### 3514 results for qubit oscillator frequency

Contributors: Eugene Grichuk, Margarita Kuzmina, Eduard Manykin

Date: 2010-09-26

**qubit** simulates the behavior of electric field of
polarized light beam...**qubit** model has been designed as a
stochastic **oscillator** formed by a pair...**qubits** that is
exploited as a computation resource in one-way quantum
...**qubit** cluster, is designed, and system of equations for
network dynamics...one-**qubit**
gates are suggested. Changing of cluster entanglement degree...**oscillators** is
proposed for modeling of a cluster of entangled **qubits** ...**oscillators** with chaotically modulated limit cycle radii and
**frequencies**...**oscillators**...**qubit** model has been designed as a
stochastic oscillator formed by a pair ... A network of coupled stochastic **oscillators** is
proposed for modeling of a cluster of entangled **qubits** that is
exploited as a computation resource in one-way quantum
computation schemes. A **qubit** model has been designed as a
stochastic **oscillator** formed by a pair of coupled limit cycle
**oscillators** with chaotically modulated limit cycle radii and
**frequencies**. The **qubit** simulates the behavior of electric field of
polarized light beam and adequately imitates the states of two-level
quantum system. A cluster of entangled **qubits** can be associated
with a beam of polarized light, light polarization degree being
directly related to cluster entanglement degree. Oscillatory network,
imitating **qubit** cluster, is designed, and system of equations for
network dynamics has been written. The constructions of one-**qubit**
gates are suggested. Changing of cluster entanglement degree caused
by measurements can be exactly calculated.

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Contributors: unknown

Date: 2015-01-01

High-**frequency** **oscillations**...**frequency** ictal patterns, ictal high-**frequency** **oscillations**, hyperexcitability...High-**frequency** **oscillations** (HFOs, 80–500Hz) from intracranial electroencephalography...**frequency** **oscillations**, CFIP=conventional **frequency** ictal patterns. ... High-**frequency** **oscillations** (HFOs, 80–500Hz) from intracranial electroencephalography (EEG) may represent a biomarker of epileptogenicity for epilepsy. We explored the relationship between ictal HFOs and hyperexcitability with a view to improving surgical outcome.

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Contributors: unknown

Date: 2010-09-13

**frequency** spectra of a coupling **oscillation** and the associated phase-coupled...high-**frequency** bursts that are coupled to a common low-**frequency** **oscillation**...**frequency** spectra produced by the tensor decomposition. All identified...**oscillation**. This mechanism requires multiple physiologically different...**frequency** spectrum. We showed that, with this source definition, the array...**frequency** spectra (both real-valued), which is exactly the structure that...high-**frequency** **oscillations** that are phase-coupled to a low-**frequency** ...high-**frequency** **oscillations**. Using human intracranial EEG (iEEG) data,...**oscillation**. In panel d, with green and index f, we show the **frequency**...low-**frequency** coupling **oscillation** and the others generating phase-coupled...**frequencies** of the coupling **oscillations** (vertical axis). The central ...**frequency** spectrum) and in terms of the neuronal network that generates...**frequency** spectra, then they can be extracted by means of tensor decomposition...**oscillations** are much more widespread than the ones for the associated...**frequency** of the coupling **oscillation**), respectively, y-axes (showing ... Spatially distributed coherent **oscillations** provide temporal windows of excitability that allow for interactions between distinct neuronal groups. It has been hypothesized that this mechanism for neuronal communication is realized by bursts of high-**frequency** **oscillations** that are phase-coupled to a low-**frequency** spatially distributed coupling **oscillation**. This mechanism requires multiple physiologically different interacting sources, one generating the low-**frequency** coupling **oscillation** and the others generating phase-coupled high-**frequency** **oscillations**. Using human intracranial EEG (iEEG) data, we provide evidence for multiple oscillatory patterns, as characterized on the basis of their spatial maps (topographies) and their **frequency** spectra. In fact, we show that the spatial maps for the coupling **oscillations** are much more widespread than the ones for the associated phase-coupled bursts. Second, in the majority of the patterns of phase-amplitude coupling (PAC), phase-coupled bursts of high-**frequency** activity are synchronized across brain areas. Third and last, working memory operations affect the PAC strength in a heterogeneous way: in some PAC patterns, working memory operations increase their strength, whereas in others they decrease it.

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Contributors: unknown

Date: 2016-01-08

**Frequency** main effects on ALFF. Hot colors represent increased ALFF in...**frequency** (slow-4 and slow-5) interaction effects on ALFF. The regions...low-**frequency** **oscillation** (LFO) of major depressive disorder (MDD) patients...**frequency** dependent....Low-**frequency** **oscillation**...**Frequency** dependence...**frequency** interaction effects on ALFF (hot colors): the left ventromedial...**frequency** on ALFF. (a) The group main effects on ALFF. Hot colors represent...**frequency** (slow-4 and slow-5) interaction effects on ALFF. ... We conducted this fMRI study to examine whether the alterations in amplitudes of low-**frequency** **oscillation** (LFO) of major depressive disorder (MDD) patients were **frequency** dependent.

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Contributors: unknown

Date: 2009-10-06

**Oscillation** **Frequency**...**Frequency** in Moth AL Model...**Oscillation** Coherence, Not **Frequency**...**oscillation** **frequency**. However, changing the concentration of the odor...**Oscillations** in the Moth MB and AL...**Oscillations** in Model of Moth AL...**oscillations** suddenly slowed as net olfactory receptor neuron (ORN) output...**oscillations**? In the olfactory system of the moth, we found that odors...**frequency**. Our recordings in vivo and computational models based on these...**oscillation** **frequency** is set by the adaptation and saturation of this ... 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.

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Contributors: Weidinger, Daniel, Gruebele, Martin

Date: 2007-07-10

**Qubits** are encoded in 2n vibrational computing states on the ground electronic ... Accurate rotation-vibration energy levels and transition dipoles of the molecule thiophosgene are used to model the execution of quantum gates with shaped laser pulses. **Qubits** are encoded in 2n vibrational computing states on the ground electronic surface of the molecule. Computations are carried out by cycling amplitude between these computing states and a gateway state with a shaped laser pulse. The shaped pulse that performs the computation in the computing states is represented by a physical model of a 128-1024 channel pulse shaper. Pulse shapes are optimized with a standard genetic algorithm, yielding experimentally realizable computing pulses. We study the robustness of optimization as a function of the vibrational states selected, rotational level structure, additional vibrational levels not assigned to the computation, and compensation for laser power variation across a molecular ensemble.

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Contributors: unknown

Date: 2013-10-29

**frequency** **oscillations** at which pHFO amplitudes were maximal was inconsistent...**oscillations** (p<0.01). These results suggest that increased CFC between...**frequency** **oscillations** (pHFOs) have been proposed to be robust markers...**oscillations**, whereas pathological ripple amplitudes preferentially occurred...**frequency** amplitude by low-**frequency** phase. In the seizure-onset zone,...**oscillations** is observed. There is also less cross-**frequency** coupling ...**frequency** range has been shown to be modulated by phase of lower **frequency**...**oscillations** during the ictal period. In the interictal period, no specific...**frequency** **oscillations**...Cross-**frequency** coupling...**frequency** phase, we measured the preferred slow oscillatory phase at which...**frequencies** (x-axis) by the phases of other narrow-band **frequencies** (y-axis...**oscillations** (p=0.14 and p=0.68, respectively). At seizure termination...**frequency** **oscillations**, sorted by concurrent low-**frequency** phase into ...**oscillations**. Here, we tested the hypothesis that dynamic cross-**frequency** ... Pathological high **frequency** **oscillations** (pHFOs) have been proposed to be robust markers of epileptic cortex. Oscillatory activity below this **frequency** range has been shown to be modulated by phase of lower **frequency** **oscillations**. Here, we tested the hypothesis that dynamic cross-**frequency** interactions involving pHFOs are concentrated within the epileptogenic cortex. Intracranial electroencephalographic recordings from 17 children with medically-intractable epilepsy secondary to focal cortical dysplasia were obtained. A time-resolved analysis was performed to determine topographic concentrations and dynamic changes in cross-**frequency** amplitude-to-phase coupling (CFC). CFC between pHFOs and the phase of theta and alpha rhythms was found to be significantly elevated in the seizure-onset zone compared to non-epileptic regions (p<0.01). Data simulations showed that elevated CFC could not be attributed to the presence of sharp transients or other signal properties. The phase of low **frequency** **oscillations** at which pHFO amplitudes were maximal was inconsistent at seizure initiation, yet consistently at the trough of the low **frequency** rhythm at seizure termination. Amplitudes of pHFOs were most significantly modulated by the phase of alpha-band **oscillations** (p<0.01). These results suggest that increased CFC between pHFO amplitude and alpha phase may constitute a marker of epileptogenic brain areas and may be relevant for understanding seizure dynamics.

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Contributors: unknown

Date: 2013-08-15

**oscillation** dynamics of a sessile drop, such as resonance **frequency** and...**frequencies** (**frequency** range of 20–110Hz in which the first and second...**frequency**, but strongly affects the **oscillation** amplitude and peak width...**oscillation**....**oscillation** amplitude, in response to different AC voltages (80 and 100Vrms...**frequency**...**Frequency** response at harmonic and subharmonic **frequencies**....**frequency**....**oscillating** drops with different viscosities along with AC **frequency** at...**oscillating** drops with different viscosities for different **frequencies**...**frequency**. In addition, drop **oscillation** in the resonance mode is no longer...**oscillation** ... The effects of drop viscosity on **oscillation** dynamics of a sessile drop, such as resonance **frequency** and **oscillation** amplitude, in response to different AC voltages (80 and 100Vrms, corresponding to the electrowetting number η of 0.25 and 0.39, respectively) and **frequencies** (**frequency** range of 20–110Hz in which the first and second resonance **frequencies** exist) were investigated, based on both experiments and theoretical modeling. The results show that drop viscosity rarely affects resonance **frequency**, but strongly affects the **oscillation** amplitude and peak width of the resonance **frequency**. In addition, drop **oscillation** in the resonance mode is no longer observed, when the drop viscosity is over the critical value, which increases with applied AC voltage. A theoretical model predicts the **oscillation** dynamics of sessile drops within the error level of ±5%, except for the drop with high viscosity (60mPas). Moreover, the friction coefficient obtained by fitting the theoretical models is nearly proportional to the drop viscosity. Finally, an empirical relationship, in which the maximum amplitude is inversely proportional to the drop viscosity, is obtained.

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Contributors: unknown

Date: 2009-04-28

**frequencies**. The amplitude and **frequency** of these **oscillations** are continuously...**Oscillation** Cycle...**Oscillation** Cycles...**Oscillations** In Vivo...**Frequency** in Simple Model of CA3 Circuit...**oscillation** **frequency**. So, by rapidly balancing excitation with inhibition...**oscillation** predicts the interval to the next. Using in vivo and in vitro...**Oscillation** Amplitude Predicts Latency to Next **Oscillation** Cycle...**oscillation** amplitude and **frequency** vary rapidly, from one cycle to the...**oscillations** over a wide band of **frequencies**. ... Neurons recruited for local computations exhibit rhythmic activity at gamma **frequencies**. The amplitude and **frequency** of these **oscillations** are continuously modulated depending on stimulus and behavioral state. This modulation is believed to crucially control information flow across cortical areas. Here we report that in the rat hippocampus gamma **oscillation** amplitude and **frequency** vary rapidly, from one cycle to the next. Strikingly, the amplitude of one **oscillation** predicts the interval to the next. Using in vivo and in vitro whole-cell recordings, we identify the underlying mechanism. We show that cycle-by-cycle fluctuations in amplitude reflect changes in synaptic excitation spanning over an order of magnitude. Despite these rapid variations, synaptic excitation is immediately and proportionally counterbalanced by inhibition. These rapid adjustments in inhibition instantaneously modulate **oscillation** **frequency**. So, by rapidly balancing excitation with inhibition, the hippocampal network is able to swiftly modulate gamma **oscillations** over a wide band of **frequencies**.

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Contributors: unknown

**frequency** throughout the SO cycle....**frequency**. (C) Phase response curve of a type I neuronal **oscillator**: any...**frequencies**, with maximal FO power for these two **frequencies** in antiphase...**frequency** modulation within a single FO **frequency** band, as shown in (A...cross-**frequency** coupling, including neural representations of multiple...cross-**frequency** coupling (CFC) network with sparse-spiking FO **oscillations**...**oscillations**...**oscillations** are ubiquitously observed in the mammalian brain, but it ...cross-**frequency** coupling. We show that different types of neural **oscillators**...**oscillation** (FO). Plot shows FO amplitude as a function of slow **oscillation**...**frequencies** (pointing to distinct **oscillation**-generation mechanisms) should...cross-**frequency** interactions yield distinct signatures in neural dynamics...cross-**frequency** coupling...**oscillators**. Neural **oscillations** in distinct **frequency** bands are generated...**frequency** of type I and type II neurons as a function of driving-current...**oscillations** at different timescales has recently received much attention...Cross-**Frequency** Neural Coupling. (A–D) Cross-**frequency** coupling (CFC) ... 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.

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