Wibral, Michael

[["dc.bibliographiccitation.journal","Frontiers in Neuroinformatics"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Lizier, Joseph T."],["dc.contributor.author","Vögler, Sebastian"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Galuske, Ralf"],["dc.date.accessioned","2022-06-08T07:57:34Z"],["dc.date.available","2022-06-08T07:57:34Z"],["dc.date.issued","2014"],["dc.identifier.doi","10.3389/fninf.2014.00001"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110138"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1662-5196"],["dc.title","Local active information storage as a tool to understand distributed neural information processing"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e55809"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Pampu, Nicolae"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Siebenhühner, Felix"],["dc.contributor.author","Seiwert, Hannes"],["dc.contributor.author","Lindner, Michael"],["dc.contributor.author","Lizier, Joseph T."],["dc.contributor.author","Vicente, Raul"],["dc.date.accessioned","2022-06-08T07:57:25Z"],["dc.date.available","2022-06-08T07:57:25Z"],["dc.date.issued","2013"],["dc.identifier.doi","10.1371/journal.pone.0055809"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110086"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1932-6203"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Measuring Information-Transfer Delays"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Munk, Matthias HJ"],["dc.contributor.author","Wibral, Michael"],["dc.date.accessioned","2022-06-08T07:57:16Z"],["dc.date.available","2022-06-08T07:57:16Z"],["dc.date.issued","2009"],["dc.identifier.doi","10.1186/1471-2202-10-40"],["dc.identifier.pii","1044"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110042"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1471-2202"],["dc.title","Subsampling effects in neuronal avalanche distributions recorded in vivo"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","25"],["dc.bibliographiccitation.journal","Brain and Cognition"],["dc.bibliographiccitation.lastpage","38"],["dc.bibliographiccitation.volume","112"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Kay, Jim W."],["dc.contributor.author","Lizier, Joseph T."],["dc.contributor.author","Phillips, William A."],["dc.date.accessioned","2022-06-08T07:57:45Z"],["dc.date.available","2022-06-08T07:57:45Z"],["dc.date.issued","2017"],["dc.description.sponsorship","Neuronale Koordination Forschungsschwerpunkt Frankfurt"],["dc.description.sponsorship","German Ministry for Education and Research"],["dc.description.sponsorship"," Bernstein Center for Computational Neuroscience https://doi.org/10.13039/501100002348"],["dc.identifier.doi","10.1016/j.bandc.2015.09.004"],["dc.identifier.pii","S027826261530021X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110206"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","0278-2626"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Partial information decomposition as a unified approach to the specification of neural goal functions"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","6500"],["dc.bibliographiccitation.journal","Science"],["dc.bibliographiccitation.volume","369"],["dc.contributor.author","Dehning, Jonas"],["dc.contributor.author","Zierenberg, Johannes"],["dc.contributor.author","Spitzner, F. Paul"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Neto, Joao Pinheiro"],["dc.contributor.author","Wilczek, Michael"],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2022-03-01T11:47:20Z"],["dc.date.available","2022-03-01T11:47:20Z"],["dc.date.issued","2020"],["dc.description.abstract","INTRODUCTION When faced with the outbreak of a novel epidemic such as coronavirus disease 2019 (COVID-19), rapid response measures are required by individuals, as well as by society as a whole, to mitigate the spread of the virus. During this initial, time-critical period, neither the central epidemiological parameters nor the effectiveness of interventions such as cancellation of public events, school closings, or social distancing is known. RATIONALE As one of the key epidemiological parameters, we inferred the spreading rate λ from confirmed SARS-CoV-2 infections using the example of Germany. We apply Bayesian inference based on Markov chain Monte Carlo sampling to a class of compartmental models [susceptible-infected-recovered (SIR)]. Our analysis characterizes the temporal change of the spreading rate and allows us to identify potential change points. Furthermore, it enables short-term forecast scenarios that assume various degrees of social distancing. A detailed description is provided in the accompanying paper, and the models, inference, and forecasts are available on GitHub (https://github.com/Priesemann-Group/covid19_inference_forecast). Although we apply the model to Germany, our approach can be readily adapted to other countries or regions. RESULTS In Germany, interventions to contain the COVID-19 outbreak were implemented in three steps over 3 weeks: (i) Around 9 March 2020, large public events such as soccer matches were canceled; (ii) around 16 March 2020, schools, childcare facilities, and many stores were closed; and (iii) on 23 March 2020, a far-reaching contact ban (Kontaktsperre) was imposed by government authorities; this included the prohibition of even small public gatherings as well as the closing of restaurants and all nonessential stores. From the observed case numbers of COVID-19, we can quantify the impact of these measures on the disease spread using change point analysis. Essentially, we find that at each change point the spreading rate λ decreased by ~40%. At the first change point, assumed around 9 March 2020, λ decreased from 0.43 to 0.25, with 95% credible intervals (CIs) of [0.35, 0.51] and [0.20, 0.30], respectively. At the second change point, assumed around 16 March 2020, λ decreased to 0.15 (CI [0.12, 0.20]). Both changes in λ slowed the spread of the virus but still implied exponential growth (see red and orange traces in the figure). To contain the disease spread, i.e., to turn exponential growth into a decline of new cases, the spreading rate has to be smaller than the recovery rate μ = 0.13 (CI [0.09, 0.18]). This critical transition was reached with the third change point, which resulted in λ = 0.09 (CI [0.06, 0.13]; see blue trace in the figure), assumed around 23 March 2020. From the peak position of daily new cases, one could conclude that the transition from growth to decline was already reached at the end of March. However, the observed transient decline can be explained by a short-term effect that originates from a sudden change in the spreading rate (see Fig. 2C in the main text). As long as interventions and the concurrent individual behavior frequently change the spreading rate, reliable short- and long-term forecasts are very difficult. As the figure shows, the three example scenarios (representing the effects up to the first, second, and third change point) quickly diverge from each other and, consequently, span a considerable range of future case numbers. Inference and subsequent forecasts are further complicated by the delay of ~2 weeks between an intervention and the first useful estimates of the new λ (which are derived from the reported case numbers). Because of this delay, any uncertainty in the magnitude of social distancing in the previous 2 weeks can have a major impact on the case numbers in the subsequent 2 weeks. Beyond 2 weeks, the case numbers depend on our future behavior, for which we must make explicit assumptions. In sum, future interventions (such as lifting restrictions) should be implemented cautiously to respect the delayed visibility of their effects. CONCLUSION We developed a Bayesian framework for the spread of COVID-19 to infer central epidemiological parameters and the timing and magnitude of intervention effects. With such an approach, the effects of interventions can be assessed in a timely manner. Future interventions and lifting of restrictions can be modeled as additional change points, enabling short-term forecasts for case numbers. In general, our approach may help to infer the efficiency of measures taken in other countries and inform policy-makers about tightening, loosening, and selecting appropriate measures for containment of COVID-19."],["dc.identifier.doi","10.1126/science.abb9789"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/103992"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-531"],["dc.relation.eissn","1095-9203"],["dc.relation.issn","0036-8075"],["dc.title","Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","51"],["dc.bibliographiccitation.journal","Current Opinion in Neurobiology"],["dc.bibliographiccitation.lastpage","61"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Aru, Juhan"],["dc.contributor.author","Aru, Jaan"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Lana, Luiz"],["dc.contributor.author","Pipa, Gordon"],["dc.contributor.author","Singer, Wolf"],["dc.contributor.author","Vicente, Raul"],["dc.date.accessioned","2022-06-08T07:57:48Z"],["dc.date.available","2022-06-08T07:57:48Z"],["dc.date.issued","2015"],["dc.description.sponsorship","Max Planck Society"],["dc.description.sponsorship","LOEWE"],["dc.description.sponsorship"," https://doi.org/10.13039/501100003493 Hertie Foundation"],["dc.description.sponsorship"," https://doi.org/10.13039/501100002301 Estonian Ministry of Science and Education"],["dc.description.sponsorship","German Ministry for Education and Research"],["dc.identifier.doi","10.1016/j.conb.2014.08.002"],["dc.identifier.pii","S0959438814001640"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110216"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.issn","0959-4388"],["dc.rights.uri","https://www.elsevier.com/tdm/userlicense/1.0/"],["dc.title","Untangling cross-frequency coupling in neuroscience"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","eLife"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Shorten, David P"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Lizier, Joseph T"],["dc.date.accessioned","2022-04-01T10:02:08Z"],["dc.date.available","2022-04-01T10:02:08Z"],["dc.date.issued","2022"],["dc.description.abstract","The brains of many organisms are capable of complicated distributed computation underpinned by a highly advanced information processing capacity. Although substantial progress has been made towards characterising the information flow component of this capacity in mature brains, there is a distinct lack of work characterising its emergence during neural development. This lack of progress has been largely driven by the lack of effective estimators of information processing operations for the spiking data available for developing neural networks. Here, we leverage recent advances in this estimation task in order to quantify the changes in transfer entropy during development. We do so by studying the changes in the intrinsic dynamics of the spontaneous activity of developing dissociated neural cell cultures. We find that the quantity of information flowing across these networks undergoes a dramatic increase across development. Moreover, the spatial structure of these flows exhibits a tendency to lock in at the point when they arise, after which there is a substantial temporal correlation in the information flows across recording days. We analyse the flow of information during the crucial periods of population bursts. We find that, during these bursts, nodes tend to undertake specialised computational roles as either transmitters, mediators or receivers of information, with these roles tending to align with their average spike ordering - either early, mid or late in the bursts. Further, we find that the specialised computational roles occupied by nodes during bursts are regularly locked-in when the information flows are established. Finally, we briefly compare these results to information flows in a model network developing according to an STDP learning rule from a state of independent firing to synchronous bursting. The phenomena of large increases in information flow, early lock-in of information flow spatial structure and computational roles based on burst position were also observed in this model, hinting at the broader generality of these phenomena."],["dc.identifier.doi","10.7554/eLife.74651"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/105829"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-530"],["dc.relation.eissn","2050-084X"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Early lock-in of structured and specialised information flows during neural development"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","108"],["dc.bibliographiccitation.journal","Frontiers in Systems Neuroscience"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Valderrama, Mario"],["dc.contributor.author","Pröpper, Robert"],["dc.contributor.author","Le Van Quyen, Michel"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Triesch, Jochen"],["dc.contributor.author","Nikolić, Danko"],["dc.contributor.author","Munk, Matthias H. J."],["dc.date.accessioned","2019-07-09T11:41:30Z"],["dc.date.available","2019-07-09T11:41:30Z"],["dc.date.issued","2014"],["dc.description.abstract","In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy."],["dc.identifier.doi","10.3389/fnsys.2014.00108"],["dc.identifier.fs","606598"],["dc.identifier.pmid","25009473"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/12143"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58446"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1662-5137"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Spike avalanches in vivo suggest a driven, slightly subcritical brain state."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Contreras, Sebastian"],["dc.contributor.author","Dehning, Jonas"],["dc.contributor.author","Loidolt, Matthias"],["dc.contributor.author","Zierenberg, Johannes"],["dc.contributor.author","Spitzner, F. Paul"],["dc.contributor.author","Urrea-Quintero, Jorge H."],["dc.contributor.author","Mohr, Sebastian B."],["dc.contributor.author","Wilczek, Michael"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2021-04-14T08:30:18Z"],["dc.date.available","2021-04-14T08:30:18Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1038/s41467-020-20699-8"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83184"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation.eissn","2041-1723"],["dc.title","The challenges of containing SARS-CoV-2 via test-trace-and-isolate"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Lindner, Michael"],["dc.contributor.author","Vicente, Raul"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Wibral, Michael"],["dc.date.accessioned","2022-06-08T07:57:16Z"],["dc.date.available","2022-06-08T07:57:16Z"],["dc.date.issued","2011"],["dc.description.abstract","Abstract Background Transfer entropy (TE) is a measure for the detection of directed interactions. Transfer entropy is an information theoretic implementation of Wiener's principle of observational causality. It offers an approach to the detection of neuronal interactions that is free of an explicit model of the interactions. Hence, it offers the power to analyze linear and nonlinear interactions alike. This allows for example the comprehensive analysis of directed interactions in neural networks at various levels of description. Here we present the open-source MATLAB toolbox TRENTOOL that allows the user to handle the considerable complexity of this measure and to validate the obtained results using non-parametrical statistical testing. We demonstrate the use of the toolbox and the performance of the algorithm on simulated data with nonlinear (quadratic) coupling and on local field potentials (LFP) recorded from the retina and the optic tectum of the turtle (Pseudemys scripta elegans) where a neuronal one-way connection is likely present. Results In simulated data TE detected information flow in the simulated direction reliably with false positives not exceeding the rates expected under the null hypothesis. In the LFP data we found directed interactions from the retina to the tectum, despite the complicated signal transformations between these stages. No false positive interactions in the reverse directions were detected. Conclusions TRENTOOL is an implementation of transfer entropy and mutual information analysis that aims to support the user in the application of this information theoretic measure. TRENTOOL is implemented as a MATLAB toolbox and available under an open source license (GPL v3). For the use with neural data TRENTOOL seamlessly integrates with the popular FieldTrip toolbox."],["dc.identifier.doi","10.1186/1471-2202-12-119"],["dc.identifier.pii","2478"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/110044"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-575"],["dc.relation.eissn","1471-2202"],["dc.title","TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropy"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]