PMID:2251287

Neural oscillations as a bridge between glutamatergic system and emotional behaviors in simulated microgravity-induced mice.

PubMed

Shang, Xueliang; Xu, Bo; Li, Qun; Zhai, Baohui; Xu, Xiaxia; Zhang, Tao

2017-01-15

This study aims to investigate if neural oscillations can play a role as a bridge between the alteration of glutamatergic system and emotional behaviors in simulated microgravity (SM) mice. Adult male C57BL/6J mice were randomly divided into two groups: SM and control groups. The animal model was established by hindlimb unloading (HU). The mice were exposed to HU continued for 14days. Weight and sucrose consumption were measured. The degree of anxious and depressive was evaluated by Open field test and Elevated plus maze test. Local field potentials were recorded in the hippocampal perforant path (PP) and dentate gyrus (DG) regions. The NMDAR2A/2B (NR2A/2B) subunits expression and glutamate level were measured by Western and high performance liquid chromatography (HPLC), respectively. After 14days, SM mice exhibited depressive-like and anxiety-like behaviors, while the expression of NR2A/2B subunits and the glutamate level were significantly decreased in the SM group. Moreover, the power distribution of theta (3-8Hz) was decreased by HU, which further significantly attenuated the identical-frequency strength of phase synchronization and the neural information flow at theta rhythm on the PP-DG pathway. The theta-gamma phase synchronization strength was also significantly reduced by HU. The data imply that the neural oscillations measurements is a sign of the emotional behaviors impairment and the glutamatergic system change induced by HU. Copyright © 2016 Elsevier B.V. All rights reserved.

A driven active mass damper by using output of a neural oscillator (effects of position control system changes on vibration mitigation performance)

NASA Astrophysics Data System (ADS)

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

2015-03-01

In this paper, a design method for a PD controller, which is a part of a new active mass damper system using a neural oscillator for high-rise buildings, is proposed. The new system mimicking the motion of bipedal mammals is a quite simple system, which has the neural oscillator synchronizing with the acceleration response of the structure. The travel distance and direction of the auxiliary mass of the active mass damper is decided by the output of the neural oscillator, and then, the auxiliary mass is transferred to the decided location by using the PD controller. Therefore, the performance of the PD controller must be evaluated by the vibration energy absorbing efficiency by the system. In order to bring the actual path driven by the PD controller in closer alignment with the ideal path, which is assumed to be a sinusoidal wave under resonance, firstly, the path of the auxiliary mass driven by the PD controller is analytically derived, and the inner product between the vector of ideal and analytical path is evaluated. And then, the PD gain is decided by the maximum value of the inner product. Finally, numerical simulations confirm the validity of the proposed design method of the PD controller.

Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions.

PubMed

Hyafil, Alexandre; Giraud, Anne-Lise; Fontolan, Lorenzo; Gutkin, Boris

2015-11-01

Neural oscillations are ubiquitously observed in the mammalian brain, but it has proven difficult to tie oscillatory patterns to specific cognitive operations. Notably, the coupling between neural oscillations at different timescales has recently received much attention, both from experimentalists and theoreticians. We review the mechanisms underlying various forms of this cross-frequency coupling. We show that different types of neural oscillators and cross-frequency interactions yield distinct signatures in neural dynamics. Finally, we associate these mechanisms with several putative functions of cross-frequency coupling, including neural representations of multiple environmental items, communication over distant areas, internal clocking of neural processes, and modulation of neural processing based on temporal predictions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

PubMed

Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain

PubMed Central

Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L.; Aziz, Tipu Z.; Wang, Shouyan

2018-01-01

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

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

Nonequilibrium landscape theory of neural networks.

PubMed

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

2013-11-05

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.

Circuit oscillations in odor perception and memory.

PubMed

Kay, Leslie M

2014-01-01

Olfactory system neural oscillations as seen in the local field potential have been studied for many decades. Recent research has shown that there is a functional role for the most studied gamma oscillations (40-100Hz in rats and mice, and 20Hz in insects), without which fine odor discrimination is poor. When these oscillations are increased artificially, fine discrimination is increased, and when rats learn difficult and highly overlapping odor discriminations, gamma is increased in power. Because of the depth of study on this oscillation, it is possible to point to specific changes in neural firing patterns as represented by the increase in gamma oscillation amplitude. However, we know far less about the mechanisms governing beta oscillations (15-30Hz in rats and mice), which are best associated with associative learning of responses to odor stimuli. These oscillations engage every part of the olfactory system that has so far been tested, plus the hippocampus, and the beta oscillation frequency band is the one that is most reliably coherent with other regions during odor processing. Respiratory oscillations overlapping with the theta frequency band (2-12Hz) are associated with odor sniffing and normal breathing in rats. They also show coupling in some circumstances between olfactory areas and rare coupling between the hippocampus and olfactory bulb. The latter occur in specific learning conditions in which coherence strength is negatively or positively correlated with performance, depending on the task. There is still much to learn about the role of neural oscillations in learning and memory, but techniques that have been brought to bear on gamma oscillations (current source density, computational modeling, slice physiology, behavioral studies) should deliver much needed knowledge of these events. © 2014 Elsevier B.V. All rights reserved.

Temporal structure of neuronal population oscillations with empirical model decomposition

NASA Astrophysics Data System (ADS)

Li, Xiaoli

2006-08-01

Frequency analysis of neuronal oscillation is very important for understanding the neural information processing and mechanism of disorder in the brain. This Letter addresses a new method to analyze the neuronal population oscillations with empirical mode decomposition (EMD). Following EMD of neuronal oscillation, a series of intrinsic mode functions (IMFs) are obtained, then Hilbert transform of IMFs can be used to extract the instantaneous time frequency structure of neuronal oscillation. The method is applied to analyze the neuronal oscillation in the hippocampus of epileptic rats in vivo, the results show the neuronal oscillations have different descriptions during the pre-ictal, seizure onset and ictal periods of the epileptic EEG at the different frequency band. This new method is very helpful to provide a view for the temporal structure of neural oscillation.

Unified neural field theory of brain dynamics underlying oscillations in Parkinson's disease and generalized epilepsies.

PubMed

Müller, E J; van Albada, S J; Kim, J W; Robinson, P A

2017-09-07

The mechanisms underlying pathologically synchronized neural oscillations in Parkinson's disease (PD) and generalized epilepsies are explored in parallel via a physiologically-based neural field model of the corticothalamic-basal ganglia (CTBG) system. The basal ganglia (BG) are approximated as a single effective population and their roles in the modulation of oscillatory dynamics of the corticothalamic (CT) system and vice versa are analyzed. In addition to normal EEG rhythms, enhanced activity around 4 Hz and 20 Hz exists in the model, consistent with the characteristic frequencies observed in PD. These rhythms result from resonances in loops formed between the BG and CT populations, analogous to those that underlie epileptic oscillations in a previous CT model, and which are still present in the combined CTBG system. Dopamine depletion is argued to weaken the dampening of these loop resonances in PD, and network connections then explain the significant coherence observed between BG, thalamic, and cortical population activity around 4-8 Hz and 20 Hz. Parallels between the afferent and efferent connection sites of the thalamic reticular nucleus (TRN) and BG predict low dopamine to correspond to a reduced likelihood of tonic-clonic (grand mal) seizures, which agrees with experimental findings. Furthermore, the model predicts an increased likelihood of absence (petit mal) seizure resulting from pathologically low dopamine levels in accordance with experimental observations. Suppression of absence seizure activity is demonstrated when afferent and efferent BG connections to the CT system are strengthened, which is consistent with other CTBG modeling studies. The BG are demonstrated to have a suppressive effect on activity of the CTBG system near tonic-clonic seizure states, which provides insight into the reported efficacy of current treatments in BG circuits. Sleep states of the TRN are also found to suppress pathological PD activity in accordance with

Encoding of Olfactory Information with Oscillating Neural Assemblies

NASA Astrophysics Data System (ADS)

Laurent, Gilles; Davidowitz, Hananel

1994-09-01

In the brain, fast oscillations of local field potentials, which are thought to arise from the coherent and rhythmic activity of large numbers of neurons, were observed first in the olfactory system and have since been described in many neocortical areas. The importance of these oscillations in information coding, however, is controversial. Here, local field potential and intracellular recordings were obtained from the antennal lobe and mushroom body of the locust Schistocerca americana. Different odors evoked coherent oscillations in different, but usually overlapping, ensembles of neurons. The phase of firing of individual neurons relative to the population was not dependent on the odor. The components of a coherently oscillating ensemble of neurons changed over the duration of a single exposure to an odor. It is thus proposed that odors are encoded by specific but dynamic assemblies of coherently oscillating neurons. Such distributed and temporal representation of complex sensory signals may facilitate combinatorial coding and associative learning in these, and possibly other, sensory networks.

Predicting neural network firing pattern from phase resetting curve

NASA Astrophysics Data System (ADS)

Oprisan, Sorinel; Oprisan, Ana

2007-04-01

Autonomous neural networks called central pattern generators (CPG) are composed of endogenously bursting neurons and produce rhythmic activities, such as flying, swimming, walking, chewing, etc. Simplified CPGs for quadrupedal locomotion and swimming are modeled by a ring of neural oscillators such that the output of one oscillator constitutes the input for the subsequent neural oscillator. The phase response curve (PRC) theory discards the detailed conductance-based description of the component neurons of a network and reduces them to ``black boxes'' characterized by a transfer function, which tabulates the transient change in the intrinsic period of a neural oscillator subject to external stimuli. Based on open-loop PRC, we were able to successfully predict the phase-locked period and relative phase between neurons in a half-center network. We derived existence and stability criteria for heterogeneous ring neural networks that are in good agreement with experimental data.

Neural Entrainment to Polyrhythms: A Comparison of Musicians and Non-musicians.

PubMed

Stupacher, Jan; Wood, Guilherme; Witte, Matthias

2017-01-01

Music can be thought of as a dynamic path over time. In most cases, the rhythmic structure of this path, such as specific sequences of strong and weak beats or recurring patterns, allows us to predict what and particularly when sounds are going to happen. Without this ability we would not be able to entrain body movements to music, like we do when we dance. By combining EEG and behavioral measures, the current study provides evidence illustrating the importance of ongoing neural oscillations at beat-related frequencies-i.e., neural entrainment-for tracking and predicting musical rhythms. Participants (13 musicians and 13 non-musicians) listened to drum rhythms that switched from a quadruple rhythm to a 3-over-4 polyrhythm. After a silent period of ~2-3 s, participants had to decide whether a target stimulus was presented on time with the triple beat of the polyrhythm, too early, or too late. Results showed that neural oscillations reflected the rhythmic structure of both the simple quadruple rhythm and the more complex polyrhythm with no differences between musicians and non-musicians. During silent periods, the observation of time-frequency plots and more commonly used frequency spectra analyses suggest that beat-related neural oscillations were more pronounced in musicians compared to non-musicians. Neural oscillations during silent periods are not driven by an external input and therefore are thought to reflect top-down controlled endogenous neural entrainment. The functional relevance of endogenous neural entrainment was demonstrated by a positive correlation between the amplitude of task-relevant neural oscillations during silent periods and the number of correctly identified target stimuli. In sum, our findings add to the evidence supporting the neural resonance theory of pulse and meter. Furthermore, they indicate that beat-related top-down controlled neural oscillations can exist without external stimulation and suggest that those endogenous oscillations

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.

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

Neural computational modeling reveals a major role of corticospinal gating of central oscillations in the generation of essential tremor.

PubMed

Qu, Hong-En; Niu, Chuanxin M; Li, Si; Hao, Man-Zhao; Hu, Zi-Xiang; Xie, Qing; Lan, Ning

2017-12-01

Essential tremor, also referred to as familial tremor, is an autosomal dominant genetic disease and the most common movement disorder. It typically involves a postural and motor tremor of the hands, head or other part of the body. Essential tremor is driven by a central oscillation signal in the brain. However, the corticospinal mechanisms involved in the generation of essential tremor are unclear. Therefore, in this study, we used a neural computational model that includes both monosynaptic and multisynaptic corticospinal pathways interacting with a propriospinal neuronal network. A virtual arm model is driven by the central oscillation signal to simulate tremor activity behavior. Cortical descending commands are classified as alpha or gamma through monosynaptic or multisynaptic corticospinal pathways, which converge respectively on alpha or gamma motoneurons in the spinal cord. Several scenarios are evaluated based on the central oscillation signal passing down to the spinal motoneurons via each descending pathway. The simulated behaviors are compared with clinical essential tremor characteristics to identify the corticospinal pathways responsible for transmitting the central oscillation signal. A propriospinal neuron with strong cortical inhibition performs a gating function in the generation of essential tremor. Our results indicate that the propriospinal neuronal network is essential for relaying the central oscillation signal and the production of essential tremor.

Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value Associations.

PubMed

Marshall, Tom R; den Boer, Sebastiaan; Cools, Roshan; Jensen, Ole; Fallon, Sean James; Zumer, Johanna M

2018-01-01

Selective attention is reflected neurally in changes in the power of posterior neural oscillations in the alpha (8-12 Hz) and gamma (40-100 Hz) bands. Although a neural mechanism that allows relevant information to be selectively processed has its advantages, it may lead to lucrative or dangerous information going unnoticed. Neural systems are also in place for processing rewarding and punishing information. Here, we examine the interaction between selective attention (left vs. right) and stimulus's learned value associations (neutral, punished, or rewarded) and how they compete for control of posterior neural oscillations. We found that both attention and stimulus-value associations influenced neural oscillations. Whereas selective attention had comparable effects on alpha and gamma oscillations, value associations had dissociable effects on these neural markers of attention. Salient targets (associated with positive and negative outcomes) hijacked changes in alpha power-increasing hemispheric alpha lateralization when salient targets were attended, decreasing it when they were being ignored. In contrast, hemispheric gamma-band lateralization was specifically abolished by negative distractors. Source analysis indicated occipital generators of both attentional and value effects. Thus, posterior cortical oscillations support both the ability to selectively attend while at the same time retaining the ability to remain sensitive to valuable features in the environment. Moreover, the versatility of our attentional system to respond separately to salient from merely positively valued stimuli appears to be carried out by separate neural processes reflected in different frequency bands.

The modular modality frame model: continuous body state estimation and plausibility-weighted information fusion.

PubMed

Ehrenfeld, Stephan; Butz, Martin V

2013-02-01

Humans show admirable capabilities in movement planning and execution. They can perform complex tasks in various contexts, using the available sensory information very effectively. Body models and continuous body state estimations appear necessary to realize such capabilities. We introduce the Modular Modality Frame (MMF) model, which maintains a highly distributed, modularized body model continuously updating, modularized probabilistic body state estimations over time. Modularization is realized with respect to modality frames, that is, sensory modalities in particular frames of reference and with respect to particular body parts. We evaluate MMF performance on a simulated, nine degree of freedom arm in 3D space. The results show that MMF is able to maintain accurate body state estimations despite high sensor and motor noise. Moreover, by comparing the sensory information available in different modality frames, MMF can identify faulty sensory measurements on the fly. In the near future, applications to lightweight robot control should be pursued. Moreover, MMF may be enhanced with neural encodings by introducing neural population codes and learning techniques. Finally, more dexterous goal-directed behavior should be realized by exploiting the available redundant state representations.

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

Individual differences and time-varying features of modular brain architecture.

PubMed

Liao, Xuhong; Cao, Miao; Xia, Mingrui; He, Yong

2017-05-15

Recent studies have suggested that human brain functional networks are topologically organized into functionally specialized but inter-connected modules to facilitate efficient information processing and highly flexible cognitive function. However, these studies have mainly focused on group-level network modularity analyses using "static" functional connectivity approaches. How these extraordinary modular brain structures vary across individuals and spontaneously reconfigure over time remain largely unknown. Here, we employed multiband resting-state functional MRI data (N=105) from the Human Connectome Project and a graph-based modularity analysis to systematically investigate individual variability and dynamic properties in modular brain networks. We showed that the modular structures of brain networks dramatically vary across individuals, with higher modular variability primarily in the association cortex (e.g., fronto-parietal and attention systems) and lower variability in the primary systems. Moreover, brain regions spontaneously changed their module affiliations on a temporal scale of seconds, which cannot be simply attributable to head motion and sampling error. Interestingly, the spatial pattern of intra-subject dynamic modular variability largely overlapped with that of inter-subject modular variability, both of which were highly reproducible across repeated scanning sessions. Finally, the regions with remarkable individual/temporal modular variability were closely associated with network connectors and the number of cognitive components, suggesting a potential contribution to information integration and flexible cognitive function. Collectively, our findings highlight individual modular variability and the notable dynamic characteristics in large-scale brain networks, which enhance our understanding of the neural substrates underlying individual differences in a variety of cognition and behaviors. Copyright © 2017 Elsevier Inc. All rights reserved.

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…

Beta/Gamma Oscillations and Event-Related Potentials Indicate Aberrant Multisensory Processing in Schizophrenia

PubMed Central

Balz, Johanna; Roa Romero, Yadira; Keil, Julian; Krebber, Martin; Niedeggen, Michael; Gallinat, Jürgen; Senkowski, Daniel

2016-01-01

Recent behavioral and neuroimaging studies have suggested multisensory processing deficits in patients with schizophrenia (SCZ). Thus far, the neural mechanisms underlying these deficits are not well understood. Previous studies with unisensory stimulation have shown altered neural oscillations in SCZ. As such, altered oscillations could contribute to aberrant multisensory processing in this patient group. To test this assumption, we conducted an electroencephalography (EEG) study in 15 SCZ and 15 control participants in whom we examined neural oscillations and event-related potentials (ERPs) in the sound-induced flash illusion (SIFI). In the SIFI multiple auditory stimuli that are presented alongside a single visual stimulus can induce the illusory percept of multiple visual stimuli. In SCZ and control participants we compared ERPs and neural oscillations between trials that induced an illusion and trials that did not induce an illusion. On the behavioral level, SCZ (55.7%) and control participants (55.4%) did not significantly differ in illusion rates. The analysis of ERPs revealed diminished amplitudes and altered multisensory processing in SCZ compared to controls around 135 ms after stimulus onset. Moreover, the analysis of neural oscillations revealed altered 25–35 Hz power after 100 to 150 ms over occipital scalp for SCZ compared to controls. Our findings extend previous observations of aberrant neural oscillations in unisensory perception paradigms. They suggest that altered ERPs and altered occipital beta/gamma band power reflect aberrant multisensory processing in SCZ. PMID:27999553

Applying Neural Networks to Air Force Personnel Analysis

DTIC Science & Technology

1992-03-01

Asakawa, Yoda, and Takeoka (1990); Atlas, Cole, Conner, EI-Sharkawi, Marks, Muthusamy, and Barnard (1990); Leung and Zue (1989); and Denker, Gardner...Transactions on Neural Networks, 1(2), 239-242. Kimoto, T., Asakawa, K., Yoda, M., & Takeoka , M. (1990). Stock market prediction with modular neural

Cannabinoid receptor-mediated disruption of sensory gating and neural oscillations: A translational study in rats and humans.

PubMed

Skosnik, Patrick D; Hajós, Mihály; Cortes-Briones, Jose A; Edwards, Chad R; Pittman, Brian P; Hoffmann, William E; Sewell, Andrew R; D'Souza, Deepak C; Ranganathan, Mohini

2018-06-01

Cannabis use has been associated with altered sensory gating and neural oscillations. However, it is unclear which constituent in cannabis is responsible for these effects, or whether these are cannabinoid receptor 1 (CB1R) mediated. Therefore, the present study in humans and rats examined whether cannabinoid administration would disrupt sensory gating and evoked oscillations utilizing electroencephalography (EEG) and local field potentials (LFPs), respectively. Human subjects (n = 15) completed four test days during which they received intravenous delta-9-tetrahydrocannabinol (Δ 9 -THC), cannabidiol (CBD), Δ 9 -THC + CBD, or placebo. Subjects engaged in a dual-click paradigm, and outcome measures included P50 gating ratio (S2/S1) and evoked power to S1 and S2. In order to examine CB1R specificity, rats (n = 6) were administered the CB1R agonist CP-55940, CP-55940+AM-251 (a CB1R antagonist), or vehicle using the same paradigm. LFPs were recorded from CA3 and entorhinal cortex. Both Δ 9 -THC (p < 0.007) and Δ 9 -THC + CBD (p < 0.004) disrupted P50 gating ratio compared to placebo, while CBD alone had no effect. Δ 9 -THC (p < 0.048) and Δ 9 -THC + CBD (p < 0.035) decreased S1 evoked theta power, and in the Δ 9 -THC condition, S1 theta negatively correlated with gating ratios (r = -0.629, p < 0.012 (p < 0.048 adjusted)). In rats, CP-55940 disrupted gating in both brain regions (p < 0.0001), and this was reversed by AM-251. Further, CP-55940 decreased evoked theta (p < 0.0077) and gamma (p < 0.011) power to S1, which was partially blocked by AM-251. These convergent human/animal data suggest that CB1R agonists disrupt sensory gating by altering neural oscillations in the theta-band. Moreover, this suggests that the endocannabinoid system mediates theta oscillations relevant to perception and cognition. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

Atypical neural synchronization to speech envelope modulations in dyslexia.

PubMed

De Vos, Astrid; Vanvooren, Sophie; Vanderauwera, Jolijn; Ghesquière, Pol; Wouters, Jan

2017-01-01

A fundamental deficit in the synchronization of neural oscillations to temporal information in speech could underlie phonological processing problems in dyslexia. In this study, the hypothesis of a neural synchronization impairment is investigated more specifically as a function of different neural oscillatory bands and temporal information rates in speech. Auditory steady-state responses to 4, 10, 20 and 40Hz modulations were recorded in normal reading and dyslexic adolescents to measure neural synchronization of theta, alpha, beta and low-gamma oscillations to syllabic and phonemic rate information. In comparison to normal readers, dyslexic readers showed reduced non-synchronized theta activity, reduced synchronized alpha activity and enhanced synchronized beta activity. Positive correlations between alpha synchronization and phonological skills were found in normal readers, but were absent in dyslexic readers. In contrast, dyslexic readers exhibited positive correlations between beta synchronization and phonological skills. Together, these results suggest that auditory neural synchronization of alpha and beta oscillations is atypical in dyslexia, indicating deviant neural processing of both syllabic and phonemic rate information. Impaired synchronization of alpha oscillations in particular demonstrated to be the most prominent neural anomaly possibly hampering speech and phonological processing in dyslexic readers. Copyright © 2016 Elsevier Inc. All rights reserved.

Signal Processing, Pattern Formation and Adaptation in Neural Oscillators

DTIC Science & Technology

2016-11-29

nonlinear oscillations of outer hair cells. We obtained analytical forms for auditory tuning curves of both unidirectionally and bidirectionally coupled...oscillations of outer hair cells in the cochlea, mode-locking of chopper cells to sound in the cochlear nucleus, and entrainment of cortical...oscillations of outer hair cells (e.g., Fredrickson-Hemsing, Ji, Bruinsma, & Bozovic, 2012), mode-locking of choppers in the cochlear nucleus (e.g., Laudanski

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art.

PubMed

Lin, Chia-Shu; Liu, Yong; Huang, Wei-Yuan; Lu, Chia-Feng; Teng, Shin; Ju, Tzong-Ching; He, Yong; Wu, Yu-Te; Jiang, Tianzi; Hsieh, Jen-Chuen

2013-01-01

Artistic training is a complex learning that requires the meticulous orchestration of sophisticated polysensory, motor, cognitive, and emotional elements of mental capacity to harvest an aesthetic creation. In this study, we investigated the architecture of the resting-state functional connectivity networks from professional painters, dancers and pianists. Using a graph-based network analysis, we focused on the art-related changes of modular organization and functional hubs in the resting-state functional connectivity network. We report that the brain architecture of artists consists of a hierarchical modular organization where art-unique and artistic form-specific brain states collectively mirror the mind states of virtuosos. We show that even in the resting state, this type of extraordinary and long-lasting training can macroscopically imprint a neural network system of spontaneous activity in which the related brain regions become functionally and topologically modularized in both domain-general and domain-specific manners. The attuned modularity reflects a resilient plasticity nurtured by long-term experience.

Sculpting the Intrinsic Modular Organization of Spontaneous Brain Activity by Art

PubMed Central

Lin, Chia-Shu; Liu, Yong; Huang, Wei-Yuan; Lu, Chia-Feng; Teng, Shin; Ju, Tzong-Ching; He, Yong; Wu, Yu-Te; Jiang, Tianzi; Hsieh, Jen-Chuen

2013-01-01

Artistic training is a complex learning that requires the meticulous orchestration of sophisticated polysensory, motor, cognitive, and emotional elements of mental capacity to harvest an aesthetic creation. In this study, we investigated the architecture of the resting-state functional connectivity networks from professional painters, dancers and pianists. Using a graph-based network analysis, we focused on the art-related changes of modular organization and functional hubs in the resting-state functional connectivity network. We report that the brain architecture of artists consists of a hierarchical modular organization where art-unique and artistic form-specific brain states collectively mirror the mind states of virtuosos. We show that even in the resting state, this type of extraordinary and long-lasting training can macroscopically imprint a neural network system of spontaneous activity in which the related brain regions become functionally and topologically modularized in both domain-general and domain-specific manners. The attuned modularity reflects a resilient plasticity nurtured by long-term experience. PMID:23840527

Aging affects the balance of neural entrainment and top-down neural modulation in the listening brain

PubMed Central

Henry, Molly J.; Herrmann, Björn; Kunke, Dunja; Obleser, Jonas

2017-01-01

Healthy aging is accompanied by listening difficulties, including decreased speech comprehension, that stem from an ill-understood combination of sensory and cognitive changes. Here, we use electroencephalography to demonstrate that auditory neural oscillations of older adults entrain less firmly and less flexibly to speech-paced (∼3 Hz) rhythms than younger adults’ during attentive listening. These neural entrainment effects are distinct in magnitude and origin from the neural response to sound per se. Non-entrained parieto-occipital alpha (8–12 Hz) oscillations are enhanced in young adults, but suppressed in older participants, during attentive listening. Entrained neural phase and task-induced alpha amplitude exert opposite, complementary effects on listening performance: higher alpha amplitude is associated with reduced entrainment-driven behavioural performance modulation. Thus, alpha amplitude as a task-driven, neuro-modulatory signal can counteract the behavioural corollaries of neural entrainment. Balancing these two neural strategies may present new paths for intervention in age-related listening difficulties. PMID:28654081

Global competition and local cooperation in a network of neural oscillators

NASA Astrophysics Data System (ADS)

Terman, David; Wang, DeLiang

An architecture of locally excitatory, globally inhibitory oscillator networks is proposed and investigated both analytically and by computer simulation. The model for each oscillator corresponds to a standard relaxation oscillator with two time scales. Oscillators are locally coupled by a scheme that resembles excitatory synaptic coupling, and each oscillator also inhibits other oscillators through a common inhibitor. Oscillators are driven to be oscillatory by external stimulation. The network exhibits a mechanism of selective gating, whereby an oscillator jumping up to its active phase rapidly recruits the oscillators stimulated by the same pattern, while preventing the other oscillators from jumping up. We show analytically that with the selective gating mechanism, the network rapidly achieves both synchronization within blocks of oscillators that are stimulated by connected regions and desynchronization between different blocks. Computer simulations demonstrate the model's promising ability for segmenting multiple input patterns in real time. This model lays a physical foundation for the oscillatory correlation theory of feature binding and may provide an effective computational framework for scene segmentation and figure/ ground segregation.

The role of cortical oscillations in a spiking neural network model of the basal ganglia.

PubMed

Fountas, Zafeirios; Shanahan, Murray

2017-01-01

Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. In particular, alpha (8-12Hz) and beta (13-30Hz) oscillations activate the direct BG pathway, and favour the modulation of the indirect and hyper-direct pathways via the subthalamic nucleus-globus pallidus loop. In contrast, gamma (30-90Hz) frequencies block the information flow from the cortex completely through activation of the indirect pathway. Finally, below alpha, all pathways decay gradually and the system gives rise to spontaneous activity generated in the globus pallidus. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. These two findings suggest new insights into the pathophysiology of specific BG disorders.

The role of cortical oscillations in a spiking neural network model of the basal ganglia

PubMed Central

Fountas, Zafeirios; Shanahan, Murray

2017-01-01

Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. In particular, alpha (8–12Hz) and beta (13–30Hz) oscillations activate the direct BG pathway, and favour the modulation of the indirect and hyper-direct pathways via the subthalamic nucleus—globus pallidus loop. In contrast, gamma (30–90Hz) frequencies block the information flow from the cortex completely through activation of the indirect pathway. Finally, below alpha, all pathways decay gradually and the system gives rise to spontaneous activity generated in the globus pallidus. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. These two findings suggest new insights into the pathophysiology of specific BG disorders. PMID:29236724

Theta band oscillations reflect more than entrainment: behavioral and neural evidence demonstrates an active chunking process.

PubMed

Teng, Xiangbin; Tian, Xing; Doelling, Keith; Poeppel, David

2017-10-17

Parsing continuous acoustic streams into perceptual units is fundamental to auditory perception. Previous studies have uncovered a cortical entrainment mechanism in the delta and theta bands (~1-8 Hz) that correlates with formation of perceptual units in speech, music, and other quasi-rhythmic stimuli. Whether cortical oscillations in the delta-theta bands are passively entrained by regular acoustic patterns or play an active role in parsing the acoustic stream is debated. Here, we investigate cortical oscillations using novel stimuli with 1/f modulation spectra. These 1/f signals have no rhythmic structure but contain information over many timescales because of their broadband modulation characteristics. We chose 1/f modulation spectra with varying exponents of f, which simulate the dynamics of environmental noise, speech, vocalizations, and music. While undergoing magnetoencephalography (MEG) recording, participants listened to 1/f stimuli and detected embedded target tones. Tone detection performance varied across stimuli of different exponents and can be explained by local signal-to-noise ratio computed using a temporal window around 200 ms. Furthermore, theta band oscillations, surprisingly, were observed for all stimuli, but robust phase coherence was preferentially displayed by stimuli with exponents 1 and 1.5. We constructed an auditory processing model to quantify acoustic information on various timescales and correlated the model outputs with the neural results. We show that cortical oscillations reflect a chunking of segments, > 200 ms. These results suggest an active auditory segmentation mechanism, complementary to entrainment, operating on a timescale of ~200 ms to organize acoustic information. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

PubMed

Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

Metastability and Inter-Band Frequency Modulation in Networks of Oscillating Spiking Neuron Populations

PubMed Central

Bhowmik, David; Shanahan, Murray

2013-01-01

Groups of neurons firing synchronously are hypothesized to underlie many cognitive functions such as attention, associative learning, memory, and sensory selection. Recent theories suggest that transient periods of synchronization and desynchronization provide a mechanism for dynamically integrating and forming coalitions of functionally related neural areas, and that at these times conditions are optimal for information transfer. Oscillating neural populations display a great amount of spectral complexity, with several rhythms temporally coexisting in different structures and interacting with each other. This paper explores inter-band frequency modulation between neural oscillators using models of quadratic integrate-and-fire neurons and Hodgkin-Huxley neurons. We vary the structural connectivity in a network of neural oscillators, assess the spectral complexity, and correlate the inter-band frequency modulation. We contrast this correlation against measures of metastable coalition entropy and synchrony. Our results show that oscillations in different neural populations modulate each other so as to change frequency, and that the interaction of these fluctuating frequencies in the network as a whole is able to drive different neural populations towards episodes of synchrony. Further to this, we locate an area in the connectivity space in which the system directs itself in this way so as to explore a large repertoire of synchronous coalitions. We suggest that such dynamics facilitate versatile exploration, integration, and communication between functionally related neural areas, and thereby supports sophisticated cognitive processing in the brain. PMID:23614040

Oscillatory phase dynamics in neural entrainment underpin illusory percepts of time.

PubMed

Herrmann, Björn; Henry, Molly J; Grigutsch, Maren; Obleser, Jonas

2013-10-02

Neural oscillatory dynamics are a candidate mechanism to steer perception of time and temporal rate change. While oscillator models of time perception are strongly supported by behavioral evidence, a direct link to neural oscillations and oscillatory entrainment has not yet been provided. In addition, it has thus far remained unaddressed how context-induced illusory percepts of time are coded for in oscillator models of time perception. To investigate these questions, we used magnetoencephalography and examined the neural oscillatory dynamics that underpin pitch-induced illusory percepts of temporal rate change. Human participants listened to frequency-modulated sounds that varied over time in both modulation rate and pitch, and judged the direction of rate change (decrease vs increase). Our results demonstrate distinct neural mechanisms of rate perception: Modulation rate changes directly affected listeners' rate percept as well as the exact frequency of the neural oscillation. However, pitch-induced illusory rate changes were unrelated to the exact frequency of the neural responses. The rate change illusion was instead linked to changes in neural phase patterns, which allowed for single-trial decoding of percepts. That is, illusory underestimations or overestimations of perceived rate change were tightly coupled to increased intertrial phase coherence and changes in cerebro-acoustic phase lag. The results provide insight on how illusory percepts of time are coded for by neural oscillatory dynamics.

Fox proteins are modular competency factors for facial cartilage and tooth specification.

PubMed

Xu, Pengfei; Balczerski, Bartosz; Ciozda, Amanda; Louie, Kristin; Oralova, Veronika; Huysseune, Ann; Crump, J Gage

2018-06-26

Facial form depends on the precise positioning of cartilage, bone, and tooth fields in the embryonic pharyngeal arches. How complex signaling information is integrated to specify these cell types remains a mystery. We find that modular expression of Forkhead domain transcription factors (Fox proteins) in the zebrafish face arises through integration of Hh, Fgf, Bmp, Edn1 and Jagged-Notch pathways. Whereas loss of C-class Fox proteins results in reduced upper facial cartilages, loss of F-class Fox proteins results in distal jaw truncations and absent midline cartilages and teeth. We show that Fox proteins are required for Sox9a to promote chondrogenic gene expression. Fox proteins are sufficient in neural crest-derived cells for cartilage development, and neural crest-specific misexpression of Fox proteins expands the cartilage domain but inhibits bone. These results support a modular role for Fox proteins in establishing the competency of progenitors to form cartilage and teeth in the face. © 2018. Published by The Company of Biologists Ltd.

Time Series Decomposition into Oscillation Components and Phase Estimation.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-02-01

Many time series are naturally considered as a superposition of several oscillation components. For example, electroencephalogram (EEG) time series include oscillation components such as alpha, beta, and gamma. We propose a method for decomposing time series into such oscillation components using state-space models. Based on the concept of random frequency modulation, gaussian linear state-space models for oscillation components are developed. In this model, the frequency of an oscillator fluctuates by noise. Time series decomposition is accomplished by this model like the Bayesian seasonal adjustment method. Since the model parameters are estimated from data by the empirical Bayes' method, the amplitudes and the frequencies of oscillation components are determined in a data-driven manner. Also, the appropriate number of oscillation components is determined with the Akaike information criterion (AIC). In this way, the proposed method provides a natural decomposition of the given time series into oscillation components. In neuroscience, the phase of neural time series plays an important role in neural information processing. The proposed method can be used to estimate the phase of each oscillation component and has several advantages over a conventional method based on the Hilbert transform. Thus, the proposed method enables an investigation of the phase dynamics of time series. Numerical results show that the proposed method succeeds in extracting intermittent oscillations like ripples and detecting the phase reset phenomena. We apply the proposed method to real data from various fields such as astronomy, ecology, tidology, and neuroscience.

Gamma oscillations in a nonlinear regime: a minimal model approach using heterogeneous integrate-and-fire networks.

PubMed

Bathellier, Brice; Carleton, Alan; Gerstner, Wulfram

2008-12-01

Fast oscillations and in particular gamma-band oscillation (20-80 Hz) are commonly observed during brain function and are at the center of several neural processing theories. In many cases, mathematical analysis of fast oscillations in neural networks has been focused on the transition between irregular and oscillatory firing viewed as an instability of the asynchronous activity. But in fact, brain slice experiments as well as detailed simulations of biological neural networks have produced a large corpus of results concerning the properties of fully developed oscillations that are far from this transition point. We propose here a mathematical approach to deal with nonlinear oscillations in a network of heterogeneous or noisy integrate-and-fire neurons connected by strong inhibition. This approach involves limited mathematical complexity and gives a good sense of the oscillation mechanism, making it an interesting tool to understand fast rhythmic activity in simulated or biological neural networks. A surprising result of our approach is that under some conditions, a change of the strength of inhibition only weakly influences the period of the oscillation. This is in contrast to standard theoretical and experimental models of interneuron network gamma oscillations (ING), where frequency tightly depends on inhibition strength, but it is similar to observations made in some in vitro preparations in the hippocampus and the olfactory bulb and in some detailed network models. This result is explained by the phenomenon of suppression that is known to occur in strongly coupled oscillating inhibitory networks but had not yet been related to the behavior of oscillation frequency.

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

Information-geometric measures estimate neural interactions during oscillatory brain states

PubMed Central

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain. PMID:24605089

Information-geometric measures estimate neural interactions during oscillatory brain states.

PubMed

Nie, Yimin; Fellous, Jean-Marc; Tatsuno, Masami

2014-01-01

The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG), a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

The neural architecture of expert calendar calculation: a matter of strategy?

PubMed

Fehr, Thorsten; Wallace, Gregory L; Erhard, Peter; Herrmann, Manfred

2011-08-01

Savants and prodigies are individuals with exceptional skills in particular mental domains. In the present study we used functional magnetic resonance imaging to examine neural correlates of calendar calculation in two individuals, a savant with Asperger's disorder and a self-taught mathematical prodigy. If there is a modular neural organization of exceptional performance in a specific mental domain, calendar calculation should be reflected in a considerable overlap in the recruitment of brain circuits across expert individuals. However, considerable individual differences in activation patterns during calendar calculation were noted. The present results indicate that activation patterns produced by complex mental processing, such as calendar calculation, seem to be influenced strongly by learning history and idiosyncratic strategy usage rather than a modular neural organization. Thus, well-known individual differences in complex cognition play a major role even in experts with exceptional abilities in a particular mental domain and should in particular be considered when examining the neural architecture of complex mental processes and skills.

Thalamo-cortical communication, glutamatergic neurotransmission and neural oscillations: A unique window into the origins of ScZ?

PubMed

Pratt, Judith; Dawson, Neil; Morris, Brain J; Grent-'t-Jong, Tineke; Roux, Frederic; Uhlhaas, Peter J

2017-02-01

The thalamus has recently received renewed interest in systems-neuroscience and schizophrenia (ScZ) research because of emerging evidence highlighting its important role in coordinating functional interactions in cortical-subcortical circuits. Moreover, higher cognitive functions, such as working memory and attention, have been related to thalamo-cortical interactions, providing a novel perspective for the understanding of the neural substrate of cognition. The current review will support this perspective by summarizing evidence on the crucial role of neural oscillations in facilitating thalamo-cortical (TC) interactions during normal brain functioning and their potential impairment in ScZ. Specifically, we will focus on the relationship between NMDA-R mediated (glutamatergic) neurotransmission in TC-interactions. To this end, we will first review the functional anatomy and neurotransmitters in thalamic circuits, followed by a review of the oscillatory signatures and cognitive processes supported by TC-circuits. In the second part of the paper, data from preclinical research as well as human studies will be summarized that have implicated TC-interactions as a crucial target for NMDA-receptor hypofunctioning. Finally, we will compare these neural signatures with current evidence from ScZ-research, suggesting a potential overlap between alterations in TC-circuits as the result of NMDA-R deficits and stage-specific alterations in large-scale networks in ScZ. Copyright © 2016 Elsevier B.V. All rights reserved.

Modular structure of functional networks in olfactory memory.

PubMed

Meunier, David; Fonlupt, Pierre; Saive, Anne-Lise; Plailly, Jane; Ravel, Nadine; Royet, Jean-Pierre

2014-07-15

) accounted for most of the observed differences in signed modularity. Taken together, our results provided some evidence that the neural networks involved in odor recognition memory are organized into modules and that these modular partitions are linked to behavioral performance and individual strategies. Copyright © 2014 Elsevier Inc. All rights reserved.

Selective population rate coding: a possible computational role of gamma oscillations in selective attention.

PubMed

Masuda, Naoki

2009-12-01

Selective attention is often accompanied by gamma oscillations in local field potentials and spike field coherence in brain areas related to visual, motor, and cognitive information processing. Gamma oscillations are implicated to play an important role in, for example, visual tasks including object search, shape perception, and speed detection. However, the mechanism by which gamma oscillations enhance cognitive and behavioral performance of attentive subjects is still elusive. Using feedforward fan-in networks composed of spiking neurons, we examine a possible role for gamma oscillations in selective attention and population rate coding of external stimuli. We implement the concept proposed by Fries ( 2005 ) that under dynamic stimuli, neural populations effectively communicate with each other only when there is a good phase relationship among associated gamma oscillations. We show that the downstream neural population selects a specific dynamic stimulus received by an upstream population and represents it by population rate coding. The encoded stimulus is the one for which gamma rhythm in the corresponding upstream population is resonant with the downstream gamma rhythm. The proposed role for gamma oscillations in stimulus selection is to enable top-down control, a neural version of time division multiple access used in communication engineering.

The neurophysiological and evolutionary considerations of close combat: A modular approach.

PubMed

Dervenis, Kostas; Tsialogiannis, Evangelos

2017-01-01

Close Combat may be identified as a physical confrontation involving armed or unarmed fighting, lethal and/or non-lethal methods, or even simply escape from and/or de-escalation of the confrontation. Our model hypothesizes that distinct areas of the brain are utilized for specific levels of violence, based on evolutionary criteria, and that these levels of violence bring into effect distinct physiological criteria and kinesiology. This model is outlined similar to Paul D. MacLean's triune brain theory, but incorporates distinct processes inherent to the autonomic nervous system (i.e. a "quadrune brain"), and correlates the observed level of violence to a particular response to a specific neural complex associated with very specific reactive kinesiology in the body. Our hypothesis is that the reverse also holds true: specific movements, scenarios and breathing will "activate" corresponding neural centres that in turn correlate to a respective level of violence. Moreover, socio-historic records bear out the premise that specific behavioural violations of social protocols act as "triggers" for assaultive and lethal force involving weapons, and it is very likely that these triggers (and the concomitant decision to engage in assault or lethal force) are processed through neural centres in what McLean has described as his "limbic system." A modular system of close combat is being researched and developed in accord with the above, readily adaptable to the level of violence professional peacekeepers and law enforcement officers may encounter in the course of their duties, but also directly relevant to the self-protection needs of civilians and youth. Distinct modular training regimes have been identified and developed for situations involving escape from a threat, submission of an adversary, and assaultive/lethal force, with the hope of strengthening neural bridges between the four neural complexes postulated in our model, and therefore via these bridges limiting adverse

Dysfunction of sensory oscillations in Autism Spectrum Disorder

PubMed Central

Simon, David M.; Wallace, Mark T.

2016-01-01

Autism Spectrum Disorder (ASD) is a highly prevalent developmental disability characterized by deficits in social communication and interaction, restricted interests, and repetitive behaviors. Recently, anomalous sensory and perceptual function has gained an increased level of recognition as an important feature of ASD. A specific impairment in the ability to integrate information across brain networks has been proposed to contribute to these disruptions. A crucial mechanism for these integrative processes is the rhythmic synchronization of neuronal excitability across neural populations; collectively known as oscillations. In ASD there is believed to be a deficit in the ability to efficiently couple functional neural networks using these oscillations. This review discusses evidence for disruptions in oscillatory synchronization in ASD, and how disturbance of this neural mechanism contributes to alterations in sensory and perceptual function. The review also frames oscillatory data from the perspective of prevailing neurobiologically-inspired theories of ASD. PMID:27451342

Fast fMRI can detect oscillatory neural activity in humans.

PubMed

Lewis, Laura D; Setsompop, Kawin; Rosen, Bruce R; Polimeni, Jonathan R

2016-10-25

Oscillatory neural dynamics play an important role in the coordination of large-scale brain networks. High-level cognitive processes depend on dynamics evolving over hundreds of milliseconds, so measuring neural activity in this frequency range is important for cognitive neuroscience. However, current noninvasive neuroimaging methods are not able to precisely localize oscillatory neural activity above 0.2 Hz. Electroencephalography and magnetoencephalography have limited spatial resolution, whereas fMRI has limited temporal resolution because it measures vascular responses rather than directly recording neural activity. We hypothesized that the recent development of fast fMRI techniques, combined with the extra sensitivity afforded by ultra-high-field systems, could enable precise localization of neural oscillations. We tested whether fMRI can detect neural oscillations using human visual cortex as a model system. We detected small oscillatory fMRI signals in response to stimuli oscillating at up to 0.75 Hz within single scan sessions, and these responses were an order of magnitude larger than predicted by canonical linear models. Simultaneous EEG-fMRI and simulations based on a biophysical model of the hemodynamic response to neuronal activity suggested that the blood oxygen level-dependent response becomes faster for rapidly varying stimuli, enabling the detection of higher frequencies than expected. Accounting for phase delays across voxels further improved detection, demonstrating that identifying vascular delays will be of increasing importance with higher-frequency activity. These results challenge the assumption that the hemodynamic response is slow, and demonstrate that fMRI has the potential to map neural oscillations directly throughout the brain.

Contour interpolation: A case study in Modularity of Mind.

PubMed

Keane, Brian P

2018-05-01

In his monograph Modularity of Mind (1983), philosopher Jerry Fodor argued that mental architecture can be partly decomposed into computational organs termed modules, which were characterized as having nine co-occurring features such as automaticity, domain specificity, and informational encapsulation. Do modules exist? Debates thus far have been framed very generally with few, if any, detailed case studies. The topic is important because it has direct implications on current debates in cognitive science and because it potentially provides a viable framework from which to further understand and make hypotheses about the mind's structure and function. Here, the case is made for the modularity of contour interpolation, which is a perceptual process that represents non-visible edges on the basis of how surrounding visible edges are spatiotemporally configured. There is substantial evidence that interpolation is domain specific, mandatory, fast, and developmentally well-sequenced; that it produces representationally impoverished outputs; that it relies upon a relatively fixed neural architecture that can be selectively impaired; that it is encapsulated from belief and expectation; and that its inner workings cannot be fathomed through conscious introspection. Upon differentiating contour interpolation from a higher-order contour representational ability ("contour abstraction") and upon accommodating seemingly inconsistent experimental results, it is argued that interpolation is modular to the extent that the initiating conditions for interpolation are strong. As interpolated contours become more salient, the modularity features emerge. The empirical data, taken as a whole, show that at least certain parts of the mind are modularly organized. Copyright © 2018 Elsevier B.V. All rights reserved.

The Amount of Time Dilation for Visual Flickers Corresponds to the Amount of Neural Entrainments Measured by EEG.

PubMed

Hashimoto, Yuki; Yotsumoto, Yuko

2018-01-01

The neural basis of time perception has long attracted the interests of researchers. Recently, a conceptual model consisting of neural oscillators was proposed and validated by behavioral experiments that measured the dilated duration in perception of a flickering stimulus (Hashimoto and Yotsumoto, 2015). The model proposed that flickering stimuli cause neural entrainment of oscillators, resulting in dilated time perception. In this study, we examined the oscillator-based model of time perception, by collecting electroencephalography (EEG) data during an interval-timing task. Initially, subjects observed a stimulus, either flickering at 10-Hz or constantly illuminated. The subjects then reproduced the duration of the stimulus by pressing a button. As reported in previous studies, the subjects reproduced 1.22 times longer durations for flickering stimuli than for continuously illuminated stimuli. The event-related potential (ERP) during the observation of a flicker oscillated at 10 Hz, reflecting the 10-Hz neural activity phase-locked to the flicker. Importantly, the longer reproduced duration was associated with a larger amplitude of the 10-Hz ERP component during the inter-stimulus interval, as well as during the presentation of the flicker. The correlation between the reproduced duration and the 10-Hz oscillation during the inter-stimulus interval suggested that the flicker-induced neural entrainment affected time dilation. While the 10-Hz flickering stimuli induced phase-locked entrainments at 10 Hz, we also observed event-related desynchronizations of spontaneous neural oscillations in the alpha-frequency range. These could be attributed to the activation of excitatory neurons while observing the flicker stimuli. In addition, neural activity at approximately the alpha frequency increased during the reproduction phase, indicating that flicker-induced neural entrainment persisted even after the offset of the flicker. In summary, our results suggest that the

Modular neuron-based body estimation: maintaining consistency over different limbs, modalities, and frames of reference

PubMed Central

Ehrenfeld, Stephan; Herbort, Oliver; Butz, Martin V.

2013-01-01

This paper addresses the question of how the brain maintains a probabilistic body state estimate over time from a modeling perspective. The neural Modular Modality Frame (nMMF) model simulates such a body state estimation process by continuously integrating redundant, multimodal body state information sources. The body state estimate itself is distributed over separate, but bidirectionally interacting modules. nMMF compares the incoming sensory and present body state information across the interacting modules and fuses the information sources accordingly. At the same time, nMMF enforces body state estimation consistency across the modules. nMMF is able to detect conflicting sensory information and to consequently decrease the influence of implausible sensor sources on the fly. In contrast to the previously published Modular Modality Frame (MMF) model, nMMF offers a biologically plausible neural implementation based on distributed, probabilistic population codes. Besides its neural plausibility, the neural encoding has the advantage of enabling (a) additional probabilistic information flow across the separate body state estimation modules and (b) the representation of arbitrary probability distributions of a body state. The results show that the neural estimates can detect and decrease the impact of false sensory information, can propagate conflicting information across modules, and can improve overall estimation accuracy due to additional module interactions. Even bodily illusions, such as the rubber hand illusion, can be simulated with nMMF. We conclude with an outlook on the potential of modeling human data and of invoking goal-directed behavioral control. PMID:24191151

Neural Entrainment to the Beat: The "Missing-Pulse" Phenomenon.

PubMed

Tal, Idan; Large, Edward W; Rabinovitch, Eshed; Wei, Yi; Schroeder, Charles E; Poeppel, David; Zion Golumbic, Elana

2017-06-28

Most humans have a near-automatic inclination to tap, clap, or move to the beat of music. The capacity to extract a periodic beat from a complex musical segment is remarkable, as it requires abstraction from the temporal structure of the stimulus. It has been suggested that nonlinear interactions in neural networks result in cortical oscillations at the beat frequency, and that such entrained oscillations give rise to the percept of a beat or a pulse. Here we tested this neural resonance theory using MEG recordings as female and male individuals listened to 30 s sequences of complex syncopated drumbeats designed so that they contain no net energy at the pulse frequency when measured using linear analysis. We analyzed the spectrum of the neural activity while listening and compared it to the modulation spectrum of the stimuli. We found enhanced neural response in the auditory cortex at the pulse frequency. We also showed phase locking at the times of the missing pulse, even though the pulse was absent from the stimulus itself. Moreover, the strength of this pulse response correlated with individuals' speed in finding the pulse of these stimuli, as tested in a follow-up session. These findings demonstrate that neural activity at the pulse frequency in the auditory cortex is internally generated rather than stimulus-driven. The current results are both consistent with neural resonance theory and with models based on nonlinear response of the brain to rhythmic stimuli. The results thus help narrow the search for valid models of beat perception. SIGNIFICANCE STATEMENT Humans perceive music as having a regular pulse marking equally spaced points in time, within which musical notes are temporally organized. Neural resonance theory (NRT) provides a theoretical model explaining how an internal periodic representation of a pulse may emerge through nonlinear coupling between oscillating neural systems. After testing key falsifiable predictions of NRT using MEG recordings, we

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

PubMed

Mizuhara, Hiroaki; Sato, Naoyuki; Yamaguchi, Yoko

2015-05-01

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

Role of local network oscillations in resting-state functional connectivity.

PubMed

Cabral, Joana; Hugues, Etienne; Sporns, Olaf; Deco, Gustavo

2011-07-01

Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes

Modular synthetic inverters from zinc finger proteins and small RNAs

DOE PAGES

Hsia, Justin; Holtz, William J.; Maharbiz, Michel M.; ...

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

Noise-Induced Synchronization among Sub-RF CMOS Analog Oscillators for Skew-Free Clock Distribution

NASA Astrophysics Data System (ADS)

Utagawa, Akira; Asai, Tetsuya; Hirose, Tetsuya; Amemiya, Yoshihito

We present on-chip oscillator arrays synchronized by random noises, aiming at skew-free clock distribution on synchronous digital systems. Nakao et al. recently reported that independent neural oscillators can be synchronized by applying temporal random impulses to the oscillators [1], [2]. We regard neural oscillators as independent clock sources on LSIs; i. e., clock sources are distributed on LSIs, and they are forced to synchronize through the use of random noises. We designed neuron-based clock generators operating at sub-RF region (<1GHz) by modifying the original neuron model to a new model that is suitable for CMOS implementation with 0.25-μm CMOS parameters. Through circuit simulations, we demonstrate that i) the clock generators are certainly synchronized by pseudo-random noises and ii) clock generators exhibited phase-locked oscillations even if they had small device mismatches.

Modular networks with delayed coupling: Synchronization and frequency control

NASA Astrophysics Data System (ADS)

Maslennikov, Oleg V.; Nekorkin, Vladimir I.

2014-07-01

We study the collective dynamics of modular networks consisting of map-based neurons which generate irregular spike sequences. Three types of intramodule topology are considered: a random Erdös-Rényi network, a small-world Watts-Strogatz network, and a scale-free Barabási-Albert network. The interaction between the neurons of different modules is organized by relatively sparse connections with time delay. For all the types of the network topology considered, we found that with increasing delay two regimes of module synchronization alternate with each other: inphase and antiphase. At the same time, the average rate of collective oscillations decreases within each of the time-delay intervals corresponding to a particular synchronization regime. A dual role of the time delay is thus established: controlling a synchronization mode and degree and controlling an average network frequency. Furthermore, we investigate the influence on the modular synchronization by other parameters: the strength of intermodule coupling and the individual firing rate.

Neuromorphic computing with nanoscale spintronic oscillators.

PubMed

Torrejon, Jacob; Riou, Mathieu; Araujo, Flavio Abreu; Tsunegi, Sumito; Khalsa, Guru; Querlioz, Damien; Bortolotti, Paolo; Cros, Vincent; Yakushiji, Kay; Fukushima, Akio; Kubota, Hitoshi; Yuasa, Shinji; Stiles, Mark D; Grollier, Julie

2017-07-26

Neurons in the brain behave as nonlinear oscillators, which develop rhythmic activity and interact to process information. Taking inspiration from this behaviour to realize high-density, low-power neuromorphic computing will require very large numbers of nanoscale nonlinear oscillators. A simple estimation indicates that to fit 10 8 oscillators organized in a two-dimensional array inside a chip the size of a thumb, the lateral dimension of each oscillator must be smaller than one micrometre. However, nanoscale devices tend to be noisy and to lack the stability that is required to process data in a reliable way. For this reason, despite multiple theoretical proposals and several candidates, including memristive and superconducting oscillators, a proof of concept of neuromorphic computing using nanoscale oscillators has yet to be demonstrated. Here we show experimentally that a nanoscale spintronic oscillator (a magnetic tunnel junction) can be used to achieve spoken-digit recognition with an accuracy similar to that of state-of-the-art neural networks. We also determine the regime of magnetization dynamics that leads to the greatest performance. These results, combined with the ability of the spintronic oscillators to interact with each other, and their long lifetime and low energy consumption, open up a path to fast, parallel, on-chip computation based on networks of oscillators.

Concurrent evolution of feature extractors and modular artificial neural networks

NASA Astrophysics Data System (ADS)

Hannak, Victor; Savakis, Andreas; Yang, Shanchieh Jay; Anderson, Peter

2009-05-01

This paper presents a new approach for the design of feature-extracting recognition networks that do not require expert knowledge in the application domain. Feature-Extracting Recognition Networks (FERNs) are composed of interconnected functional nodes (feurons), which serve as feature extractors, and are followed by a subnetwork of traditional neural nodes (neurons) that act as classifiers. A concurrent evolutionary process (CEP) is used to search the space of feature extractors and neural networks in order to obtain an optimal recognition network that simultaneously performs feature extraction and recognition. By constraining the hill-climbing search functionality of the CEP on specific parts of the solution space, i.e., individually limiting the evolution of feature extractors and neural networks, it was demonstrated that concurrent evolution is a necessary component of the system. Application of this approach to a handwritten digit recognition task illustrates that the proposed methodology is capable of producing recognition networks that perform in-line with other methods without the need for expert knowledge in image processing.

Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex.

PubMed

Zhang, Honghui; Watrous, Andrew J; Patel, Ansh; Jacobs, Joshua

2018-06-01

Human cognition requires the coordination of neural activity across widespread brain networks. Here, we describe a new mechanism for large-scale coordination in the human brain: traveling waves of theta and alpha oscillations. Examining direct brain recordings from neurosurgical patients performing a memory task, we found contiguous clusters of cortex in individual patients with oscillations at specific frequencies within 2 to 15 Hz. These oscillatory clusters displayed spatial phase gradients, indicating that they formed traveling waves that propagated at ∼0.25-0.75 m/s. Traveling waves were relevant behaviorally because their propagation correlated with task events and was more consistent when subjects performed the task well. Human traveling theta and alpha waves can be modeled by a network of coupled oscillators because the direction of wave propagation correlated with the spatial orientation of local frequency gradients. Our findings suggest that oscillations support brain connectivity by organizing neural processes across space and time. Copyright © 2018 Elsevier Inc. All rights reserved.

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].

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

PubMed

Maguire, Mandy J; Abel, Alyson D

2013-10-01

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 and 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. Copyright © 2013 Elsevier Ltd. All rights reserved.

Inhibition delay increases neural network capacity through Stirling transform.

PubMed

Nogaret, Alain; King, Alastair

2018-03-01

Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2)^{-N}-fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.

Inhibition delay increases neural network capacity through Stirling transform

NASA Astrophysics Data System (ADS)

Nogaret, Alain; King, Alastair

2018-03-01

Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2) -N-fold increase in capacity for an N -neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.

Topological dimension tunes activity patterns in hierarchical modular networks

NASA Astrophysics Data System (ADS)

Safari, Ali; Moretti, Paolo; Muñoz, Miguel A.

2017-11-01

Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both segregation and integration of neural information. Here, we propose an extensive analysis of the critical spreading rate (or ‘epidemic’ threshold)—separating a phase with endemic persistent activity from one in which activity ceases—on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs and, most importantly, we elucidate the role played by the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.

Widespread neural oscillations in the delta band dissociate rule convergence from rule divergence during creative idea generation.

PubMed

Boot, Nathalie; Baas, Matthijs; Mühlfeld, Elisabeth; de Dreu, Carsten K W; van Gaal, Simon

2017-09-01

Critical to creative cognition and performance is both the generation of multiple alternative solutions in response to open-ended problems (divergent thinking) and a series of cognitive operations that converges on the correct or best possible answer (convergent thinking). Although the neural underpinnings of divergent and convergent thinking are still poorly understood, several electroencephalography (EEG) studies point to differences in alpha-band oscillations between these thinking modes. We reason that, because most previous studies employed typical block designs, these pioneering findings may mainly reflect the more sustained aspects of creative processes that extend over longer time periods, and that still much is unknown about the faster-acting neural mechanisms that dissociate divergent from convergent thinking during idea generation. To this end, we developed a new event-related paradigm, in which we measured participants' tendency to implicitly follow a rule set by examples, versus breaking that rule, during the generation of novel names for specific categories (e.g., pasta, planets). This approach allowed us to compare the oscillatory dynamics of rule convergent and rule divergent idea generation and at the same time enabled us to measure spontaneous switching between these thinking modes on a trial-to-trial basis. We found that, relative to more systematic, rule convergent thinking, rule divergent thinking was associated with widespread decreases in delta band activity. Therefore, this study contributes to advancing our understanding of the neural underpinnings of creativity by addressing some methodological challenges that neuroscientific creativity research faces. Copyright © 2017 Elsevier Ltd. All rights reserved.

Neuromorphic computing with nanoscale spintronic oscillators

PubMed Central

Torrejon, Jacob; Riou, Mathieu; Araujo, Flavio Abreu; Tsunegi, Sumito; Khalsa, Guru; Querlioz, Damien; Bortolotti, Paolo; Cros, Vincent; Fukushima, Akio; Kubota, Hitoshi; Yuasa, Shinji; Stiles, M. D.; Grollier, Julie

2017-01-01

Neurons in the brain behave as non-linear oscillators, which develop rhythmic activity and interact to process information1. Taking inspiration from this behavior to realize high density, low power neuromorphic computing will require huge numbers of nanoscale non-linear oscillators. Indeed, a simple estimation indicates that, in order to fit a hundred million oscillators organized in a two-dimensional array inside a chip the size of a thumb, their lateral dimensions must be smaller than one micrometer. However, despite multiple theoretical proposals2–5, and several candidates such as memristive6 or superconducting7 oscillators, there is no proof of concept today of neuromorphic computing with nano-oscillators. Indeed, nanoscale devices tend to be noisy and to lack the stability required to process data in a reliable way. Here, we show experimentally that a nanoscale spintronic oscillator8,9 can achieve spoken digit recognition with accuracies similar to state of the art neural networks. We pinpoint the regime of magnetization dynamics leading to highest performance. These results, combined with the exceptional ability of these spintronic oscillators to interact together, their long lifetime, and low energy consumption, open the path to fast, parallel, on-chip computation based on networks of oscillators. PMID:28748930

Towards cortex sized artificial neural systems.

PubMed

Johansson, Christopher; Lansner, Anders

2007-01-01

We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the cortical circuitry. A characterizing feature of the model is that it is based on the modularization of neocortex into hypercolumns and minicolumns. Both a floating- and fixed-point arithmetic implementation of the model are presented along with simulation results. We conclude that an implementation on a cluster computer is not communication but computation bounded. A mouse and rat cortex sized version of our model executes in 44% and 23% of real-time respectively. Further, an instance of the model with 1.6 x 10(6) units and 2 x 10(11) connections performed noise reduction and pattern completion. These implementations represent the current frontier of large-scale abstract neural network simulations in terms of network size and running speed.

Dissecting the Function of Hippocampal Oscillations in a Human Anxiety Model

PubMed Central

Khemka, Saurabh

2017-01-01

Neural oscillations in hippocampus and medial prefrontal cortex (mPFC) are a hallmark of rodent anxiety models that build on conflict between approach and avoidance. Yet, the function of these oscillations, and their expression in humans, remain elusive. Here, we used magnetoencephalography (MEG) to investigate neural oscillations in a task that simulated approach–avoidance conflict, wherein 23 male and female human participants collected monetary tokens under a threat of virtual predation. Probability of threat was signaled by color and learned beforehand by direct experience. Magnitude of threat corresponded to a possible monetary loss, signaled as a quantity. We focused our analyses on an a priori defined region-of-interest, the bilateral hippocampus. Oscillatory power under conflict was linearly predicted by threat probability in a location consistent with right mid-hippocampus. This pattern was specific to the hippocampus, most pronounced in the gamma band, and not explained by spatial movement or anxiety-like behavior. Gamma power was modulated by slower theta rhythms, and this theta modulation increased with threat probability. Furthermore, theta oscillations in the same location showed greater synchrony with mPFC theta with increased threat probability. Strikingly, these findings were not seen in relation to an increase in threat magnitude, which was explicitly signaled as a quantity and induced similar behavioral responses as learned threat probability. Thus, our findings suggest that the expression of hippocampal and mPFC oscillatory activity in the context of anxiety is specifically linked to threat memory. These findings resonate with neurocomputational accounts of the role played by hippocampal oscillations in memory. SIGNIFICANCE STATEMENT We use a biologically relevant approach–avoidance conflict test in humans while recording neural oscillations with magnetoencephalography to investigate the expression and function of hippocampal oscillations in

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization.

PubMed

Westphal, Andrew J; Wang, Siliang; Rissman, Jesse

2017-03-29

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity-a measure of network segregation-is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal control

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

Frontal Preparatory Neural Oscillations Associated with Cognitive Control: A Developmental Study Comparing Young Adults and Adolescents

PubMed Central

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

2016-01-01

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 (16–38 Hz) power in the dorsal lateral prefrontal cortex (DLPFC), enhanced alpha- to low beta-band (10–18 Hz) power in the frontal eye field (FEF) that predicted performance, and increased cross-frequency alpha-beta (10–26 Hz) 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 prefrontal connectivity and strengthening of inhibition signaling for suppressing task-incompatible processes. PMID:27173759

Nonsinusoidal Beta Oscillations Reflect Cortical Pathophysiology in Parkinson's Disease.

PubMed

Cole, Scott R; van der Meij, Roemer; Peterson, Erik J; de Hemptinne, Coralie; Starr, Philip A; Voytek, Bradley

2017-05-03

Oscillations in neural activity play a critical role in neural computation and communication. There is intriguing new evidence that the nonsinusoidal features of the oscillatory waveforms may inform underlying physiological and pathophysiological characteristics. Time-domain waveform analysis approaches stand in contrast to traditional Fourier-based methods, which alter or destroy subtle waveform features. Recently, it has been shown that the waveform features of oscillatory beta (13-30 Hz) events, a prominent motor cortical oscillation, may reflect near-synchronous excitatory synaptic inputs onto cortical pyramidal neurons. Here we analyze data from invasive human primary motor cortex (M1) recordings from patients with Parkinson's disease (PD) implanted with a deep brain stimulator (DBS) to test the hypothesis that the beta waveform becomes less sharp with DBS, suggesting that M1 input synchrony may be decreased. We find that, in PD, M1 beta oscillations have sharp, asymmetric, nonsinusoidal features, specifically asymmetries in the ratio between the sharpness of the beta peaks compared with the troughs. This waveform feature is nearly perfectly correlated with beta-high gamma phase-amplitude coupling ( r = 0.94), a neural index previously shown to track PD-related motor deficit. Our results suggest that the pathophysiological beta generator is altered by DBS, smoothing out the beta waveform. This has implications not only for the interpretation of the physiological mechanism by which DBS reduces PD-related motor symptoms, but more broadly for our analytic toolkit in general. That is, the often-overlooked time-domain features of oscillatory waveforms may carry critical physiological information about neural processes and dynamics. SIGNIFICANCE STATEMENT To better understand the neural basis of cognition and disease, we need to understand how groups of neurons interact to communicate with one another. For example, there is evidence that parkinsonian bradykinesia

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

Prestimulus neural oscillations inhibit visual perception via modulation of response gain.

PubMed

Chaumon, Maximilien; Busch, Niko A

2014-11-01

The ongoing state of the brain radically affects how it processes sensory information. How does this ongoing brain activity interact with the processing of external stimuli? Spontaneous oscillations in the alpha range are thought to inhibit sensory processing, but little is known about the psychophysical mechanisms of this inhibition. We recorded ongoing brain activity with EEG while human observers performed a visual detection task with stimuli of different contrast intensities. To move beyond qualitative description, we formally compared psychometric functions obtained under different levels of ongoing alpha power and evaluated the inhibitory effect of ongoing alpha oscillations in terms of contrast or response gain models. This procedure opens the way to understanding the actual functional mechanisms by which ongoing brain activity affects visual performance. We found that strong prestimulus occipital alpha oscillations-but not more anterior mu oscillations-reduce performance most strongly for stimuli of the highest intensities tested. This inhibitory effect is best explained by a divisive reduction of response gain. Ongoing occipital alpha oscillations thus reflect changes in the visual system's input/output transformation that are independent of the sensory input to the system. They selectively scale the system's response, rather than change its sensitivity to sensory information.

A modular, closed-loop platform for intracranial stimulation in people with neurological disorders.

PubMed

Sarma, Anish A; Crocker, Britni; Cash, Sydney S; Truccolo, Wilson

2016-08-01

Neuromodulation systems based on electrical stimulation can be used to investigate, probe, and potentially treat a range of neurological disorders. The effects of ongoing neural state and dynamics on stimulation response, and of stimulation parameters on neural state, have broad implications for the development of closed-loop neuro-modulation approaches. We describe the development of a modular, low-latency platform for pre-clinical, closed-loop neuromodulation studies with human participants. We illustrate the uses of the platform in a stimulation case study with a person with epilepsy undergoing neuro-monitoring prior to resective surgery. We demonstrate the efficacy of the system by tracking interictal epileptiform discharges in the local field potential to trigger intracranial electrical stimulation, and show that the response to stimulation depends on the neural state.

Locking of correlated neural activity to ongoing oscillations

PubMed Central

Helias, Moritz

2017-01-01

Population-wide oscillations are ubiquitously observed in mesoscopic signals of cortical activity. In these network states a global oscillatory cycle modulates the propensity of neurons to fire. Synchronous activation of neurons has been hypothesized to be a separate channel of signal processing information in the brain. A salient question is therefore if and how oscillations interact with spike synchrony and in how far these channels can be considered separate. Experiments indeed showed that correlated spiking co-modulates with the static firing rate and is also tightly locked to the phase of beta-oscillations. While the dependence of correlations on the mean rate is well understood in feed-forward networks, it remains unclear why and by which mechanisms correlations tightly lock to an oscillatory cycle. We here demonstrate that such correlated activation of pairs of neurons is qualitatively explained by periodically-driven random networks. We identify the mechanisms by which covariances depend on a driving periodic stimulus. Mean-field theory combined with linear response theory yields closed-form expressions for the cyclostationary mean activities and pairwise zero-time-lag covariances of binary recurrent random networks. Two distinct mechanisms cause time-dependent covariances: the modulation of the susceptibility of single neurons (via the external input and network feedback) and the time-varying variances of single unit activities. For some parameters, the effectively inhibitory recurrent feedback leads to resonant covariances even if mean activities show non-resonant behavior. Our analytical results open the question of time-modulated synchronous activity to a quantitative analysis. PMID:28604771

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

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

Modular Fixturing System

NASA Technical Reports Server (NTRS)

Littell, Justin Anderson (Inventor); Street, Jon P. (Inventor)

2017-01-01

The modular fixturing system of the present invention is modular, reusable and capable of significant customization, both in terms of system radius and system height, allowing it to be arranged and rearranged in numerous unique configurations. The system includes multiple modular stanchions having stanchion shafts and stanchion feet that removably attach to apertures in a table. Angle brackets attached to the modular stanchions support shelves. These shelves in turn provide support to work pieces during fabrication processes such as welding.

Episodic Memory Retrieval Benefits from a Less Modular Brain Network Organization

PubMed Central

2017-01-01

Most complex cognitive tasks require the coordinated interplay of multiple brain networks, but the act of retrieving an episodic memory may place especially heavy demands for communication between the frontoparietal control network (FPCN) and the default mode network (DMN), two networks that do not strongly interact with one another in many task contexts. We applied graph theoretical analysis to task-related fMRI functional connectivity data from 20 human participants and found that global brain modularity—a measure of network segregation—is markedly reduced during episodic memory retrieval relative to closely matched analogical reasoning and visuospatial perception tasks. Individual differences in modularity were correlated with memory task performance, such that lower modularity levels were associated with a lower false alarm rate. Moreover, the FPCN and DMN showed significantly elevated coupling with each other during the memory task, which correlated with the global reduction in brain modularity. Both networks also strengthened their functional connectivity with the hippocampus during the memory task. Together, these results provide a novel demonstration that reduced modularity is conducive to effective episodic retrieval, which requires close collaboration between goal-directed control processes supported by the FPCN and internally oriented self-referential processing supported by the DMN. SIGNIFICANCE STATEMENT Modularity, an index of the degree to which nodes of a complex system are organized into discrete communities, has emerged as an important construct in the characterization of brain connectivity dynamics. We provide novel evidence that the modularity of the human brain is reduced when individuals engage in episodic memory retrieval, relative to other cognitive tasks, and that this state of lower modularity is associated with improved memory performance. We propose a neural systems mechanism for this finding where the nodes of the frontoparietal

The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

PubMed

Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

2018-01-01

Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

Modular entanglement.

PubMed

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

2011-02-04

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.

Age-related changes in neural oscillations supporting context memory retrieval

PubMed Central

Strunk, Jonathan; James, Taylor; Arndt, Jason; Duarte, Audrey

2018-01-01

Recent evidence suggests that directing attention toward single item-context associations during encoding improves young and older adults’ context memory performance and reduces demands on executive functions during retrieval. In everyday situations, there are many event features competing for our attention, and our ability to successfully recover those details may depend on our ability to ignore others. Failures of selective attention may contribute to older adults’ context memory impairments. In the current electroencephalogram (EEG) study, we assessed the effects of age on processes supporting successful context memory retrieval of selectively attended features as indexed by neural oscillations. During encoding, young and older adults were directed to attend to a picture of an object and its relationship to one of two concurrently presented contextual details: a color or scene. At retrieval, we tested their memory for the object, its attended and unattended context features, and their confidence for both the attended and unattended features. Both groups showed greater memory for attended than unattended contextual features. However, older adults showed evidence of hyper-binding between attended and unattended context features while the young adults did not. EEG results in the theta band suggest that young and older adults recollect similar amounts of information but brain-behavior correlations suggest that this information was supportive of contextual memory performance, particularly for young adults. By contrast, sustained beta desynchronization, indicative of sensory reactivation and episodic reconstruction, was correlated with contextual memory performance for older adults only. We conclude that older adults’ inhibition deficits during encoding reduced the selectivity of their contextual memories, which led to reliance on executive functions like episodic reconstruction to support successful memory retrieval. PMID:28237686

High-frequency neural activity predicts word parsing in ambiguous speech streams

PubMed Central

Basirat, Anahita; Azizi, Leila; van Wassenhove, Virginie

2016-01-01

During speech listening, the brain parses a continuous acoustic stream of information into computational units (e.g., syllables or words) necessary for speech comprehension. Recent neuroscientific hypotheses have proposed that neural oscillations contribute to speech parsing, but whether they do so on the basis of acoustic cues (bottom-up acoustic parsing) or as a function of available linguistic representations (top-down linguistic parsing) is unknown. In this magnetoencephalography study, we contrasted acoustic and linguistic parsing using bistable speech sequences. While listening to the speech sequences, participants were asked to maintain one of the two possible speech percepts through volitional control. We predicted that the tracking of speech dynamics by neural oscillations would not only follow the acoustic properties but also shift in time according to the participant's conscious speech percept. Our results show that the latency of high-frequency activity (specifically, beta and gamma bands) varied as a function of the perceptual report. In contrast, the phase of low-frequency oscillations was not strongly affected by top-down control. Whereas changes in low-frequency neural oscillations were compatible with the encoding of prelexical segmentation cues, high-frequency activity specifically informed on an individual's conscious speech percept. PMID:27605528

Measurement of Long Baseline Neutrino Oscillations and Improvements from Deep Learning

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

Psihas, Fernanda

NOvA is a long-baseline neutrino oscillation experiment which measures the oscillation of muon neutrinos from the NuMI beam at Fermilab after they travel through the Earth for 810 km. In this dissertation I describe the operations and monitoring of the detectors which make it possible to record over 98% of the delivered neutrino beam. I also present reconstruction and identification techniques using deep convolutional neural networks (CNNs), which are applicable to multiple analyses. Lastly, I detail the oscillation analyses in themore » $$\

Local linear approximation of the Jacobian matrix better captures phase resetting of neural limit cycle oscillators.

PubMed

Oprisan, Sorinel Adrian

2014-01-01

One effect of any external perturbations, such as presynaptic inputs, received by limit cycle oscillators when they are part of larger neural networks is a transient change in their firing rate, or phase resetting. A brief external perturbation moves the figurative point outside the limit cycle, a geometric perturbation that we mapped into a transient change in the firing rate, or a temporal phase resetting. In order to gain a better qualitative understanding of the link between the geometry of the limit cycle and the phase resetting curve (PRC), we used a moving reference frame with one axis tangent and the others normal to the limit cycle. We found that the stability coefficients associated with the unperturbed limit cycle provided good quantitative predictions of both the tangent and the normal geometric displacements induced by external perturbations. A geometric-to-temporal mapping allowed us to correctly predict the PRC while preserving the intuitive nature of this geometric approach.

Electrical switching and oscillations in vanadium dioxide

NASA Astrophysics Data System (ADS)

Pergament, Alexander; Velichko, Andrey; Belyaev, Maksim; Putrolaynen, Vadim

2018-05-01

We have studied electrical switching with S-shaped I-V characteristics in two-terminal MOM devices based on vanadium dioxide thin films. The switching effect is associated with the metal-insulator phase transition. Relaxation oscillations are observed in circuits with VO2-based switches. Dependences of the oscillator critical frequency Fmax, threshold power and voltage, as well as the time of current rise, on the switching structure size are obtained by numerical simulation. The empirical dependence of the threshold voltage on the switching region dimensions and film thickness is found. It is shown that, for the VO2 channel sizes of 10 × 10 nm, Fmax can reach the value of 300 MHz at a film thickness of 20 nm. Next, it is shown that oscillatory neural networks can be implemented on the basis of coupled VO2 oscillators. For the weak capacitive coupling, we revealed the dependence of the phase difference upon synchronization on the coupling capacitance value. When the switches are scaled down, the limiting time of synchronization is reduced to Ts 13 μs, and the number of oscillation periods for the entering to the synchronization mode remains constant, Ns 17. In the case of weak thermal coupling in the synchronization mode, we observe in-phase behavior of oscillators, and there is a certain range of parameters of the supply current, in which the synchronization effect becomes possible. With a decrease in dimensions, a decrease in the thermal coupling action radius is observed, which can vary in the range from 0.5 to 50 μm for structures with characteristic dimensions of 0.1-5 μm, respectively. Thermal coupling may have a promising effect for realization of a 3D integrated oscillatory neural network.

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.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance.

PubMed

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators.

Brain Oscillations in Sport: Toward EEG Biomarkers of Performance

PubMed Central

Cheron, Guy; Petit, Géraldine; Cheron, Julian; Leroy, Axelle; Cebolla, Anita; Cevallos, Carlos; Petieau, Mathieu; Hoellinger, Thomas; Zarka, David; Clarinval, Anne-Marie; Dan, Bernard

2016-01-01

Brain dynamics is at the basis of top performance accomplishment in sports. The search for neural biomarkers of performance remains a challenge in movement science and sport psychology. The non-invasive nature of high-density electroencephalography (EEG) recording has made it a most promising avenue for providing quantitative feedback to practitioners and coaches. Here, we review the current relevance of the main types of EEG oscillations in order to trace a perspective for future practical applications of EEG and event-related potentials (ERP) in sport. In this context, the hypotheses of unified brain rhythms and continuity between wake and sleep states should provide a functional template for EEG biomarkers in sport. The oscillations in the thalamo-cortical and hippocampal circuitry including the physiology of the place cells and the grid cells provide a frame of reference for the analysis of delta, theta, beta, alpha (incl.mu), and gamma oscillations recorded in the space field of human performance. Based on recent neuronal models facilitating the distinction between the different dynamic regimes (selective gating and binding) in these different oscillations we suggest an integrated approach articulating together the classical biomechanical factors (3D movements and EMG) and the high-density EEG and ERP signals to allow finer mathematical analysis to optimize sport performance, such as microstates, coherency/directionality analysis and neural generators. PMID:26955362

Theta oscillations promote temporal sequence learning.

PubMed

Crivelli-Decker, Jordan; Hsieh, Liang-Tien; Clarke, Alex; Ranganath, Charan

2018-05-17

Many theoretical models suggest that neural oscillations play a role in learning or retrieval of temporal sequences, but the extent to which oscillations support sequence representation remains unclear. To address this question, we used scalp electroencephalography (EEG) to examine oscillatory activity over learning of different object sequences. Participants made semantic decisions on each object as they were presented in a continuous stream. For three "Consistent" sequences, the order of the objects was always fixed. Activity during Consistent sequences was compared to "Random" sequences that consisted of the same objects presented in a different order on each repetition. Over the course of learning, participants made faster semantic decisions to objects in Consistent, as compared to objects in Random sequences. Thus, participants were able to use sequence knowledge to predict upcoming items in Consistent sequences. EEG analyses revealed decreased oscillatory power in the theta (4-7 Hz) band at frontal sites following decisions about objects in Consistent sequences, as compared with objects in Random sequences. The theta power difference between Consistent and Random only emerged in the second half of the task, as participants were more effectively able to predict items in Consistent sequences. Moreover, we found increases in parieto-occipital alpha (10-13 Hz) and beta (14-28 Hz) power during the pre-response period for objects in Consistent sequences, relative to objects in Random sequences. Linear mixed effects modeling revealed that single trial theta oscillations were related to reaction time for future objects in a sequence, whereas beta and alpha oscillations were only predictive of reaction time on the current trial. These results indicate that theta and alpha/beta activity preferentially relate to future and current events, respectively. More generally our findings highlight the importance of band-specific neural oscillations in the learning of

Emergent Oscillations in Networks of Stochastic Spiking Neurons

PubMed Central

van Drongelen, Wim; Cowan, Jack D.

2011-01-01

Networks of neurons produce diverse patterns of oscillations, arising from the network's global properties, the propensity of individual neurons to oscillate, or a mixture of the two. Here we describe noisy limit cycles and quasi-cycles, two related mechanisms underlying emergent oscillations in neuronal networks whose individual components, stochastic spiking neurons, do not themselves oscillate. Both mechanisms are shown to produce gamma band oscillations at the population level while individual neurons fire at a rate much lower than the population frequency. Spike trains in a network undergoing noisy limit cycles display a preferred period which is not found in the case of quasi-cycles, due to the even faster decay of phase information in quasi-cycles. These oscillations persist in sparsely connected networks, and variation of the network's connectivity results in variation of the oscillation frequency. A network of such neurons behaves as a stochastic perturbation of the deterministic Wilson-Cowan equations, and the network undergoes noisy limit cycles or quasi-cycles depending on whether these have limit cycles or a weakly stable focus. These mechanisms provide a new perspective on the emergence of rhythmic firing in neural networks, showing the coexistence of population-level oscillations with very irregular individual spike trains in a simple and general framework. PMID:21573105

Age-related changes in neural oscillations supporting context memory retrieval.

PubMed

Strunk, Jonathan; James, Taylor; Arndt, Jason; Duarte, Audrey

2017-06-01

Recent evidence suggests that directing attention toward single item-context associations during encoding improves young and older adults' context memory performance and reduces demands on executive functions during retrieval. In everyday situations, there are many event features competing for our attention, and our ability to successfully recover those details may depend on our ability to ignore others. Failures of selective attention may contribute to older adults' context memory impairments. In the current electroencephalogram (EEG) study, we assessed the effects of age on processes supporting successful context memory retrieval of selectively attended features as indexed by neural oscillations. During encoding, young and older adults were directed to attend to a picture of an object and its relationship to one of two concurrently presented contextual details: a color or scene. At retrieval, we tested their memory for the object, its attended and unattended context features, and their confidence for both the attended and unattended features. Both groups showed greater memory for attended than unattended contextual features. However, older adults showed evidence of hyper-binding between attended and unattended context features while the young adults did not. EEG results in the theta band suggest that young and older adults recollect similar amounts of information but brain-behavior correlations suggest that this information was supportive of contextual memory performance, particularly for young adults. By contrast, sustained beta desynchronization, indicative of sensory reactivation and episodic reconstruction, was correlated with contextual memory performance for older adults only. We conclude that older adults' inhibition deficits during encoding reduced the selectivity of their contextual memories, which led to reliance on executive functions like episodic reconstruction to support successful memory retrieval. Copyright © 2017 Elsevier Ltd. All rights

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

The Rhythm of Perception: Entrainment to Acoustic Rhythms Induces Subsequent Perceptual Oscillation.

PubMed

Hickok, Gregory; Farahbod, Haleh; Saberi, Kourosh

2015-07-01

Acoustic rhythms are pervasive in speech, music, and environmental sounds. Recent evidence for neural codes representing periodic information suggests that they may be a neural basis for the ability to detect rhythm. Further, rhythmic information has been found to modulate auditory-system excitability, which provides a potential mechanism for parsing the acoustic stream. Here, we explored the effects of a rhythmic stimulus on subsequent auditory perception. We found that a low-frequency (3 Hz), amplitude-modulated signal induces a subsequent oscillation of the perceptual detectability of a brief nonperiodic acoustic stimulus (1-kHz tone); the frequency but not the phase of the perceptual oscillation matches the entrained stimulus-driven rhythmic oscillation. This provides evidence that rhythmic contexts have a direct influence on subsequent auditory perception of discrete acoustic events. Rhythm coding is likely a fundamental feature of auditory-system design that predates the development of explicit human enjoyment of rhythm in music or poetry. © The Author(s) 2015.

Impact of delays on the synchronization transitions of modular neuronal networks with hybrid synapses

NASA Astrophysics Data System (ADS)

Liu, Chen; Wang, Jiang; Yu, Haitao; Deng, Bin; Wei, Xile; Tsang, Kaiming; Chan, Wailok

2013-09-01

The combined effects of the information transmission delay and the ratio of the electrical and chemical synapses on the synchronization transitions in the hybrid modular neuronal network are investigated in this paper. Numerical results show that the synchronization of neuron activities can be either promoted or destroyed as the information transmission delay increases, irrespective of the probability of electrical synapses in the hybrid-synaptic network. Interestingly, when the number of the electrical synapses exceeds a certain level, further increasing its proportion can obviously enhance the spatiotemporal synchronization transitions. Moreover, the coupling strength has a significant effect on the synchronization transition. The dominated type of the synapse always has a more profound effect on the emergency of the synchronous behaviors. Furthermore, the results of the modular neuronal network structures demonstrate that excessive partitioning of the modular network may result in the dramatic detriment of neuronal synchronization. Considering that information transmission delays are inevitable in intra- and inter-neuronal networks communication, the obtained results may have important implications for the exploration of the synchronization mechanism underlying several neural system diseases such as Parkinson's Disease.

High-frequency neural activity predicts word parsing in ambiguous speech streams.

PubMed

Kösem, Anne; Basirat, Anahita; Azizi, Leila; van Wassenhove, Virginie

2016-12-01

During speech listening, the brain parses a continuous acoustic stream of information into computational units (e.g., syllables or words) necessary for speech comprehension. Recent neuroscientific hypotheses have proposed that neural oscillations contribute to speech parsing, but whether they do so on the basis of acoustic cues (bottom-up acoustic parsing) or as a function of available linguistic representations (top-down linguistic parsing) is unknown. In this magnetoencephalography study, we contrasted acoustic and linguistic parsing using bistable speech sequences. While listening to the speech sequences, participants were asked to maintain one of the two possible speech percepts through volitional control. We predicted that the tracking of speech dynamics by neural oscillations would not only follow the acoustic properties but also shift in time according to the participant's conscious speech percept. Our results show that the latency of high-frequency activity (specifically, beta and gamma bands) varied as a function of the perceptual report. In contrast, the phase of low-frequency oscillations was not strongly affected by top-down control. Whereas changes in low-frequency neural oscillations were compatible with the encoding of prelexical segmentation cues, high-frequency activity specifically informed on an individual's conscious speech percept. Copyright © 2016 the American Physiological Society.

Category-Specific Neural Oscillations Predict Recall Organization During Memory Search

PubMed Central

Morton, Neal W.; Kahana, Michael J.; Rosenberg, Emily A.; Baltuch, Gordon H.; Litt, Brian; Sharan, Ashwini D.; Sperling, Michael R.; Polyn, Sean M.

2013-01-01

Retrieved-context models of human memory propose that as material is studied, retrieval cues are constructed that allow one to target particular aspects of past experience. We examined the neural predictions of these models by using electrocorticographic/depth recordings and scalp electroencephalography (EEG) to characterize category-specific oscillatory activity, while participants studied and recalled items from distinct, neurally discriminable categories. During study, these category-specific patterns predict whether a studied item will be recalled. In the scalp EEG experiment, category-specific activity during study also predicts whether a given item will be recalled adjacent to other same-category items, consistent with the proposal that a category-specific retrieval cue is used to guide memory search. Retrieved-context models suggest that integrative neural circuitry is involved in the construction and maintenance of the retrieval cue. Consistent with this hypothesis, we observe category-specific patterns that rise in strength as multiple same-category items are studied sequentially, and find that individual differences in this category-specific neural integration during study predict the degree to which a participant will use category information to organize memory search. Finally, we track the deployment of this retrieval cue during memory search: Category-specific patterns are stronger when participants organize their responses according to the category of the studied material. PMID:22875859

Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems.

PubMed

Wang, Cong; Wang, Min; Liu, Tengfei; Hill, David J

2012-10-01

This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.

[Modular enteral nutrition in pediatrics].

PubMed

Murillo Sanchís, S; Prenafeta Ferré, M T; Sempere Luque, M D

1991-01-01

Modular Enteral Nutrition may be a substitute for Parenteral Nutrition in children with different pathologies. Study of 4 children with different pathologies selected from a group of 40 admitted to the Maternal-Childrens Hospital "Valle de Hebrón" in Barcelona, who received modular enteral nutrition. They were monitored on a daily basis by the Dietician Service. Modular enteral nutrition consists of modules of proteins, peptides, lipids, glucids and mineral salts-vitamins. 1.--Craneo-encephalic traumatisms with loss of consciousness, Feeding with a combination of parenteral nutrition and modular enteral nutrition for 7 days. In view of the tolerance and good results of the modular enteral nutrition, the parenteral nutrition was suspended and modular enteral nutrition alone used up to a total of 43 days. 2.--55% burns with 36 days of hyperproteic modular enteral nutrition together with normal feeding. A more rapid recovery was achieved with an increase in total proteins and albumin. 3.--Persistent diarrhoea with 31 days of modular enteral nutrition, 5 days on parenteral nutrition alone and 8 days on combined parenteral nutrition and modular enteral nutrition. In view of the tolerance and good results of the modular enteral nutrition, the parenteral nutrition was suspended. 4.--Mucoviscidosis with a total of 19 days on modular enteral nutrition, 12 of which were exclusively on modular enteral nutrition and 7 as a night supplement to normal feeding. We administered proteic intakes of up to 20% of the total calorific intake and in concentrations of up to 1.2 calories/ml of the final preparation, always with a good tolerance. Modular enteral nutrition can and should be used as a substitute for parenteral nutrition in children with different pathologies, thus preventing the complications inherent in parenteral nutrition.

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.

Frontal Alpha Oscillations and Attentional Control: A Virtual Reality Neurofeedback Study.

PubMed

Berger, Anna M; Davelaar, Eddy J

2018-05-15

Two competing views about alpha oscillations suggest that cortical alpha reflect either cortical inactivity or cortical processing efficiency. We investigated the role of alpha oscillations in attentional control, as measured with a Stroop task. We used neurofeedback to train 22 participants to increase their level of alpha amplitude. Based on the conflict/control loop theory, we selected to train prefrontal alpha and focus on the Gratton effect as an index of deployment of attentional control. We expected an increase or a decrease in the Gratton effect with increase in neural learning depending on whether frontal alpha oscillations reflect cortical idling or enhanced processing efficiency, respectively. In order to induce variability in neural learning beyond natural occurring individual differences, we provided half of the participants with feedback on alpha amplitude in a 3-dimensional (3D) virtual reality environment and the other half received feedback in a 2D environment. Our results showed variable neural learning rates, with larger rates in the 3D compared to the 2D group, corroborating prior evidence of individual differences in EEG-based learning and the influence of a virtual environment. Regression analyses revealed a significant association between the learning rate and changes on deployment of attentional control, with larger learning rates being associated with larger decreases in the Gratton effect. This association was not modulated by feedback medium. The study supports the view of frontal alpha oscillations being associated with efficient neurocognitive processing and demonstrates the utility of neurofeedback training in addressing theoretical questions in the non-neurofeedback literature. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

Neural networks for sign language translation

NASA Astrophysics Data System (ADS)

Wilson, Beth J.; Anspach, Gretel

1993-09-01

A neural network is used to extract relevant features of sign language from video images of a person communicating in American Sign Language or Signed English. The key features are hand motion, hand location with respect to the body, and handshape. A modular hybrid design is under way to apply various techniques, including neural networks, in the development of a translation system that will facilitate communication between deaf and hearing people. One of the neural networks described here is used to classify video images of handshapes into their linguistic counterpart in American Sign Language. The video image is preprocessed to yield Fourier descriptors that encode the shape of the hand silhouette. These descriptors are then used as inputs to a neural network that classifies their shapes. The network is trained with various examples from different signers and is tested with new images from new signers. The results have shown that for coarse handshape classes, the network is invariant to the type of camera used to film the various signers and to the segmentation technique.

Cross-frequency coupling of brain oscillations in studying motivation and emotion.

PubMed

Schutter, Dennis J L G; Knyazev, Gennady G

2012-03-01

Research has shown that brain functions are realized by simultaneous oscillations in various frequency bands. In addition to examining oscillations in pre-specified bands, interactions and relations between the different frequency bandwidths is another important aspect that needs to be considered in unraveling the workings of the human brain and its functions. In this review we provide evidence that studying interdependencies between brain oscillations may be a valuable approach to study the electrophysiological processes associated with motivation and emotional states. Studies will be presented showing that amplitude-amplitude coupling between delta-alpha and delta-beta oscillations varies as a function of state anxiety and approach-avoidance-related motivation, and that changes in the association between delta-beta oscillations can be observed following successful psychotherapy. Together these studies suggest that cross-frequency coupling of brain oscillations may contribute to expanding our understanding of the neural processes underlying motivation and emotion.

Brain state-dependent recruitment of high-frequency oscillations in the human hippocampus.

PubMed

Billeke, Pablo; Ossandon, Tomas; Stockle, Marcelo; Perrone-Bertolotti, Marcela; Kahane, Philippe; Lachaux, Jean-Philippe; Fuentealba, Pablo

2017-09-01

Ripples are high-frequency bouts of coordinated hippocampal activity believed to be crucial for information transfer and memory formation. We used intracortical macroelectrodes to record neural activity in the human hippocampus of awake subjects undergoing surgical treatment for refractory epilepsy and distinguished two populations of ripple episodes based on their frequency spectrum. The phase-coupling of one population, slow ripples (90-110 Hz), to cortical delta oscillations was differentially modulated by cognitive task; whereas the second population, fast ripples (130-170 Hz), was not seemingly correlated to local neural activity. Furthermore, as cognitive tasks changed, the ongoing coordination of neural activity associated to slow ripples progressively augmented along the parahippocampal axis. Thus, during resting states, slow ripples were coordinated in restricted hippocampal territories; whereas during active states, such as attentionally-demanding tasks, high frequency activity emerged across the hippocampus and parahippocampal cortex, that was synchronized with slow ripples, consistent with ripples supporting information transfer and coupling anatomically distant regions. Hence, our results provide further evidence of neural diversity in hippocampal high-frequency oscillations and their association to cognitive processing in humans. Copyright © 2017 Elsevier Ltd. All rights reserved.

Some properties of asymmetric Hopfield neural networks with finite time of transition between states

NASA Astrophysics Data System (ADS)

Suleimenov, Ibragim; Mun, Grigoriy; Panchenko, Sergey; Pak, Ivan

2016-11-01

There were implemented samples of asymmetric Hopfield neural networks which have finite time of transition from one state to another. It was shown that in such systems, various oscillation modes could occur. It was revealed that the oscillation of the output signal of certain neuron could be treated as extra logical variable, which describes the state of the neuron. Asymmetric Hopfield neural networks are described in terms of ternary logic. Such logic may be employed in image recognition procedure.

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.

PubMed

Corzo, Gerald; Solomatine, Dimitri

2007-05-01

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.

A modular optical sensor

NASA Astrophysics Data System (ADS)

Conklin, John Albert

This dissertation presents the design of a modular, fiber-optic sensor and the results obtained from testing the modular sensor. The modular fiber-optic sensor is constructed in such manner that the sensor diaphragm can be replaced with different configurations to detect numerous physical phenomena. Additionally, different fiber-optic detection systems can be attached to the sensor. Initially, the modular sensor was developed to be used by university of students to investigate realistic optical sensors and detection systems to prepare for advance studies of micro-optical mechanical systems (MOMS). The design accomplishes this by doing two things. First, the design significantly lowers the costs associated with studying optical sensors by modularizing the sensor design. Second, the sensor broadens the number of physical phenomena that students can apply optical sensing techniques to in a fiber optics sensor course. The dissertation is divided into seven chapters covering the historical development of fiber-optic sensors, a theoretical overview of fiber-optic sensors, the design, fabrication, and the testing of the modular sensor developed in the course of this work. Chapter 1 discusses, in detail, how this dissertation is organized and states the purpose of the dissertation. Chapter 2 presents an historical overview of the development of optical fibers, optical pressure sensors, and fibers, optical pressure sensors, and optical microphones. Chapter 3 reviews the theory of multi-fiber optic detection systems, optical microphones, and pressure sensors. Chapter 4 presents the design details of the modular, optical sensor. Chapter 5 delves into how the modular sensor is fabricated and how the detection systems are constructed. Chapter 6 presents the data collected from the microphone and pressure sensor configurations of the modular sensor. Finally, Chapter 7 discusses the data collected and draws conclusions about the design based on the data collected. Chapter 7 also

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. Copyright © 2016 Elsevier Ltd. All rights reserved.

Frequency Agile Tm,Ho:YLF Local Oscillator for a Scanning Doppler wind Lidar in Earth Orbit

NASA Technical Reports Server (NTRS)

Menzies, Robert T.; Hemmati, Hamid; Esproles, Carlos

1997-01-01

A compact cw Tm,Ho:YLF laser with single-mode tunability over +/-4 GHz has been developed into a modular unit containing an isolator and photomixer for offset tuning of the LO from a master oscillator which controls the frequency of a Doppler lidar transmitter. This and an alternative diode laser LO will be described.

Learning characteristics of a space-time neural network as a tether skiprope observer

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1992-01-01

The Software Technology Laboratory at JSC is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

Stochastic dynamics of uncoupled neural oscillators: Fokker-Planck studies with the finite element method

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

Galan, Roberto F.; Urban, Nathaniel N.; Center for the Neural Basis of Cognition, Mellon Institute, Pittsburgh, Pennsylvania 15213

We have investigated the effect of the phase response curve on the dynamics of oscillators driven by noise in two limit cases that are especially relevant for neuroscience. Using the finite element method to solve the Fokker-Planck equation we have studied (i) the impact of noise on the regularity of the oscillations quantified as the coefficient of variation, (ii) stochastic synchronization of two uncoupled phase oscillators driven by correlated noise, and (iii) their cross-correlation function. We show that, in general, the limit of type II oscillators is more robust to noise and more efficient at synchronizing by correlated noise thanmore » type I.« less

Portable modular detection system

DOEpatents

Brennan, James S [Rodeo, CA; Singh, Anup [Danville, CA; Throckmorton, Daniel J [Tracy, CA; Stamps, James F [Livermore, CA

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.

Metabolic modulation of neuronal gamma-band oscillations.

PubMed

Vodovozov, Wadim; Schneider, Justus; Elzoheiry, Shehabeldin; Hollnagel, Jan-Oliver; Lewen, Andrea; Kann, Oliver

2018-05-28

Gamma oscillations (30-100 Hz) represent a physiological fast brain rhythm that occurs in many cortex areas in awake mammals, including humans. They associate with sensory perception, voluntary movement, and memory formation and require precise synaptic transmission between excitatory glutamatergic neurons and inhibitory GABAergic interneurons such as parvalbumin-positive basket cells. Notably, gamma oscillations are exquisitely sensitive to shortage in glucose and oxygen supply (metabolic stress), with devastating consequences for higher cognitive functions. Herein, we explored the robustness of gamma oscillations against changes in the availability of alternative energy substrates and amino acids, which is partially regulated by glial cells such as astrocytes. We used organotypic slice cultures of the rat hippocampus expressing acetylcholine-induced persistent gamma oscillations under normoxic recording conditions (20% oxygen fraction). Our main findings are (1) partial substitution of glucose with pyruvate and the ketone body β-hydroxybutyrate increases the frequency of gamma oscillations, even at different stages of neuronal tissue development. (2) Supplementation with the astrocytic neurotransmitter precursor glutamine has no effect on the properties of gamma oscillations. (3) Supplementation with glycine increases power, frequency, and inner coherence of gamma oscillations in a dose-dependent manner. (4) During these treatments switches to other frequency bands or pathological network states such as neural burst firing or synchronized epileptic activity are absent. Our study indicates that cholinergic gamma oscillations show general robustness against these changes in nutrient and amino acid composition of the cerebrospinal fluid; however, modulation of their properties may impact on cortical information processing under physiological and pathophysiological conditions.

Genetic influences on phase synchrony of brain oscillations supporting response inhibition.

PubMed

Müller, Viktor; Anokhin, Andrey P; Lindenberger, Ulman

2017-05-01

Phase synchronization of neuronal oscillations is a fundamental mechanism underlying cognitive processing and behavior, including context-dependent response production and inhibition. Abnormalities in neural synchrony can lead to abnormal information processing and contribute to cognitive and behavioral deficits in neuropsychiatric disorders. However, little is known about genetic and environmental contributions to individual differences in cortical oscillatory dynamics underlying response inhibition. This study examined heritability of event-related phase synchronization of brain oscillations in 302 young female twins including 94 MZ and 57 DZ pairs performing a cued Go/No-Go version of the Continuous Performance Test (CPT). We used the Phase Locking Index (PLI) to assess inter-trial phase clustering (synchrony) in several frequency bands in two time intervals after stimulus onset (0-300 and 301-600ms). Response inhibition (i.e., successful response suppression in No-Go trials) was characterized by a transient increase in phase synchronization of delta- and theta-band oscillations in the fronto-central midline region. Genetic analysis showed significant heritability of the phase locking measures related to response inhibition, with 30 to 49% of inter-individual variability being accounted for by genetic factors. This is the first study providing evidence for heritability of task-related neural synchrony. The present results suggest that PLI can serve as an indicator of genetically transmitted individual differences in neural substrates of response inhibition. Copyright © 2016 Elsevier B.V. All rights reserved.

Learning characteristics of a space-time neural network as a tether skiprope observer

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1993-01-01

The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

Why Go Modular? A Review of Modular A-Level Mathematics.

ERIC Educational Resources Information Center

Taverner, Sally; Wright, Martin

1997-01-01

Attitudes, academic intentions, and attainment of students gaining a grade in A-level (Advanced level) mathematics were compared for those who followed a modular course and those assessed at the end of two years of study. Overall, the final grades of those assessed modularly were half a grade higher. (JOW)

Dysrhythmias of the respiratory oscillator

NASA Astrophysics Data System (ADS)

Paydarfar, David; Buerkel, Daniel M.

1995-03-01

Breathing is regulated by a central neural oscillator that produces rhythmic output to the respiratory muscles. Pathological disturbances in rhythm (dysrhythmias) are observed in the breathing pattern of children and adults with neurological and cardiopulmonary diseases. The mechanisms responsible for genesis of respiratory dysrhythmias are poorly understood. The present studies take a novel approach to this problem. The basic postulate is that the rhythm of the respiratory oscillator can be altered by a variety of stimuli. When the oscillator recovers its rhythm after such perturbations, its phase may be reset relative to the original rhythm. The amount of phase resetting is dependent upon stimulus parameters and the level of respiratory drive. The long-range hypothesis is that respiratory dysrhythmias can be induced by stimuli that impinge upon or arise within the respiratory oscillator with certain combinations of strength and timing relative to the respiratory cycle. Animal studies were performed in anesthetized or decerebrate preparations. Neural respiratory rhythmicity is represented by phrenic nerve activity, allowing use of open-loop experimental conditions which avoid negative chemical feedback associated with changes in ventilation. In animal experiments, respiratory dysrhythmias can be induced by stimuli having specific combinations of strength and timing. Newborn animals readily exhibit spontaneous dysrhythmias which become more prominent at lower respiratory drives. In human subjects, swallowing was studied as a physiological perturbation of respiratory rhythm, causing a pattern of phase resetting that is characterized topologically as type 0. Computational studies of the Bonhoeffer-van der Pol (BvP) equations, whose qualitative behavior is representative of many excitable systems, supports a unified interpretation of these experimental findings. Rhythmicity is observed when the BvP model exhibits recurrent periods of excitation alternating with

Alpha oscillations and their impairment in affective and post-traumatic stress disorders.

PubMed

Eidelman-Rothman, Moranne; Levy, Jonathan; Feldman, Ruth

2016-09-01

Affective and anxiety disorders are debilitating conditions characterized by impairments in cognitive and social functioning. Elucidating their neural underpinnings may assist in improving diagnosis and developing targeted interventions. Neural oscillations are fundamental for brain functioning. Specifically, oscillations in the alpha frequency range (alpha rhythms) are prevalent in the awake, conscious brain and play an important role in supporting perceptual, cognitive, and social processes. We review studies utilizing various alpha power measurements to assess abnormalities in brain functioning in affective and anxiety disorders as well as obsessive compulsive and post-traumatic stress disorders. Despite some inconsistencies, studies demonstrate associations between aberrant alpha patterns and these disorders both in response to specific cognitive and emotional tasks and during a resting state. We conclude by discussing methodological considerations and future directions, and underscore the need for much further research on the role of alpha functionality in social contexts. As social dysfunction accompanies most psychiatric conditions, research on alpha's involvement in social processes may provide a unique window into the neural mechanisms underlying these disorders. Copyright © 2016 Elsevier Ltd. All rights reserved.

Quasispecies theory for evolution of modularity.

PubMed

Park, Jeong-Man; Niestemski, Liang Ren; Deem, Michael W

2015-01-01

Biological systems are modular, and this modularity evolves over time and in different environments. A number of observations have been made of increased modularity in biological systems under increased environmental pressure. We here develop a quasispecies theory for the dynamics of modularity in populations of these systems. We show how the steady-state fitness in a randomly changing environment can be computed. We derive a fluctuation dissipation relation for the rate of change of modularity and use it to derive a relationship between rate of environmental changes and rate of growth of modularity. We also find a principle of least action for the evolved modularity at steady state. Finally, we compare our predictions to simulations of protein evolution and find them to be consistent.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

NASA Astrophysics Data System (ADS)

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-12-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks.

Functional complexity emerging from anatomical constraints in the brain: the significance of network modularity and rich-clubs

PubMed Central

Zamora-López, Gorka; Chen, Yuhan; Deco, Gustavo; Kringelbach, Morten L.; Zhou, Changsong

2016-01-01

The large-scale structural ingredients of the brain and neural connectomes have been identified in recent years. These are, similar to the features found in many other real networks: the arrangement of brain regions into modules and the presence of highly connected regions (hubs) forming rich-clubs. Here, we examine how modules and hubs shape the collective dynamics on networks and we find that both ingredients lead to the emergence of complex dynamics. Comparing the connectomes of C. elegans, cats, macaques and humans to surrogate networks in which either modules or hubs are destroyed, we find that functional complexity always decreases in the perturbed networks. A comparison between simulated and empirically obtained resting-state functional connectivity indicates that the human brain, at rest, lies in a dynamical state that reflects the largest complexity its anatomical connectome can host. Last, we generalise the topology of neural connectomes into a new hierarchical network model that successfully combines modular organisation with rich-club forming hubs. This is achieved by centralising the cross-modular connections through a preferential attachment rule. Our network model hosts more complex dynamics than other hierarchical models widely used as benchmarks. PMID:27917958

Modularization and Flexibilization.

ERIC Educational Resources Information Center

Van Meel, R. M.

Publications in the fields of educational science, organization theory, and project management were analyzed to identify the possibilities that modularization offers to institutions of higher professional education and to obtain background information for use in developing a method for modularization in higher professional education. It was…

Self-organized modularization in evolutionary algorithms.

PubMed

Dauscher, Peter; Uthmann, Thomas

2005-01-01

The principle of modularization has proven to be extremely successful in the field of technical applications and particularly for Software Engineering purposes. The question to be answered within the present article is whether mechanisms can also be identified within the framework of Evolutionary Computation that cause a modularization of solutions. We will concentrate on processes, where modularization results only from the typical evolutionary operators, i.e. selection and variation by recombination and mutation (and not, e.g., from special modularization operators). This is what we call Self-Organized Modularization. Based on a combination of two formalizations by Radcliffe and Altenberg, some quantitative measures of modularity are introduced. Particularly, we distinguish Built-in Modularity as an inherent property of a genotype and Effective Modularity, which depends on the rest of the population. These measures can easily be applied to a wide range of present Evolutionary Computation models. It will be shown, both theoretically and by simulation, that under certain conditions, Effective Modularity (as defined within this paper) can be a selection factor. This causes Self-Organized Modularization to take place. The experimental observations emphasize the importance of Effective Modularity in comparison with Built-in Modularity. Although the experimental results have been obtained using a minimalist toy model, they can lead to a number of consequences for existing models as well as for future approaches. Furthermore, the results suggest a complex self-amplification of highly modular equivalence classes in the case of respected relations. Since the well-known Holland schemata are just the equivalence classes of respected relations in most Simple Genetic Algorithms, this observation emphasizes the role of schemata as Building Blocks (in comparison with arbitrary subsets of the search space).

Neural fate decisions mediated by combinatorial regulation of Hes1 and miR-9.

PubMed

Li, Shanshan; Liu, Yanwei; Liu, Zengrong; Wang, Ruiqi

2016-01-01

In the nervous system, Hes1 shows an oscillatory manner in neural progenitors but a persistent one in neurons. Many models involving Hes1 have been provided for the study of neural differentiation but few of them take the role of microRNA into account. It is known that a microRNA, miR-9, plays crucial roles in modulating Hes1 oscillations. However, the roles of miR-9 in controlling Hes1 oscillations and inducing transition between different cell fates still need to be further explored. Here we provide a mathematical model to show the interaction between miR-9 and Hes1, with the aim of understanding how the Hes1 oscillations are produced, how they are controlled, and further, how they are terminated. Based on the experimental findings, the model demonstrates the essential roles of Hes1 and miR-9 in regulating the dynamics of the system. In particular, the model suggests that the balance between miR-9 and Hes1 plays important roles in the choice between progenitor maintenance and neural differentiation. In addition, the synergistic (or antagonistic) effects of several important regulations are investigated so as to elucidate the effects of combinatorial regulation in neural decision-making. Our model provides a qualitative mechanism for understanding the process in neural fate decisions regulated by Hes1 and miR-9.

Synchronization in neural nets

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.; Haggerty, John

1988-01-01

The paper presents an artificial neural network concept (the Synchronizable Oscillator Networks) where the instants of individual firings in the form of point processes constitute the only form of information transmitted between joining neurons. In the model, neurons fire spontaneously and regularly in the absence of perturbation. When interaction is present, the scheduled firings are advanced or delayed by the firing of neighboring neurons. Networks of such neurons become global oscillators which exhibit multiple synchronizing attractors. From arbitrary initial states, energy minimization learning procedures can make the network converge to oscillatory modes that satisfy multi-dimensional constraints. Such networks can directly represent routing and scheduling problems that consist of ordering sequences of events.

Basal Ganglia Beta Oscillations Accompany Cue Utilization

PubMed Central

Leventhal, Daniel K.; Gage, Gregory J.; Schmidt, Robert; Pettibone, Jeffrey R.; Case, Alaina C.; Berke, Joshua D.

2012-01-01

SUMMARY Beta oscillations in cortical-basal ganglia (BG) circuits have been implicated in normal movement suppression and motor impairment in Parkinson’s disease. To dissect the functional correlates of these rhythms we compared neural activity during four distinct variants of a cued choice task in rats. Brief beta (~20 Hz) oscillations occurred simultaneously throughout the cortical-BG network, both spontaneously and at precise moments of task performance. Beta phase was rapidly reset in response to salient cues, yet increases in beta power were not rigidly linked to cues, movements, or movement suppression. Rather, beta power was enhanced after cues were used to determine motor output. We suggest that beta oscillations reflect a postdecision stabilized state of cortical-BG networks, which normally reduces interference from alternative potential actions. The abnormally strong beta seen in Parkinson’s Disease may reflect overstabilization of these networks, producing pathological persistence of the current motor state. PMID:22325204

Numerical Study of the Complex Temporal Pattern of Spontaneous Oscillation in Bullfrog Saccular Hair Cells

NASA Astrophysics Data System (ADS)

Roongthumskul, Yuttana; Fredrickson-Hemsing, Lea; Kao, Albert; Bozovic, Dolores

2011-11-01

Hair bundles of the bullfrog sacculus display spontaneous oscillations that show complex temporal profiles. Quiescent intervals are typically interspersed with oscillations, analogous to bursting behavior observed in neural systems. By introducing slow calcium dynamics into the theoretical model of bundle mechanics, we reproduce numerically the multi-mode oscillations and explore the effects of internal parameters on the temporal profiles and the frequency tuning of their linear response functions. We also study the effects of mechanical overstimulation on the oscillatory behavior.

Localizing Tortoise Nests by Neural Networks.

PubMed

Barbuti, Roberto; Chessa, Stefano; Micheli, Alessio; Pucci, Rita

2016-01-01

The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating). Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS) which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN). We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours), the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

Sensory Entrainment Mechanisms in Auditory Perception: Neural Synchronization Cortico-Striatal Activation.

PubMed

Sameiro-Barbosa, Catia M; Geiser, Eveline

2016-01-01

The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system.

Implementing neural nets with programmable logic

NASA Technical Reports Server (NTRS)

Vidal, Jacques J.

1988-01-01

Networks of Boolean programmable logic modules are presented as one purely digital class of artificial neural nets. The approach contrasts with the continuous analog framework usually suggested. Programmable logic networks are capable of handling many neural-net applications. They avoid some of the limitations of threshold logic networks and present distinct opportunities. The network nodes are called dynamically programmable logic modules. They can be implemented with digitally controlled demultiplexers. Each node performs a Boolean function of its inputs which can be dynamically assigned. The overall network is therefore a combinational circuit and its outputs are Boolean global functions of the network's input variables. The approach offers definite advantages for VLSI implementation, namely, a regular architecture with limited connectivity, simplicity of the control machinery, natural modularity, and the support of a mature technology.

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules.

PubMed

Ursino, Mauro; Magosso, Elisa; La Cara, Giuseppe-Emiliano; Cuppini, Cristiano

2006-09-01

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical

A Modularized Counselor-Education Program.

ERIC Educational Resources Information Center

Miller, Thomas V.; Dimattia, Dominic J.

1978-01-01

Counselor-education programs may be enriched through the use of modularized learning experiences. This article notes several recent articles on competency-based counselor education, the concepts of simulation and modularization, and describes the process of developing a modularized master's program at the University of Bridgeport in Connecticut.…

Robotic hand with modular extensions

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

Salisbury, Curt Michael; Quigley, Morgan

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.

Signal Processing in Periodically Forced Gradient Frequency Neural Networks

PubMed Central

Kim, Ji Chul; Large, Edward W.

2015-01-01

Oscillatory instability at the Hopf bifurcation is a dynamical phenomenon that has been suggested to characterize active non-linear processes observed in the auditory system. Networks of oscillators poised near Hopf bifurcation points and tuned to tonotopically distributed frequencies have been used as models of auditory processing at various levels, but systematic investigation of the dynamical properties of such oscillatory networks is still lacking. Here we provide a dynamical systems analysis of a canonical model for gradient frequency neural networks driven by a periodic signal. We use linear stability analysis to identify various driven behaviors of canonical oscillators for all possible ranges of model and forcing parameters. The analysis shows that canonical oscillators exhibit qualitatively different sets of driven states and transitions for different regimes of model parameters. We classify the parameter regimes into four main categories based on their distinct signal processing capabilities. This analysis will lead to deeper understanding of the diverse behaviors of neural systems under periodic forcing and can inform the design of oscillatory network models of auditory signal processing. PMID:26733858

Sound asleep: processing and retention of slow oscillation phase-targeted stimuli.

PubMed

Cox, Roy; Korjoukov, Ilia; de Boer, Marieke; Talamini, Lucia M

2014-01-01

The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep.

Sound Asleep: Processing and Retention of Slow Oscillation Phase-Targeted Stimuli

PubMed Central

Cox, Roy; Korjoukov, Ilia; de Boer, Marieke; Talamini, Lucia M.

2014-01-01

The sleeping brain retains some residual information processing capacity. Although direct evidence is scarce, a substantial literature suggests the phase of slow oscillations during deep sleep to be an important determinant for stimulus processing. Here, we introduce an algorithm for predicting slow oscillations in real-time. Using this approach to present stimuli directed at both oscillatory up and down states, we show neural stimulus processing depends importantly on the slow oscillation phase. During ensuing wakefulness, however, we did not observe differential brain or behavioral responses to these stimulus categories, suggesting no enduring memories were formed. We speculate that while simpler forms of learning may occur during sleep, neocortically based memories are not readily established during deep sleep. PMID:24999803

Fast Dynamical Coupling Enhances Frequency Adaptation of Oscillators for Robotic Locomotion Control

PubMed Central

Nachstedt, Timo; Tetzlaff, Christian; Manoonpong, Poramate

2017-01-01

Rhythmic neural signals serve as basis of many brain processes, in particular of locomotion control and generation of rhythmic movements. It has been found that specific neural circuits, named central pattern generators (CPGs), are able to autonomously produce such rhythmic activities. In order to tune, shape and coordinate the produced rhythmic activity, CPGs require sensory feedback, i.e., external signals. Nonlinear oscillators are a standard model of CPGs and are used in various robotic applications. A special class of nonlinear oscillators are adaptive frequency oscillators (AFOs). AFOs are able to adapt their frequency toward the frequency of an external periodic signal and to keep this learned frequency once the external signal vanishes. AFOs have been successfully used, for instance, for resonant tuning of robotic locomotion control. However, the choice of parameters for a standard AFO is characterized by a trade-off between the speed of the adaptation and its precision and, additionally, is strongly dependent on the range of frequencies the AFO is confronted with. As a result, AFOs are typically tuned such that they require a comparably long time for their adaptation. To overcome the problem, here, we improve the standard AFO by introducing a novel adaptation mechanism based on dynamical coupling strengths. The dynamical adaptation mechanism enhances both the speed and precision of the frequency adaptation. In contrast to standard AFOs, in this system, the interplay of dynamics on short and long time scales enables fast as well as precise adaptation of the oscillator for a wide range of frequencies. Amongst others, a very natural implementation of this mechanism is in terms of neural networks. The proposed system enables robotic applications which require fast retuning of locomotion control in order to react to environmental changes or conditions. PMID:28377710

Modular organization and hospital performance.

PubMed

Kuntz, Ludwig; Vera, Antonio

2007-02-01

The concept of modularization represents a modern form of organization, which contains the vertical disaggregation of the firm and the use of market mechanisms within hierarchies. The objective of this paper is to examine whether the use of modular structures has a positive effect on hospital performance. The empirical section makes use of multiple regression analyses and leads to the main result that modularization does not have a positive effect on hospital performance. However, the analysis also finds out positive efficiency effects of two central ideas of modularization, namely process orientation and internal market mechanisms.

Interaction of oscillations, and their suppression via deep brain stimulation, in a model of the cortico-basal ganglia network.

PubMed

Kang, Guiyeom; Lowery, Madeleine M

2013-03-01

Growing evidence suggests that synchronized neural oscillations in the cortico-basal ganglia network may play a critical role in the pathophysiology of Parkinson's disease. In this study, a new model of the closed loop network is used to explore the generation and interaction of network oscillations and their suppression through deep brain stimulation (DBS). Under simulated dopamine depletion conditions, increased gain through the hyperdirect pathway resulted in the interaction of neural oscillations at different frequencies in the cortex and subthalamic nucleus (STN), leading to the emergence of synchronized oscillations at a new intermediate frequency. Further increases in synaptic gain resulted in the cortex driving synchronous oscillatory activity throughout the network. When DBS was added to the model a progressive reduction in STN power at the tremor and beta frequencies was observed as the frequency of stimulation was increased, with resonance effects occurring for low frequency DBS (40 Hz) in agreement with experimental observations. The results provide new insights into the mechanisms by which synchronous oscillations can arise within the network and how DBS may suppress unwanted oscillatory activity.

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.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network.

PubMed

Jadi, Monika P; Sejnowski, Terrence J

2014-04-21

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

Modular Design in Treaty Verification Equipment

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

Macarthur, Duncan Whittemore; Benz, Jacob; Tolk, Keith

2015-01-27

It is widely believed that modular design is a good thing. However, there are often few explicit arguments, or even an agreed range of definitions, to back up this belief. In this paper, we examine the potential range of design modularity, the implications of various amounts of modularity, and the advantages and disadvantages of each level of modular construction. We conclude with a comparison of the advantages and disadvantages of each type, as well as discuss many caveats that should be observed to take advantage of the positive features of modularity and minimize the effects of the negative. The tradeoffsmore » described in this paper will be evaluated during the conceptual design to determine what amount of modularity should be included.« less

Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation.

PubMed

Kim, Bowon; Kocsis, Bernat; Hwang, Eunjin; Kim, Youngsoo; Strecker, Robert E; McCarley, Robert W; Choi, Jee Hyun

2017-02-28

Homeostatic rebound in rapid eye movement (REM) sleep normally occurs after acute sleep deprivation, but REM sleep rebound settles on a persistently elevated level despite continued accumulation of REM sleep debt during chronic sleep restriction (CSR). Using high-density EEG in mice, we studied how this pattern of global regulation is implemented in cortical regions with different functions and network architectures. We found that across all areas, slow oscillations repeated the behavioral pattern of persistent enhancement during CSR, whereas high-frequency oscillations showed progressive increases. This pattern followed a common rule despite marked topographic differences. The findings suggest that REM sleep slow oscillations may translate top-down homeostatic control to widely separated brain regions whereas fast oscillations synchronizing local neuronal ensembles escape this global command. These patterns of EEG oscillation changes are interpreted to reconcile two prevailing theories of the function of sleep, synaptic homeostasis and sleep dependent memory consolidation.

Modular interdependency in complex dynamical systems.

PubMed

Watson, Richard A; Pollack, Jordan B

2005-01-01

Herbert A. Simon's characterization of modularity in dynamical systems describes subsystems as having dynamics that are approximately independent of those of other subsystems (in the short term). This fits with the general intuition that modules must, by definition, be approximately independent. In the evolution of complex systems, such modularity may enable subsystems to be modified and adapted independently of other subsystems, whereas in a nonmodular system, modifications to one part of the system may result in deleterious side effects elsewhere in the system. But this notion of modularity and its effect on evolvability is not well quantified and is rather simplistic. In particular, modularity need not imply that intermodule dependences are weak or unimportant. In dynamical systems this is acknowledged by Simon's suggestion that, in the long term, the dynamical behaviors of subsystems do interact with one another, albeit in an "aggregate" manner--but this kind of intermodule interaction is omitted in models of modularity for evolvability. In this brief discussion we seek to unify notions of modularity in dynamical systems with notions of how modularity affects evolvability. This leads to a quantifiable measure of modularity and a different understanding of its effect on evolvability.

Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches

PubMed Central

Lowet, Eric; Roberts, Mark J.; Bonizzi, Pietro; Karel, Joël; De Weerd, Peter

2016-01-01

Synchronization or phase-locking between oscillating neuronal groups is considered to be important for coordination of information among cortical networks. Spectral coherence is a commonly used approach to quantify phase locking between neural signals. We systematically explored the validity of spectral coherence measures for quantifying synchronization among neural oscillators. To that aim, we simulated coupled oscillatory signals that exhibited synchronization dynamics using an abstract phase-oscillator model as well as interacting gamma-generating spiking neural networks. We found that, within a large parameter range, the spectral coherence measure deviated substantially from the expected phase-locking. Moreover, spectral coherence did not converge to the expected value with increasing signal-to-noise ratio. We found that spectral coherence particularly failed when oscillators were in the partially (intermittent) synchronized state, which we expect to be the most likely state for neural synchronization. The failure was due to the fast frequency and amplitude changes induced by synchronization forces. We then investigated whether spectral coherence reflected the information flow among networks measured by transfer entropy (TE) of spike trains. We found that spectral coherence failed to robustly reflect changes in synchrony-mediated information flow between neural networks in many instances. As an alternative approach we explored a phase-locking value (PLV) method based on the reconstruction of the instantaneous phase. As one approach for reconstructing instantaneous phase, we used the Hilbert Transform (HT) preceded by Singular Spectrum Decomposition (SSD) of the signal. PLV estimates have broad applicability as they do not rely on stationarity, and, unlike spectral coherence, they enable more accurate estimations of oscillatory synchronization across a wide range of different synchronization regimes, and better tracking of synchronization-mediated information

Event-Related Oscillations in Alcoholism Research: A Review

PubMed Central

Pandey, Ashwini K; Kamarajan, Chella; Rangaswamy, Madhavi; Porjesz, Bernice

2013-01-01

Alcohol dependence is characterized as a multi-factorial disorder caused by a complex interaction between genetic and environmental liabilities across development. A variety of neurocognitive deficits/dysfunctions involving impairments in different brain regions and/or neural circuitries have been associated with chronic alcoholism, as well as with a predisposition to develop alcoholism. Several neurobiological and neurobehavioral approaches and methods of analyses have been used to understand the nature of these neurocognitive impairments/deficits in alcoholism. In the present review, we have examined relatively novel methods of analyses of the brain signals that are collectively referred to as event-related oscillations (EROs) and show promise to further our understanding of human brain dynamics while performing various tasks. These new measures of dynamic brain processes have exquisite temporal resolution and allow the study of neural networks underlying responses to sensory and cognitive events, thus providing a closer link to the physiology underlying them. Here, we have reviewed EROs in the study of alcoholism, their usefulness in understanding dynamical brain functions/dysfunctions associated with alcoholism as well as their utility as effective endophenotypes to identify and understand genes associated with both brain oscillations and alcoholism. PMID:24273686

Modular analysis of biological networks.

PubMed

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation

PubMed Central

Kim, Bowon; Kocsis, Bernat; Hwang, Eunjin; Kim, Youngsoo; Strecker, Robert E.; McCarley, Robert W.; Choi, Jee Hyun

2017-01-01

Homeostatic rebound in rapid eye movement (REM) sleep normally occurs after acute sleep deprivation, but REM sleep rebound settles on a persistently elevated level despite continued accumulation of REM sleep debt during chronic sleep restriction (CSR). Using high-density EEG in mice, we studied how this pattern of global regulation is implemented in cortical regions with different functions and network architectures. We found that across all areas, slow oscillations repeated the behavioral pattern of persistent enhancement during CSR, whereas high-frequency oscillations showed progressive increases. This pattern followed a common rule despite marked topographic differences. The findings suggest that REM sleep slow oscillations may translate top-down homeostatic control to widely separated brain regions whereas fast oscillations synchronizing local neuronal ensembles escape this global command. These patterns of EEG oscillation changes are interpreted to reconcile two prevailing theories of the function of sleep, synaptic homeostasis and sleep dependent memory consolidation. PMID:28193862

Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network.

PubMed

Goto, Hayato

2016-02-22

The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.

Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network

PubMed Central

Goto, Hayato

2016-01-01

The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence. PMID:26899997

Bifurcation-based adiabatic quantum computation with a nonlinear oscillator network

NASA Astrophysics Data System (ADS)

Goto, Hayato

2016-02-01

The dynamics of nonlinear systems qualitatively change depending on their parameters, which is called bifurcation. A quantum-mechanical nonlinear oscillator can yield a quantum superposition of two oscillation states, known as a Schrödinger cat state, via quantum adiabatic evolution through its bifurcation point. Here we propose a quantum computer comprising such quantum nonlinear oscillators, instead of quantum bits, to solve hard combinatorial optimization problems. The nonlinear oscillator network finds optimal solutions via quantum adiabatic evolution, where nonlinear terms are increased slowly, in contrast to conventional adiabatic quantum computation or quantum annealing, where quantum fluctuation terms are decreased slowly. As a result of numerical simulations, it is concluded that quantum superposition and quantum fluctuation work effectively to find optimal solutions. It is also notable that the present computer is analogous to neural computers, which are also networks of nonlinear components. Thus, the present scheme will open new possibilities for quantum computation, nonlinear science, and artificial intelligence.

Cosmic-ray discrimination capabilities of /ΔE-/E silicon nuclear telescopes using neural networks

NASA Astrophysics Data System (ADS)

Ambriola, M.; Bellotti, R.; Cafagna, F.; Castellano, M.; Ciacio, F.; Circella, M.; Marzo, C. N. D.; Montaruli, T.

2000-02-01

An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the ``mixture of experts'' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a ``classical'' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within /1% - with that of the classical one, with efficiency of the order of /98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.

Modular invariant inflation

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

Kobayashi, Tatsuo; Nitta, Daisuke; Urakawa, Yuko

2016-08-08

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,more » 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 V{sub ht}, but it also has a non-negligible deviation from V{sub ht}. 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.« less

Optogenetic Stimulation Shifts the Excitability of Cerebral Cortex from Type I to Type II: Oscillation Onset and Wave Propagation.

PubMed

Heitmann, Stewart; Rule, Michael; Truccolo, Wilson; Ermentrout, Bard

2017-01-01

Constant optogenetic stimulation targeting both pyramidal cells and inhibitory interneurons has recently been shown to elicit propagating waves of gamma-band (40-80 Hz) oscillations in the local field potential of non-human primate motor cortex. The oscillations emerge with non-zero frequency and small amplitude-the hallmark of a type II excitable medium-yet they also propagate far beyond the stimulation site in the manner of a type I excitable medium. How can neural tissue exhibit both type I and type II excitability? We investigated the apparent contradiction by modeling the cortex as a Wilson-Cowan neural field in which optogenetic stimulation was represented by an external current source. In the absence of any external current, the model operated as a type I excitable medium that supported propagating waves of gamma oscillations similar to those observed in vivo. Applying an external current to the population of inhibitory neurons transformed the model into a type II excitable medium. The findings suggest that cortical tissue normally operates as a type I excitable medium but it is locally transformed into a type II medium by optogenetic stimulation which predominantly targets inhibitory neurons. The proposed mechanism accounts for the graded emergence of gamma oscillations at the stimulation site while retaining propagating waves of gamma oscillations in the non-stimulated tissue. It also predicts that gamma waves can be emitted on every second cycle of a 100 Hz oscillation. That prediction was subsequently confirmed by re-analysis of the neurophysiological data. The model thus offers a theoretical account of how optogenetic stimulation alters the excitability of cortical neural fields.

Survey of Modular Military Vehicles: Benefits and Burdens

DTIC Science & Technology

2016-01-01

Survey of Modular Military Vehicles: BENEFITS and BURDENS Jean M. Dasch and David J. Gorsich Modularity in military vehicle design is generally...considered a positive attribute that promotes adaptability, resilience, and cost savings. The benefits and burdens of modularity are considered by...Engineering Center, vehicles were considered based on horizontal modularity , vertical modularity , and distributed modularity . Examples were given for each

Oscillator networks with tissue-specific circadian clocks in plants.

PubMed

Inoue, Keisuke; Araki, Takashi; Endo, Motomu

2017-09-08

Many organisms rely on circadian clocks to synchronize their biological processes with the 24-h rotation of the earth. In mammals, the circadian clock consists of a central clock in the suprachiasmatic nucleus and peripheral clocks in other tissues. The central clock is tightly coupled to synchronize rhythmicity and can organize peripheral clocks through neural and hormonal signals. In contrast to mammals, it has long been assumed that the circadian clocks in each plant cell is able to be entrained by external light, and they are only weakly coupled to each other. Recently, however, several reports have demonstrated that plants have unique oscillator networks with tissue-specific circadian clocks. Here, we introduce our current view regarding tissue-specific properties and oscillator networks of plant circadian clocks. Accumulating evidence suggests that plants have multiple oscillators, which show distinct properties and reside in different tissues. A direct tissue-isolation technique and micrografting have clearly demonstrated that plants have hierarchical oscillator networks consisting of multiple tissue-specific clocks. Copyright © 2017. Published by Elsevier Ltd.

Acetylcholine Release in Prefrontal Cortex Promotes Gamma Oscillations and Theta-Gamma Coupling during Cue Detection.

PubMed

Howe, William M; Gritton, Howard J; Lusk, Nicholas A; Roberts, Erik A; Hetrick, Vaughn L; Berke, Joshua D; Sarter, Martin

2017-03-22

The capacity for using external cues to guide behavior ("cue detection") constitutes an essential aspect of attention and goal-directed behavior. The cortical cholinergic input system, via phasic increases in prefrontal acetylcholine release, plays an essential role in attention by mediating such cue detection. However, the relationship between cholinergic signaling during cue detection and neural activity dynamics in prefrontal networks remains unclear. Here we combined subsecond measures of cholinergic signaling, neurophysiological recordings, and cholinergic receptor blockade to delineate the cholinergic contributions to prefrontal oscillations during cue detection in rats. We first confirmed that detected cues evoke phasic acetylcholine release. These cholinergic signals were coincident with increased neuronal synchrony across several frequency bands and the emergence of theta-gamma coupling. Muscarinic and nicotinic cholinergic receptors both contributed specifically to gamma synchrony evoked by detected cues, but the effects of blocking the two receptor subtypes were dissociable. Blocking nicotinic receptors primarily attenuated high-gamma oscillations occurring during the earliest phases of the cue detection process, while muscarinic (M1) receptor activity was preferentially involved in the transition from high to low gamma power that followed and corresponded to the mobilization of networks involved in cue-guided decision making. Detected cues also promoted coupling between gamma and theta oscillations, and both nicotinic and muscarinic receptor activity contributed to this process. These results indicate that acetylcholine release coordinates neural oscillations during the process of cue detection. SIGNIFICANCE STATEMENT The capacity of learned cues to direct attention and guide responding ("cue detection") is a key component of goal-directed behavior. Rhythmic neural activity and increases in acetylcholine release in the prefrontal cortex contribute to

Acetylcholine Release in Prefrontal Cortex Promotes Gamma Oscillations and Theta–Gamma Coupling during Cue Detection

PubMed Central

Hetrick, Vaughn L.; Berke, Joshua D.

2017-01-01

The capacity for using external cues to guide behavior (“cue detection”) constitutes an essential aspect of attention and goal-directed behavior. The cortical cholinergic input system, via phasic increases in prefrontal acetylcholine release, plays an essential role in attention by mediating such cue detection. However, the relationship between cholinergic signaling during cue detection and neural activity dynamics in prefrontal networks remains unclear. Here we combined subsecond measures of cholinergic signaling, neurophysiological recordings, and cholinergic receptor blockade to delineate the cholinergic contributions to prefrontal oscillations during cue detection in rats. We first confirmed that detected cues evoke phasic acetylcholine release. These cholinergic signals were coincident with increased neuronal synchrony across several frequency bands and the emergence of theta–gamma coupling. Muscarinic and nicotinic cholinergic receptors both contributed specifically to gamma synchrony evoked by detected cues, but the effects of blocking the two receptor subtypes were dissociable. Blocking nicotinic receptors primarily attenuated high-gamma oscillations occurring during the earliest phases of the cue detection process, while muscarinic (M1) receptor activity was preferentially involved in the transition from high to low gamma power that followed and corresponded to the mobilization of networks involved in cue-guided decision making. Detected cues also promoted coupling between gamma and theta oscillations, and both nicotinic and muscarinic receptor activity contributed to this process. These results indicate that acetylcholine release coordinates neural oscillations during the process of cue detection. SIGNIFICANCE STATEMENT The capacity of learned cues to direct attention and guide responding (“cue detection”) is a key component of goal-directed behavior. Rhythmic neural activity and increases in acetylcholine release in the prefrontal cortex

Respiratory Control in Stuttering Speakers: Evidence from Respiratory High-Frequency Oscillations.

ERIC Educational Resources Information Center

Denny, Margaret; Smith, Anne

2000-01-01

This study examined whether stuttering speakers (N=10) differed from fluent speakers in relations between the neural control systems for speech and life support. It concluded that in some stuttering speakers the relations between respiratory controllers are atypical, but that high participation by the high frequency oscillation-producing circuitry…

EEG oscillations entrain their phase to high-level features of speech sound.

PubMed

Zoefel, Benedikt; VanRullen, Rufin

2016-01-01

Phase entrainment of neural oscillations, the brain's adjustment to rhythmic stimulation, is a central component in recent theories of speech comprehension: the alignment between brain oscillations and speech sound improves speech intelligibility. However, phase entrainment to everyday speech sound could also be explained by oscillations passively following the low-level periodicities (e.g., in sound amplitude and spectral content) of auditory stimulation-and not by an adjustment to the speech rhythm per se. Recently, using novel speech/noise mixture stimuli, we have shown that behavioral performance can entrain to speech sound even when high-level features (including phonetic information) are not accompanied by fluctuations in sound amplitude and spectral content. In the present study, we report that neural phase entrainment might underlie our behavioral findings. We observed phase-locking between electroencephalogram (EEG) and speech sound in response not only to original (unprocessed) speech but also to our constructed "high-level" speech/noise mixture stimuli. Phase entrainment to original speech and speech/noise sound did not differ in the degree of entrainment, but rather in the actual phase difference between EEG signal and sound. Phase entrainment was not abolished when speech/noise stimuli were presented in reverse (which disrupts semantic processing), indicating that acoustic (rather than linguistic) high-level features play a major role in the observed neural entrainment. Our results provide further evidence for phase entrainment as a potential mechanism underlying speech processing and segmentation, and for the involvement of high-level processes in the adjustment to the rhythm of speech. Copyright © 2015 Elsevier Inc. All rights reserved.

Bifurcation of synchronous oscillations into torus in a system of two reciprocally inhibitory silicon neurons: experimental observation and modeling.

PubMed

Bondarenko, Vladimir E; Cymbalyuk, Gennady S; Patel, Girish; Deweerth, Stephen P; Calabrese, Ronald L

2004-12-01

Oscillatory activity in the central nervous system is associated with various functions, like motor control, memory formation, binding, and attention. Quasiperiodic oscillations are rarely discussed in the neurophysiological literature yet they may play a role in the nervous system both during normal function and disease. Here we use a physical system and a model to explore scenarios for how quasiperiodic oscillations might arise in neuronal networks. An oscillatory system of two mutually inhibitory neuronal units is a ubiquitous network module found in nervous systems and is called a half-center oscillator. Previously we created a half-center oscillator of two identical oscillatory silicon (analog Very Large Scale Integration) neurons and developed a mathematical model describing its dynamics. In the mathematical model, we have shown that an in-phase limit cycle becomes unstable through a subcritical torus bifurcation. However, the existence of this torus bifurcation in experimental silicon two-neuron system was not rigorously demonstrated or investigated. Here we demonstrate the torus predicted by the model for the silicon implementation of a half-center oscillator using complex time series analysis, including bifurcation diagrams, mapping techniques, correlation functions, amplitude spectra, and correlation dimensions, and we investigate how the properties of the quasiperiodic oscillations depend on the strengths of coupling between the silicon neurons. The potential advantages and disadvantages of quasiperiodic oscillations (torus) for biological neural systems and artificial neural networks are discussed.

Gamma and Beta Oscillations Define a Sequence of Neurocognitive Modes Present in Odor Processing

PubMed Central

Frederick, Donald E.; Brown, Austin; Brim, Elizabeth; Mehta, Nisarg; Vujovic, Mark

2016-01-01

Olfactory system beta (15–35 Hz) and gamma (40–110 Hz) oscillations of the local field potential in mammals have both been linked to odor learning and discrimination. Gamma oscillations represent the activity of a local network within the olfactory bulb, and beta oscillations represent engagement of a systemwide network. Here, we test whether beta and gamma oscillations represent different cognitive modes using the different demands of go/no-go and two-alternative choice tasks that previously were suggested to favor beta or gamma oscillations, respectively. We reconcile previous studies and show that both beta and gamma oscillations occur in both tasks, with gamma dominating the early odor sampling period (2–4 sniffs) and beta dominating later. The relative power and coherence of both oscillations depend separately on multiple factors within both tasks without categorical differences across tasks. While the early/gamma-associated period occurs in all trials, rats can perform above chance without the later/beta-associated period. Longer sampling, which includes beta oscillations, is associated with better performance. Gamma followed by beta oscillations therefore represents a sequence of cognitive and neural states during odor discrimination, which can be separately modified depending on the demands of a task and odor discrimination. Additionally, fast (85 Hz) and slow (70 Hz) olfactory bulb gamma oscillation sub-bands have been hypothesized to represent tufted and mitral cell networks, respectively (Manabe and Mori, 2013). We find that fast gamma favors the early and slow gamma the later (beta-dominated) odor-sampling period and that the relative contributions of these oscillations are consistent across tasks. SIGNIFICANCE STATEMENT Olfactory system gamma (40–110 Hz) and beta (15–35 Hz) oscillations of the local field potential indicate different neural firing statistics and functional circuits. We show that gamma and beta oscillations occur in

Gamma and Beta Oscillations Define a Sequence of Neurocognitive Modes Present in Odor Processing.

PubMed

Frederick, Donald E; Brown, Austin; Brim, Elizabeth; Mehta, Nisarg; Vujovic, Mark; Kay, Leslie M

2016-07-20

Olfactory system beta (15-35 Hz) and gamma (40-110 Hz) oscillations of the local field potential in mammals have both been linked to odor learning and discrimination. Gamma oscillations represent the activity of a local network within the olfactory bulb, and beta oscillations represent engagement of a systemwide network. Here, we test whether beta and gamma oscillations represent different cognitive modes using the different demands of go/no-go and two-alternative choice tasks that previously were suggested to favor beta or gamma oscillations, respectively. We reconcile previous studies and show that both beta and gamma oscillations occur in both tasks, with gamma dominating the early odor sampling period (2-4 sniffs) and beta dominating later. The relative power and coherence of both oscillations depend separately on multiple factors within both tasks without categorical differences across tasks. While the early/gamma-associated period occurs in all trials, rats can perform above chance without the later/beta-associated period. Longer sampling, which includes beta oscillations, is associated with better performance. Gamma followed by beta oscillations therefore represents a sequence of cognitive and neural states during odor discrimination, which can be separately modified depending on the demands of a task and odor discrimination. Additionally, fast (85 Hz) and slow (70 Hz) olfactory bulb gamma oscillation sub-bands have been hypothesized to represent tufted and mitral cell networks, respectively (Manabe and Mori, 2013). We find that fast gamma favors the early and slow gamma the later (beta-dominated) odor-sampling period and that the relative contributions of these oscillations are consistent across tasks. Olfactory system gamma (40-110 Hz) and beta (15-35 Hz) oscillations of the local field potential indicate different neural firing statistics and functional circuits. We show that gamma and beta oscillations occur in stereotyped sequence during odor sampling

Modular Neuronal Assemblies Embodied in a Closed-Loop Environment: Toward Future Integration of Brains and Machines

PubMed Central

Tessadori, Jacopo; Bisio, Marta; Martinoia, Sergio; Chiappalone, Michela

2012-01-01

Behaviors, from simple to most complex, require a two-way interaction with the environment and the contribution of different brain areas depending on the orchestrated activation of neuronal assemblies. In this work we present a new hybrid neuro-robotic architecture based on a neural controller bi-directionally connected to a virtual robot implementing a Braitenberg vehicle aimed at avoiding obstacles. The robot is characterized by proximity sensors and wheels, allowing it to navigate into a circular arena with obstacles of different sizes. As neural controller, we used hippocampal cultures dissociated from embryonic rats and kept alive over Micro Electrode Arrays (MEAs) for 3–8 weeks. The developed software architecture guarantees a bi-directional exchange of information between the natural and the artificial part by means of simple linear coding/decoding schemes. We used two different kinds of experimental preparation: “random” and “modular” populations. In the second case, the confinement was assured by a polydimethylsiloxane (PDMS) mask placed over the surface of the MEA device, thus defining two populations interconnected via specific microchannels. The main results of our study are: (i) neuronal cultures can be successfully interfaced to an artificial agent; (ii) modular networks show a different dynamics with respect to random culture, both in terms of spontaneous and evoked electrophysiological patterns; (iii) the robot performs better if a reinforcement learning paradigm (i.e., a tetanic stimulation delivered to the network following each collision) is activated, regardless of the modularity of the culture; (iv) the robot controlled by the modular network further enhances its capabilities in avoiding obstacles during the short-term plasticity trial. The developed paradigm offers a new framework for studying, in simplified model systems, neuro-artificial bi-directional interfaces for the development of new strategies for brain-machine interaction

Modular Power Standard for Space Explorations Missions

NASA Technical Reports Server (NTRS)

Oeftering, Richard C.; Gardner, Brent G.

2016-01-01

Future human space exploration will most likely be composed of assemblies of multiple modular spacecraft elements with interconnected electrical power systems. An electrical system composed of a standardized set modular building blocks provides significant development, integration, and operational cost advantages. The modular approach can also provide the flexibility to configure power systems to meet the mission needs. A primary goal of the Advanced Exploration Systems (AES) Modular Power System (AMPS) project is to establish a Modular Power Standard that is needed to realize these benefits. This paper is intended to give the space exploration community a "first look" at the evolving Modular Power Standard and invite their comments and technical contributions.

Neural entrainment to the rhythmic structure of music.

PubMed

Tierney, Adam; Kraus, Nina

2015-02-01

The neural resonance theory of musical meter explains musical beat tracking as the result of entrainment of neural oscillations to the beat frequency and its higher harmonics. This theory has gained empirical support from experiments using simple, abstract stimuli. However, to date there has been no empirical evidence for a role of neural entrainment in the perception of the beat of ecologically valid music. Here we presented participants with a single pop song with a superimposed bassoon sound. This stimulus was either lined up with the beat of the music or shifted away from the beat by 25% of the average interbeat interval. Both conditions elicited a neural response at the beat frequency. However, although the on-the-beat condition elicited a clear response at the first harmonic of the beat, this frequency was absent in the neural response to the off-the-beat condition. These results support a role for neural entrainment in tracking the metrical structure of real music and show that neural meter tracking can be disrupted by the presentation of contradictory rhythmic cues.

Modular control of varied locomotor tasks in children with incomplete spinal cord injuries

PubMed Central

Tester, Nicole J.; Kautz, Steven A.; Howland, Dena R.; Clark, David J.; Garvan, Cyndi; Behrman, Andrea L.

2013-01-01

A module is a functional unit of the nervous system that specifies functionally relevant patterns of muscle activation. In adults, four to five modules account for muscle activation during walking. Neurological injury alters modular control and is associated with walking impairments. The effect of neurological injury on modular control in children is unknown and may differ from adults due to their immature and developing nervous systems. We examined modular control of locomotor tasks in children with incomplete spinal cord injuries (ISCIs) and control children. Five controls (8.6 ± 2.7 yr of age) and five children with ISCIs (8.6 ± 3.7 yr of age performed treadmill walking, overground walking, pedaling, supine lower extremity flexion/extension, stair climbing, and crawling. Electromyograms (EMGs) were recorded in bilateral leg muscles. Nonnegative matrix factorization was applied, and the minimum number of modules required to achieve 90% of the “variance accounted for” (VAF) was calculated. On average, 3.5 modules explained muscle activation in the controls, whereas 2.4 modules were required in the children with ISCIs. To determine if control is similar across tasks, the module weightings identified from treadmill walking were used to reconstruct the EMGs from each of the other tasks. This resulted in VAF values exceeding 86% for each child and each locomotor task. Our results suggest that 1) modularity is constrained in children with ISCIs and 2) for each child, similar neural control mechanisms are used across locomotor tasks. These findings suggest that interventions that activate the neuromuscular system to enhance walking also may influence the control of other locomotor tasks. PMID:23761702

Neuronal ensemble for visual working memory via interplay of slow and fast oscillations.

PubMed

Mizuhara, Hiroaki; Yamaguchi, Yoko

2011-05-01

The current focus of studies on neural entities for memory maintenance is on the interplay between fast neuronal oscillations in the gamma band and slow oscillations in the theta or delta band. The hierarchical coupling of slow and fast oscillations is crucial for the rehearsal of sensory inputs for short-term storage, as well as for binding sensory inputs that are represented in spatially segregated cortical areas. However, no experimental evidence for the binding of spatially segregated information has yet been presented for memory maintenance in humans. In the present study, we actively manipulated memory maintenance performance with an attentional blink procedure during human scalp electroencephalography (EEG) recordings and identified that slow oscillations are enhanced when memory maintenance is successful. These slow oscillations accompanied fast oscillations in the gamma frequency range that appeared at spatially segregated scalp sites. The amplitude of the gamma oscillation at these scalp sites was simultaneously enhanced at an EEG phase of the slow oscillation. Successful memory maintenance appears to be achieved by a rehearsal of sensory inputs together with a coordination of distributed fast oscillations at a preferred timing of the slow oscillations. © 2011 The Authors. European Journal of Neuroscience © 2011 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Analysis of the characteristics of the synchronous clusters in the adaptive Kuramoto network and neural network of the epileptic brain

NASA Astrophysics Data System (ADS)

Hramov, Alexander E.; Kharchenko, Alexander A.; Makarov, Vladimir V.; Khramova, Marina V.; Koronovskii, Alexey A.; Pavlov, Alexey N.; Dana, Syamal K.

2016-04-01

In the paper we study the mechanisms of phase synchronization in the adaptive model network of Kuramoto oscillators and the neural network of brain by consideration of the integral characteristics of the observed networks signals. As the integral characteristics of the model network we consider the summary signal produced by the oscillators. Similar to the model situation we study the ECoG signal as the integral characteristic of neural network of the brain. We show that the establishment of the phase synchronization results in the increase of the peak, corresponding to synchronized oscillators, on the wavelet energy spectrum of the integral signals. The observed correlation between the phase relations of the elements and the integral characteristics of the whole network open the way to detect the size of synchronous clusters in the neural networks of the epileptic brain before and during seizure.

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.

Theory for the Emergence of Modularity in Complex Systems

NASA Astrophysics Data System (ADS)

Deem, Michael; Park, Jeong-Man

2013-03-01

Biological systems are modular, and this modularity evolves over time and in different environments. A number of observations have been made of increased modularity in biological systems under increased environmental pressure. We here develop a theory for the dynamics of modularity in these systems. We find a principle of least action for the evolved modularity at long times. In addition, we find a fluctuation dissipation relation for the rate of change of modularity at short times. We discuss a number of biological and social systems that can be understood with this framework. The modularity of the protein-protein interaction network increases when yeast are exposed to heat shock, and the modularity of the protein-protein networks in both yeast and E. coli appears to have increased over evolutionary time. Food webs in low-energy, stressful environments are more modular than those in plentiful environments, arid ecologies are more modular during droughts, and foraging of sea otters is more modular when food is limiting. The modularity of social networks changes over time: stock brokers instant messaging networks are more modular under stressful market conditions, criminal networks are more modular under increased police pressure, and world trade network modularity has decreased

GABA-B Agonist Baclofen Normalizes Auditory-Evoked Neural Oscillations and Behavioral Deficits in the Fmr1 Knockout Mouse Model of Fragile X Syndrome

PubMed Central

Featherstone, R.; Naschek, M.; Nam, J.; Du, A.; Wright, S.; Weger, R.; Akuzawa, S.

2017-01-01

Abstract Fragile X syndrome is a genetic condition resulting from FMR1 gene mutation that leads to intellectual disability, autism-like symptoms, and sensory hypersensitivity. Arbaclofen, a GABA-B agonist, has shown efficacy in some individuals with FXS but has become unavailable after unsuccessful clinical trials, prompting interest in publicly available, racemic baclofen. The present study investigated whether racemic baclofen can remediate abnormalities of neural circuit function, sensory processing, and behavior in Fmr1 knockout mice, a rodent model of fragile X syndrome. Fmr1 knockout mice showed increased baseline and auditory-evoked high-frequency gamma (30–80 Hz) power relative to C57BL/6 controls, as measured by electroencephalography. These deficits were accompanied by decreased T maze spontaneous alternation, decreased social interactions, and increased open field center time, suggestive of diminished working memory, sociability, and anxiety-like behavior, respectively. Abnormal auditory-evoked gamma oscillations, working memory, and anxiety-related behavior were normalized by treatment with baclofen, but impaired sociability was not. Improvements in working memory were evident predominantly in mice whose auditory-evoked gamma oscillations were dampened by baclofen. These findings suggest that racemic baclofen may be useful for targeting sensory and cognitive disturbances in fragile X syndrome. PMID:28451631

Low-complexity nonlinear adaptive filter based on a pipelined bilinear recurrent neural network.

PubMed

Zhao, Haiquan; Zeng, Xiangping; He, Zhengyou

2011-09-01

To reduce the computational complexity of the bilinear recurrent neural network (BLRNN), a novel low-complexity nonlinear adaptive filter with a pipelined bilinear recurrent neural network (PBLRNN) is presented in this paper. The PBLRNN, inheriting the modular architectures of the pipelined RNN proposed by Haykin and Li, comprises a number of BLRNN modules that are cascaded in a chained form. Each module is implemented by a small-scale BLRNN with internal dynamics. Since those modules of the PBLRNN can be performed simultaneously in a pipelined parallelism fashion, it would result in a significant improvement of computational efficiency. Moreover, due to nesting module, the performance of the PBLRNN can be further improved. To suit for the modular architectures, a modified adaptive amplitude real-time recurrent learning algorithm is derived on the gradient descent approach. Extensive simulations are carried out to evaluate the performance of the PBLRNN on nonlinear system identification, nonlinear channel equalization, and chaotic time series prediction. Experimental results show that the PBLRNN provides considerably better performance compared to the single BLRNN and RNN models.

Patterns recognition of electric brain activity using artificial neural networks

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Pchelintseva, S. V.; Runnova, A. E.; Hramov, A. E.

2017-04-01

An approach for the recognition of various cognitive processes in the brain activity in the perception of ambiguous images. On the basis of developed theoretical background and the experimental data, we propose a new classification of oscillating patterns in the human EEG by using an artificial neural network approach. After learning of the artificial neural network reliably identified cube recognition processes, for example, left-handed or right-oriented Necker cube with different intensity of their edges, construct an artificial neural network based on Perceptron architecture and demonstrate its effectiveness in the pattern recognition of the EEG in the experimental.

Implementing Modular A Levels.

ERIC Educational Resources Information Center

Holding, Gordon

This document, which is designed for curriculum managers at British further education (FE) colleges, presents basic information on the implementation and perceived benefits of the General Certificate of Education (GCE) modular A (Advanced) levels. The information was synthesized from a survey of 12 FE colleges that introduced the modular A levels…

Non-equilibrium physics of neural networks for leaning, memory and decision making: landscape and flux perspectives

NASA Astrophysics Data System (ADS)

Wang, Jin

Cognitive behaviors are determined by underlying neural networks. Many brain functions, such as learning and memory, can be described by attractor dynamics. We developed a theoretical framework for global dynamics by quantifying the landscape associated with the steady state probability distributions and steady state curl flux, measuring the degree of non-equilibrium through detailed balance breaking. 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. Both landscape and flux determine the kinetic paths and speed of decision making. The kinetics and global stability of decision making are explored by quantifying the landscape topography through the barrier heights and the mean first passage time. The theoretical predictions are in agreement with experimental observations: more errors occur under time pressure. We quantitatively explored two mechanisms of the speed-accuracy tradeoff with speed emphasis and further uncovered the tradeoffs among speed, accuracy, and energy cost. Our results show an optimal balance among speed, accuracy, and the energy cost in decision making. We uncovered possible mechanisms of changes of mind and how mind changes improve performance in decision processes. Our landscape approach can help facilitate an understanding of the underlying physical mechanisms of cognitive processes and identify the key elements in neural networks.

Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation

PubMed Central

Zanto, Theodore P.; van Schouwenburg, Martine R.; Gazzaley, Adam

2017-01-01

Multitasking is associated with the generation of stimulus-locked theta (4–7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13–30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement. PMID:28562642

Enhancement of multitasking performance and neural oscillations by transcranial alternating current stimulation.

PubMed

Hsu, Wan-Yu; Zanto, Theodore P; van Schouwenburg, Martine R; Gazzaley, Adam

2017-01-01

Multitasking is associated with the generation of stimulus-locked theta (4-7 Hz) oscillations arising from prefrontal cortex (PFC). Transcranial alternating current stimulation (tACS) is a non-invasive brain stimulation technique that influences endogenous brain oscillations. Here, we investigate whether applying alternating current stimulation within the theta frequency band would affect multitasking performance, and explore tACS effects on neurophysiological measures. Brief runs of bilateral PFC theta-tACS were applied while participants were engaged in a multitasking paradigm accompanied by electroencephalography (EEG) data collection. Unlike an active control group, a tACS stimulation group showed enhancement of multitasking performance after a 90-minute session (F1,35 = 6.63, p = 0.01, ηp2 = 0.16; effect size = 0.96), coupled with significant modulation of posterior beta (13-30 Hz) activities (F1,32 = 7.66, p = 0.009, ηp2 = 0.19; effect size = 0.96). Across participant regression analyses indicated that those participants with greater increases in frontal theta, alpha and beta oscillations exhibited greater multitasking performance improvements. These results indicate frontal theta-tACS generates benefits on multitasking performance accompanied by widespread neuronal oscillatory changes, and suggests that future tACS studies with extended treatments are worth exploring as promising tools for cognitive enhancement.

Oscillations contribute to memory consolidation by changing criticality and stability in the brain

NASA Astrophysics Data System (ADS)

Wu, Jiaxing; Skilling, Quinton; Ognjanovski, Nicolette; Aton, Sara; Zochowski, Michal

Oscillations are a universal feature of every level of brain dynamics and have been shown to contribute to many brain functions. To investigate the fundamental mechanism underpinning oscillatory activity, the properties of heterogeneous networks are compared in situations with and without oscillations. Our results show that both network criticality and stability are changed in the presence of oscillations. Criticality describes the network state of neuronal avalanche, a cascade of bursts of action potential firing in neural network. Stability measures how stable the spike timing relationship between neuron pairs is over time. Using a detailed spiking model, we found that the branching parameter σ changes relative to oscillation and structural network properties, corresponding to transmission among different critical states. Also, analysis of functional network structures shows that the oscillation helps to stabilize neuronal representation of memory. Further, quantitatively similar results are observed in biological data recorded in vivo. In summary, we have observed that, by regulating the neuronal firing pattern, oscillations affect both criticality and stability properties of the network, and thus contribute to memory formation.

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.

Neural network identification of aircraft nonlinear aerodynamic characteristics

NASA Astrophysics Data System (ADS)

Egorchev, M. V.; Tiumentsev, Yu V.

2018-02-01

The simulation problem for the controlled aircraft motion is considered in the case of imperfect knowledge of the modeling object and its operating conditions. The work aims to develop a class of modular semi-empirical dynamic models that combine the capabilities of theoretical and neural network modeling. We consider the use of semi-empirical neural network models for solving the problem of identifying aerodynamic characteristics of an aircraft. We also discuss the formation problem for a representative set of data characterizing the behavior of a simulated dynamic system, which is one of the critical tasks in the synthesis of ANN-models. The effectiveness of the proposed approach is demonstrated using a simulation example of the aircraft angular motion and identifying the corresponding coefficients of aerodynamic forces and moments.

Modular workcells: modern methods for laboratory automation.

PubMed

Felder, R A

1998-12-01

Laboratory automation is beginning to become an indispensable survival tool for laboratories facing difficult market competition. However, estimates suggest that only 8% of laboratories will be able to afford total laboratory automation systems. Therefore, automation vendors have developed alternative hardware configurations called 'modular automation', to fit the smaller laboratory. Modular automation consists of consolidated analyzers, integrated analyzers, modular workcells, and pre- and post-analytical automation. These terms will be defined in this paper. Using a modular automation model, the automated core laboratory will become a site where laboratory data is evaluated by trained professionals to provide diagnostic information to practising physicians. Modem software information management and process control tools will complement modular hardware. Proper standardization that will allow vendor-independent modular configurations will assure success of this revolutionary new technology.

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.

Desynchronization of slow oscillations in the basal ganglia during natural sleep.

PubMed

Mizrahi-Kliger, Aviv D; Kaplan, Alexander; Israel, Zvi; Bergman, Hagai

2018-05-01

Slow oscillations of neuronal activity alternating between firing and silence are a hallmark of slow-wave sleep (SWS). These oscillations reflect the default activity present in all mammalian species, and are ubiquitous to anesthesia, brain slice preparations, and neuronal cultures. In all these cases, neuronal firing is highly synchronous within local circuits, suggesting that oscillation-synchronization coupling may be a governing principle of sleep physiology regardless of anatomical connectivity. To investigate whether this principle applies to overall brain organization, we recorded the activity of individual neurons from basal ganglia (BG) structures and the thalamocortical (TC) network over 70 full nights of natural sleep in two vervet monkeys. During SWS, BG neurons manifested slow oscillations (∼0.5 Hz) in firing rate that were as prominent as in the TC network. However, in sharp contrast to any neural substrate explored thus far, the slow oscillations in all BG structures were completely desynchronized between individual neurons. Furthermore, whereas in the TC network single-cell spiking was locked to slow oscillations in the local field potential (LFP), the BG LFP exhibited only weak slow oscillatory activity and failed to entrain nearby cells. We thus show that synchrony is not inherent to slow oscillations, and propose that the BG desynchronization of slow oscillations could stem from its unique anatomy and functional connectivity. Finally, we posit that BG slow-oscillation desynchronization may further the reemergence of slow-oscillation traveling waves from multiple independent origins in the frontal cortex, thus significantly contributing to normal SWS.

Efficient “Communication through Coherence” Requires Oscillations Structured to Minimize Interference between Signals

PubMed Central

Akam, Thomas E.; Kullmann, Dimitri M.

2012-01-01

The ‘communication through coherence’ (CTC) hypothesis proposes that selective communication among neural networks is achieved by coherence between firing rate oscillation in a sending region and gain modulation in a receiving region. Although this hypothesis has stimulated extensive work, it remains unclear whether the mechanism can in principle allow reliable and selective information transfer. Here we use a simple mathematical model to investigate how accurately coherent gain modulation can filter a population-coded target signal from task-irrelevant distracting inputs. We show that selective communication can indeed be achieved, although the structure of oscillatory activity in the target and distracting networks must satisfy certain previously unrecognized constraints. Firstly, the target input must be differentiated from distractors by the amplitude, phase or frequency of its oscillatory modulation. When distracting inputs oscillate incoherently in the same frequency band as the target, communication accuracy is severely degraded because of varying overlap between the firing rate oscillations of distracting inputs and the gain modulation in the receiving region. Secondly, the oscillatory modulation of the target input must be strong in order to achieve a high signal-to-noise ratio relative to stochastic spiking of individual neurons. Thus, whilst providing a quantitative demonstration of the power of coherent oscillatory gain modulation to flexibly control information flow, our results identify constraints imposed by the need to avoid interference between signals, and reveal a likely organizing principle for the structure of neural oscillations in the brain. PMID:23144603

Modular High Voltage Power Supply

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

Newell, Matthew R.

The goal of this project is to develop a modular high voltage power supply that will meet the needs of safeguards applications and provide a modular plug and play supply for use with standard electronic racks.

The phase of prestimulus alpha oscillations affects tactile perception.

PubMed

Ai, Lei; Ro, Tony

2014-03-01

Previous studies have shown that neural oscillations in the 8- to 12-Hz range influence sensory perception. In the current study, we examined whether both the power and phase of these mu/alpha oscillations predict successful conscious tactile perception. Near-threshold tactile stimuli were applied to the left hand while electroencephalographic (EEG) activity was recorded over the contralateral right somatosensory cortex. We found a significant inverted U-shaped relationship between prestimulus mu/alpha power and detection rate, suggesting that there is an intermediate level of alpha power that is optimal for tactile perception. We also found a significant difference in phase angle concentration at stimulus onset that predicted whether the upcoming tactile stimulus was perceived or missed. As has been shown in the visual system, these findings suggest that these mu/alpha oscillations measured over somatosensory areas exert a strong inhibitory control on tactile perception and that pulsed inhibition by these oscillations shapes the state of brain activity necessary for conscious perception. They further suggest that these common phasic processing mechanisms across different sensory modalities and brain regions may reflect a common underlying encoding principle in perceptual processing that leads to momentary windows of perceptual awareness.

Theta Oscillations During Active Sleep Synchronize The Developing Rubro-Hippocampal Sensorimotor Network

PubMed Central

Rio-Bermudez, Carlos Del; Kim, Jangjin; Sokoloff, Greta; Blumberg, Mark S.

2017-01-01

Summary Neuronal oscillations comprise a fundamental mechanism by which distant neural structures establish and express functional connectivity. Long-range functional connectivity between the hippocampus and other forebrain structures is enabled by theta oscillations. Here we show for the first time that the infant rat red nucleus (RN)—a brainstem sensorimotor structure— exhibits theta (4-7 Hz) oscillations restricted primarily to periods of active (REM) sleep. At postnatal day (P) 8, theta is expressed as brief bursts immediately following myoclonic twitches; by P12, theta oscillations are expressed continuously across bouts of active sleep. Simultaneous recordings from the hippocampus and RN at P12 show that theta oscillations in both structures are coherent, co-modulated, and mutually interactive during active sleep. Critically, at P12, inactivation of the medial septum eliminates theta in both structures. The developmental emergence of theta-dependent functional coupling between the hippocampus and RN parallels that between the hippocampus and prefrontal cortex. Accordingly, disruptions in the early expression of theta could underlie the cognitive and sensorimotor deficits associated with neurodevelopmental disorders such as autism and schizophrenia. PMID:28479324

Dynamics of delay-coupled FitzHugh-Nagumo neural rings.

PubMed

Mao, Xiaochen; Sun, Jianqiao; Li, Shaofan

2018-01-01

This paper studies the dynamical behaviors of a pair of FitzHugh-Nagumo neural networks with bidirectional delayed couplings. It presents a detailed analysis of delay-independent and delay-dependent stabilities and the existence of bifurcated oscillations. Illustrative examples are performed to validate the analytical results and to discover interesting phenomena. It is shown that the network exhibits a variety of complicated activities, such as multiple stability switches, the coexistence of periodic and quasi-periodic oscillations, the coexistence of periodic and chaotic orbits, and the coexisting chaotic attractors.

Dynamics of delay-coupled FitzHugh-Nagumo neural rings

NASA Astrophysics Data System (ADS)

Mao, Xiaochen; Sun, Jianqiao; Li, Shaofan

2018-01-01

This paper studies the dynamical behaviors of a pair of FitzHugh-Nagumo neural networks with bidirectional delayed couplings. It presents a detailed analysis of delay-independent and delay-dependent stabilities and the existence of bifurcated oscillations. Illustrative examples are performed to validate the analytical results and to discover interesting phenomena. It is shown that the network exhibits a variety of complicated activities, such as multiple stability switches, the coexistence of periodic and quasi-periodic oscillations, the coexistence of periodic and chaotic orbits, and the coexisting chaotic attractors.

Ictal high frequency oscillations distinguish two types of seizure territories in humans

PubMed Central

Weiss, Shennan A.; Banks, Garrett P.; McKhann, Guy M.; Goodman, Robert R.; Emerson, Ronald G.; Trevelyan, Andrew J.

2013-01-01

High frequency oscillations have been proposed as a clinically useful biomarker of seizure generating sites. We used a unique set of human microelectrode array recordings (four patients, 10 seizures), in which propagating seizure wavefronts could be readily identified, to investigate the basis of ictal high frequency activity at the cortical (subdural) surface. Sustained, repetitive transient increases in high gamma (80–150 Hz) amplitude, phase-locked to the low-frequency (1–25 Hz) ictal rhythm, correlated with strong multi-unit firing bursts synchronized across the core territory of the seizure. These repetitive high frequency oscillations were seen in recordings from subdural electrodes adjacent to the microelectrode array several seconds after seizure onset, following ictal wavefront passage. Conversely, microelectrode recordings demonstrating only low-level, heterogeneous neural firing correlated with a lack of high frequency oscillations in adjacent subdural recording sites, despite the presence of a strong low-frequency signature. Previously, we reported that this pattern indicates a failure of the seizure to invade the area, because of a feedforward inhibitory veto mechanism. Because multi-unit firing rate and high gamma amplitude are closely related, high frequency oscillations can be used as a surrogate marker to distinguish the core seizure territory from the surrounding penumbra. We developed an efficient measure to detect delayed-onset, sustained ictal high frequency oscillations based on cross-frequency coupling between high gamma amplitude and the low-frequency (1–25 Hz) ictal rhythm. When applied to the broader subdural recording, this measure consistently predicted the timing or failure of ictal invasion, and revealed a surprisingly small and slowly spreading seizure core surrounded by a far larger penumbral territory. Our findings thus establish an underlying neural mechanism for delayed-onset, sustained ictal high frequency oscillations, and

Space time neural networks for tether operations in space

NASA Technical Reports Server (NTRS)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1993-01-01

A space shuttle flight scheduled for 1992 will attempt to prove the feasibility of operating tethered payloads in earth orbit. due to the interaction between the Earth's magnetic field and current pulsing through the tether, the tethered system may exhibit a circular transverse oscillation referred to as the 'skiprope' phenomenon. Effective damping of skiprope motion depends on rapid and accurate detection of skiprope magnitude and phase. Because of non-linear dynamic coupling, the satellite attitude behavior has characteristic oscillations during the skiprope motion. Since the satellite attitude motion has many other perturbations, the relationship between the skiprope parameters and attitude time history is very involved and non-linear. We propose a Space-Time Neural Network implementation for filtering satellite rate gyro data to rapidly detect and predict skiprope magnitude and phase. Training and testing of the skiprope detection system will be performed using a validated Orbital Operations Simulator and Space-Time Neural Network software developed in the Software Technology Branch at NASA's Lyndon B. Johnson Space Center.

PCSIM: A Parallel Simulation Environment for Neural Circuits Fully Integrated with Python

PubMed Central

Pecevski, Dejan; Natschläger, Thomas; Schuch, Klaus

2008-01-01

The Parallel Circuit SIMulator (PCSIM) is a software package for simulation of neural circuits. It is primarily designed for distributed simulation of large scale networks of spiking point neurons. Although its computational core is written in C++, PCSIM's primary interface is implemented in the Python programming language, which is a powerful programming environment and allows the user to easily integrate the neural circuit simulator with data analysis and visualization tools to manage the full neural modeling life cycle. The main focus of this paper is to describe PCSIM's full integration into Python and the benefits thereof. In particular we will investigate how the automatically generated bidirectional interface and PCSIM's object-oriented modular framework enable the user to adopt a hybrid modeling approach: using and extending PCSIM's functionality either employing pure Python or C++ and thus combining the advantages of both worlds. Furthermore, we describe several supplementary PCSIM packages written in pure Python and tailored towards setting up and analyzing neural simulations. PMID:19543450

Modular properties of 6d (DELL) systems

NASA Astrophysics Data System (ADS)

Aminov, G.; Mironov, A.; Morozov, A.

2017-11-01

If super-Yang-Mills theory possesses the exact conformal invariance, there is an additional modular invariance under the change of the complex bare charge [InlineMediaObject not available: see fulltext.]. The low-energy Seiberg-Witten prepotential ℱ( a), however, is not explicitly invariant, because the flat moduli also change a - → a D = ∂ℱ/∂ a. In result, the prepotential is not a modular form and depends also on the anomalous Eisenstein series E 2. This dependence is usually described by the universal MNW modular anomaly equation. We demonstrate that, in the 6 d SU( N) theory with two independent modular parameters τ and \\widehat{τ} , the modular anomaly equation changes, because the modular transform of τ is accompanied by an ( N -dependent!) shift of \\widehat{τ} and vice versa. This is a new peculiarity of double-elliptic systems, which deserves further investigation.

Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks

PubMed Central

Solanka, Lukas; van Rossum, Mark CW; Nolan, Matthew F

2015-01-01

Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength. DOI: http://dx.doi.org/10.7554/eLife.06444.001 PMID:26146940

Center for Neural Engineering: applications of pulse-coupled neural networks

NASA Astrophysics Data System (ADS)

Malkani, Mohan; Bodruzzaman, Mohammad; Johnson, John L.; Davis, Joel

1999-03-01

Pulsed-Coupled Neural Network (PCNN) is an oscillatory model neural network where grouping of cells and grouping among the groups that form the output time series (number of cells that fires in each input presentation also called `icon'). This is based on the synchronicity of oscillations. Recent work by Johnson and others demonstrated the functional capabilities of networks containing such elements for invariant feature extraction using intensity maps. PCNN thus presents itself as a more biologically plausible model with solid functional potential. This paper will present the summary of several projects and their results where we successfully applied PCNN. In project one, the PCNN was applied for object recognition and classification through a robotic vision system. The features (icons) generated by the PCNN were then fed into a feedforward neural network for classification. In project two, we developed techniques for sensory data fusion. The PCNN algorithm was implemented and tested on a B14 mobile robot. The PCNN-based features were extracted from the images taken from the robot vision system and used in conjunction with the map generated by data fusion of the sonar and wheel encoder data for the navigation of the mobile robot. In our third project, we applied the PCNN for speaker recognition. The spectrogram image of speech signals are fed into the PCNN to produce invariant feature icons which are then fed into a feedforward neural network for speaker identification.

The Role of Prefrontal Dopamine D1 Receptors in the Neural Mechanisms of Associative Learning

PubMed Central

Puig, M. Victoria; Miller, Earl K.

2013-01-01

Summary Dopamine is thought to play a major role in learning. However, while dopamine D1 receptors (D1Rs) in the prefrontal cortex (PFC) have been shown to modulate working memory-related neural activity, their role in the cellular basis of learning is unknown. We recorded activity from multiple electrodes while injecting the D1R antagonist SCH23390 in the lateral PFC as monkeys learned visuomotor associations. Blocking D1Rs impaired learning of novel associations and decreased cognitive flexibility, but spared performance of already familiar associations. This suggests a greater role for prefrontal D1Rs in learning new, than performing familiar, associations. There was a corresponding greater decrease in neural selectivity and increase in alpha and beta oscillations in local field potentials for novel than familiar associations. Our results suggest that weak stimulation of D1Rs observed in aging and psychiatric disorders may impair learning and PFC function by reducing neural selectivity and exacerbating neural oscillations associated with inattention and cognitive deficits. PMID:22681691

Oscillating water column structural model

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

Copeland, Guild; Bull, Diana L; Jepsen, Richard Alan

2014-09-01

An oscillating water column (OWC) wave energy converter is a structure with an opening to the ocean below the free surface, i.e. a structure with a moonpool. Two structural models for a non-axisymmetric terminator design OWC, the Backward Bent Duct Buoy (BBDB) are discussed in this report. The results of this structural model design study are intended to inform experiments and modeling underway in support of the U.S. Department of Energy (DOE) initiated Reference Model Project (RMP). A detailed design developed by Re Vision Consulting used stiffeners and girders to stabilize the structure against the hydrostatic loads experienced by amore » BBDB device. Additional support plates were added to this structure to account for loads arising from the mooring line attachment points. A simplified structure was designed in a modular fashion. This simplified design allows easy alterations to the buoyancy chambers and uncomplicated analysis of resulting changes in buoyancy.« less

Scale-Free Neural and Physiological Dynamics in Naturalistic Stimuli Processing

PubMed Central

Lin, Amy

2016-01-01

Abstract Neural activity recorded at multiple spatiotemporal scales is dominated by arrhythmic fluctuations without a characteristic temporal periodicity. Such activity often exhibits a 1/f-type power spectrum, in which power falls off with increasing frequency following a power-law function: P(f)∝1/fβ, which is indicative of scale-free dynamics. Two extensively studied forms of scale-free neural dynamics in the human brain are slow cortical potentials (SCPs)—the low-frequency (<5 Hz) component of brain field potentials—and the amplitude fluctuations of α oscillations, both of which have been shown to carry important functional roles. In addition, scale-free dynamics characterize normal human physiology such as heartbeat dynamics. However, the exact relationships among these scale-free neural and physiological dynamics remain unclear. We recorded simultaneous magnetoencephalography and electrocardiography in healthy subjects in the resting state and while performing a discrimination task on scale-free dynamical auditory stimuli that followed different scale-free statistics. We observed that long-range temporal correlation (captured by the power-law exponent β) in SCPs positively correlated with that of heartbeat dynamics across time within an individual and negatively correlated with that of α-amplitude fluctuations across individuals. In addition, across individuals, long-range temporal correlation of both SCP and α-oscillation amplitude predicted subjects’ discrimination performance in the auditory task, albeit through antagonistic relationships. These findings reveal interrelations among different scale-free neural and physiological dynamics and initial evidence for the involvement of scale-free neural dynamics in the processing of natural stimuli, which often exhibit scale-free dynamics. PMID:27822495

Adaptive multi-resolution Modularity for detecting communities in networks

NASA Astrophysics Data System (ADS)

Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

2018-02-01

Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

Modular thought in the circuit analysis

NASA Astrophysics Data System (ADS)

Wang, Feng

2018-04-01

Applied to solve the problem of modular thought, provides a whole for simplification's method, the complex problems have become of, and the study of circuit is similar to the above problems: the complex connection between components, make the whole circuit topic solution seems to be more complex, and actually components the connection between the have rules to follow, this article mainly tells the story of study on the application of the circuit modular thought. First of all, this paper introduces the definition of two-terminal network and the concept of two-terminal network equivalent conversion, then summarizes the common source resistance hybrid network modular approach, containing controlled source network modular processing method, lists the common module, typical examples analysis.

A novel joint-processing adaptive nonlinear equalizer using a modular recurrent neural network for chaotic communication systems.

PubMed

Zhao, Haiquan; Zeng, Xiangping; Zhang, Jiashu; Liu, Yangguang; Wang, Xiaomin; Li, Tianrui

2011-01-01

To eliminate nonlinear channel distortion in chaotic communication systems, a novel joint-processing adaptive nonlinear equalizer based on a pipelined recurrent neural network (JPRNN) is proposed, using a modified real-time recurrent learning (RTRL) algorithm. Furthermore, an adaptive amplitude RTRL algorithm is adopted to overcome the deteriorating effect introduced by the nesting process. Computer simulations illustrate that the proposed equalizer outperforms the pipelined recurrent neural network (PRNN) and recurrent neural network (RNN) equalizers. Copyright © 2010 Elsevier Ltd. All rights reserved.

Modular optical detector system

DOEpatents

Horn, Brent A [Livermore, CA; Renzi, Ronald F [Tracy, CA

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.

Decoding and Reconstructing the Focus of Spatial Attention from the Topography of Alpha-band Oscillations.

PubMed

Samaha, Jason; Sprague, Thomas C; Postle, Bradley R

2016-08-01

Many aspects of perception and cognition are supported by activity in neural populations that are tuned to different stimulus features (e.g., orientation, spatial location, color). Goal-directed behavior, such as sustained attention, requires a mechanism for the selective prioritization of contextually appropriate representations. A candidate mechanism of sustained spatial attention is neural activity in the alpha band (8-13 Hz), whose power in the human EEG covaries with the focus of covert attention. Here, we applied an inverted encoding model to assess whether spatially selective neural responses could be recovered from the topography of alpha-band oscillations during spatial attention. Participants were cued to covertly attend to one of six spatial locations arranged concentrically around fixation while EEG was recorded. A linear classifier applied to EEG data during sustained attention demonstrated successful classification of the attended location from the topography of alpha power, although not from other frequency bands. We next sought to reconstruct the focus of spatial attention over time by applying inverted encoding models to the topography of alpha power and phase. Alpha power, but not phase, allowed for robust reconstructions of the specific attended location beginning around 450 msec postcue, an onset earlier than previous reports. These results demonstrate that posterior alpha-band oscillations can be used to track activity in feature-selective neural populations with high temporal precision during the deployment of covert spatial attention.

Neural synchronization as a hypothetical explanation of the psychoanalytic unconscious.

PubMed

Ceylan, Mehmet Emin; Dönmez, Aslıhan; Ünsalver, Barış Önen; Evrensel, Alper

2016-02-01

Cognitive scientists have tried to explain the neural mechanisms of unconscious mental states such as coma, epileptic seizures, and anesthesia-induced unconsciousness. However these types of unconscious states are different from the psychoanalytic unconscious. In this review, we aim to present our hypothesis about the neural correlates underlying psychoanalytic unconscious. To fulfill this aim, we firstly review the previous explanations about the neural correlates of conscious and unconscious mental states, such as brain oscillations, synchronicity of neural networks, and cognitive binding. By doing so, we hope to lay a neuroscientific ground for our hypothesis about neural correlates of psychoanalytic unconscious; parallel but unsynchronized neural networks between different layers of consciousness and unconsciousness. Next, we propose a neuroscientific mechanism about how the repressed mental events reach the conscious awareness; the lock of neural synchronization between two mental layers of conscious and unconscious. At the last section, we will discuss the data about schizophrenia as a clinical example of our proposed hypothesis. Copyright © 2015 Elsevier Inc. All rights reserved.

Breakdown of the brain’s functional network modularity with awareness

PubMed Central

Godwin, Douglass; Barry, Robert L.; Marois, René

2015-01-01

Neurobiological theories of awareness propose divergent accounts of the spatial extent of brain changes that support conscious perception. Whereas focal theories posit mostly local regional changes, global theories propose that awareness emerges from the propagation of neural signals across a broad extent of sensory and association cortex. Here we tested the scalar extent of brain changes associated with awareness using graph theoretical analysis applied to functional connectivity data acquired at ultra-high field while subjects performed a simple masked target detection task. We found that awareness of a visual target is associated with a degradation of the modularity of the brain’s functional networks brought about by an increase in intermodular functional connectivity. These results provide compelling evidence that awareness is associated with truly global changes in the brain’s functional connectivity. PMID:25759440

Modularity-like objective function in annotated networks

NASA Astrophysics Data System (ADS)

Xie, Jia-Rong; Wang, Bing-Hong

2017-12-01

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

Resonant Interneurons Can Increase Robustness of Gamma Oscillations.

PubMed

Tikidji-Hamburyan, Ruben A; Martínez, Joan José; White, John A; Canavier, Carmen C

2015-11-25

Gamma oscillations are believed to play a critical role in in information processing, encoding, and retrieval. Inhibitory interneuronal network gamma (ING) oscillations may arise from a coupled oscillator mechanism in which individual neurons oscillate or from a population oscillator in which individual neurons fire sparsely and stochastically. All ING mechanisms, including the one proposed herein, rely on alternating waves of inhibition and windows of opportunity for spiking. The coupled oscillator model implemented with Wang-Buzsáki model neurons is not sufficiently robust to heterogeneity in excitatory drive, and therefore intrinsic frequency, to account for in vitro models of ING. Similarly, in a tightly synchronized regime, the stochastic population oscillator model is often characterized by sparse firing, whereas interneurons both in vivo and in vitro do not fire sparsely during gamma, but rather on average every other cycle. We substituted so-called resonator neural models, which exhibit class 2 excitability and postinhibitory rebound (PIR), for the integrators that are typically used. This results in much greater robustness to heterogeneity that actually increases as the average participation in spikes per cycle approximates physiological levels. Moreover, dynamic clamp experiments that show autapse-induced firing in entorhinal cortical interneurons support the idea that PIR can serve as a network gamma mechanism. Furthermore, parvalbumin-positive (PV(+)) cells were much more likely to display both PIR and autapse-induced firing than GAD2(+) cells, supporting the view that PV(+) fast-firing basket cells are more likely to exhibit class 2 excitability than other types of inhibitory interneurons. Gamma oscillations are believed to play a critical role in information processing, encoding, and retrieval. Networks of inhibitory interneurons are thought to be essential for these oscillations. We show that one class of interneurons with an abrupt onset of firing

On the classification of weakly integral modular categories

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

Bruillard, Paul; Galindo, César; Ng, Siu-Hung

In this paper we classify all modular categories of dimension 4m, where m is an odd square-free integer, and all rank 6 and rank 7 weakly integral modular categories. This completes the classification of weakly integral modular categories through rank 7. In particular, our results imply that all integral modular categories of rank at most 7 are pointed (that is, every simple object has dimension 1). All the non-integral (but weakly integral) modular categories of ranks 6 and 7 have dimension 4m, with m an odd square free integer, so their classification is an application of our main result. Themore » classification of rank 7 integral modular categories is facilitated by an analysis of the two group actions on modular categories: the Galois group of the field generated by the entries of the S-matrix and the group of invertible isomorphism classes of objects. We derive some valuable arithmetic consequences from these actions.« less

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

PubMed Central

Stevens, Alexander A.; Tappon, Sarah C.; Garg, Arun; Fair, Damien A.

2012-01-01

Background Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Methodology/Principal Findings Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. Conclusions/Significance The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules

GABA level, gamma oscillation, and working memory performance in schizophrenia

PubMed Central

Chen, Chi-Ming A.; Stanford, Arielle D.; Mao, Xiangling; Abi-Dargham, Anissa; Shungu, Dikoma C.; Lisanby, Sarah H.; Schroeder, Charles E.; Kegeles, Lawrence S.

2014-01-01

A relationship between working memory impairment, disordered neuronal oscillations, and abnormal prefrontal GABA function has been hypothesized in schizophrenia; however, in vivo GABA measurements and gamma band neural synchrony have not yet been compared in schizophrenia. This case–control pilot study (N = 24) compared baseline and working memory task-induced neuronal oscillations acquired with high-density electroencephalograms (EEGs) to GABA levels measured in vivo with magnetic resonance spectroscopy. Working memory performance, baseline GABA level in the left dorsolateral prefrontal cortex (DLPFC), and measures of gamma oscillations from EEGs at baseline and during a working memory task were obtained. A major limitation of this study is a relatively small sample size for several analyses due to the integration of diverse methodologies and participant compliance. Working memory performance was significantly lower for patients than for controls. During the working memory task, patients (n = 7) had significantly lower amplitudes in gamma oscillations than controls (n = 9). However, both at rest and across working memory stages, there were significant correlations between gamma oscillation amplitude and left DLPFC GABA level. Peak gamma frequency during the encoding stage of the working memory task (n = 16) significantly correlated with GABA level and working memory performance. Despite gamma band amplitude deficits in patients across working memory stages, both baseline and working memory-induced gamma oscillations showed strong dependence on baseline GABA levels in patients and controls. These findings suggest a critical role for GABA function in gamma band oscillations, even under conditions of system and cognitive impairments as seen in schizophrenia. PMID:24749063

GABA level, gamma oscillation, and working memory performance in schizophrenia.

PubMed

Chen, Chi-Ming A; Stanford, Arielle D; Mao, Xiangling; Abi-Dargham, Anissa; Shungu, Dikoma C; Lisanby, Sarah H; Schroeder, Charles E; Kegeles, Lawrence S

2014-01-01

A relationship between working memory impairment, disordered neuronal oscillations, and abnormal prefrontal GABA function has been hypothesized in schizophrenia; however, in vivo GABA measurements and gamma band neural synchrony have not yet been compared in schizophrenia. This case-control pilot study (N = 24) compared baseline and working memory task-induced neuronal oscillations acquired with high-density electroencephalograms (EEGs) to GABA levels measured in vivo with magnetic resonance spectroscopy. Working memory performance, baseline GABA level in the left dorsolateral prefrontal cortex (DLPFC), and measures of gamma oscillations from EEGs at baseline and during a working memory task were obtained. A major limitation of this study is a relatively small sample size for several analyses due to the integration of diverse methodologies and participant compliance. Working memory performance was significantly lower for patients than for controls. During the working memory task, patients (n = 7) had significantly lower amplitudes in gamma oscillations than controls (n = 9). However, both at rest and across working memory stages, there were significant correlations between gamma oscillation amplitude and left DLPFC GABA level. Peak gamma frequency during the encoding stage of the working memory task (n = 16) significantly correlated with GABA level and working memory performance. Despite gamma band amplitude deficits in patients across working memory stages, both baseline and working memory-induced gamma oscillations showed strong dependence on baseline GABA levels in patients and controls. These findings suggest a critical role for GABA function in gamma band oscillations, even under conditions of system and cognitive impairments as seen in schizophrenia.

Neural Crest Origins of the Neck and Shoulder

PubMed Central

Matsuoka, Toshiyuki; Ahlberg, Per E.; Kessaris, Nicoletta; Iannarelli, Palma; Dennehy, Ulla; Richardson, William D.; McMahon, Andrew P.; Koentges, Georgy

2005-01-01

Summary The neck and shoulder region of vertebrates has undergone a complex evolutionary history. In order to identify its underlying mechanisms we map the destinations of embryonic neural crest and mesodermal stem cells using novel Cre-recombinase mediated transgenesis. The single-cell resolution of this genetic labelling reveals cryptic cell boundaries traversing seemingly homogeneous skeleton of neck and shoulders. Within this complex assembly of bones and muscles we discern a precise code of connectivity that mesenchymal stem cells of neural crest and mesodermal origin both obey as they form muscle scaffolds. Neural crest anchors the head onto the anterior lining of the shoulder girdle, while a Hox gene controlled mesoderm links trunk muscles to the posterior neck and shoulder skeleton. The skeleton that we identify as neural crest is specifically affected in human Klippel-Feil syndrome, Sprengel’s deformity and Arnold-Chiari I/II malformation, providing first insights into their likely aetiology. We identify genes involved in the cellular modularity of neck and shoulder skeleton and propose a new methodology for determining skeletal homologies that is based on muscle attachments. This has allowed us to trace the whereabouts of the cleithrum, the major shoulder bone of extinct land vertebrate ancestors which appears to survive as the scapular spine in living mammals. PMID:16034409

A Formal Theory for Modular ERDF Ontologies

NASA Astrophysics Data System (ADS)

Analyti, Anastasia; Antoniou, Grigoris; Damásio, Carlos Viegas

The success of the Semantic Web is impossible without any form of modularity, encapsulation, and access control. In an earlier paper, we extended RDF graphs with weak and strong negation, as well as derivation rules. The ERDF #n-stable model semantics of the extended RDF framework (ERDF) is defined, extending RDF(S) semantics. In this paper, we propose a framework for modular ERDF ontologies, called modular ERDF framework, which enables collaborative reasoning over a set of ERDF ontologies, while support for hidden knowledge is also provided. In particular, the modular ERDF stable model semantics of modular ERDF ontologies is defined, extending the ERDF #n-stable model semantics. Our proposed framework supports local semantics and different points of view, local closed-world and open-world assumptions, and scoped negation-as-failure. Several complexity results are provided.

Selective attention on representations in working memory: cognitive and neural mechanisms.

PubMed

Ku, Yixuan

2018-01-01

Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory.

Localization of beta and high-frequency oscillations within the subthalamic nucleus region.

PubMed

van Wijk, B C M; Pogosyan, A; Hariz, M I; Akram, H; Foltynie, T; Limousin, P; Horn, A; Ewert, S; Brown, P; Litvak, V

2017-01-01

Parkinsonian bradykinesia and rigidity are typically associated with excessive beta band oscillations in the subthalamic nucleus. Recently another spectral peak has been identified that might be implicated in the pathophysiology of the disease: high-frequency oscillations (HFO) within the 150-400 Hz range. Beta-HFO phase-amplitude coupling (PAC) has been found to correlate with severity of motor impairment. However, the neuronal origin of HFO and its usefulness as a potential target for deep brain stimulation remain to be established. For example, it is unclear whether HFO arise from the same neural populations as beta oscillations. We intraoperatively recorded local field potentials from the subthalamic nucleus while advancing DBS electrodes in 2 mm steps from 4 mm above the surgical target point until 2 mm below, resulting in 4 recording sites. Data from 26 nuclei from 14 patients were analysed. For each trajectory, we identified the recording site with the largest spectral peak in the beta range (13-30 Hz), and the largest peak in the HFO range separately. In addition, we identified the recording site with the largest beta-HFO PAC. Recording sites with largest beta power and largest HFO power coincided in 50% of cases. In the other 50%, HFO was more likely to be detected at a more superior recording site in the target area. PAC followed more closely the site with largest HFO (45%) than beta power (27%). HFO are likely to arise from spatially close, but slightly more superior neural populations than beta oscillations. Further work is necessary to determine whether the different activities can help fine-tune deep brain stimulation targeting.

Network recruitment to coherent oscillations in a hippocampal computer model

PubMed Central

Krieger, Abba; Litt, Brian

2011-01-01

Coherent neural oscillations represent transient synchronization of local neuronal populations in both normal and pathological brain activity. These oscillations occur at or above gamma frequencies (>30 Hz) and often are propagated to neighboring tissue under circumstances that are both normal and abnormal, such as gamma binding or seizures. The mechanisms that generate and propagate these oscillations are poorly understood. In the present study we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of stochastic resonance and coherence resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. “Epileptic” high-frequency oscillations (HFOs), produced by pathologies such as increased synaptic activity and recurrent connections, were superior at recruiting neighboring tissue. “Normal” HFOs, associated with fast firing of inhibitory cells and sparse pyramidal cell firing, tended to suppress surrounding cells and showed very limited ability to recruit. These findings point to synaptic noise and physiological coupling as important targets for understanding the generation and propagation of both normal and pathological HFOs, suggesting potential new diagnostic and therapeutic approaches to human disorders such as epilepsy. PMID:21273309

Modular health services: a single case study approach to the applicability of modularity to residential mental healthcare

PubMed Central

2014-01-01

Background The Dutch mental healthcare sector has to decrease costs by reducing intramural capacity with one third by 2020 and treating more patients in outpatient care. This transition necessitates enabling patients to become as self-supporting as possible, by customising the residential care they receive to their needs for self-development. Theoretically, modularity might help mental healthcare institutions with this. Modularity entails the decomposition of a healthcare service in parts that can be mixed-and-matched in a variety of ways, and combined form a functional whole. It brings about easier and better configuration, increased transparency and more variety without increasing costs. Aim: this study aims to explore the applicability of the modularity concept to the residential care provided in Assisted Living Facilities (ALFs) of Dutch mental healthcare institutions. Methods A single case study is carried out at the centre for psychosis in Etten-Leur, part of the GGz Breburg IMPACT care group. The design enables in-depth analysis of a case in a specific context. This is considered appropriate since theory concerning healthcare modularity is in an early stage of development. The present study can be considered a pilot case. Data were gathered by means of interviews, observations and documentary analysis. Results At the centre for psychosis, the majority of the residential care can be decomposed in modules, which can be grouped in service bundles and sub-bundles; the service customisation process is sufficiently fit to apply modular thinking; and interfaces for most of the categories are present. Hence, the prerequisites for modular residential care offerings are already largely fulfilled. For not yet fulfilled aspects of these prerequisites, remedies are available. Conclusion The modularity concept seems applicable to the residential care offered by the ALF of the mental healthcare institution under study. For a successful implementation of modularity however

Modular health services: a single case study approach to the applicability of modularity to residential mental healthcare.

PubMed

Soffers, Rutger; Meijboom, Bert; van Zaanen, Jos; van der Feltz-Cornelis, Christina

2014-05-09

The Dutch mental healthcare sector has to decrease costs by reducing intramural capacity with one third by 2020 and treating more patients in outpatient care. This transition necessitates enabling patients to become as self-supporting as possible, by customising the residential care they receive to their needs for self-development. Theoretically, modularity might help mental healthcare institutions with this. Modularity entails the decomposition of a healthcare service in parts that can be mixed-and-matched in a variety of ways, and combined form a functional whole. It brings about easier and better configuration, increased transparency and more variety without increasing costs. this study aims to explore the applicability of the modularity concept to the residential care provided in Assisted Living Facilities (ALFs) of Dutch mental healthcare institutions. A single case study is carried out at the centre for psychosis in Etten-Leur, part of the GGz Breburg IMPACT care group. The design enables in-depth analysis of a case in a specific context. This is considered appropriate since theory concerning healthcare modularity is in an early stage of development. The present study can be considered a pilot case. Data were gathered by means of interviews, observations and documentary analysis. At the centre for psychosis, the majority of the residential care can be decomposed in modules, which can be grouped in service bundles and sub-bundles; the service customisation process is sufficiently fit to apply modular thinking; and interfaces for most of the categories are present. Hence, the prerequisites for modular residential care offerings are already largely fulfilled. For not yet fulfilled aspects of these prerequisites, remedies are available. The modularity concept seems applicable to the residential care offered by the ALF of the mental healthcare institution under study. For a successful implementation of modularity however, some steps should be taken by the ALF

Astrocytes contribute to gamma oscillations and recognition memory.

PubMed

Lee, Hosuk Sean; Ghetti, Andrea; Pinto-Duarte, António; Wang, Xin; Dziewczapolski, Gustavo; Galimi, Francesco; Huitron-Resendiz, Salvador; Piña-Crespo, Juan C; Roberts, Amanda J; Verma, Inder M; Sejnowski, Terrence J; Heinemann, Stephen F

2014-08-12

Glial cells are an integral part of functional communication in the brain. Here we show that astrocytes contribute to the fast dynamics of neural circuits that underlie normal cognitive behaviors. In particular, we found that the selective expression of tetanus neurotoxin (TeNT) in astrocytes significantly reduced the duration of carbachol-induced gamma oscillations in hippocampal slices. These data prompted us to develop a novel transgenic mouse model, specifically with inducible tetanus toxin expression in astrocytes. In this in vivo model, we found evidence of a marked decrease in electroencephalographic (EEG) power in the gamma frequency range in awake-behaving mice, whereas neuronal synaptic activity remained intact. The reduction in cortical gamma oscillations was accompanied by impaired behavioral performance in the novel object recognition test, whereas other forms of memory, including working memory and fear conditioning, remained unchanged. These results support a key role for gamma oscillations in recognition memory. Both EEG alterations and behavioral deficits in novel object recognition were reversed by suppression of tetanus toxin expression. These data reveal an unexpected role for astrocytes as essential contributors to information processing and cognitive behavior.

Modular Courses in British Higher Education: A Critical Assessment

ERIC Educational Resources Information Center

Church, Clive

1975-01-01

The trends towards modular course structures is examined. British conceptions of modularization are compared with American interpretations of modular instruction, the former shown to be concerned almost exclusively with content, the latter attempting more radical changes in students' learning behavior. Rationales for British modular schemes are…

An Intrinsic Role of Beta Oscillations in Memory for Time Estimation.

PubMed

Wiener, Martin; Parikh, Alomi; Krakow, Arielle; Coslett, H Branch

2018-05-22

The neural mechanisms underlying time perception are of vital importance to a comprehensive understanding of behavior and cognition. Recent work has suggested a supramodal role for beta oscillations in measuring temporal intervals. However, the precise function of beta oscillations and whether their manipulation alters timing has yet to be determined. To accomplish this, we first re-analyzed two, separate EEG datasets and demonstrate that beta oscillations are associated with the retention and comparison of a memory standard for duration. We next conducted a study of 20 human participants using transcranial alternating current stimulation (tACS), over frontocentral cortex, at alpha and beta frequencies, during a visual temporal bisection task, finding that beta stimulation exclusively shifts the perception of time such that stimuli are reported as longer in duration. Finally, we decomposed trialwise choice data with a drift diffusion model of timing, revealing that the shift in timing is caused by a change in the starting point of accumulation, rather than the drift rate or threshold. Our results provide evidence for the intrinsic involvement of beta oscillations in the perception of time, and point to a specific role for beta oscillations in the encoding and retention of memory for temporal intervals.

Serial, Covert, Shifts of Attention during Visual Search are Reflected by the Frontal Eye Fields and Correlated with Population Oscillations

PubMed Central

Buschman, Timothy J.; Miller, Earl K.

2009-01-01

Attention regulates the flood of sensory information into a manageable stream, and so understanding how attention is controlled is central to understanding cognition. Competing theories suggest visual search involves serial and/or parallel allocation of attention, but there is little direct, neural, evidence for either mechanism. Two monkeys were trained to covertly search an array for a target stimulus under visual search (endogenous) and pop-out (exogenous) conditions. Here we present neural evidence in the frontal eye fields (FEF) for serial, covert shifts of attention during search but not pop-out. Furthermore, attention shifts reflected in FEF spiking activity were correlated with 18–34 Hz oscillations in the local field potential, suggesting a ‘clocking’ signal. This provides direct neural evidence that primates can spontaneously adopt a serial search strategy and that these serial covert shifts of attention are directed by the FEF. It also suggests that neuron population oscillations may regulate the timing of cognitive processing. PMID:19679077

The Modular need for the Division Signal Battalion

DTIC Science & Technology

2017-06-09

findings and analyzes them to expand on them. It is with these findings and subsequent analysis that the case studies shape the answer to the three...These case studies focus on the signal leadership development and how it occurred in the pre-modular force structure, during modularity, and the...the comparative case study research. The case studies focus on signal leader development in a pre-modular signal force, a modular signal force, and

Auditory priming improves neural synchronization in auditory-motor entrainment.

PubMed

Crasta, Jewel E; Thaut, Michael H; Anderson, Charles W; Davies, Patricia L; Gavin, William J

2018-05-22

Neurophysiological research has shown that auditory and motor systems interact during movement to rhythmic auditory stimuli through a process called entrainment. This study explores the neural oscillations underlying auditory-motor entrainment using electroencephalography. Forty young adults were randomly assigned to one of two control conditions, an auditory-only condition or a motor-only condition, prior to a rhythmic auditory-motor synchronization condition (referred to as combined condition). Participants assigned to the auditory-only condition auditory-first group) listened to 400 trials of auditory stimuli presented every 800 ms, while those in the motor-only condition (motor-first group) were asked to tap rhythmically every 800 ms without any external stimuli. Following their control condition, all participants completed an auditory-motor combined condition that required tapping along with auditory stimuli every 800 ms. As expected, the neural processes for the combined condition for each group were different compared to their respective control condition. Time-frequency analysis of total power at an electrode site on the left central scalp (C3) indicated that the neural oscillations elicited by auditory stimuli, especially in the beta and gamma range, drove the auditory-motor entrainment. For the combined condition, the auditory-first group had significantly lower evoked power for a region of interest representing sensorimotor processing (4-20 Hz) and less total power in a region associated with anticipation and predictive timing (13-16 Hz) than the motor-first group. Thus, the auditory-only condition served as a priming facilitator of the neural processes in the combined condition, more so than the motor-only condition. Results suggest that even brief periods of rhythmic training of the auditory system leads to neural efficiency facilitating the motor system during the process of entrainment. These findings have implications for interventions

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.

Towards a Formal Basis for Modular Safety Cases

NASA Technical Reports Server (NTRS)

Denney, Ewen; Pai, Ganesh

2015-01-01

Safety assurance using argument-based safety cases is an accepted best-practice in many safety-critical sectors. Goal Structuring Notation (GSN), which is widely used for presenting safety arguments graphically, provides a notion of modular arguments to support the goal of incremental certification. Despite the efforts at standardization, GSN remains an informal notation whereas the GSN standard contains appreciable ambiguity especially concerning modular extensions. This, in turn, presents challenges when developing tools and methods to intelligently manipulate modular GSN arguments. This paper develops the elements of a theory of modular safety cases, leveraging our previous work on formalizing GSN arguments. Using example argument structures we highlight some ambiguities arising through the existing guidance, present the intuition underlying the theory, clarify syntax, and address modular arguments, contracts, well-formedness and well-scopedness of modules. Based on this theory, we have a preliminary implementation of modular arguments in our toolset, AdvoCATE.

Diurnal influences on electrophysiological oscillations and coupling in the dorsal striatum and cerebellar cortex of the anesthetized rat

PubMed Central

Frederick, Ariana; Bourget-Murray, Jonathan; Chapman, C. Andrew; Amir, Shimon; Courtemanche, Richard

2014-01-01

Circadian rhythms modulate behavioral processes over a 24 h period through clock gene expression. What is largely unknown is how these molecular influences shape neural activity in different brain areas. The clock gene Per2 is rhythmically expressed in the striatum and the cerebellum and its expression is linked with daily fluctuations in extracellular dopamine levels and D2 receptor activity. Electrophysiologically, dopamine depletion enhances striatal local field potential (LFP) oscillations. We investigated if LFP oscillations and synchrony were influenced by time of day, potentially via dopamine mechanisms. To assess the presence of a diurnal effect, oscillatory power and coherence were examined in the striatum and cerebellum of rats under urethane anesthesia at four different times of day zeitgeber time (ZT1, 7, 13 and 19—indicating number of hours after lights turned on in a 12:12 h light-dark cycle). We also investigated the diurnal response to systemic raclopride, a D2 receptor antagonist. Time of day affected the proportion of LFP oscillations within the 0–3 Hz band and the 3–8 Hz band. In both the striatum and the cerebellum, slow oscillations were strongest at ZT1 and weakest at ZT13. A 3–8 Hz oscillation was present when the slow oscillation was lowest, with peak 3–8 Hz activity occurring at ZT13. Raclopride enhanced the slow oscillations, and had the greatest effect at ZT13. Within the striatum and with the cerebellum, 0–3 Hz coherence was greatest at ZT1, when the slow oscillations were strongest. Coherence was also affected the most by raclopride at ZT13. Our results suggest that neural oscillations in the cerebellum and striatum, and the synchrony between these areas, are modulated by time of day, and that these changes are influenced by dopamine manipulation. This may provide insight into how circadian gene transcription patterns influence network electrophysiology. Future experiments will address how these network alterations are

Modular standards for emerging avionics technologies

NASA Astrophysics Data System (ADS)

Radcliffe, B.; Boaz, J.

The present investigation is concerned with modular standards for the integration of new avionics technologies into production aircraft, taking into account also major retrofit programs. It is pointed out that avionics systems are about to undergo drastic changes in the partitioning of functions and judicious sharing of resources. These changes have the potential to significantly improve reliability and maintainability, and to reduce costs. Attention is given to a definition of the modular avionics concept, the existing module program, the development approach, development progress on the modular avionics standard, and the future of avionics installation standards.

Frontal and occipital-parietal alpha oscillations distinguish between stimulus conflict and response conflict

PubMed Central

Tang, Dandan; Hu, Li; Lei, Yi; Li, Hong; Chen, Antao

2015-01-01

Conflicts between target and distraction can occur at the level of both stimulus and response processing. However, the neural oscillations underlying occurrence of the interference in different levels have not been understood well. Here, we reveal such a neural oscillation modulation by combining a 4:2 mapping design (two targets are mapped into one response key) with a practice paradigm (pretest, practice, and posttest) when healthy human participants were performing a novel color-word flanker task. Response time (RT) results revealed constant stimulus conflict (SC, stimulus incongruent minus congruent, SI-CO) but increased response conflict (RC, response incongruent minus stimulus incongruent, RI-SI) with practice. Event-related potential (ERP) results demonstrated stable P3 amplitude differences for the SI-CO in the centro-parietal region across practice, which may reflect maintenance of the stimulus processing; and significantly larger P3 amplitudes in the same region for the RI relative to SI trial type in posttest, which may reflect inhibition of the distraction response. Further, neural oscillatory results showed that with practice, the lower alpha band in the frontal region and the upper alpha band in the occipital-parietal region distinguished between stimulus- and response-conflicts, respectively, suggesting that practice reduces the alertness (sensitiveness) of the brain to conflict occurrence, and enhances stimulus-response associations. PMID:26300758

ERPs and oscillations during encoding predict retrieval of digit memory in superior mnemonists.

PubMed

Pan, Yafeng; Li, Xianchun; Chen, Xi; Ku, Yixuan; Dong, Yujie; Dou, Zheng; He, Lin; Hu, Yi; Li, Weidong; Zhou, Xiaolin

2017-10-01

Previous studies have consistently demonstrated that superior mnemonists (SMs) outperform normal individuals in domain-specific memory tasks. However, the neural correlates of memory-related processes remain unclear. In the current EEG study, SMs and control participants performed a digit memory task during which their brain activity was recorded. Chinese SMs used a digit-image mnemonic for encoding digits, in which they associated 2-digit groups with images immediately after the presentation of each even-position digit in sequences. Behaviorally, SMs' memory of digit sequences was better than the controls'. During encoding in the study phase, SMs showed an increased right central P2 (150-250ms post onset) and a larger right posterior high-alpha (10-14Hz, 500-1720ms) oscillation on digits at even-positions compared with digits at odd-positions. Both P2 and high-alpha oscillations in the study phase co-varied with performance in the recall phase, but only in SMs, indicating that neural dynamics during encoding could predict successful retrieval of digit memory in SMs. Our findings suggest that representation of a digit sequence in SMs using mnemonics may recruit both the early-stage attention allocation process and the sustained information preservation process. This study provides evidence for the role of dynamic and efficient neural encoding processes in mnemonists. Copyright © 2017. Published by Elsevier Inc.

Reduced frontal theta oscillations indicate altered crossmodal prediction error processing in schizophrenia

PubMed Central

Keil, Julian; Balz, Johanna; Gallinat, Jürgen; Senkowski, Daniel

2016-01-01

Our brain generates predictions about forthcoming stimuli and compares predicted with incoming input. Failures in predicting events might contribute to hallucinations and delusions in schizophrenia (SZ). When a stimulus violates prediction, neural activity that reflects prediction error (PE) processing is found. While PE processing deficits have been reported in unisensory paradigms, it is unknown whether SZ patients (SZP) show altered crossmodal PE processing. We measured high-density electroencephalography and applied source estimation approaches to investigate crossmodal PE processing generated by audiovisual speech. In SZP and healthy control participants (HC), we used an established paradigm in which high- and low-predictive visual syllables were paired with congruent or incongruent auditory syllables. We examined crossmodal PE processing in SZP and HC by comparing differences in event-related potentials and neural oscillations between incongruent and congruent high- and low-predictive audiovisual syllables. In both groups event-related potentials between 206 and 250 ms were larger in high- compared with low-predictive syllables, suggesting intact audiovisual incongruence detection in the auditory cortex of SZP. The analysis of oscillatory responses revealed theta-band (4–7 Hz) power enhancement in high- compared with low-predictive syllables between 230 and 370 ms in the frontal cortex of HC but not SZP. Thus aberrant frontal theta-band oscillations reflect crossmodal PE processing deficits in SZ. The present study suggests a top-down multisensory processing deficit and highlights the role of dysfunctional frontal oscillations for the SZ psychopathology. PMID:27358314

Neural Network Analysis on the NOvA Experiment

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

Safford, Twymun K.; Himmel, Alex

NOνA is collaboration of 180 scientists and engineers from 28 institutions which plans to study neutrino oscillations using the existing NuMI neutrino beam at Fermilab. The NOνA experiment is designed to search for oscillations of muon neutrinos to electron neutrinos by comparing the electron neutrino event rate measured at the Fermilab site with the electron neutrino event rate measured at a location just south of International Falls, MN 810 kilometers distant from Fermilab. If oscillations occur, the far site will see the appearance of electrons in the muon neutrino beam produced at Fermilab. The presence of background radiation obscures themore » desired particles and trails to be observed. Using neural network analysis, the goal of the project was to implement machine learning to automate the removal of background radiation to render pixel maps of the particle trajectories.« less

Modular Apparatus and Method for Attaching Multiple Devices

NASA Technical Reports Server (NTRS)

Okojie, Robert S (Inventor)

2015-01-01

A modular apparatus for attaching sensors and electronics is disclosed. The modular apparatus includes a square recess including a plurality of cavities and a reference cavity such that a pressure sensor can be connected to the modular apparatus. The modular apparatus also includes at least one voltage input hole and at least one voltage output hole operably connected to each of the plurality of cavities such that voltage can be applied to the pressure sensor and received from the pressure sensor.

Effects of aberrant gamma frequency oscillations on prepulse inhibition.

PubMed

Jones, Nigel C; Anderson, Paul; Rind, Gil; Sullivan, Caley; van den Buuse, Maarten; O'Brien, Terence J

2014-10-01

Emerging literature implicates abnormalities in gamma frequency oscillations in the pathophysiology of schizophrenia, with hypofunction of N-methyl-D-aspartate (NMDA) receptors implicated as a key factor. Prepulse inhibition (PPI) is a behavioural measure of sensorimotor gating, which is disrupted in schizophrenia. We studied relationships between ongoing and sensory-evoked gamma oscillations and PPI using pharmacological interventions designed to increase gamma oscillations (ketamine, MK-801); reduce gamma oscillations (LY379268); or disrupt PPI (amphetamine). We predicted that elevating ongoing gamma power would lead to increased 'neural noise' in cortical circuits, dampened sensory-evoked gamma responses and disrupted behaviour. Wistar rats were implanted with EEG recording electrodes. They received ketamine (5 mg/kg), MK-801 (0.16 mg/kg), amphetamine (0.5 mg/kg), LY379268 (3 mg/kg) or vehicle and underwent PPI sessions with concurrent EEG recording. Ketamine and MK-801 increased the power of ongoing gamma oscillations and caused time-matched disruptions of PPI, while amphetamine marginally affected ongoing gamma power. In contrast, LY379268 reduced ongoing gamma power, but had no effect on PPI. The sensory gamma response evoked by the prepulse was reduced following treatment with all psychotomimetics, associating with disruptions in PPI. This was most noticeable following treatment with NMDA receptor antagonists. We found that ketamine and MK-801 increase ongoing gamma power and reduce evoked gamma power, both of which are related to disruptions in sensorimotor gating. This appears to be due to antagonism of NMDA receptors, since amphetamine and LY379268 differentially impacted these outcomes and possess different neuropharmacological substrates. Aberrant gamma frequency oscillations caused by NMDA receptor hypofunction may mediate the sensory processing deficits observed in schizophrenia.

Modular Universal Scalable Ion-trap Quantum Computer

DTIC Science & Technology

2016-06-02

SECURITY CLASSIFICATION OF: The main goal of the original MUSIQC proposal was to construct and demonstrate a modular and universally- expandable ion...Distribution Unlimited UU UU UU UU 02-06-2016 1-Aug-2010 31-Jan-2016 Final Report: Modular Universal Scalable Ion-trap Quantum Computer The views...P.O. Box 12211 Research Triangle Park, NC 27709-2211 Ion trap quantum computation, scalable modular architectures REPORT DOCUMENTATION PAGE 11

A Smarter Brain Is Associated with Stronger Neural Interaction in Healthy Young Females: A Resting EEG Coherence Study

ERIC Educational Resources Information Center

Lee, Tien-Wen; Wu, Yu-Te; Yu, Younger W.-Y.; Wu, Hung-Chi; Chen, Tai-Jui

2012-01-01

General intelligence, the "g" factor, is a major issue in psychology and neuroscience. However, the neural mechanism of the "g" factor is still not clear. It is suggested that the "g" factor should be non-modular (a property across the brain) and show good colinearity with various cognitive tests. This study examines…

Using oceanic-atmospheric oscillations for long lead time streamflow forecasting

NASA Astrophysics Data System (ADS)

Kalra, Ajay; Ahmad, Sajjad

2009-03-01

We present a data-driven model, Support Vector Machine (SVM), for long lead time streamflow forecasting using oceanic-atmospheric oscillations. The SVM is based on statistical learning theory that uses a hypothesis space of linear functions based on Kernel approach and has been used to predict a quantity forward in time on the basis of training from past data. The strength of SVM lies in minimizing the empirical classification error and maximizing the geometric margin by solving inverse problem. The SVM model is applied to three gages, i.e., Cisco, Green River, and Lees Ferry in the Upper Colorado River Basin in the western United States. Annual oceanic-atmospheric indices, comprising Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and El Nino-Southern Oscillations (ENSO) for a period of 1906-2001 are used to generate annual streamflow volumes with 3 years lead time. The SVM model is trained with 86 years of data (1906-1991) and tested with 10 years of data (1992-2001). On the basis of correlation coefficient, root means square error, and Nash Sutcliffe Efficiency Coefficient the model shows satisfactory results, and the predictions are in good agreement with measured streamflow volumes. Sensitivity analysis, performed to evaluate the effect of individual and coupled oscillations, reveals a strong signal for ENSO and NAO indices as compared to PDO and AMO indices for the long lead time streamflow forecast. Streamflow predictions from the SVM model are found to be better when compared with the predictions obtained from feedforward back propagation artificial neural network model and linear regression.

Selective attention on representations in working memory: cognitive and neural mechanisms

PubMed Central

2018-01-01

Selective attention and working memory are inter-dependent core cognitive functions. It is critical to allocate attention on selected targets during the capacity-limited working memory processes to fulfill the goal-directed behavior. The trends of research on both topics are increasing exponentially in recent years, and it is considered that selective attention and working memory share similar underlying neural mechanisms. Different types of attention orientation in working memory are introduced by distinctive cues, and the means using retrospective cues are strengthened currently as it is manipulating the representation in memory, instead of the perceptual representation. The cognitive and neural mechanisms of the retro-cue effects are further reviewed, as well as the potential molecular mechanism. The frontal-parietal network that is involved in both attention and working memory is also the neural candidate for attention orientation during working memory. Neural oscillations in the gamma and alpha/beta oscillations may respectively be employed for the feedforward and feedback information transfer between the sensory cortices and the association cortices. Dopamine and serotonin systems might interact with each other subserving the communication between memory and attention. In conclusion, representations which attention shifts towards are strengthened, while representations which attention moves away from are degraded. Studies on attention orientation during working memory indicates the flexibility of the processes of working memory, and the beneficial way that overcome the limited capacity of working memory. PMID:29629245

Generalized epidemic process on modular networks.

PubMed

Chung, Kihong; Baek, Yongjoo; Kim, Daniel; Ha, Meesoon; Jeong, Hawoong

2014-05-01

Social reinforcement and modular structure are two salient features observed in the spreading of behavior through social contacts. In order to investigate the interplay between these two features, we study the generalized epidemic process on modular networks with equal-sized finite communities and adjustable modularity. Using the analytical approach originally applied to clique-based random networks, we show that the system exhibits a bond-percolation type continuous phase transition for weak social reinforcement, whereas a discontinuous phase transition occurs for sufficiently strong social reinforcement. Our findings are numerically verified using the finite-size scaling analysis and the crossings of the bimodality coefficient.

Representation of Cognitive Reappraisal Goals in Frontal Gamma Oscillations

PubMed Central

Kang, Jae-Hwan; Jeong, Ji Woon; Kim, Hyun Taek; Kim, Sang Hee; Kim, Sung-Phil

2014-01-01

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35–55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals

Representation of cognitive reappraisal goals in frontal gamma oscillations.

PubMed

Kang, Jae-Hwan; Jeong, Ji Woon; Kim, Hyun Taek; Kim, Sang Hee; Kim, Sung-Phil

2014-01-01

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35-55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks

PubMed Central

Clune, Jeff

2017-01-01

A long-term goal of AI is to produce agents that can learn a diversity of skills throughout their lifetimes and continuously improve those skills via experience. A longstanding obstacle towards that goal is catastrophic forgetting, which is when learning new information erases previously learned information. Catastrophic forgetting occurs in artificial neural networks (ANNs), which have fueled most recent advances in AI. A recent paper proposed that catastrophic forgetting in ANNs can be reduced by promoting modularity, which can limit forgetting by isolating task information to specific clusters of nodes and connections (functional modules). While the prior work did show that modular ANNs suffered less from catastrophic forgetting, it was not able to produce ANNs that possessed task-specific functional modules, thereby leaving the main theory regarding modularity and forgetting untested. We introduce diffusion-based neuromodulation, which simulates the release of diffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. up or down regulate) learning in a spatial region. On the simple diagnostic problem from the prior work, diffusion-based neuromodulation 1) induces task-specific learning in groups of nodes and connections (task-specific localized learning), which 2) produces functional modules for each subtask, and 3) yields higher performance by eliminating catastrophic forgetting. Overall, our results suggest that diffusion-based neuromodulation promotes task-specific localized learning and functional modularity, which can help solve the challenging, but important problem of catastrophic forgetting. PMID:29145413

Modular support blocks for fluid lines

NASA Technical Reports Server (NTRS)

Dimino, J. M.; Deskin, R. D.

1974-01-01

Modular line block comprises matched modular elements machined to accept fluid lines of different diameters. Modules can support different fluid-line configurations. Top and bottom surfaces are machined to accept dovetail strip used for holding modules together. End modules have holes drilled through to accept fastening screws.

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)

Future Concepts for Modular, Intelligent Aerospace Power Systems

NASA Technical Reports Server (NTRS)

Button, Robert M.; Soeder, James F.

2004-01-01

Nasa's resent commitment to Human and Robotic Space Exploration obviates the need for more affordable and sustainable systems and missions. Increased use of modularity and on-board intelligent technologies will enable these lofty goals. To support this new paradigm, an advanced technology program to develop modular, intelligent power management and distribution (PMAD) system technologies is presented. The many benefits to developing and including modular functionality in electrical power components and systems are shown to include lower costs and lower mass for highly reliable systems. The details of several modular technologies being developed by NASA are presented, broken down into hierarchical levels. Modularity at the device level, including the use of power electronic building blocks, is shown to provide benefits in lowering the development time and costs of new power electronic components.

Implicit Contractive Mappings in Modular Metric and Fuzzy Metric Spaces

PubMed Central

Hussain, N.; Salimi, P.

2014-01-01

The notion of modular metric spaces being a natural generalization of classical modulars over linear spaces like Lebesgue, Orlicz, Musielak-Orlicz, Lorentz, Orlicz-Lorentz, and Calderon-Lozanovskii spaces was recently introduced. In this paper we investigate the existence of fixed points of generalized α-admissible modular contractive mappings in modular metric spaces. As applications, we derive some new fixed point theorems in partially ordered modular metric spaces, Suzuki type fixed point theorems in modular metric spaces and new fixed point theorems for integral contractions. In last section, we develop an important relation between fuzzy metric and modular metric and deduce certain new fixed point results in triangular fuzzy metric spaces. Moreover, some examples are provided here to illustrate the usability of the obtained results. PMID:25003157

Reviving oscillations in coupled nonlinear oscillators.

PubMed

Zou, Wei; Senthilkumar, D V; Zhan, Meng; Kurths, Jürgen

2013-07-05

By introducing a processing delay in the coupling, we find that it can effectively annihilate the quenching of oscillation, amplitude death (AD), in a network of coupled oscillators by switching the stability of AD. It revives the oscillation in the AD regime to retain sustained rhythmic functioning of the networks, which is in sharp contrast to the propagation delay with the tendency to induce AD. This processing delay-induced phenomenon occurs both with and without the propagation delay. Further this effect is rather general from two coupled to networks of oscillators in all known scenarios that can exhibit AD, and it has a wide range of applications where sustained oscillations should be retained for proper functioning of the systems.

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.

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.

Automatic Modeling and Simulation of Modular Robots

NASA Astrophysics Data System (ADS)

Jiang, C.; Wei, H.; Zhang, Y.

2018-03-01

The ability of reconfiguration makes modular robots have the ability of adaptable, low-cost, self-healing and fault-tolerant. It can also be applied to a variety of mission situations. In this manuscript, a robot platform which relied on the module library was designed, based on the screw theory and module theory. Then, the configuration design method of the modular robot was proposed. And the different configurations of modular robot system have been built, including industrial mechanical arms, the mobile platform, six-legged robot and 3D exoskeleton manipulator. Finally, the simulation and verification of one system among them have been made, using the analyses of screw kinematics and polynomial planning. The results of experiments demonstrate the feasibility and superiority of this modular system.

Teleoperated Modular Robots for Lunar Operations

NASA Technical Reports Server (NTRS)

Globus, Al; Hornby, Greg; Larchev, Greg; Hancher, Matt; Cannon, Howard; Lohn, Jason

2004-01-01

Solar system exploration is currently carried out by special purpose robots exquisitely designed for the anticipated tasks. However, all contingencies for in situ resource utilization (ISRU), human habitat preparation, and exploration will be difficult to anticipate. Furthermore, developing the necessary special purpose mechanisms for deployment and other capabilities is difficult and error prone. For example, the Galileo high gain antenna never opened, severely restricting the quantity of data returned by the spacecraft. Also, deployment hardware is used only once. To address these problems, we are developing teleoperated modular robots for lunar missions, including operations in transit from Earth. Teleoperation of lunar systems from Earth involves a three second speed-of-light delay, but experiment suggests that interactive operations are feasible.' Modular robots typically consist of many identical modules that pass power and data between them and can be reconfigured for different tasks providing great flexibility, inherent redundancy and graceful degradation as modules fail. Our design features a number of different hub, link, and joint modules to simplify the individual modules, lower structure cost, and provide specialized capabilities. Modular robots are well suited for space applications because of their extreme flexibility, inherent redundancy, high-density packing, and opportunities for mass production. Simple structural modules can be manufactured from lunar regolith in situ using molds or directed solar sintering. Software to direct and control modular robots is difficult to develop. We have used genetic algorithms to evolve both the morphology and control system for walking modular robots3 We are currently using evolvable system technology to evolve controllers for modular robots in the ISS glove box. Development of lunar modular robots will require software and physical simulators, including regolith simulation, to enable design and test of robot

Abnormal late visual responses and alpha oscillations in neurofibromatosis type 1: a link to visual and attention deficits

PubMed Central

2014-01-01

Background Neurofibromatosis type 1 (NF1) affects several areas of cognitive function including visual processing and attention. We investigated the neural mechanisms underlying the visual deficits of children and adolescents with NF1 by studying visual evoked potentials (VEPs) and brain oscillations during visual stimulation and rest periods. Methods Electroencephalogram/event-related potential (EEG/ERP) responses were measured during visual processing (NF1 n = 17; controls n = 19) and idle periods with eyes closed and eyes open (NF1 n = 12; controls n = 14). Visual stimulation was chosen to bias activation of the three detection mechanisms: achromatic, red-green and blue-yellow. Results We found significant differences between the groups for late chromatic VEPs and a specific enhancement in the amplitude of the parieto-occipital alpha amplitude both during visual stimulation and idle periods. Alpha modulation and the negative influence of alpha oscillations in visual performance were found in both groups. Conclusions Our findings suggest abnormal later stages of visual processing and enhanced amplitude of alpha oscillations supporting the existence of deficits in basic sensory processing in NF1. Given the link between alpha oscillations, visual perception and attention, these results indicate a neural mechanism that might underlie the visual sensitivity deficits and increased lapses of attention observed in individuals with NF1. PMID:24559228

Olfactory bulb gamma oscillations are enhanced with task demands.

PubMed

Beshel, Jennifer; Kopell, Nancy; Kay, Leslie M

2007-08-01

Fast oscillations in neural assemblies have been proposed as a mechanism to facilitate stimulus representation in a variety of sensory systems across animal species. In the olfactory system, intervention studies suggest that oscillations in the gamma frequency range play a role in fine odor discrimination. However, there is still no direct evidence that such oscillations are intrinsically altered in intact systems to aid in stimulus disambiguation. Here we show that gamma oscillatory power in the rat olfactory bulb during a two-alternative choice task is modulated in the intact system according to task demands with dramatic increases in gamma power during discrimination of molecularly similar odorants in contrast to dissimilar odorants. This elevation in power evolves over the course of criterion performance, is specific to the gamma frequency band (65-85 Hz), and is independent of changes in the theta or beta frequency band range. Furthermore, these high amplitude gamma oscillations are restricted to the olfactory bulb, such that concurrent piriform cortex recordings show no evidence of enhanced gamma power during these high-amplitude events. Our results display no modulation in the power of beta oscillations (15-28 Hz) shown previously to increase with odor learning in a Go/No-go task, and we suggest that the oscillatory profile of the olfactory system may be influenced by both odor discrimination demands and task type. The results reported here indicate that enhancement of local gamma power may reflect a switch in the dynamics of the system to a strategy that optimizes stimulus resolution when input signals are ambiguous.

Lateralization in Alpha-Band Oscillations Predicts the Locus and Spatial Distribution of Attention.

PubMed

Ikkai, Akiko; Dandekar, Sangita; Curtis, Clayton E

2016-01-01

Attending to a task-relevant location changes how neural activity oscillates in the alpha band (8-13Hz) in posterior visual cortical areas. However, a clear understanding of the relationships between top-down attention, changes in alpha oscillations in visual cortex, and attention performance are still poorly understood. Here, we tested the degree to which the posterior alpha power tracked the locus of attention, the distribution of attention, and how well the topography of alpha could predict the locus of attention. We recorded magnetoencephalographic (MEG) data while subjects performed an attention demanding visual discrimination task that dissociated the direction of attention from the direction of a saccade to indicate choice. On some trials, an endogenous cue predicted the target's location, while on others it contained no spatial information. When the target's location was cued, alpha power decreased in sensors over occipital cortex contralateral to the attended visual field. When the cue did not predict the target's location, alpha power again decreased in sensors over occipital cortex, but bilaterally, and increased in sensors over frontal cortex. Thus, the distribution and the topography of alpha reliably indicated the locus of covert attention. Together, these results suggest that alpha synchronization reflects changes in the excitability of populations of neurons whose receptive fields match the locus of attention. This is consistent with the hypothesis that alpha oscillations reflect the neural mechanisms by which top-down control of attention biases information processing and modulate the activity of neurons in visual cortex.

Forecasting ENSO events: A neural network-extended EOF approach

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

Tangang, F.T.; Tang, B.; Monahan, A.H.

The authors constructed neural network models to forecast the sea surface temperature anomalies (SSTA) for three regions: Nino 4. Nino 3.5, and Nino 3, representing the western-central, the central, and the eastern-central parts of the equatorial Pacific Ocean, respectively. The inputs were the extended empirical orthogonal functions (EEOF) of the sea level pressure (SLP) field that covered the tropical Indian and Pacific Oceans and evolved for a duration of 1 yr. The EEOFs greatly reduced the size of the neural networks from those of the authors` earlier papers using EOFs. The Nino 4 region appeared to be the best forecastedmore » region, with useful skills up to a year lead time for the 1982-93 forecast period. By network pruning analysis and spectral analysis, four important inputs were identified: modes 1, 2, and 6 of the SLP EEOFs and the SSTA persistence. Mode 1 characterized the low-frequency oscillation (LFO, with 4-5-yr period), and was seen as the typical ENSO signal, while mode 2, with a period of 2-5 yr, characterized the quasi-biennial oscillation (QBO) plus the LFO. Mode 6 was dominated by decadal and interdecadal variations. Thus, forecasting ENSO required information from the QBO, and the decadal-interdecadal oscillations. The nonlinearity of the networks tended to increase with lead time and to become stronger for the eastern regions of the equatorial Pacific Ocean. 35 refs., 14 figs., 4 tabs.« less

Mesoscopic chaos mediated by Drude electron-hole plasma in silicon optomechanical oscillators

PubMed Central

Wu, Jiagui; Huang, Shu-Wei; Huang, Yongjun; Zhou, Hao; Yang, Jinghui; Liu, Jia-Ming; Yu, Mingbin; Lo, Guoqiang; Kwong, Dim-Lee; Duan, Shukai; Wei Wong, Chee

2017-01-01

Chaos has revolutionized the field of nonlinear science and stimulated foundational studies from neural networks, extreme event statistics, to physics of electron transport. Recent studies in cavity optomechanics provide a new platform to uncover quintessential architectures of chaos generation and the underlying physics. Here, we report the generation of dynamical chaos in silicon-based monolithic optomechanical oscillators, enabled by the strong and coupled nonlinearities of two-photon absorption induced Drude electron–hole plasma. Deterministic chaotic oscillation is achieved, and statistical and entropic characterization quantifies the chaos complexity at 60 fJ intracavity energies. The correlation dimension D2 is determined at 1.67 for the chaotic attractor, along with a maximal Lyapunov exponent rate of about 2.94 times the fundamental optomechanical oscillation for fast adjacent trajectory divergence. Nonlinear dynamical maps demonstrate the subharmonics, bifurcations and stable regimes, along with distinct transitional routes into chaos. This provides a CMOS-compatible and scalable architecture for understanding complex dynamics on the mesoscopic scale. PMID:28598426

Telepathology: design of a modular system.

PubMed

Brauchli, K; Christen, H; Meyer, P; Haroske, G; Meyer, W; Kunze, K D; Otto, R; Oberholzer, M

2000-01-01

Although telepathology systems have been developed for more than a decade, they are still not a widespread tool for routine diagnostic applications. Lacking interoperability, software that is not satisfying user needs as well as high costs have been identified as reasons. In this paper we would like to demonstrate that with a clear separation of the tasks required for a telepathology application, telepathology systems can be built in a modular way, where many modules can be implemented using standard software components. With such a modular design, systems can be easily adapted to changing user needs and new technological developments and it is easier to integrate modular systems into existing environments.

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.

Synchronization and spatiotemporal patterns in coupled phase oscillators on a weighted planar network

NASA Astrophysics Data System (ADS)

Kagawa, Yuki; Takamatsu, Atsuko

2009-04-01

To reveal the relation between network structures found in two-dimensional biological systems, such as protoplasmic tube networks in the plasmodium of true slime mold, and spatiotemporal oscillation patterns emerged on the networks, we constructed coupled phase oscillators on weighted planar networks and investigated their dynamics. Results showed that the distribution of edge weights in the networks strongly affects (i) the propensity for global synchronization and (ii) emerging ratios of oscillation patterns, such as traveling and concentric waves, even if the total weight is fixed. In-phase locking, traveling wave, and concentric wave patterns were, respectively, observed most frequently in uniformly weighted, center weighted treelike, and periphery weighted ring-shaped networks. Controlling the global spatiotemporal patterns with the weight distribution given by the local weighting (coupling) rules might be useful in biological network systems including the plasmodial networks and neural networks in the brain.

Modular space station

NASA Technical Reports Server (NTRS)

1972-01-01

The modular space station comprising small, shuttle-launched modules, and characterized by low initial cost and incremental manning, is described. The initial space station is designed to be delivered into orbit by three space shuttles and assembled in space. The three sections are the power/subsystems module, the crew/operations module, and the general purpose laboratory module. It provides for a crew of six. Subsequently duplicate/crew/operations and power/subsystems modules will be mated to the original modules, and provide for an additional six crewmen. A total of 17 research and applications modules is planned, three of which will be free-flying modules. Details are given on the program plan, modular characteristics, logistics, experiment support capability and requirements, operations analysis, design support analyses, and shuttle interfaces.

Visualization of Notch signaling oscillation in cells and tissues.

PubMed

Shimojo, Hiromi; Harima, Yukiko; Kageyama, Ryoichiro

2014-01-01

The Notch signaling effectors Hes1 and Hes7 exhibit oscillatory expression with a period of about 2-3 h during embryogenesis. Hes1 oscillation is important for proliferation and differentiation of neural stem cells, whereas Hes7 oscillation regulates periodic formation of somites. Continuous expression of Hes1 and Hes7 inhibits these developmental processes. Thus, expression dynamics are very important for gene functions, but it is difficult to distinguish between oscillatory and persistent expression by conventional methods such as in situ hybridization and immunostaining. Here, we describe time-lapse imaging methods using destabilized luciferase reporters and a highly sensitive cooled charge-coupled device camera, which can monitor dynamic gene expression. Furthermore, the expression of two genes can be examined simultaneously by a dual reporter system using two-color luciferase reporters. Time-lapse imaging analyses reveal how dynamically gene expression changes in many biological events.

Cessation of oscillations in a chemo-mechanical oscillator

NASA Astrophysics Data System (ADS)

Phogat, Richa; Tiwari, Ishant; Kumar, Pawan; Rivera, Marco; Parmananda, Punit

2018-06-01

In this paper, different methods for cessation of oscillations in a chemo-mechanical oscillator [mercury beating heart (MBH)] are presented. The first set of experiments were carried out on a single MBH oscillator. To achieve cessation of oscillations, two protocols, namely, inverted feedback and delayed feedback were employed. In the second set of experiments, two quasi-identical MBH oscillators are considered. They are first synchronized via a bidirectional attractive coupling. These two synchronized oscillators are thereafter coupled with a unidirectional repulsive coupling and the system dynamics were observed. Subsequently, in the next protocol, the effect of a unidirectional delay coupling on the two synchronized oscillators was explored. The cessation of oscillations in all the above experimental setups was observed as the feedback/coupling was switched on at a suitable strength. Oscillatory dynamics of the system were restored when the feedback/coupling was switched off.

Configurable double-sided modular jet impingement assemblies for electronics cooling

DOEpatents

Zhou, Feng; Dede, Ercan Mehmet

2018-05-22

A modular jet impingement assembly includes an inlet tube fluidly coupled to a fluid inlet, an outlet tube fluidly coupled to a fluid outlet, and a modular manifold having a first distribution recess extending into a first side of the modular manifold, a second distribution recess extending into a second side of the modular manifold, a plurality of inlet connection tubes positioned at an inlet end of the modular manifold, and a plurality of outlet connection tubes positioned at an outlet end of the modular manifold. A first manifold insert is removably positioned within the first distribution recess, a second manifold insert is removably positioned within the second distribution recess, and a first and second heat transfer plate each removably coupled to the modular manifold. The first and second heat transfer plates each comprise an impingement surface.

Modular femoral neck fracture after primary total hip arthroplasty.

PubMed

Sotereanos, Nicholas G; Sauber, Timothy J; Tupis, Todd T

2013-01-01

The use of modular femoral stems in primary total hip arthroplasty has increased considerably in recent years. These modular components offer the surgeon the ability to independently alter version, offset, and length of the femoral component of a hip arthroplasty. This increases the surgeon's ability to accurately recreate the relevant anatomy but increases the possibilities of corrosion and fracture. Multiple case reports have highlighted fractures of these modular components. We present a case of a fracture of a modular design that has had no previously reported modular neck fractures. The patient was informed that data concerning the case would be submitted, and he consented. Copyright © 2013 Elsevier Inc. All rights reserved.

Synchronization of heteroclinic circuits through learning in coupled neural networks

NASA Astrophysics Data System (ADS)

Selskii, Anton; Makarov, Valeri A.

2016-01-01

The synchronization of oscillatory activity in neural networks is usually implemented by coupling the state variables describing neuronal dynamics. Here we study another, but complementary mechanism based on a learning process with memory. A driver network, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven network, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner can "copy" the coupling structure and thus synchronize oscillations with the teacher. The replication of the WLC dynamics occurs for intermediate memory lengths only, consequently, the learner network exhibits a phenomenon of learning resonance.

Neural plasticity and its initiating conditions in tinnitus.

PubMed

Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

The gravity duals of modular Hamiltonians

DOE PAGES

Jafferis, Daniel L.; Suh, S. Josephine

2016-09-12

In this study, we investigate modular Hamiltonians defined with respect to arbitrary spatial regions in quantum field theory states which have semi-classical gravity duals. We find prescriptions in the gravity dual for calculating the action of the modular Hamiltonian on its defining state, including its dual metric, and also on small excitations around the state. Curiously, use of the covariant holographic entanglement entropy formula leads us to the conclusion that the modular Hamiltonian, which in the quantum field theory acts only in the causal completion of the region, does not commute with bulk operators whose entire gauge-invariant description is space-likemore » to the causal completion of the region.« less

The gravity duals of modular Hamiltonians

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

Jafferis, Daniel L.; Suh, S. Josephine

In this study, we investigate modular Hamiltonians defined with respect to arbitrary spatial regions in quantum field theory states which have semi-classical gravity duals. We find prescriptions in the gravity dual for calculating the action of the modular Hamiltonian on its defining state, including its dual metric, and also on small excitations around the state. Curiously, use of the covariant holographic entanglement entropy formula leads us to the conclusion that the modular Hamiltonian, which in the quantum field theory acts only in the causal completion of the region, does not commute with bulk operators whose entire gauge-invariant description is space-likemore » to the causal completion of the region.« less

Memory as the "whole brain work": a large-scale model based on "oscillations in super-synergy".

PubMed

Başar, Erol

2005-01-01

According to recent trends, memory depends on several brain structures working in concert across many levels of neural organization; "memory is a constant work-in progress." The proposition of a brain theory based on super-synergy in neural populations is most pertinent for the understanding of this constant work in progress. This report introduces a new model on memory basing on the processes of EEG oscillations and Brain Dynamics. This model is shaped by the following conceptual and experimental steps: 1. The machineries of super-synergy in the whole brain are responsible for formation of sensory-cognitive percepts. 2. The expression "dynamic memory" is used for memory processes that evoke relevant changes in alpha, gamma, theta and delta activities. The concerted action of distributed multiple oscillatory processes provides a major key for understanding of distributed memory. It comprehends also the phyletic memory and reflexes. 3. The evolving memory, which incorporates reciprocal actions or reverberations in the APLR alliance and during working memory processes, is especially emphasized. 4. A new model related to "hierarchy of memories as a continuum" is introduced. 5. The notions of "longer activated memory" and "persistent memory" are proposed instead of long-term memory. 6. The new analysis to recognize faces emphasizes the importance of EEG oscillations in neurophysiology and Gestalt analysis. 7. The proposed basic framework called "Memory in the Whole Brain Work" emphasizes that memory and all brain functions are inseparable and are acting as a "whole" in the whole brain. 8. The role of genetic factors is fundamental in living system settings and oscillations and accordingly in memory, according to recent publications. 9. A link from the "whole brain" to "whole body," and incorporation of vegetative and neurological system, is proposed, EEG oscillations and ultraslow oscillations being a control parameter.

Neural Entrainment in Drum Rhythms with Silent Breaks: Evidence from Steady-state Evoked and Event-related Potentials.

PubMed

Stupacher, Jan; Witte, Matthias; Hove, Michael J; Wood, Guilherme

2016-12-01

The fusion of rhythm, beat perception, and movement is often summarized under the term "entrainment" and becomes obvious when we effortlessly tap our feet or snap our fingers to the pulse of music. Entrainment to music involves a large network of brain structures, and neural oscillations at beat-related frequencies can help elucidate how this network is connected. Here, we used EEG to investigate steady-state evoked potentials (SSEPs) and event-related potentials (ERPs) during listening and tapping to drum clips with different rhythmic structures that were interrupted by silent breaks of 2-6 sec. This design allowed us to address the question of whether neural entrainment processes persist after the physical presence of musical rhythms and to link neural oscillations and event-related neural responses. During stimulus presentation, SSEPs were elicited in both tasks (listening and tapping). During silent breaks, SSEPs were only present in the tapping task. Notably, the amplitude of the N1 ERP component was more negative after longer silent breaks, and both N1 and SSEP results indicate that neural entrainment was increased when listening to drum rhythms compared with an isochronous metronome. Taken together, this suggests that neural entrainment to music is not solely driven by the physical input but involves endogenous timing processes. Our findings break ground for a tighter linkage between steady-state and transient evoked neural responses in rhythm processing. Beyond music perception, they further support the crucial role of entrained oscillatory activity in shaping sensory, motor, and cognitive processes in general.

Brain modularity controls the critical behavior of spontaneous activity.

PubMed

Russo, R; Herrmann, H J; de Arcangelis, L

2014-03-13

The human brain exhibits a complex structure made of scale-free highly connected modules loosely interconnected by weaker links to form a small-world network. These features appear in healthy patients whereas neurological diseases often modify this structure. An important open question concerns the role of brain modularity in sustaining the critical behaviour of spontaneous activity. Here we analyse the neuronal activity of a model, successful in reproducing on non-modular networks the scaling behaviour observed in experimental data, on a modular network implementing the main statistical features measured in human brain. We show that on a modular network, regardless the strength of the synaptic connections or the modular size and number, activity is never fully scale-free. Neuronal avalanches can invade different modules which results in an activity depression, hindering further avalanche propagation. Critical behaviour is solely recovered if inter-module connections are added, modifying the modular into a more random structure.

Modular assembly of optical nanocircuits.

PubMed

Shi, Jinwei; Monticone, Francesco; Elias, Sarah; Wu, Yanwen; Ratchford, Daniel; Li, Xiaoqin; Alù, Andrea

2014-05-29

A key element enabling the microelectronic technology advances of the past decades has been the conceptualization of complex circuits with versatile functionalities as being composed of the proper combination of basic 'lumped' circuit elements (for example, inductors and capacitors). In contrast, modern nanophotonic systems are still far from a similar level of sophistication, partially because of the lack of modularization of their response in terms of basic building blocks. Here we demonstrate the design, assembly and characterization of relatively complex photonic nanocircuits by accurately positioning a number of metallic and dielectric nanoparticles acting as modular lumped elements. The nanoparticle clusters produce the desired spectral response described by simple circuit rules and are shown to be dynamically reconfigurable by modifying the direction or polarization of impinging signals. Our work represents an important step towards extending the powerful modular design tools of electronic circuits into nanophotonic systems.

Modular assembly of optical nanocircuits

NASA Astrophysics Data System (ADS)

Shi, Jinwei; Monticone, Francesco; Elias, Sarah; Wu, Yanwen; Ratchford, Daniel; Li, Xiaoqin; Alù, Andrea

2014-05-01

A key element enabling the microelectronic technology advances of the past decades has been the conceptualization of complex circuits with versatile functionalities as being composed of the proper combination of basic ‘lumped’ circuit elements (for example, inductors and capacitors). In contrast, modern nanophotonic systems are still far from a similar level of sophistication, partially because of the lack of modularization of their response in terms of basic building blocks. Here we demonstrate the design, assembly and characterization of relatively complex photonic nanocircuits by accurately positioning a number of metallic and dielectric nanoparticles acting as modular lumped elements. The nanoparticle clusters produce the desired spectral response described by simple circuit rules and are shown to be dynamically reconfigurable by modifying the direction or polarization of impinging signals. Our work represents an important step towards extending the powerful modular design tools of electronic circuits into nanophotonic systems.

Synchronization transition in neuronal networks composed of chaotic or non-chaotic oscillators.

PubMed

Xu, Kesheng; Maidana, Jean Paul; Castro, Samy; Orio, Patricio

2018-05-30

Chaotic dynamics has been shown in the dynamics of neurons and neural networks, in experimental data and numerical simulations. Theoretical studies have proposed an underlying role of chaos in neural systems. Nevertheless, whether chaotic neural oscillators make a significant contribution to network behaviour and whether the dynamical richness of neural networks is sensitive to the dynamics of isolated neurons, still remain open questions. We investigated synchronization transitions in heterogeneous neural networks of neurons connected by electrical coupling in a small world topology. The nodes in our model are oscillatory neurons that - when isolated - can exhibit either chaotic or non-chaotic behaviour, depending on conductance parameters. We found that the heterogeneity of firing rates and firing patterns make a greater contribution than chaos to the steepness of the synchronization transition curve. We also show that chaotic dynamics of the isolated neurons do not always make a visible difference in the transition to full synchrony. Moreover, macroscopic chaos is observed regardless of the dynamics nature of the neurons. However, performing a Functional Connectivity Dynamics analysis, we show that chaotic nodes can promote what is known as multi-stable behaviour, where the network dynamically switches between a number of different semi-synchronized, metastable states.

A model for the neural control of pineal periodicity

NASA Astrophysics Data System (ADS)

de Oliveira Cruz, Frederico Alan; Soares, Marilia Amavel Gomes; Cortez, Celia Martins

2016-12-01

The aim of this work was verify if a computational model associating the synchronization dynamics of coupling oscillators to a set of synaptic transmission equations would be able to simulate the control of pineal by a complex neural pathway that connects the retina to this gland. Results from the simulations showed that the frequency and temporal firing patterns were in the range of values found in literature.

Dissociable Decoding of Spatial Attention and Working Memory from EEG Oscillations and Sustained Potentials.

PubMed

Bae, Gi-Yeul; Luck, Steven J

2018-01-10

In human scalp EEG recordings, both sustained potentials and alpha-band oscillations are present during the delay period of working memory tasks and may therefore reflect the representation of information in working memory. However, these signals may instead reflect support mechanisms rather than the actual contents of memory. In particular, alpha-band oscillations have been tightly tied to spatial attention and may not reflect location-independent memory representations per se. To determine how sustained and oscillating EEG signals are related to attention and working memory, we attempted to decode which of 16 orientations was being held in working memory by human observers (both women and men). We found that sustained EEG activity could be used to decode the remembered orientation of a stimulus, even when the orientation of the stimulus varied independently of its location. Alpha-band oscillations also carried clear information about the location of the stimulus, but they provided little or no information about orientation independently of location. Thus, sustained potentials contain information about the object properties being maintained in working memory, consistent with previous evidence of a tight link between these potentials and working memory capacity. In contrast, alpha-band oscillations primarily carry location information, consistent with their link to spatial attention. SIGNIFICANCE STATEMENT Working memory plays a key role in cognition, and working memory is impaired in several neurological and psychiatric disorders. Previous research has suggested that human scalp EEG recordings contain signals that reflect the neural representation of information in working memory. However, to conclude that a neural signal actually represents the object being remembered, it is necessary to show that the signal contains fine-grained information about that object. Here, we show that sustained voltages in human EEG recordings contain fine-grained information about the

A Modular Laser Graphics Projection System

NASA Astrophysics Data System (ADS)

Newswanger, Craig D.

1984-05-01

WED Enterprises has designed and built a modular projection system for the presentation of animated laser shows. This system was designed specifically for use in Disney theme shows. Its modular design allows it to be adapted to many show situations with simple hardware and software adjustments. The primary goals were superior animation, long life, low maintenance and stand alone operation.

Full characterization of modular values for finite-dimensional systems

NASA Astrophysics Data System (ADS)

Ho, Le Bin; Imoto, Nobuyuki

2016-06-01

Kedem and Vaidman obtained a relationship between the spin-operator modular value and its weak value for specific coupling strengths [14]. Here we give a general expression for the modular value in the n-dimensional Hilbert space using the weak values up to (n - 1)th order of an arbitrary observable for any coupling strength, assuming non-degenerated eigenvalues. For two-dimensional case, it shows a linear relationship between the weak value and the modular value. We also relate the modular value of the sum of observables to the weak value of their product.

Complex Dynamical Networks Constructed with Fully Controllable Nonlinear Nanomechanical Oscillators.

PubMed

Fon, Warren; Matheny, Matthew H; Li, Jarvis; Krayzman, Lev; Cross, Michael C; D'Souza, Raissa M; Crutchfield, James P; Roukes, Michael L

2017-10-11

Control of the global parameters of complex networks has been explored experimentally in a variety of contexts. Yet, the more difficult prospect of realizing arbitrary network architectures, especially analog physical networks that provide dynamical control of individual nodes and edges, has remained elusive. Given the vast hierarchy of time scales involved, it also proves challenging to measure a complex network's full internal dynamics. These span from the fastest nodal dynamics to very slow epochs over which emergent global phenomena, including network synchronization and the manifestation of exotic steady states, eventually emerge. Here, we demonstrate an experimental system that satisfies these requirements. It is based upon modular, fully controllable, nonlinear radio frequency nanomechanical oscillators, designed to form the nodes of complex dynamical networks with edges of arbitrary topology. The dynamics of these oscillators and their surrounding network are analog and continuous-valued and can be fully interrogated in real time. They comprise a piezoelectric nanomechanical membrane resonator, which serves as the frequency-determining element within an electrical feedback circuit. This embodiment permits network interconnections entirely within the electrical domain and provides unprecedented node and edge control over a vast region of parameter space. Continuous measurement of the instantaneous amplitudes and phases of every constituent oscillator node are enabled, yielding full and detailed network data without reliance upon statistical quantities. We demonstrate the operation of this platform through the real-time capture of the dynamics of a three-node ring network as it evolves from the uncoupled state to full synchronization.

A modular multiple use system for precise time and frequency measurement and distribution

NASA Technical Reports Server (NTRS)

Reinhardt, V. S.; Adams, W. S.; Lee, G. M.; Bush, R. L.

1978-01-01

A modular CAMAC based system is described which was developed to meet a variety of precise time and frequency measurement and distribution needs. The system was based on a generalization of the dual mixer concept. By using a 16 channel 100 ns event clock, the system can intercompare the phase of 16 frequency standards with subpicosecond resolution. The system has a noise floor of 26 fs and a long term stability on the order of 1 ps or better. The system also used a digitally controlled crystal oscillator in a control loop to provide an offsettable 5 MHz output with subpicosecond phase tracking capability. A detailed description of the system is given including theory of operation and performance. A method to improve the performance of the dual mixer technique is discussed when phase balancing of the two input ports cannot be accomplished.

Modular survivable satellite support

NASA Astrophysics Data System (ADS)

Wagner, R. E.

The development of a highly mobile, survivable satellite system from the Transportable Mobile Ground Station (T/MGS) is proposed. The addition of advanced capabilities to the T/MGS such as telemetry processing equipment, and the flexibility of a modularly designed system are examined. The need to increase survivability and mobility while reducing life cycle costs is discussed. A modular survivable satellite support system which consists of a 40-foot van, a diesel tractor, and a multimedia communications subsystem is described. The use of planar and phased arrays to improve transportability and new materials and structural designs to enhance hardness are discussed. Diagrams of the system are provided.

Linear analysis of auto-organization in Hebbian neural networks.

PubMed

Carlos Letelier, J; Mpodozis, J

1995-01-01

The self-organization of neurotopies where neural connections follow Hebbian dynamics is framed in terms of linear operator theory. A general and exact equation describing the time evolution of the overall synaptic strength connecting two neural laminae is derived. This linear matricial equation, which is similar to the equations used to describe oscillating systems in physics, is modified by the introduction of non-linear terms, in order to capture self-organizing (or auto-organizing) processes. The behavior of a simple and small system, that contains a non-linearity that mimics a metabolic constraint, is analyzed by computer simulations. The emergence of a simple "order" (or degree of organization) in this low-dimensionality model system is discussed.

46 CFR 181.450 - Independent modular smoke detecting units.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 46 Shipping 7 2013-10-01 2013-10-01 false Independent modular smoke detecting units. 181.450... Independent modular smoke detecting units. (a) An independent modular smoke detecting unit must: (1) Meet UL 217 (incorporated by reference, see 46 CFR 175.600) and be listed as a “Single Station Smoke detector...

46 CFR 181.450 - Independent modular smoke detecting units.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 46 Shipping 7 2012-10-01 2012-10-01 false Independent modular smoke detecting units. 181.450... Independent modular smoke detecting units. (a) An independent modular smoke detecting unit must: (1) Meet UL 217 (incorporated by reference, see 46 CFR 175.600) and be listed as a “Single Station Smoke detector...

46 CFR 181.450 - Independent modular smoke detecting units.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 46 Shipping 7 2011-10-01 2011-10-01 false Independent modular smoke detecting units. 181.450... Independent modular smoke detecting units. (a) An independent modular smoke detecting unit must: (1) Meet UL 217 (incorporated by reference, see 46 CFR 175.600) and be listed as a “Single Station Smoke detector...

46 CFR 181.450 - Independent modular smoke detecting units.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 46 Shipping 7 2010-10-01 2010-10-01 false Independent modular smoke detecting units. 181.450... Independent modular smoke detecting units. (a) An independent modular smoke detecting unit must: (1) Meet UL 217 (incorporated by reference, see 46 CFR 175.600) and be listed as a “Single Station Smoke detector...

46 CFR 181.450 - Independent modular smoke detecting units.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 46 Shipping 7 2014-10-01 2014-10-01 false Independent modular smoke detecting units. 181.450... Independent modular smoke detecting units. (a) An independent modular smoke detecting unit must: (1) Meet UL 217 (incorporated by reference, see 46 CFR 175.600) and be listed as a “Single Station Smoke detector...

Self-oscillation

NASA Astrophysics Data System (ADS)

Jenkins, Alejandro

2013-04-01

Physicists are very familiar with forced and parametric resonance, but usually not with self-oscillation, a property of certain dynamical systems that gives rise to a great variety of vibrations, both useful and destructive. In a self-oscillator, the driving force is controlled by the oscillation itself so that it acts in phase with the velocity, causing a negative damping that feeds energy into the vibration: no external rate needs to be adjusted to the resonant frequency. The famous collapse of the Tacoma Narrows bridge in 1940, often attributed by introductory physics texts to forced resonance, was actually a self-oscillation, as was the swaying of the London Millennium Footbridge in 2000. Clocks are self-oscillators, as are bowed and wind musical instruments. The heart is a “relaxation oscillator”, i.e., a non-sinusoidal self-oscillator whose period is determined by sudden, nonlinear switching at thresholds. We review the general criterion that determines whether a linear system can self-oscillate. We then describe the limiting cycles of the simplest nonlinear self-oscillators, as well as the ability of two or more coupled self-oscillators to become spontaneously synchronized (“entrained”). We characterize the operation of motors as self-oscillation and prove a theorem about their limit efficiency, of which Carnot’s theorem for heat engines appears as a special case. We briefly discuss how self-oscillation applies to servomechanisms, Cepheid variable stars, lasers, and the macroeconomic business cycle, among other applications. Our emphasis throughout is on the energetics of self-oscillation, often neglected by the literature on nonlinear dynamical systems.

Convergent evolution of modularity in metabolic networks through different community structures.

PubMed

Zhou, Wanding; Nakhleh, Luay

2012-09-14

It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network

Convergent evolution of modularity in metabolic networks through different community structures

PubMed Central

2012-01-01

Background It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new

A new bio-inspired stimulator to suppress hyper-synchronized neural firing in a cortical network.

PubMed

Amiri, Masoud; Amiri, Mahmood; Nazari, Soheila; Faez, Karim

2016-12-07

Hyper-synchronous neural oscillations are the character of several neurological diseases such as epilepsy. On the other hand, glial cells and particularly astrocytes can influence neural synchronization. Therefore, based on the recent researches, a new bio-inspired stimulator is proposed which basically is a dynamical model of the astrocyte biophysical model. The performance of the new stimulator is investigated on a large-scale, cortical network. Both excitatory and inhibitory synapses are also considered in the simulated spiking neural network. The simulation results show that the new stimulator has a good performance and is able to reduce recurrent abnormal excitability which in turn avoids the hyper-synchronous neural firing in the spiking neural network. In this way, the proposed stimulator has a demand controlled characteristic and is a good candidate for deep brain stimulation (DBS) technique to successfully suppress the neural hyper-synchronization. Copyright © 2016 Elsevier Ltd. All rights reserved.

Modular multiplication in GF(p) for public-key cryptography

NASA Astrophysics Data System (ADS)

Olszyna, Jakub

Modular multiplication forms the basis of modular exponentiation which is the core operation of the RSA cryptosystem. It is also present in many other cryptographic algorithms including those based on ECC and HECC. Hence, an efficient implementation of PKC relies on efficient implementation of modular multiplication. The paper presents a survey of most common algorithms for modular multiplication along with hardware architectures especially suitable for cryptographic applications in energy constrained environments. The motivation for studying low-power and areaefficient modular multiplication algorithms comes from enabling public-key security for ultra-low power devices that can perform under constrained environments like wireless sensor networks. Serial architectures for GF(p) are analyzed and presented. Finally proposed architectures are verified and compared according to the amount of power dissipated throughout the operation.

Z-Score-Based Modularity for Community Detection in Networks

PubMed Central

Miyauchi, Atsushi; Kawase, Yasushi

2016-01-01

Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function. PMID:26808270

Reduced-Order Modeling for Flutter/LCO Using Recurrent Artificial Neural Network

NASA Technical Reports Server (NTRS)

Yao, Weigang; Liou, Meng-Sing

2012-01-01

The present study demonstrates the efficacy of a recurrent artificial neural network to provide a high fidelity time-dependent nonlinear reduced-order model (ROM) for flutter/limit-cycle oscillation (LCO) modeling. An artificial neural network is a relatively straightforward nonlinear method for modeling an input-output relationship from a set of known data, for which we use the radial basis function (RBF) with its parameters determined through a training process. The resulting RBF neural network, however, is only static and is not yet adequate for an application to problems of dynamic nature. The recurrent neural network method [1] is applied to construct a reduced order model resulting from a series of high-fidelity time-dependent data of aero-elastic simulations. Once the RBF neural network ROM is constructed properly, an accurate approximate solution can be obtained at a fraction of the cost of a full-order computation. The method derived during the study has been validated for predicting nonlinear aerodynamic forces in transonic flow and is capable of accurate flutter/LCO simulations. The obtained results indicate that the present recurrent RBF neural network is accurate and efficient for nonlinear aero-elastic system analysis

Lateralization in Alpha-Band Oscillations Predicts the Locus and Spatial Distribution of Attention

PubMed Central

Ikkai, Akiko; Dandekar, Sangita; Curtis, Clayton E.

2016-01-01

Attending to a task-relevant location changes how neural activity oscillates in the alpha band (8–13Hz) in posterior visual cortical areas. However, a clear understanding of the relationships between top-down attention, changes in alpha oscillations in visual cortex, and attention performance are still poorly understood. Here, we tested the degree to which the posterior alpha power tracked the locus of attention, the distribution of attention, and how well the topography of alpha could predict the locus of attention. We recorded magnetoencephalographic (MEG) data while subjects performed an attention demanding visual discrimination task that dissociated the direction of attention from the direction of a saccade to indicate choice. On some trials, an endogenous cue predicted the target’s location, while on others it contained no spatial information. When the target’s location was cued, alpha power decreased in sensors over occipital cortex contralateral to the attended visual field. When the cue did not predict the target’s location, alpha power again decreased in sensors over occipital cortex, but bilaterally, and increased in sensors over frontal cortex. Thus, the distribution and the topography of alpha reliably indicated the locus of covert attention. Together, these results suggest that alpha synchronization reflects changes in the excitability of populations of neurons whose receptive fields match the locus of attention. This is consistent with the hypothesis that alpha oscillations reflect the neural mechanisms by which top-down control of attention biases information processing and modulate the activity of neurons in visual cortex. PMID:27144717

Modular Rocket Engine Control Software (MRECS)

NASA Technical Reports Server (NTRS)

Tarrant, Charlie; Crook, Jerry

1997-01-01

The Modular Rocket Engine Control Software (MRECS) Program is a technology demonstration effort designed to advance the state-of-the-art in launch vehicle propulsion systems. Its emphasis is on developing and demonstrating a modular software architecture for a generic, advanced engine control system that will result in lower software maintenance (operations) costs. It effectively accommodates software requirements changes that occur due to hardware. technology upgrades and engine development testing. Ground rules directed by MSFC were to optimize modularity and implement the software in the Ada programming language. MRECS system software and the software development environment utilize Commercial-Off-the-Shelf (COTS) products. This paper presents the objectives and benefits of the program. The software architecture, design, and development environment are described. MRECS tasks are defined and timing relationships given. Major accomplishment are listed. MRECS offers benefits to a wide variety of advanced technology programs in the areas of modular software, architecture, reuse software, and reduced software reverification time related to software changes. Currently, the program is focused on supporting MSFC in accomplishing a Space Shuttle Main Engine (SSME) hot-fire test at Stennis Space Center and the Low Cost Boost Technology (LCBT) Program.

Modular Knowledge Representation and Reasoning in the Semantic Web

NASA Astrophysics Data System (ADS)

Serafini, Luciano; Homola, Martin

Construction of modular ontologies by combining different modules is becoming a necessity in ontology engineering in order to cope with the increasing complexity of the ontologies and the domains they represent. The modular ontology approach takes inspiration from software engineering, where modularization is a widely acknowledged feature. Distributed reasoning is the other side of the coin of modular ontologies: given an ontology comprising of a set of modules, it is desired to perform reasoning by combination of multiple reasoning processes performed locally on each of the modules. In the last ten years, a number of approaches for combining logics has been developed in order to formalize modular ontologies. In this chapter, we survey and compare the main formalisms for modular ontologies and distributed reasoning in the Semantic Web. We select four formalisms build on formal logical grounds of Description Logics: Distributed Description Logics, ℰ-connections, Package-based Description Logics and Integrated Distributed Description Logics. We concentrate on expressivity and distinctive modeling features of each framework. We also discuss reasoning capabilities of each framework.

Curriculum Development through YTS Modular Credit Accumulation.

ERIC Educational Resources Information Center

Further Education Unit, London (England).

This document reports the evaluation of the collaborately developed Modular Training Framework (MainFrame), a British curriculum development project, built around a commitment to a competency-based, modular credit accumulation program. The collaborators were three local education authorities (LEAs), those of Bedfordshire, Haringey, and Sheffield,…

Decrease in early right alpha band phase synchronization and late gamma band oscillations in processing syntax in music.

PubMed

Ruiz, María Herrojo; Koelsch, Stefan; Bhattacharya, Joydeep

2009-04-01

The present study investigated the neural correlates associated with the processing of music-syntactical irregularities as compared with regular syntactic structures in music. Previous studies reported an early ( approximately 200 ms) right anterior negative component (ERAN) by traditional event-related-potential analysis during music-syntactical irregularities, yet little is known about the underlying oscillatory and synchronization properties of brain responses which are supposed to play a crucial role in general cognition including music perception. First we showed that the ERAN was primarily represented by low frequency (<8 Hz) brain oscillations. Further, we found that music-syntactical irregularities as compared with music-syntactical regularities, were associated with (i) an early decrease in the alpha band (9-10 Hz) phase synchronization between right fronto-central and left temporal brain regions, and (ii) a late ( approximately 500 ms) decrease in gamma band (38-50 Hz) oscillations over fronto-central brain regions. These results indicate a weaker degree of long-range integration when the musical expectancy is violated. In summary, our results reveal neural mechanisms of music-syntactic processing that operate at different levels of cortical integration, ranging from early decrease in long-range alpha phase synchronization to late local gamma oscillations. 2008 Wiley-Liss, Inc.

Hierarchical functional modularity in the resting-state human brain.

PubMed

Ferrarini, Luca; Veer, Ilya M; Baerends, Evelinda; van Tol, Marie-José; Renken, Remco J; van der Wee, Nic J A; Veltman, Dirk J; Aleman, André; Zitman, Frans G; Penninx, Brenda W J H; van Buchem, Mark A; Reiber, Johan H C; Rombouts, Serge A R B; Milles, Julien

2009-07-01

Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain. Copyright 2009 Wiley-Liss, Inc

A Realtime Active Feedback Control System For Coupled Nonlinear Chemical Oscillators

NASA Astrophysics Data System (ADS)

Tompkins, Nathan; Fraden, Seth

2012-02-01

We study the manipulation and control of oscillatory networks. As a model system we use an emulsion of Belousov-Zhabotinsky (BZ) oscillators packed on a hexagonal lattice. Each drop is observed and perturbed by a Programmable Illumination Microscope (PIM). The PIM allows us to track individual BZ oscillators, calculate the phase and order parameters of every drop, and selectively perturb specific drops with photo illumination, all in realtime. To date we have determined the native attractor patterns for drops in 1D arrays and 2D hexagonal packing as a function of coupling strength as well as determined methods to move the system from one attractor basin to another. Current work involves implementing these attractor control methods with our experimental system and future work will likely include implementing a model neural network for use with photo controllable BZ emulsions.

Quenching oscillating behaviors in fractional coupled Stuart-Landau oscillators

NASA Astrophysics Data System (ADS)

Sun, Zhongkui; Xiao, Rui; Yang, Xiaoli; Xu, Wei

2018-03-01

Oscillation quenching has been widely studied during the past several decades in fields ranging from natural sciences to engineering, but investigations have so far been restricted to oscillators with an integer-order derivative. Here, we report the first study of amplitude death (AD) in fractional coupled Stuart-Landau oscillators with partial and/or complete conjugate couplings to explore oscillation quenching patterns and dynamics. It has been found that the fractional-order derivative impacts the AD state crucially. The area of the AD state increases along with the decrease of the fractional-order derivative. Furthermore, by introducing and adjusting a limiting feedback factor in coupling links, the AD state can be well tamed in fractional coupled oscillators. Hence, it provides one an effective approach to analyze and control the oscillating behaviors in fractional coupled oscillators.

Manufactured Housing--The Modular Home in Texas.

ERIC Educational Resources Information Center

Sindt, Roger P.

This report deals principally with modular homes (permanently sited structures) although it also presents some recent information on mobile homes. In 1976, modular home construction companies were surveyed in Texas and across the United States to assess the extent of their construction activity and market penetration and to gather some insight…

Data handling for the modular observatory

NASA Technical Reports Server (NTRS)

Taber, J. E.

1975-01-01

The current paper summarizes work undertaken at TRW for the EOS satellite and related missions, and it presents conclusions that lead to a flexible and low-cost overall system implementation. It shows how the usual communication and data handling functions must be altered to meet the modularization ground rules, and it demonstrates the modularization that is possible in the handling of wideband payload data both on board and on the ground.

Inferring oscillatory modulation in neural spike trains

PubMed Central

Arai, Kensuke; Kass, Robert E.

2017-01-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak. PMID:28985231

Connectional Modularity of Top-Down and Bottom-Up Multimodal Inputs to the Lateral Cortex of the Mouse Inferior Colliculus

PubMed Central

Lesicko, Alexandria M.H.; Hristova, Teodora S.; Maigler, Kathleen C.

2016-01-01

The lateral cortex of the inferior colliculus receives information from both auditory and somatosensory structures and is thought to play a role in multisensory integration. Previous studies in the rat have shown that this nucleus contains a series of distinct anatomical modules that stain for GAD-67 as well as other neurochemical markers. In the present study, we sought to better characterize these modules in the mouse inferior colliculus and determine whether the connectivity of other neural structures with the lateral cortex is spatially related to the distribution of these neurochemical modules. Staining for GAD-67 and other markers revealed a single modular network throughout the rostrocaudal extent of the mouse lateral cortex. Somatosensory inputs from the somatosensory cortex and dorsal column nuclei were found to terminate almost exclusively within these modular zones. However, projections from the auditory cortex and central nucleus of the inferior colliculus formed patches that interdigitate with the GAD-67-positive modules. These results suggest that the lateral cortex of the mouse inferior colliculus exhibits connectional as well as neurochemical modularity and may contain multiple segregated processing streams. This finding is discussed in the context of other brain structures in which neuroanatomical and connectional modularity have functional consequences. SIGNIFICANCE STATEMENT Many brain regions contain subnuclear microarchitectures, such as the matrix-striosome organization of the basal ganglia or the patch-interpatch organization of the visual cortex, that shed light on circuit complexities. In the present study, we demonstrate the presence of one such micro-organization in the rodent inferior colliculus. While this structure is typically viewed as an auditory integration center, its lateral cortex appears to be involved in multisensory operations and receives input from somatosensory brain regions. We show here that the lateral cortex can be

Synchronizing theta oscillations with direct-current stimulation strengthens adaptive control in the human brain.

PubMed

Reinhart, Robert M G; Zhu, Julia; Park, Sohee; Woodman, Geoffrey F

2015-07-28

Executive control and flexible adjustment of behavior following errors are essential to adaptive functioning. Loss of adaptive control may be a biomarker of a wide range of neuropsychiatric disorders, particularly in the schizophrenia spectrum. Here, we provide support for the view that oscillatory activity in the frontal cortex underlies adaptive adjustments in cognitive processing following errors. Compared with healthy subjects, patients with schizophrenia exhibited low frequency oscillations with abnormal temporal structure and an absence of synchrony over medial-frontal and lateral-prefrontal cortex following errors. To demonstrate that these abnormal oscillations were the origin of the impaired adaptive control in patients with schizophrenia, we applied noninvasive dc electrical stimulation over the medial-frontal cortex. This noninvasive stimulation descrambled the phase of the low-frequency neural oscillations that synchronize activity across cortical regions. Following stimulation, the behavioral index of adaptive control was improved such that patients were indistinguishable from healthy control subjects. These results provide unique causal evidence for theories of executive control and cortical dysconnectivity in schizophrenia.

Performance evaluation of coherent Ising machines against classical neural networks

NASA Astrophysics Data System (ADS)

Haribara, Yoshitaka; Ishikawa, Hitoshi; Utsunomiya, Shoko; Aihara, Kazuyuki; Yamamoto, Yoshihisa

2017-12-01

The coherent Ising machine is expected to find a near-optimal solution in various combinatorial optimization problems, which has been experimentally confirmed with optical parametric oscillators and a field programmable gate array circuit. The similar mathematical models were proposed three decades ago by Hopfield et al in the context of classical neural networks. In this article, we compare the computational performance of both models.

Learning of spatio-temporal codes in a coupled oscillator system.

PubMed

Orosz, Gábor; Ashwin, Peter; Townley, Stuart

2009-07-01

In this paper, we consider a learning strategy that allows one to transmit information between two coupled phase oscillator systems (called teaching and learning systems) via frequency adaptation. The dynamics of these systems can be modeled with reference to a number of partially synchronized cluster states and transitions between them. Forcing the teaching system by steady but spatially nonhomogeneous inputs produces cyclic sequences of transitions between the cluster states, that is, information about inputs is encoded via a "winnerless competition" process into spatio-temporal codes. The large variety of codes can be learned by the learning system that adapts its frequencies to those of the teaching system. We visualize the dynamics using "weighted order parameters (WOPs)" that are analogous to "local field potentials" in neural systems. Since spatio-temporal coding is a mechanism that appears in olfactory systems, the developed learning rules may help to extract information from these neural ensembles.

A Daily Oscillation in the Fundamental Frequency and Amplitude of Harmonic Syllables of Zebra Finch Song

PubMed Central

Wood, William E.; Osseward, Peter J.; Roseberry, Thomas K.; Perkel, David J.

2013-01-01

Complex motor skills are more difficult to perform at certain points in the day (for example, shortly after waking), but the daily trajectory of motor-skill error is more difficult to predict. By undertaking a quantitative analysis of the fundamental frequency (FF) and amplitude of hundreds of zebra finch syllables per animal per day, we find that zebra finch song follows a previously undescribed daily oscillation. The FF and amplitude of harmonic syllables rises across the morning, reaching a peak near mid-day, and then falls again in the late afternoon until sleep. This oscillation, although somewhat variable, is consistent across days and across animals and does not require serotonin, as animals with serotonergic lesions maintained daily oscillations. We hypothesize that this oscillation is driven by underlying physiological factors which could be shared with other taxa. Song production in zebra finches is a model system for studying complex learned behavior because of the ease of gathering comprehensive behavioral data and the tractability of the underlying neural circuitry. The daily oscillation that we describe promises to reveal new insights into how time of day affects the ability to accomplish a variety of complex learned motor skills. PMID:24312654

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.

Robust autoassociative memory with coupled networks of Kuramoto-type oscillators

NASA Astrophysics Data System (ADS)

Heger, Daniel; Krischer, Katharina

2016-08-01

Uncertain recognition success, unfavorable scaling of connection complexity, or dependence on complex external input impair the usefulness of current oscillatory neural networks for pattern recognition or restrict technical realizations to small networks. We propose a network architecture of coupled oscillators for pattern recognition which shows none of the mentioned flaws. Furthermore we illustrate the recognition process with simulation results and analyze the dynamics analytically: Possible output patterns are isolated attractors of the system. Additionally, simple criteria for recognition success are derived from a lower bound on the basins of attraction.

Time Within:. the Perceptual Rivalry Switch as a Neural Clock

NASA Astrophysics Data System (ADS)

Pettigrew, John D.; Tilden, Jan D.

2005-10-01

Attention is drawn to weaknesses in the case for an external, physical basis for time's perceptual phenomena, raising the possibility of a Darwinian evolutionary explanation for the apparent flow, structure and arrow of time. We develop the hypothesis that, of all arrows of time identified by physicists and philosophers, the most fundamental is the psychological arrow. Based on findings of an on-going program of empirical research, we suggest a neural basis for time phenomena in the rhythmicity and plasticity of one of the brainstem dopaminergic nuclei, the venetral tegmental area (VTA). We examine links between neural time-keeping and perceptual rivalry and discuss evidence that rivalry is mediated by the VTA which functions as an ultradian oscillator. Further research is suggested, which could challenge or support the hypothesis of the VTA as an important neural time-keeper and the subjective basis of the asymmetric phenomena of time.

Advanced Modular Power Approach to Affordable, Supportable Space Systems

NASA Technical Reports Server (NTRS)

Oeftering, Richard C.; Kimnach, Greg L.; Fincannon, James; Mckissock,, Barbara I.; Loyselle, Patricia L.; Wong, Edmond

2013-01-01

Recent studies of missions to the Moon, Mars and Near Earth Asteroids (NEA) indicate that these missions often involve several distinct separately launched vehicles that must ultimately be integrated together in-flight and operate as one unit. Therefore, it is important to see these vehicles as elements of a larger segmented spacecraft rather than separate spacecraft flying in formation. The evolution of large multi-vehicle exploration architecture creates the need (and opportunity) to establish a global power architecture that is common across all vehicles. The Advanced Exploration Systems (AES) Modular Power System (AMPS) project managed by NASA Glenn Research Center (GRC) is aimed at establishing the modular power system architecture that will enable power systems to be built from a common set of modular building blocks. The project is developing, demonstrating and evaluating key modular power technologies that are expected to minimize non-recurring development costs, reduce recurring integration costs, as well as, mission operational and support costs. Further, modular power is expected to enhance mission flexibility, vehicle reliability, scalability and overall mission supportability. The AMPS project not only supports multi-vehicle architectures but should enable multi-mission capability as well. The AMPS technology development involves near term demonstrations involving developmental prototype vehicles and field demonstrations. These operational demonstrations not only serve as a means of evaluating modular technology but also provide feedback to developers that assure that they progress toward truly flexible and operationally supportable modular power architecture.

Optimal Network Modularity for Information Diffusion

NASA Astrophysics Data System (ADS)

Nematzadeh, Azadeh; Ferrara, Emilio; Flammini, Alessandro; Ahn, Yong-Yeol

2014-08-01

We investigate the impact of community structure on information diffusion with the linear threshold model. Our results demonstrate that modular structure may have counterintuitive effects on information diffusion when social reinforcement is present. We show that strong communities can facilitate global diffusion by enhancing local, intracommunity spreading. Using both analytic approaches and numerical simulations, we demonstrate the existence of an optimal network modularity, where global diffusion requires the minimal number of early adopters.

Implementation of a Synchronized Oscillator Circuit for Fast Sensing and Labeling of Image Objects

PubMed Central

Kowalski, Jacek; Strzelecki, Michal; Kim, Hyongsuk

2011-01-01

We present an application-specific integrated circuit (ASIC) CMOS chip that implements a synchronized oscillator cellular neural network with a matrix size of 32 × 32 for object sensing and labeling in binary images. Networks of synchronized oscillators are a recently developed tool for image segmentation and analysis. Its parallel network operation is based on a “temporary correlation” theory that attempts to describe scene recognition as if performed by the human brain. The synchronized oscillations of neuron groups attract a person’s attention if he or she is focused on a coherent stimulus (image object). For more than one perceived stimulus, these synchronized patterns switch in time between different neuron groups, thus forming temporal maps that code several features of the analyzed scene. In this paper, a new oscillator circuit based on a mathematical model is proposed, and the network architecture and chip functional blocks are presented and discussed. The proposed chip is implemented in AMIS 0.35 μm C035M-D 5M/1P technology. An application of the proposed network chip for the segmentation of insulin-producing pancreatic islets in magnetic resonance liver images is presented. PMID:22163803

Local modular Hamiltonians from the quantum null energy condition

NASA Astrophysics Data System (ADS)

Koeller, Jason; Leichenauer, Stefan; Levine, Adam; Shahbazi-Moghaddam, Arvin

2018-03-01

The vacuum modular Hamiltonian K of the Rindler wedge in any relativistic quantum field theory is given by the boost generator. Here we investigate the modular Hamiltonian for more general half-spaces which are bounded by an arbitrary smooth cut of a null plane. We derive a formula for the second derivative of the modular Hamiltonian with respect to the coordinates of the cut which schematically reads K''=Tv v . This formula can be integrated twice to obtain a simple expression for the modular Hamiltonian. The result naturally generalizes the standard expression for the Rindler modular Hamiltonian to this larger class of regions. Our primary assumptions are the quantum null energy condition—an inequality between the second derivative of the von Neumann entropy of a region and the stress tensor—and its saturation in the vacuum for these regions. We discuss the validity of these assumptions in free theories and holographic theories to all orders in 1 /N .

Standardized Modular Power Interfaces for Future Space Explorations Missions

NASA Technical Reports Server (NTRS)

Oeftering, Richard

2015-01-01

Earlier studies show that future human explorations missions are composed of multi-vehicle assemblies with interconnected electric power systems. Some vehicles are often intended to serve as flexible multi-purpose or multi-mission platforms. This drives the need for power architectures that can be reconfigured to support this level of flexibility. Power system developmental costs can be reduced, program wide, by utilizing a common set of modular building blocks. Further, there are mission operational and logistics cost benefits of using a common set of modular spares. These benefits are the goals of the Advanced Exploration Systems (AES) Modular Power System (AMPS) project. A common set of modular blocks requires a substantial level of standardization in terms of the Electrical, Data System, and Mechanical interfaces. The AMPS project is developing a set of proposed interface standards that will provide useful guidance for modular hardware developers but not needlessly constrain technology options, or limit future growth in capability. In 2015 the AMPS project focused on standardizing the interfaces between the elements of spacecraft power distribution and energy storage. The development of the modular power standard starts with establishing mission assumptions and ground rules to define design application space. The standards are defined in terms of AMPS objectives including Commonality, Reliability-Availability, Flexibility-Configurability and Supportability-Reusability. The proposed standards are aimed at assembly and sub-assembly level building blocks. AMPS plans to adopt existing standards for spacecraft command and data, software, network interfaces, and electrical power interfaces where applicable. Other standards including structural encapsulation, heat transfer, and fluid transfer, are governed by launch and spacecraft environments and bound by practical limitations of weight and volume. Developing these mechanical interface standards is more difficult but

Modular, Reconfigurable, High-Energy Technology Development

NASA Technical Reports Server (NTRS)

Carrington, Connie; Howell, Joe

2006-01-01

The Modular, Reconfigurable High-Energy (MRHE) Technology Demonstrator project was to have been a series of ground-based demonstrations to mature critical technologies needed for in-space assembly of a highpower high-voltage modular spacecraft in low Earth orbit, enabling the development of future modular solar-powered exploration cargo-transport vehicles and infrastructure. MRHE was a project in the High Energy Space Systems (HESS) Program, within NASA's Exploration Systems Research and Technology (ESR&T) Program. NASA participants included Marshall Space Flight Center (MSFC), the Jet Propulsion Laboratory (JPL), and Glenn Research Center (GRC). Contractor participants were the Boeing Phantom Works in Huntsville, AL, Lockheed Martin Advanced Technology Center in Palo Alto, CA, ENTECH, Inc. in Keller, TX, and the University of AL Huntsville (UAH). MRHE's technical objectives were to mature: (a) lightweight, efficient, high-voltage, radiation-resistant solar power generation (SPG) technologies; (b) innovative, lightweight, efficient thermal management systems; (c) efficient, 100kW-class, high-voltage power delivery systems from an SPG to an electric thruster system; (d) autonomous rendezvous and docking technology for in-space assembly of modular, reconfigurable spacecraft; (e) robotic assembly of modular space systems; and (f) modular, reconfigurable distributed avionics technologies. Maturation of these technologies was to be implemented through a series of increasingly-inclusive laboratory demonstrations that would have integrated and demonstrated two systems-of-systems: (a) the autonomous rendezvous and docking of modular spacecraft with deployable structures, robotic assembly, reconfiguration both during assembly and (b) the development and integration of an advanced thermal heat pipe and a high-voltage power delivery system with a representative lightweight high-voltage SPG array. In addition, an integrated simulation testbed would have been developed

Analysis of Advanced Modular Power Systems (AMPS) for Deep Space Exploration

NASA Technical Reports Server (NTRS)

Oeftering, Richard; Soeder, James F.; Beach, Ray

2014-01-01

The Advanced Modular Power Systems (AMPS) project is developing a modular approach to spacecraft power systems for exploration beyond Earth orbit. AMPS is intended to meet the need of reducing the cost of design development, test and integration and also reducing the operational logistics cost of supporting exploration missions. AMPS seeks to establish modular power building blocks with standardized electrical, mechanical, thermal and data interfaces that can be applied across multiple exploration vehicles. The presentation discusses the results of a cost analysis that compares the cost of the modular approach against a traditional non-modular approach.

Modularization of Courses.

ERIC Educational Resources Information Center

Eastern Arizona Coll., Thatcher.

Eastern Arizona College has developed a modularized system of instruction for five vocational and vocationally related courses--Introduction to Business, Business Mathematics, English, Drafting, and Electronics. Each course is divided into independent segments of instruction and students have open-entry and exit options. This document reviews the…

θ-Band and β-Band Neural Activity Reflects Independent Syllable Tracking and Comprehension of Time-Compressed Speech.

PubMed

Pefkou, Maria; Arnal, Luc H; Fontolan, Lorenzo; Giraud, Anne-Lise

2017-08-16

Recent psychophysics data suggest that speech perception is not limited by the capacity of the auditory system to encode fast acoustic variations through neural γ activity, but rather by the time given to the brain to decode them. Whether the decoding process is bounded by the capacity of θ rhythm to follow syllabic rhythms in speech, or constrained by a more endogenous top-down mechanism, e.g., involving β activity, is unknown. We addressed the dynamics of auditory decoding in speech comprehension by challenging syllable tracking and speech decoding using comprehensible and incomprehensible time-compressed auditory sentences. We recorded EEGs in human participants and found that neural activity in both θ and γ ranges was sensitive to syllabic rate. Phase patterns of slow neural activity consistently followed the syllabic rate (4-14 Hz), even when this rate went beyond the classical θ range (4-8 Hz). The power of θ activity increased linearly with syllabic rate but showed no sensitivity to comprehension. Conversely, the power of β (14-21 Hz) activity was insensitive to the syllabic rate, yet reflected comprehension on a single-trial basis. We found different long-range dynamics for θ and β activity, with β activity building up in time while more contextual information becomes available. This is consistent with the roles of θ and β activity in stimulus-driven versus endogenous mechanisms. These data show that speech comprehension is constrained by concurrent stimulus-driven θ and low-γ activity, and by endogenous β activity, but not primarily by the capacity of θ activity to track the syllabic rhythm. SIGNIFICANCE STATEMENT Speech comprehension partly depends on the ability of the auditory cortex to track syllable boundaries with θ-range neural oscillations. The reason comprehension drops when speech is accelerated could hence be because θ oscillations can no longer follow the syllabic rate. Here, we presented subjects with comprehensible and

Functional modularity in lake-dwelling characin fishes of Mexico

PubMed Central

Bautista, Amando; Herder, Fabian; Doadrio, Ignacio

2017-01-01

Modular evolution promotes evolutionary change, allowing independent variation across morphological units. Recent studies have shown that under contrasting ecological pressures, patterns of modularity could be related to divergent evolution. The main goal of the present study was to evaluate the presence of modular evolution in two sister lacustrine species, Astyanax aeneus and A. caballeroi, which are differentiated by their trophic habits. Two different datasets were analyzed: (1) skull X-rays from 73 specimens (35 A. aeneus and 38 A. caballeroi) to characterize skull variation patterns, considering both species and sex effects. For this dataset, three different modularity hypotheses were tested, previously supported in other lacustrine divergent species; (2) a complete body shape dataset was also tested for four modularity hypotheses, which included a total of 196 individuals (110 Astyanax aeneus and 86 A. caballeroi). Skull shape showed significant differences among species and sex (P < 0.001), where Astyanax caballeroi species showed an upwardly projected mandible and larger preorbital region. For the skull dataset, the modularity hypothesis ranked first included three partitioning modules. While for the complete body dataset the best ranked hypothesis included two modules (head vs the rest of the body), being significant only for A. caballeroi. PMID:28951817

Functional modularity in lake-dwelling characin fishes of Mexico.

PubMed

Ornelas-García, Claudia Patricia; Bautista, Amando; Herder, Fabian; Doadrio, Ignacio

2017-01-01

Modular evolution promotes evolutionary change, allowing independent variation across morphological units. Recent studies have shown that under contrasting ecological pressures, patterns of modularity could be related to divergent evolution. The main goal of the present study was to evaluate the presence of modular evolution in two sister lacustrine species, Astyanax aeneus and A. caballeroi , which are differentiated by their trophic habits. Two different datasets were analyzed: (1) skull X-rays from 73 specimens (35 A. aeneus and 38 A. caballeroi ) to characterize skull variation patterns, considering both species and sex effects. For this dataset, three different modularity hypotheses were tested, previously supported in other lacustrine divergent species; (2) a complete body shape dataset was also tested for four modularity hypotheses, which included a total of 196 individuals (110 Astyanax aeneus and 86 A. caballeroi ). Skull shape showed significant differences among species and sex ( P  < 0.001), where Astyanax caballeroi species showed an upwardly projected mandible and larger preorbital region. For the skull dataset, the modularity hypothesis ranked first included three partitioning modules. While for the complete body dataset the best ranked hypothesis included two modules (head vs the rest of the body), being significant only for A. caballeroi .

On Classification of Modular Categories by Rank: Table A.1

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

Bruillard, Paul; Ng, Siu-Hung; Rowell, Eric C.

2016-04-10

The feasibility of a classification-by-rank program for modular categories follows from the Rank-Finiteness Theorem. We develop arithmetic, representation theoretic and algebraic methods for classifying modular categories by rank. As an application, we determine all possible fusion rules for all rank=5 modular categories and describe the corresponding monoidal equivalence classes.

Modular Robotic Vehicle

NASA Technical Reports Server (NTRS)

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

Modular chemiresistive sensor

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

Alam, Maksudul M.; Sampathkumaran, Uma

The present invention relates to a modular chemiresistive sensor. In particular, a modular chemiresistive sensor for hypergolic fuel and oxidizer leak detection, carbon dioxide monitoring and detection of disease biomarkers. The sensor preferably has two gold or platinum electrodes mounted on a silicon substrate where the electrodes are connected to a power source and are separated by a gap of 0.5 to 4.0 .mu.M. A polymer nanowire or carbon nanotube spans the gap between the electrodes and connects the electrodes electrically. The electrodes are further connected to a circuit board having a processor and data storage, where the processor canmore » measure current and voltage values between the electrodes and compare the current and voltage values with current and voltage values stored in the data storage and assigned to particular concentrations of a pre-determined substance such as those listed above or a variety of other substances.« less

Voluntary reduction of force variability via modulation of low-frequency oscillations.

PubMed

Park, Seoung Hoon; Casamento-Moran, Agostina; Yacoubi, Basma; Christou, Evangelos A

2017-09-01

Visual feedback can influence the force output by changing the power in frequencies below 1 Hz. However, it remains unknown whether visual guidance can help an individual reduce force variability voluntarily. The purpose of this study, therefore, was to determine whether an individual can voluntarily reduce force variability during constant contractions with visual guidance, and whether this reduction is associated with a decrease in the power of low-frequency oscillations (0-1 Hz) in force and muscle activity. Twenty young adults (27.6 ± 3.4 years) matched a force target of 15% MVC (maximal voluntary contraction) with ankle dorsiflexion. Participants performed six visually unrestricted contractions, from which we selected the trial with the least variability. Following, participants performed six visually guided contractions and were encouraged to reduce their force variability within two guidelines (±1 SD of the least variable unrestricted trial). Participants decreased the SD of force by 45% (P < 0.001) during the guided condition, without changing mean force (P > 0.2). The decrease in force variability was associated with decreased low-frequency oscillations (0-1 Hz) in force (R 2  = 0.59), which was associated with decreased low-frequency oscillations in EMG bursts (R 2  = 0.35). The reduction in low-frequency oscillations in EMG burst was positively associated with power in the interference EMG from 35 to 60 Hz (R 2  = 0.47). In conclusion, voluntary reduction of force variability is associated with decreased low-frequency oscillations in EMG bursts and consequently force output. We provide novel evidence that visual guidance allows healthy young adults to reduce force variability voluntarily likely by adjusting the low-frequency oscillations in the neural drive.

Resurgence of oscillation in coupled oscillators under delayed cyclic interaction

NASA Astrophysics Data System (ADS)

Bera, Bidesh K.; Majhi, Soumen; Ghosh, Dibakar

2017-07-01

This paper investigates the emergence of amplitude death and revival of oscillations from the suppression states in a system of coupled dynamical units interacting through delayed cyclic mode. In order to resurrect the oscillation from amplitude death state, we introduce asymmetry and feedback parameter in the cyclic coupling forms as a result of which the death region shrinks due to higher asymmetry and lower feedback parameter values for coupled oscillatory systems. Some analytical conditions are derived for amplitude death and revival of oscillations in two coupled limit cycle oscillators and corresponding numerical simulations confirm the obtained theoretical results. We also report that the death state and revival of oscillations from quenched state are possible in the network of identical coupled oscillators. The proposed mechanism has also been examined using chaotic Lorenz oscillator.

Modular Rocket Engine Control Software (MRECS)

NASA Technical Reports Server (NTRS)

Tarrant, C.; Crook, J.

1998-01-01

The Modular Rocket Engine Control Software (MRECS) Program is a technology demonstration effort designed to advance the state-of-the-art in launch vehicle propulsion systems. Its emphasis is on developing and demonstrating a modular software architecture for advanced engine control systems that will result in lower software maintenance (operations) costs. It effectively accommodates software requirement changes that occur due to hardware technology upgrades and engine development testing. Ground rules directed by MSFC were to optimize modularity and implement the software in the Ada programming language. MRECS system software and the software development environment utilize Commercial-Off-the-Shelf (COTS) products. This paper presents the objectives, benefits, and status of the program. The software architecture, design, and development environment are described. MRECS tasks are defined and timing relationships given. Major accomplishments are listed. MRECS offers benefits to a wide variety of advanced technology programs in the areas of modular software architecture, reuse software, and reduced software reverification time related to software changes. MRECS was recently modified to support a Space Shuttle Main Engine (SSME) hot-fire test. Cold Flow and Flight Readiness Testing were completed before the test was cancelled. Currently, the program is focused on supporting NASA MSFC in accomplishing development testing of the Fastrac Engine, part of NASA's Low Cost Technologies (LCT) Program. MRECS will be used for all engine development testing.

Control of Oscillation Patterns in a Symmetric Coupled Biological Oscillator System

NASA Astrophysics Data System (ADS)

Takamatsu, Atsuko; Tanaka, Reiko; Yamamoto, Takatoki; Fujii, Teruo

2003-08-01

A chain of three-oscillator system was constructed with living biological oscillators of phasmodial slime mold, Physarum polycehalum and the oscillation patterns were analyzed by the symmetric Hopf bifurcation theory using group theory. Multi-stability of oscillation patterns was observed, even when the coupling strength was fixed. This suggests that the coupling strength is not an effective parameter to obtain a desired oscillation pattern among the multiple patterns. Here we propose a method to control oscillation patterns using resonance to external stimulus and demonstrate pattern switching induced by frequency resonance given to only one of oscillators in the system.

Multi-kilowatt modularized spacecraft power processing system development

NASA Technical Reports Server (NTRS)

Andrews, R. E.; Hayden, J. H.; Hedges, R. T.; Rehmann, D. W.

1975-01-01

A review of existing information pertaining to spacecraft power processing systems and equipment was accomplished with a view towards applicability to the modularization of multi-kilowatt power processors. Power requirements for future spacecraft were determined from the NASA mission model-shuttle systems payload data study which provided the limits for modular power equipment capabilities. Three power processing systems were compared to evaluation criteria to select the system best suited for modularity. The shunt regulated direct energy transfer system was selected by this analysis for a conceptual design effort which produced equipment specifications, schematics, envelope drawings, and power module configurations.

Modular cathode assemblies and methods of using the same for electrochemical reduction

DOEpatents

Wiedmeyer, Stanley G; Barnes, Laurel A; Williamson, Mark A; Willit, James L

2014-12-02

Modular cathode assemblies are useable in electrolytic reduction systems and include a basket through which fluid electrolyte may pass and exchange charge with a material to be reduced in the basket. The basket can be divided into upper and lower sections to provide entry for the material. Example embodiment cathode assemblies may have any shape to permit modular placement at any position in reduction systems. Modular cathode assemblies include a cathode plate in the basket, to which unique and opposite electrical power may be supplied. Example embodiment modular cathode assemblies may have standardized electrical connectors. Modular cathode assemblies may be supported by a top plate of an electrolytic reduction system. Electrolytic oxide reduction systems are operated by positioning modular cathode and anode assemblies at desired positions, placing a material in the basket, and charging the modular assemblies to reduce the metal oxide.

Using a Simple Neural Network to Delineate Some Principles of Distributed Economic Choice.

PubMed

Balasubramani, Pragathi P; Moreno-Bote, Rubén; Hayden, Benjamin Y

2018-01-01

The brain uses a mixture of distributed and modular organization to perform computations and generate appropriate actions. While the principles under which the brain might perform computations using modular systems have been more amenable to modeling, the principles by which the brain might make choices using distributed principles have not been explored. Our goal in this perspective is to delineate some of those distributed principles using a neural network method and use its results as a lens through which to reconsider some previously published neurophysiological data. To allow for direct comparison with our own data, we trained the neural network to perform binary risky choices. We find that value correlates are ubiquitous and are always accompanied by non-value information, including spatial information (i.e., no pure value signals). Evaluation, comparison, and selection were not distinct processes; indeed, value signals even in the earliest stages contributed directly, albeit weakly, to action selection. There was no place, other than at the level of action selection, at which dimensions were fully integrated. No units were specialized for specific offers; rather, all units encoded the values of both offers in an anti-correlated format, thus contributing to comparison. Individual network layers corresponded to stages in a continuous rotation from input to output space rather than to functionally distinct modules. While our network is likely to not be a direct reflection of brain processes, we propose that these principles should serve as hypotheses to be tested and evaluated for future studies.

Endogenous modulation of low frequency oscillations by temporal expectations

PubMed Central

Cravo, Andre M.; Rohenkohl, Gustavo; Wyart, Valentin

2011-01-01

Recent studies have associated increasing temporal expectations with synchronization of higher frequency oscillations and suppression of lower frequencies. In this experiment, we explore a proposal that low-frequency oscillations provide a mechanism for regulating temporal expectations. We used a speeded Go/No-go task and manipulated temporal expectations by changing the probability of target presentation after certain intervals. Across two conditions, the temporal conditional probability of target events differed substantially at the first of three possible intervals. We found that reactions times differed significantly at this first interval across conditions, decreasing with higher temporal expectations. Interestingly, the power of theta activity (4–8 Hz), distributed over central midline sites, also differed significantly across conditions at this first interval. Furthermore, we found a transient coupling between theta phase and beta power after the first interval in the condition with high temporal expectation for targets at this time point. Our results suggest that the adjustments in theta power and the phase-power coupling between theta and beta contribute to a central mechanism for controlling neural excitability according to temporal expectations. PMID:21900508

Multiple D3-Instantons and Mock Modular Forms II

NASA Astrophysics Data System (ADS)

Alexandrov, Sergei; Banerjee, Sibasish; Manschot, Jan; Pioline, Boris

2018-03-01

We analyze the modular properties of D3-brane instanton corrections to the hypermultiplet moduli space in type IIB string theory compactified on a Calabi-Yau threefold. In Part I, we found a necessary condition for the existence of an isometric action of S-duality on this moduli space: the generating function of DT invariants in the large volume attractor chamber must be a vector-valued mock modular form with specified modular properties. In this work, we prove that this condition is also sufficient at two-instanton order. This is achieved by producing a holomorphic action of {SL(2,Z)} on the twistor space which preserves the holomorphic contact structure. The key step is to cancel the anomalous modular variation of the Darboux coordinates by a local holomorphic contact transformation, which is generated by a suitable indefinite theta series. For this purpose we introduce a new family of theta series of signature (2, n - 2), find their modular completion, and conjecture sufficient conditions for their convergence, which may be of independent mathematical interest.

Chemical oscillator as a generalized Rayleigh oscillator.

PubMed

Ghosh, Shyamolina; Ray, Deb Shankar

2013-10-28

We derive the conditions under which a set of arbitrary two dimensional autonomous kinetic equations can be reduced to the form of a generalized Rayleigh oscillator which admits of limit cycle solution. This is based on a linear transformation of field variables which can be found by inspection of the kinetic equations. We illustrate the scheme with the help of several chemical and bio-chemical oscillator models to show how they can be cast as a generalized Rayleigh oscillator.

24 CFR 3282.12 - Excluded structures-modular homes.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 24 Housing and Urban Development 5 2013-04-01 2013-04-01 false Excluded structures-modular homes... HOUSING AND URBAN DEVELOPMENT MANUFACTURED HOME PROCEDURAL AND ENFORCEMENT REGULATIONS General § 3282.12 Excluded structures—modular homes. (a) The purpose of this section is to provide the certification...

24 CFR 3282.12 - Excluded structures-modular homes.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 24 Housing and Urban Development 5 2010-04-01 2010-04-01 false Excluded structures-modular homes... HOUSING AND URBAN DEVELOPMENT MANUFACTURED HOME PROCEDURAL AND ENFORCEMENT REGULATIONS General § 3282.12 Excluded structures—modular homes. (a) The purpose of this section is to provide the certification...

Multiple D3-Instantons and Mock Modular Forms I

NASA Astrophysics Data System (ADS)

Alexandrov, Sergei; Banerjee, Sibasish; Manschot, Jan; Pioline, Boris

2017-07-01

We study D3-instanton corrections to the hypermultiplet moduli space in type IIB string theory compactified on a Calabi-Yau threefold. In a previous work, consistency of D3-instantons with S-duality was established at first order in the instanton expansion, using the modular properties of the M5-brane elliptic genus. We extend this analysis to the two-instanton level, where wall-crossing phenomena start playing a role. We focus on the contact potential, an analogue of the Kähler potential which must transform as a modular form under S-duality. We show that it can be expressed in terms of a suitable modification of the partition function of D4-D2-D0 BPS black holes, constructed out of the generating function of MSW invariants (the latter coincide with Donaldson-Thomas invariants in a particular chamber). Modular invariance of the contact potential then requires that, in the case where the D3-brane wraps a reducible divisor, the generating function of MSW invariants must transform as a vector-valued mock modular form, with a specific modular completion built from the MSW invariants of the constituents. Physically, this gives a powerful constraint on the degeneracies of BPS black holes. Mathematically, our result gives a universal prediction for the modular properties of Donaldson-Thomas invariants of pure two-dimensional sheaves.

Input-dependent modulation of MEG gamma oscillations reflects gain control in the visual cortex.

PubMed

Orekhova, Elena V; Sysoeva, Olga V; Schneiderman, Justin F; Lundström, Sebastian; Galuta, Ilia A; Goiaeva, Dzerasa E; Prokofyev, Andrey O; Riaz, Bushra; Keeler, Courtney; Hadjikhani, Nouchine; Gillberg, Christopher; Stroganova, Tatiana A

2018-05-31

Gamma-band oscillations arise from the interplay between neural excitation (E) and inhibition (I) and may provide a non-invasive window into the state of cortical circuitry. A bell-shaped modulation of gamma response power by increasing the intensity of sensory input was observed in animals and is thought to reflect neural gain control. Here we sought to find a similar input-output relationship in humans with MEG via modulating the intensity of a visual stimulation by changing the velocity/temporal-frequency of visual motion. In the first experiment, adult participants observed static and moving gratings. The frequency of the MEG gamma response monotonically increased with motion velocity whereas power followed a bell-shape. In the second experiment, on a large group of children and adults, we found that despite drastic developmental changes in frequency and power of gamma oscillations, the relative suppression at high motion velocities was scaled to the same range of values across the life-span. In light of animal and modeling studies, the modulation of gamma power and frequency at high stimulation intensities characterizes the capacity of inhibitory neurons to counterbalance increasing excitation in visual networks. Gamma suppression may thus provide a non-invasive measure of inhibitory-based gain control in the healthy and diseased brain.

Investigation of natural circulation instability and transients in passively safe novel modular reactor

NASA Astrophysics Data System (ADS)

Shi, Shanbin

The Purdue Novel Modular Reactor (NMR) is a new type small modular reactor (SMR) that belongs to the design of boiling water reactor (BWR). Specifically, the NMR is one third the height and area of a conventional BWR reactor pressure vessel (RPV) with an electric output of 50 MWe. The fuel cycle length of the NMR-50 is extended up to 10 years due to optimized neutronics design. The NMR-50 is designed with double passive engineering safety system. However, natural circulation BWRs (NCBWR) could experience certain operational difficulties due to flow instabilities that occur at low pressure and low power conditions. Static instabilities (i.e. flow excursion (Ledinegg) instability and flow pattern transition instability) and dynamic instabilities (i.e. density wave instability and flashing/condensation instability) pose a significant challenge in two-phase natural circulation systems. In order to experimentally study the natural circulation flow instability, a proper scaling methodology is needed to build a reduced-size test facility. The scaling analysis of the NMR uses a three-level scaling method, which was developed and applied for the design of the Purdue Multi-dimensional Integral Test Assembly (PUMA). Scaling criteria is derived from dimensionless field equations and constitutive equations. The scaling process is validated by the RELAP5 analysis for both steady state and startup transients. A new well-scaled natural circulation test facility is designed and constructed based on the scaling analysis of the NMR-50. The experimental facility is installed with different equipment to measure various thermal-hydraulic parameters such as pressure, temperature, mass flow rate and void fraction. Characterization tests are performed before the startup transient tests and quasi-steady tests to determine the loop flow resistance. The controlling system and data acquisition system are programmed with LabVIEW to realize the real-time control and data storage. The thermal

Analysis of Oscillatory Neural Activity in Series Network Models of Parkinson's Disease During Deep Brain Stimulation.

PubMed

Davidson, Clare M; de Paor, Annraoi M; Cagnan, Hayriye; Lowery, Madeleine M

2016-01-01

Parkinson's disease is a progressive, neurodegenerative disorder, characterized by hallmark motor symptoms. It is associated with pathological, oscillatory neural activity in the basal ganglia. Deep brain stimulation (DBS) is often successfully used to treat medically refractive Parkinson's disease. However, the selection of stimulation parameters is based on qualitative assessment of the patient, which can result in a lengthy tuning period and a suboptimal choice of parameters. This study explores fourth-order, control theory-based models of oscillatory activity in the basal ganglia. Describing function analysis is applied to examine possible mechanisms for the generation of oscillations in interacting nuclei and to investigate the suppression of oscillations with high-frequency stimulation. The theoretical results for the suppression of the oscillatory activity obtained using both the fourth-order model, and a previously described second-order model, are optimized to fit clinically recorded local field potential data obtained from Parkinsonian patients with implanted DBS. Close agreement between the power of oscillations recorded for a range of stimulation amplitudes is observed ( R(2)=0.69-0.99 ). The results suggest that the behavior of the system and the suppression of pathological neural oscillations with DBS is well described by the macroscopic models presented. The results also demonstrate that in this instance, a second-order model is sufficient to model the clinical data, without the need for added complexity. Describing the system behavior with computationally efficient models could aid in the identification of optimal stimulation parameters for patients in a clinical environment.

Modular cathode assemblies and methods of using the same for electrochemical reduction

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

Wiedmeyer, Stanley G.; Barnes, Laurel A.; Williamson, Mark A.

Modular cathode assemblies are useable in electrolytic reduction systems and include a basket through which fluid electrolyte may pass and exchange charge with a material to be reduced in the basket. The basket can be divided into upper and lower sections to provide entry for the material. Example embodiment cathode assemblies may have any shape to permit modular placement at any position in reduction systems. Modular cathode assemblies include a cathode plate in the basket, to which unique and opposite electrical power may be supplied. Example embodiment modular cathode assemblies may have standardized electrical connectors. Modular cathode assemblies may bemore » supported by a top plate of an electrolytic reduction system. Electrolytic oxide reduction systems are operated by positioning modular cathode and anode assemblies at desired positions, placing a material in the basket, and charging the modular assemblies to reduce the metal oxide.« less

Neuromuscular mechanisms and neural strategies in the control of time-varying muscle contractions.

PubMed

Erimaki, Sophia; Agapaki, Orsalia M; Christakos, Constantinos N

2013-09-01

The organization of the neural input to motoneurons that underlies time-varying muscle force is assumed to depend on muscle transfer characteristics and neural strategies or control modes utilizing sensory signals. We jointly addressed these interlinked, but previously studied individually and partially, issues for sinusoidal (range 0.5-5.0 Hz) force-tracking contractions of a human finger muscle. Using spectral and correlation analyses of target signal, force signal, and motor unit (MU) discharges, we studied 1) patterns of such discharges, allowing inferences on the motoneuronal input; 2) transformation of MU population activity (EMG) into quasi-sinusoidal force; and 3) relation of force oscillation to target, carrying information on the input's organization. A broad view of force control mechanisms and strategies emerged. Specifically, synchronized MU and EMG modulations, reflecting a frequency-modulated motoneuronal input, accompanied the force variations. Gain and delay drops between EMG modulation and force oscillation, critical for the appropriate organization of this input, occurred with increasing target frequency. According to our analyses, gain compensation was achieved primarily through rhythmical activation/deactivation of higher-threshold MUs and secondarily through the adaptation of the input's strength expected during tracking tasks. However, the input's timing was not adapted to delay behaviors and seemed to depend on the control modes employed. Thus, for low-frequency targets, the force oscillation was highly coherent with, but led, a target, this timing error being compatible with predictive feedforward control partly based on the target's derivatives. In contrast, the force oscillation was weakly coherent, but in phase, with high-frequency targets, suggesting control mainly based on a target's rhythm.

Dissociation of modular total hip arthroplasty at the neck-stem interface without dislocation.

PubMed

Kouzelis, A; Georgiou, C S; Megas, P

2012-12-01

Modular femoral and acetabular components are now widely used, but only a few complications related to the modularity itself have been reported. We describe a case of dissociation of the modular total hip arthroplasty (THA) at the femoral neck-stem interface during walking. The possible causes of this dissociation are discussed. Successful treatment was provided with surgical revision and replacement of the modular neck components. Surgeons who use modular components in hip arthroplasties should be aware of possible early complications in which the modularity of the prostheses is the major factor of failure.

MOBS - A modular on-board switching system

NASA Astrophysics Data System (ADS)

Berner, W.; Grassmann, W.; Piontek, M.

The authors describe a multibeam satellite system that is designed for business services and for communications at a high bit rate. The repeater is regenerative with a modular onboard switching system. It acts not only as baseband switch but also as the central node of the network, performing network control and protocol evaluation. The hardware is based on a modular bus/memory architecture with associated processors.

A Direct Position-Determination Approach for Multiple Sources Based on Neural Network Computation.

PubMed

Chen, Xin; Wang, Ding; Yin, Jiexin; Wu, Ying

2018-06-13

The most widely used localization technology is the two-step method that localizes transmitters by measuring one or more specified positioning parameters. Direct position determination (DPD) is a promising technique that directly localizes transmitters from sensor outputs and can offer superior localization performance. However, existing DPD algorithms such as maximum likelihood (ML)-based and multiple signal classification (MUSIC)-based estimations are computationally expensive, making it difficult to satisfy real-time demands. To solve this problem, we propose the use of a modular neural network for multiple-source DPD. In this method, the area of interest is divided into multiple sub-areas. Multilayer perceptron (MLP) neural networks are employed to detect the presence of a source in a sub-area and filter sources in other sub-areas, and radial basis function (RBF) neural networks are utilized for position estimation. Simulation results show that a number of appropriately trained neural networks can be successfully used for DPD. The performance of the proposed MLP-MLP-RBF method is comparable to the performance of the conventional MUSIC-based DPD algorithm for various signal-to-noise ratios and signal power ratios. Furthermore, the MLP-MLP-RBF network is less computationally intensive than the classical DPD algorithm and is therefore an attractive choice for real-time applications.

Neural architectures for robot intelligence.

PubMed

Ritter, H; Steil, J J; Nölker, C; Röthling, F; McGuire, P

2003-01-01

We argue that direct experimental approaches to elucidate the architecture of higher brains may benefit from insights gained from exploring the possibilities and limits of artificial control architectures for robot systems. We present some of our recent work that has been motivated by that view and that is centered around the study of various aspects of hand actions since these are intimately linked with many higher cognitive abilities. As examples, we report on the development of a modular system for the recognition of continuous hand postures based on neural nets, the use of vision and tactile sensing for guiding prehensile movements of a multifingered hand, and the recognition and use of hand gestures for robot teaching. Regarding the issue of learning, we propose to view real-world learning from the perspective of data-mining and to focus more strongly on the imitation of observed actions instead of purely reinforcement-based exploration. As a concrete example of such an effort we report on the status of an ongoing project in our laboratory in which a robot equipped with an attention system with a neurally inspired architecture is taught actions by using hand gestures in conjunction with speech commands. We point out some of the lessons learnt from this system, and discuss how systems of this kind can contribute to the study of issues at the junction between natural and artificial cognitive systems.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Safety concerns related to modular/prefabricated building construction.

PubMed

Fard, Maryam Mirhadi; Terouhid, Seyyed Amin; Kibert, Charles J; Hakim, Hamed

2017-03-01

The US construction industry annually experiences a relatively high rate of fatalities and injuries; therefore, improving safety practices should be considered a top priority for this industry. Modular/prefabricated building construction is a construction strategy that involves manufacturing of the whole building or some of its components off-site. This research focuses on the safety performance of the modular/prefabricated building construction sector during both manufacturing and on-site processes. This safety evaluation can serve as the starting point for improving the safety performance of this sector. Research was conducted based on Occupational Safety and Health Administration investigated accidents. The study found 125 accidents related to modular/prefabricated building construction. The details of each accident were closely examined to identify the types of injury and underlying causes. Out of 125 accidents, there were 48 fatalities (38.4%), 63 hospitalized injuries (50.4%), and 14 non-hospitalized injuries (11.2%). It was found that, the most common type of injury in modular/prefabricated construction was 'fracture', and the most common cause of accidents was 'fall'. The most frequent cause of cause (underlying and root cause) was 'unstable structure'. In this research, the accidents were also examined in terms of corresponding location, occupation, equipment as well as activities during which the accidents occurred. For improving safety records of the modular/prefabricated construction sector, this study recommends that future research be conducted on stabilizing structures during their lifting, storing, and permanent installation, securing fall protection systems during on-site assembly of components while working from heights, and developing training programmes and standards focused on modular/prefabricated construction.

Brain Modularity Mediates the Relation between Task Complexity and Performance

NASA Astrophysics Data System (ADS)

Ye, Fengdan; Yue, Qiuhai; Martin, Randi; Fischer-Baum, Simon; Ramos-Nuã+/-Ez, Aurora; Deem, Michael

Recent work in cognitive neuroscience has focused on analyzing the brain as a network, rather than a collection of independent regions. Prior studies taking this approach have found that individual differences in the degree of modularity of the brain network relate to performance on cognitive tasks. However, inconsistent results concerning the direction of this relationship have been obtained, with some tasks showing better performance as modularity increases, and other tasks showing worse performance. A recent theoretical model suggests that these inconsistencies may be explained on the grounds that high-modularity networks favor performance on simple tasks whereas low-modularity networks favor performance on complex tasks. The current study tests these predictions by relating modularity from resting-state fMRI to performance on a set of behavioral tasks. Complex and simple tasks were defined on the basis of whether they drew on executive attention. Consistent with predictions, we found a negative correlation between individuals' modularity and their performance on the complex tasks but a positive correlation with performance on the simple tasks. The results presented here provide a framework for linking measures of whole brain organization to cognitive processing.

Modularity and the spread of perturbations in complex dynamical systems

NASA Astrophysics Data System (ADS)

Kolchinsky, Artemy; Gates, Alexander J.; Rocha, Luis M.

2015-12-01

We propose a method to decompose dynamical systems based on the idea that modules constrain the spread of perturbations. We find partitions of system variables that maximize "perturbation modularity," defined as the autocovariance of coarse-grained perturbed trajectories. The measure effectively separates the fast intramodular from the slow intermodular dynamics of perturbation spreading (in this respect, it is a generalization of the "Markov stability" method of network community detection). Our approach captures variation of modular organization across different system states, time scales, and in response to different kinds of perturbations: aspects of modularity which are all relevant to real-world dynamical systems. It offers a principled alternative to detecting communities in networks of statistical dependencies between system variables (e.g., "relevance networks" or "functional networks"). Using coupled logistic maps, we demonstrate that the method uncovers hierarchical modular organization planted in a system's coupling matrix. Additionally, in homogeneously coupled map lattices, it identifies the presence of self-organized modularity that depends on the initial state, dynamical parameters, and type of perturbations. Our approach offers a powerful tool for exploring the modular organization of complex dynamical systems.