Wibral, Michael

[["dc.bibliographiccitation.artnumber","e1005511"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Wollstadt, Patricia"],["dc.contributor.author","Sellers, Kristin K."],["dc.contributor.author","Rudelt, Lucas"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Hutt, Axel"],["dc.contributor.author","Frohlich, Flavio"],["dc.contributor.author","Wibral, Michael"],["dc.date.accessioned","2019-07-09T11:43:32Z"],["dc.date.available","2019-07-09T11:43:32Z"],["dc.date.issued","2017-06"],["dc.description.abstract","The disruption of coupling between brain areas has been suggested as the mechanism underlying loss of consciousness in anesthesia. This hypothesis has been tested previously by measuring the information transfer between brain areas, and by taking reduced information transfer as a proxy for decoupling. Yet, information transfer is a function of the amount of information available in the information source-such that transfer decreases even for unchanged coupling when less source information is available. Therefore, we reconsidered past interpretations of reduced information transfer as a sign of decoupling, and asked whether impaired local information processing leads to a loss of information transfer. An important prediction of this alternative hypothesis is that changes in locally available information (signal entropy) should be at least as pronounced as changes in information transfer. We tested this prediction by recording local field potentials in two ferrets after administration of isoflurane in concentrations of 0.0%, 0.5%, and 1.0%. We found strong decreases in the source entropy under isoflurane in area V1 and the prefrontal cortex (PFC)-as predicted by our alternative hypothesis. The decrease in source entropy was stronger in PFC compared to V1. Information transfer between V1 and PFC was reduced bidirectionally, but with a stronger decrease from PFC to V1. This links the stronger decrease in information transfer to the stronger decrease in source entropy-suggesting reduced source entropy reduces information transfer. This conclusion fits the observation that the synaptic targets of isoflurane are located in local cortical circuits rather than on the synapses formed by interareal axonal projections. Thus, changes in information transfer under isoflurane seem to be a consequence of changes in local processing more than of decoupling between brain areas. We suggest that source entropy changes must be considered whenever interpreting changes in information transfer as decoupling."],["dc.identifier.doi","10.1371/journal.pcbi.1005511"],["dc.identifier.pmid","28570661"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14560"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58905"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","006"],["dc.subject.ddc","573"],["dc.subject.ddc","612"],["dc.subject.mesh","Anesthesia"],["dc.subject.mesh","Anesthetics, Inhalation"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Consciousness"],["dc.subject.mesh","Female"],["dc.subject.mesh","Ferrets"],["dc.subject.mesh","Isoflurane"],["dc.subject.mesh","Mental Processes"],["dc.subject.mesh","Prefrontal Cortex"],["dc.subject.mesh","Unconsciousness"],["dc.title","Breakdown of local information processing may underlie isoflurane anesthesia effects."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e1008927"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Rudelt, Lucas"],["dc.contributor.author","González Marx, Daniel"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.editor","Marinazzo, Daniele"],["dc.date.accessioned","2021-08-12T07:45:37Z"],["dc.date.available","2021-08-12T07:45:37Z"],["dc.date.issued","2021"],["dc.description.abstract","Information processing can leave distinct footprints on the statistics of neural spiking. For example, efficient coding minimizes the statistical dependencies on the spiking history, while temporal integration of information may require the maintenance of information over different timescales. To investigate these footprints, we developed a novel approach to quantify history dependence within the spiking of a single neuron, using the mutual information between the entire past and current spiking. This measure captures how much past information is necessary to predict current spiking. In contrast, classical time-lagged measures of temporal dependence like the autocorrelation capture how long—potentially redundant—past information can still be read out. Strikingly, we find for model neurons that our method disentangles the strength and timescale of history dependence, whereas the two are mixed in classical approaches. When applying the method to experimental data, which are necessarily of limited size, a reliable estimation of mutual information is only possible for a coarse temporal binning of past spiking, a so-called past embedding. To still account for the vastly different spiking statistics and potentially long history dependence of living neurons, we developed an embedding-optimization approach that does not only vary the number and size, but also an exponential stretching of past bins. For extra-cellular spike recordings, we found that the strength and timescale of history dependence indeed can vary independently across experimental preparations. While hippocampus indicated strong and long history dependence, in visual cortex it was weak and short, while in vitro the history dependence was strong but short. This work enables an information-theoretic characterization of history dependence in recorded spike trains, which captures a footprint of information processing that is beyond time-lagged measures of temporal dependence. To facilitate the application of the method, we provide practical guidelines and a toolbox."],["dc.identifier.doi","10.1371/journal.pcbi.1008927"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/88508"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-448"],["dc.relation.eissn","1553-7358"],["dc.title","Embedding optimization reveals long-lasting history dependence in neural spiking activity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Systems Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Wilting, Jens"],["dc.contributor.author","Dehning, Jonas"],["dc.contributor.author","Pinheiro Neto, Joao"],["dc.contributor.author","Rudelt, Lucas"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Zierenberg, Johannes"],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2022-03-01T11:44:23Z"],["dc.date.available","2022-03-01T11:44:23Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.3389/fnsys.2018.00055"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/103013"],["dc.notes.intern","DOI-Import GROB-531"],["dc.relation.eissn","1662-5137"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Operating in a Reverberating Regime Enables Rapid Tuning of Network States to Task Requirements"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]