### 38 results for qubit oscillator frequency

Contributors: Lowet, Eric, Roberts, Mark, Hadjipapas, Avgis, Peter, Alina, van der Eerden, Jan, De Weerd, Peter

Date: 2015-02-23

simulation data from lattice phase-**oscillator** model part 2...simulation data from lattice phase-**oscillator** model part 4...simulation data from lattice phase-**oscillator** model part 5...simulation data from lattice phase-**oscillator** model part 6...gamma **oscillation**...Matlab Code of a ring-shaped phase-**oscillator** model (Fig.6)...Fine-scale temporal organization of cortical activity in the gamma range (~25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise **frequency** of gamma **oscillations** varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common **frequency**. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent **frequency** modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different **oscillation** **frequencies** at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and **frequency** differences, and quantified the stimulus-related information represented by gamma phase and **frequency**. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled **oscillators**, where input drive determines the intrinsic (natural) **frequency** of **oscillators**. The gamma phase-locking, the precise phase relation and the emergent (measurable) **frequencies** were determined by two principal factors: the detuning (intrinsic **frequency** difference, i.e. local input difference) and the coupling strength. In addition to **frequency** coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower **oscillation** phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma **frequencies** could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity. ... Fine-scale temporal organization of cortical activity in the gamma range (~25–80Hz) may play a significant role in information processing, for example by neural grouping (‘binding’) and phase coding. Recent experimental studies have shown that the precise **frequency** of gamma **oscillations** varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common **frequency**. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent **frequency** modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different **oscillation** **frequencies** at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and **frequency** differences, and quantified the stimulus-related information represented by gamma phase and **frequency**. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled **oscillators**, where input drive determines the intrinsic (natural) **frequency** of **oscillators**. The gamma phase-locking, the precise phase relation and the emergent (measurable) **frequencies** were determined by two principal factors: the detuning (intrinsic **frequency** difference, i.e. local input difference) and the coupling strength. In addition to **frequency** coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower **oscillation** phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma **frequencies** could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

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Contributors: Waldman, Zachary, Chervenova, Inna, Berry, Brent, Kucewicz, Michal, Ganne, Chaitanya, He, Xiao-Song, Elahian, Bahareh, Shimamoto, Shoichi, Davis, Leon, Stein, Joel

Date: 2017-08-03

Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.
... Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.

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Contributors: Waldman, Zachary, Chervenova, Inna, Berry, Brent, Kucewicz, Michal, Ganne, Chaitanya, He, Xiao-Song, Elahian, Bahareh, Shimamoto, Shoichi, Davis, Leon, Stein, Joel

Date: 2017-08-03

Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.
... Spreadsheets and Tables in .csv, .mat, .xls used for the statistical analyses in Waldman et al., Pathological high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical analysis can be reproduced using the code available on https://github.com/shennanw/waldman_RAM/. Please contact shennan.weiss@jefferson.edu for additional data requests. The intracranial EEG recordings used for this study can be obtained at http://memory.psych.upenn.edu/RAM_Public_Data.

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Top results from Data Repository sources. Show only results like these.

Contributors: Quiroz-Juarez, Mario A., Jimenez-Ramirez, Omar, Vazquez-Medina, Ruben, Ryzhii, Elena, Ryzhii, Maxim, Aragon, Jose L.

Date: 2018-01-01

We present the code for an extended heterogeneous **oscillator** model of cardiac conduction system for generation of realistic 12 lead ECG waveforms. We incorporate an artificial RR-tachogram with the specific statistics of a heart rate, the **frequency**-domain characteristics of heart rate variability produced by Mayer and respiratory sinus arrhythmia waves, normally distributed additive noise and a baseline wander that couple the respiratory **frequency**. The standard 12 lead ECG is calculated by means of a weighted linear combination of atria and ventricle signals and thus can be fitted to clinical ECG of real subject. The model is capable to simulate accurately realistic ECG characteristics including local pathological phenomena accounting for biophysical properties of the human heart. ... We present the code for an extended heterogeneous **oscillator** model of cardiac conduction system for generation of realistic 12 lead ECG waveforms. We incorporate an artificial RR-tachogram with the specific statistics of a heart rate, the **frequency**-domain characteristics of heart rate variability produced by Mayer and respiratory sinus arrhythmia waves, normally distributed additive noise and a baseline wander that couple the respiratory **frequency**. The standard 12 lead ECG is calculated by means of a weighted linear combination of atria and ventricle signals and thus can be fitted to clinical ECG of real subject. The model is capable to simulate accurately realistic ECG characteristics including local pathological phenomena accounting for biophysical properties of the human heart.

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Contributors: Oshima, Daisuke

Date: 2015-01-01

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Contributors: Oshima, Daisuke

Date: 2015-01-01

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Contributors: unknown

Date: 2010-08-01

Comparison of 3 **oscillating** elevations in cytosolic Ca2+ (created using electrical stimulation and measured using aequorin luminescence) in Arabidopsis seedlings. Treatment 1; high **frequency** high amplitude osc., Treatment 2; high **frequency** low amplitude osc., Treatment 3; low **frequency** low amplitude osc. One biological sample per experiment processed as technical dye swaps against intreated control. ... Comparison of 3 **oscillating** elevations in cytosolic Ca2+ (created using electrical stimulation and measured using aequorin luminescence) in Arabidopsis seedlings. Treatment 1; high **frequency** high amplitude osc., Treatment 2; high **frequency** low amplitude osc., Treatment 3; low **frequency** low amplitude osc. One biological sample per experiment processed as technical dye swaps against intreated control.

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Contributors: Alagapan, Sankaraleengam, Schmidt, Stephen, Lefebvre, Je̒re̒mie , Hadar, Eldad, Shin, Hae Won, Frohlich, Flavio

Date: 2016-02-09

Modulation of Cortical **Oscillations** by Low-**Frequency** Direct Cortical Stimulation Is State-Dependent...Cortical **oscillations** play a fundamental role in organizing large-scale functional brain networks. Noninvasive brain stimulation with temporally patterned waveforms such as repetitive transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS) have been proposed to modulate these **oscillations**. Thus, these stimulation modalities represent promising new approaches for the treatment of psychiatric illnesses in which these **oscillations** are impaired. However, the mechanism by which periodic brain stimulation alters endogenous **oscillation** dynamics is debated and appears to depend on brain state. Here, we demonstrate with a static model and a neural **oscillator** model that recurrent excitation in the thalamo-cortical circuit, together with recruitment of cortico-cortical connections, can explain the enhancement of **oscillations** by brain stimulation as a function of brain state. We then performed concurrent invasive recording and stimulation of the human cortical surface to elucidate the response of cortical **oscillations** to periodic stimulation and support the findings from the computational models. We found that (1) stimulation enhanced the targeted **oscillation** power, (2) this enhancement outlasted stimulation, and (3) the effect of stimulation depended on behavioral state. Together, our results show successful target engagement of **oscillations** by periodic brain stimulation and highlight the role of nonlinear interaction between endogenous network **oscillations** and stimulation. These mechanistic insights will contribute to the design of adaptive, more targeted stimulation paradigms....Dataset accompanying publication:
"Modulation of Cortical **Oscillations** by Low-**Frequency** Direct Cortical Stimulation is State-Dependent", Alagapan, Schmidt, Lefebvre, Hadar, Shin and Frohlich
For questions, contact flavio_frohlich@med.unc.edu
The mat file consists of the following Matlab variables
**Electrode Distance**: 3 x 1 cell array containing the arrays (trial x electrode) of distance from stimulating electrode to recording electrode for the three ECoG participants. (First array corresponds to P001, Second array corresponds to P005 and Third array corresponds to P008)
**Spectra_Electrode_EC**: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-closed experiment. nTrial corresponds to number of trials, nFreq corresponds to **frequencies** at which spectral power is calculated, nChannels corresponds to number of electrodes in the analysis and nEpochs corresponds to “Before Stimulation”, “**During** Stimulation” and “**After** Stimulation” epochs.
**Spectra_Electrode_EO**: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-open experiment. The dimensions are the same as above. The first array consists of task-engaged dataset from Participant P001.
**MI_Summary**: 8 x 1 cell array consisting of 3 x 1 cell arrays of modulation indexes for the three participants. The 8 arrays stand for the modulation indexes in different epochs and different **frequencies**. Refer **MI_Summary_Names**
**MI_Summary_Names**: 8 x 1 cell array consisting of strings denoting the arrays in **MI_Summary**. **During** in text corresponds to “**During** Stimulation” epoch and **After** corresponds to “**After** Stimulation” epoch.
**f**: **Frequencies** at which spectral power was estimated.
**NetworkModel**: Matlab struct containing the time series generated by the network model and corresponding spectra. The **timeseries** consists of 4 columns – 1st column corresponds to time, 2nd column corresponds to task-engaged state data, 3rd column corresponds to eyes-open state data and 4th column corresponds to eyes-closed state data. The **spectra **struct consists of spectral powers estimated in the different epochs. 1st column of each epoch array corresponds to task-engaged state, 2nd column corresponds to eyes-open state and the 3rd column corresponds to eyes-closed state.
**SummationModel**: Matlab struct containing the time series generated by the summation model and the peak values in **spectra **before and during stimulation by varying the two strength parameters. The columns correspond to stimulation strength while rows correspond to **oscillation** strength. The **oscillation** strength parameter was varied from 0.5 to 50 in steps of 0.5 and the stimulation strength parameter was varied from 0.1 to 10 in steps of 0.1.
... Dataset accompanying publication:
"Modulation of Cortical **Oscillations** by Low-**Frequency** Direct Cortical Stimulation is State-Dependent", Alagapan, Schmidt, Lefebvre, Hadar, Shin and Frohlich
For questions, contact flavio_frohlich@med.unc.edu
The mat file consists of the following Matlab variables
**Electrode Distance**: 3 x 1 cell array containing the arrays (trial x electrode) of distance from stimulating electrode to recording electrode for the three ECoG participants. (First array corresponds to P001, Second array corresponds to P005 and Third array corresponds to P008)
**Spectra_Electrode_EC**: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-closed experiment. nTrial corresponds to number of trials, nFreq corresponds to **frequencies** at which spectral power is calculated, nChannels corresponds to number of electrodes in the analysis and nEpochs corresponds to “Before Stimulation”, “**During** Stimulation” and “**After** Stimulation” epochs.
**Spectra_Electrode_EO**: 3 x 1 cell array consisting of nTrial x nFreq x nChannels x nEpochs matrices for each subject’s eyes-open experiment. The dimensions are the same as above. The first array consists of task-engaged dataset from Participant P001.
**MI_Summary**: 8 x 1 cell array consisting of 3 x 1 cell arrays of modulation indexes for the three participants. The 8 arrays stand for the modulation indexes in different epochs and different **frequencies**. Refer **MI_Summary_Names**
**MI_Summary_Names**: 8 x 1 cell array consisting of strings denoting the arrays in **MI_Summary**. **During** in text corresponds to “**During** Stimulation” epoch and **After** corresponds to “**After** Stimulation” epoch.
**f**: **Frequencies** at which spectral power was estimated.
**NetworkModel**: Matlab struct containing the time series generated by the network model and corresponding spectra. The **timeseries** consists of 4 columns – 1st column corresponds to time, 2nd column corresponds to task-engaged state data, 3rd column corresponds to eyes-open state data and 4th column corresponds to eyes-closed state data. The **spectra **struct consists of spectral powers estimated in the different epochs. 1st column of each epoch array corresponds to task-engaged state, 2nd column corresponds to eyes-open state and the 3rd column corresponds to eyes-closed state.
**SummationModel**: Matlab struct containing the time series generated by the summation model and the peak values in **spectra **before and during stimulation by varying the two strength parameters. The columns correspond to stimulation strength while rows correspond to **oscillation** strength. The **oscillation** strength parameter was varied from 0.5 to 50 in steps of 0.5 and the stimulation strength parameter was varied from 0.1 to 10 in steps of 0.1.

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Contributors: Yoni Levy

Date: 2019-05-26

Gene **Oscillations**...The data contains 88 matrices from 44 participants, with 2 matrices for each participant.
Each matrix represents either the pain ('p') or the no-pain ('n') conditions.
In each matrix you will find 4 dimensions: trials * MEG-sensors * spectral-**frequency** (80:10:150) * time ... The data contains 88 matrices from 44 participants, with 2 matrices for each participant.
Each matrix represents either the pain ('p') or the no-pain ('n') conditions.
In each matrix you will find 4 dimensions: trials * MEG-sensors * spectral-**frequency** (80:10:150) * time

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Contributors: Hofstad, Cory Andrew

Date: 2018-01-01

Plasma Vortex Theory is the engineered application of known sciences to create efficient velocity during spaceflight using electricity and propellant gas. **Oscillation** of granulate and liquid reagents using simple harmonic motion has been shown to excite particles to form geometric patterns when using calibrated **frequencies** discovered by the late Dr. Hans Jenny. Calibration methods will be used to attain vortex formations in the reagents Lycopodium, Sulfur Hexafluoride, CO2 and Xenon. **Frequencies** which form vortex patterns in Lycopodium powder using known methods will be used to excite Sulfur Hexafluoride (density 6.17 kg/m3), at incremental partial pressures. Air-filled mass objects will be used to observe acceleration, force and velocity data for a dense gas during **oscillation** and vortex formation. Xenon gas (density 5.761 kg/m3) will be ionized by external electrode field before, during and after vortex formations are created using acoustic measures. ... Plasma Vortex Theory is the engineered application of known sciences to create efficient velocity during spaceflight using electricity and propellant gas. **Oscillation** of granulate and liquid reagents using simple harmonic motion has been shown to excite particles to form geometric patterns when using calibrated **frequencies** discovered by the late Dr. Hans Jenny. Calibration methods will be used to attain vortex formations in the reagents Lycopodium, Sulfur Hexafluoride, CO2 and Xenon. **Frequencies** which form vortex patterns in Lycopodium powder using known methods will be used to excite Sulfur Hexafluoride (density 6.17 kg/m3), at incremental partial pressures. Air-filled mass objects will be used to observe acceleration, force and velocity data for a dense gas during **oscillation** and vortex formation. Xenon gas (density 5.761 kg/m3) will be ionized by external electrode field before, during and after vortex formations are created using acoustic measures.

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