### 95 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

phase-**oscillator** model part 1...phase-**oscillator** model part 2...phase-**oscillator** model part 3...phase-**oscillator** model part 4...**oscillation** **frequencies** at nearby spatial locations. Similarly to cortical...**oscillation** phase codes, may resolve conflicting experimental observations...**frequency** with increasing input drive. The relates to the experimental...**oscillators**. The gamma phase-locking, the precise phase relation and the...**oscillators**, where input drive determines the intrinsic (natural) **frequency**...**Frequency** Modulation of Cortical Gamma **Oscillations** Shapes Spatial Synchronization...**oscillation**...**frequency** of gamma **oscillations** varies with input drive (e.g. visual contrast ... 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

high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical ... 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

high-**frequency** **oscillations** disrupt verbal memory encoding. The statistical ... 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: Lowet, Eric, Roberts, Mark Jonathan, Peter, Alina, Gips, Bart, de Weerd, Peter

Date: 2017-09-01

**frequency** modulations applies to gamma in V1, and is likely generalizable...**frequency** by increasing input current) and coupling on their phase dynamics...**frequency** difference. Crucially, the precise dynamics of **frequencies** and...**frequencies**. When similar enough, these **frequencies** continually attracted...**oscillators** influence each other’s phase relations. Hence, the fundamental...**oscillators**. With this code the effects of detuning and coupling are illustrated...**oscillating** neuronal populations to optimize information transmission ... Gamma-band synchronization coordinates brief periods of excitability in **oscillating** neuronal populations to optimize information transmission during sensation and cognition. Commonly, a stable, shared **frequency** over time is considered a condition for functional neural synchronization. Here, we demonstrate the opposite: instantaneous **frequency** modulations are critical to regulate phase relations and synchronization. In monkey visual area V1, nearby local populations driven by different visual stimulation showed different gamma **frequencies**. When similar enough, these **frequencies** continually attracted and repulsed each other, which enabled preferred phase relations to be maintained in periods of minimized **frequency** difference. Crucially, the precise dynamics of **frequencies** and phases across a wide range of stimulus conditions was predicted from a physics theory that describes how weakly coupled **oscillators** influence each other’s phase relations. Hence, the fundamental mathematical principle of synchronization through instantaneous **frequency** modulations applies to gamma in V1, and is likely generalizable to other brain regions and rhythms.

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Contributors: unknown

Date: 2010-08-01

**frequency** low amplitude osc. One biological sample per experiment processed...**oscillating** elevations in cytosolic Ca2+ (created using electrical stimulation...**frequency** high amplitude osc., Treatment 2; high **frequency** low amplitude ... 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

**Oscillations** by Low-**Frequency** Direct Cortical Stimulation Is State-Dependent...**oscillations** to periodic stimulation and support the findings from the...**Oscillations** by Low-**Frequency** Direct Cortical Stimulation is State-Dependent...**oscillation** dynamics is debated and appears to depend on brain state. ...**oscillations** are impaired. However, the mechanism by which periodic brain...**oscillations**. Thus, these stimulation modalities represent promising new...**Frequencies** at which spectral power was estimated.
NetworkModel...**frequencies** at which spectral power is calculated, nChannels corresponds...**frequencies**. Refer **MI_Summary_Names**
<strong...**oscillations** play a fundamental role in organizing large-scale functional...**oscillation** strength. The **oscillation** strength parameter was varied from ... 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: Shennan Weiss

Date: 2018-05-06

... Processed data necessary and sufficient to reproduce manuscript findings.

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Contributors: Rego Costa, Artur, Débarre, Florence, Chevin, Luis-Miguel

Date: 2017-11-28

**frequency**-dependent selection caused by competitive interactions mediated...**frequency** of chaos at each simulation time point for different values ...**oscillating** optimum for d = 70....**oscillations** of an optimal phenotype interacts with the internal dynamics...**oscillations**. In contrast, weak forcing can increase the probability of...**oscillating** optimum for d = 40. ... Among the factors that may reduce the predictability of evolution, chaos, characterized by a strong dependence on initial conditions, has received much less attention than randomness due to genetic drift or environmental stochasticity. It was recently shown that chaos in phenotypic evolution arises commonly under **frequency**-dependent selection caused by competitive interactions mediated by many traits. This result has been used to argue that chaos should often make evolutionary dynamics unpredictable. However, populations also evolve largely in response to external changing environments, and such environmental forcing is likely to influence the outcome of evolution in systems prone to chaos. We investigate how a changing environment causing **oscillations** of an optimal phenotype interacts with the internal dynamics of an eco-evolutionary system that would be chaotic in a constant environment. We show that strong environmental forcing can improve the predictability of evolution, by reducing the probability of chaos arising, and by dampening the magnitude of chaotic **oscillations**. In contrast, weak forcing can increase the probability of chaos, but it also causes evolutionary trajectories to track the environment more closely. Overall, our results indicate that, although chaos may occur in evolution, it does not necessarily undermine its predictability.

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Contributors: Papadopoulou, Anna, Knowles, L. Lacey

Date: 2017-10-18

**frequency** of completed speciation across the PRB. Our results suggest ...**oscillations**, with the aim to understand the micro- and macroevolutionary...**oscillating** sea levels. Our study highlights how a microevolutionary perspective...**oscillations** as drivers of diversification is complex and not well understood ... With shifts in island area, isolation, and cycles of island fusion-fission, the role of Quaternary sea-level **oscillations** as drivers of diversification is complex and not well understood. Here we conduct parallel comparisons of population and species divergence between two island areas of equivalent size that have been affected differently by sea-level **oscillations**, with the aim to understand the micro- and macroevolutionary dynamics associated with sea-level change. Using genome-wide datasets for a clade of seven Amphiacusta ground cricket species endemic to the Puerto Rico Bank (PRB), we found consistently deeper interspecific divergences and higher population differentiation across the unfragmented Western PRB, in comparison to the currently fragmented Eastern PRB that has experienced extreme changes in island area and connectivity during the Quaternary. We evaluate alternative hypotheses related to the microevolutionary processes (population splitting, extinction and merging) that regulate the **frequency** of completed speciation across the PRB. Our results suggest that under certain combinations of archipelago characteristics and taxon traits the repeated changes in island area and connectivity may create an opposite effect to the hypothesized “species pump” action of **oscillating** sea levels. Our study highlights how a microevolutionary perspective can complement current macroecological work on the Quaternary dynamics of island biodiversity.

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Contributors: Smith, Robert W., van Sluijs, Bob, Fleck, Christian

Date: 2017-12-02

**oscillations**. In this work we present a generalised in silico evolutionary...**oscillators**, and by performing multi-objective optimisation to find a ...**oscillators** and feed-forward loops that are optimal at balancing different...**oscillators** for experimental construction.Conclusions: In this work we ... Background: Evolution has led to the development of biological networks that are shaped by environmental signals. Elucidating, understanding and then reconstructing important network motifs is one of the principal aims of Systems & Synthetic Biology. Consequently, previous research has focused on finding optimal network structures and reaction rates that respond to pulses or produce stable **oscillations**. In this work we present a generalised in silico evolutionary algorithm that simultaneously finds network structures and reaction rates (genotypes) that can satisfy multiple defined objectives (phenotypes).Results: The key step to our approach is to translate a schema/binary-based description of biological networks into systems of ordinary differential equations (ODEs). The ODEs can then be solved numerically to provide dynamic information about an evolved networks functionality. Initially we benchmark algorithm performance by finding optimal networks that can recapitulate concentration time-series data and perform parameter optimisation on oscillatory dynamics of the Repressilator. We go on to show the utility of our algorithm by finding new designs for robust synthetic **oscillators**, and by performing multi-objective optimisation to find a set of **oscillators** and feed-forward loops that are optimal at balancing different system properties. In sum, our results not only confirm and build on previous observations but we also provide new designs of synthetic **oscillators** for experimental construction.Conclusions: In this work we have presented and tested an evolutionary algorithm that can design a biological network to produce desired output. Given that previous designs of synthetic networks have been limited to subregions of network- and parameter-space, the use of our evolutionary optimisation algorithm will enable Synthetic Biologists to construct new systems with the potential to display a wider range of complex responses.

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