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.

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.

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.

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.

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.

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.   

Contributors: Shennan Weiss

Date: 2018-05-06

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

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.

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.

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.