Priesemann, Viola

[["dc.bibliographiccitation.artnumber","e0186361"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","PLOS ONE"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Sogorski, Mathias"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2019-07-09T11:45:00Z"],["dc.date.available","2019-07-09T11:45:00Z"],["dc.date.issued","2018"],["dc.description.abstract","Musical rhythms performed by humans typically show temporal fluctuations. While they have been characterized in simple rhythmic tasks, it is an open question what is the nature of temporal fluctuations, when several musicians perform music jointly in all its natural complexity. To study such fluctuations in over 100 original jazz and rock/pop recordings played with and without metronome we developed a semi-automated workflow allowing the extraction of cymbal beat onsets with millisecond precision. Analyzing the inter-beat interval (IBI) time series revealed evidence for two long-range correlated processes characterized by power laws in the IBI power spectral densities. One process dominates on short timescales (t < 8 beats) and reflects microtiming variability in the generation of single beats. The other dominates on longer timescales and reflects slow tempo variations. Whereas the latter did not show differences between musical genres (jazz vs. rock/pop), the process on short timescales showed higher variability for jazz recordings, indicating that jazz makes stronger use of microtiming fluctuations within a measure than rock/pop. Our results elucidate principles of rhythmic performance and can inspire algorithms for artificial music generation. By studying microtiming fluctuations in original music recordings, we bridge the gap between minimalistic tapping paradigms and expressive rhythmic performances."],["dc.identifier.doi","10.1371/journal.pone.0186361"],["dc.identifier.pmid","29364920"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14999"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59138"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1932-6203"],["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.title","Correlated microtiming deviations in jazz and rock music"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Datseris, George"],["dc.contributor.author","Ziereis, Annika"],["dc.contributor.author","Albrecht, Thorsten"],["dc.contributor.author","Hagmayer, York"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Geisel, Theo"],["dc.date.accessioned","2020-12-10T18:11:11Z"],["dc.date.available","2020-12-10T18:11:11Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1038/s41598-019-55981-3"],["dc.identifier.eissn","2045-2322"],["dc.identifier.pmid","31882842"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17180"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/73916"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Microtiming Deviations and Swing Feel in Jazz"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["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.artnumber","e1006081"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.lastpage","29"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Priesemann, Viola"],["dc.contributor.author","Shriki, Oren"],["dc.date.accessioned","2018-11-16T10:48:50Z"],["dc.date.accessioned","2021-10-27T13:10:50Z"],["dc.date.available","2018-11-16T10:48:50Z"],["dc.date.available","2021-10-27T13:10:50Z"],["dc.date.issued","2018"],["dc.description.abstract","The finding of power law scaling in neural recordings lends support to the hypothesis of critical brain dynamics. However, power laws are not unique to critical systems and can arise from alternative mechanisms. Here, we investigate whether a common time-varying external drive to a set of Poisson units can give rise to neuronal avalanches and exhibit apparent criticality. To this end, we analytically derive the avalanche size and duration distributions, as well as additional measures, first for homogeneous Poisson activity, and then for slowly varying inhomogeneous Poisson activity. We show that homogeneous Poisson activity cannot give rise to power law distributions. Inhomogeneous activity can also not generate perfect power laws, but it can exhibit approximate power laws with cutoffs that are comparable to those typically observed in experiments. The mechanism of generating apparent criticality by time-varying external fields, forces or input may generalize to many other systems like dynamics of swarms, diseases or extinction cascades. Here, we illustrate the analytically derived effects for spike recordings in vivo and discuss approaches to distinguish true from apparent criticality. Ultimately, this requires causal interventions, which allow separating internal system properties from externally imposed ones."],["dc.identifier.doi","10.1371/journal.pcbi.1006081"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15667"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91537"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.issn","1553-7358"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Can a time varying external drive give rise to apparent criticality in neural systems?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","15140"],["dc.bibliographiccitation.journal","Nature Communications"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Levina, A."],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2019-07-09T11:43:22Z"],["dc.date.available","2019-07-09T11:43:22Z"],["dc.date.issued","2017"],["dc.description.abstract","In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system’s aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development."],["dc.identifier.doi","10.1038/ncomms15140"],["dc.identifier.pmid","28469176"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14462"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/58871"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/291734/EU/International IST Postdoctoral Fellowship Programme/ISTFELLOW"],["dc.relation.issn","2041-1723"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Subsampling scaling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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","108841"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","Cell Reports"],["dc.bibliographiccitation.volume","34"],["dc.contributor.author","Jähne, Sebastian"],["dc.contributor.author","Mikulasch, Fabian"],["dc.contributor.author","Heuer, Helge G.H."],["dc.contributor.author","Truckenbrodt, Sven"],["dc.contributor.author","Agüi-Gonzalez, Paola"],["dc.contributor.author","Grewe, Katharina"],["dc.contributor.author","Vogts, Angela"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2021-04-14T08:29:03Z"],["dc.date.available","2021-04-14T08:29:03Z"],["dc.date.issued","2021"],["dc.description.abstract","Synaptic transmission relies on the continual exocytosis and recycling of synaptic vesicles. Aged vesicle proteins are prevented from recycling and are eventually degraded. This implies that active synapses would lose vesicles and vesicle-associated proteins over time, unless the supply correlates to activity, to balance the losses. To test this hypothesis, we first model the quantitative relation between presynaptic spike rate and vesicle turnover. The model predicts that the vesicle supply needs to increase with the spike rate. To follow up this prediction, we measure protein turnover in individual synapses of cultured hippocampal neurons by combining nanoscale secondary ion mass spectrometry (nanoSIMS) and fluorescence microscopy. We find that turnover correlates with activity at the single-synapse level, but not with other parameters such as the abundance of synaptic vesicles or postsynaptic density proteins. We therefore suggest that the supply of newly synthesized proteins to synapses is closely connected to synaptic activity."],["dc.identifier.doi","10.1016/j.celrep.2021.108841"],["dc.identifier.pmid","33730575"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82779"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/244"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/117"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.relation","EXC 2067: Multiscale Bioimaging"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | A03: Dynamische Analyse der Remodellierung der extrazellulären Matrix (ECM) als Mechanismus der Synapsenorganisation und Plastizität"],["dc.relation.issn","2211-1247"],["dc.relation.workinggroup","RG Priesemann (Physics, Complex Systems & Neural Networks)"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.rights","CC BY-NC-ND 4.0"],["dc.title","Presynaptic activity and protein turnover are correlated at the single-synapse level"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","494"],["dc.bibliographiccitation.issue","9"],["dc.bibliographiccitation.journal","Entropy"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","Wibral, Michael"],["dc.contributor.author","Finn, Conor"],["dc.contributor.author","Wollstadt, Patricia"],["dc.contributor.author","Lizier, Joseph"],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2019-12-17T13:06:20Z"],["dc.date.accessioned","2021-10-27T13:22:41Z"],["dc.date.available","2019-12-17T13:06:20Z"],["dc.date.available","2021-10-27T13:22:41Z"],["dc.date.issued","2017"],["dc.description.abstract","Information processing performed by any system can be conceptually decomposed into the transfer, storage and modification of information—an idea dating all the way back to the work of Alan Turing. However, formal information theoretic definitions until very recently were only available for information transfer and storage, not for modification. This has changed with the extension of Shannon information theory via the decomposition of the mutual information between inputs to and the output of a process into unique, shared and synergistic contributions from the inputs, called a partial information decomposition (PID). The synergistic contribution in particular has been identified as the basis for a definition of information modification. We here review the requirements for a functional definition of information modification in neuroscience, and apply a recently proposed measure of information modification to investigate the developmental trajectory of information modification in a culture of neurons vitro, using partial information decomposition. We found that modification rose with maturation, but ultimately collapsed when redundant information among neurons took over. This indicates that this particular developing neural system initially developed intricate processing capabilities, but ultimately displayed information processing that was highly similar across neurons, possibly due to a lack of external inputs. We close by pointing out the enormous promise PID and the analysis of information modification hold for the understanding of neural systems."],["dc.identifier.doi","10.3390/e19090494"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17015"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92118"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1099-4300"],["dc.relation.issn","1099-4300"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["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.title","Quantifying Information Modification in Developing Neural Networks via Partial Information Decomposition"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Physics"],["dc.bibliographiccitation.volume","9"],["dc.contributor.affiliation","Zeraati, Roxana; \r\n\r\n1\r\nInternational Max Planck Research School for the Mechanisms of Mental Function and Dysfunction, University of Tübingen, Tübingen, Germany"],["dc.contributor.affiliation","Priesemann, Viola; \r\n\r\n3\r\nMax Planck Institute for Dynamics and Self-Organization, Göttingen, Germany"],["dc.contributor.affiliation","Levina, Anna; \r\n\r\n2\r\nMax Planck Institute for Biological Cybernetics, Tübingen, Germany"],["dc.contributor.author","Zeraati, Roxana"],["dc.contributor.author","Levina, Anna"],["dc.contributor.author","Priesemann, Viola"],["dc.date.accessioned","2021-06-01T09:42:27Z"],["dc.date.available","2021-06-01T09:42:27Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:21:08Z"],["dc.description.abstract","Self-organized criticality has been proposed to be a universal mechanism for the emergence of scale-free dynamics in many complex systems, and possibly in the brain. While such scale-free patterns were identified experimentally in many different types of neural recordings, the biological principles behind their emergence remained unknown. Utilizing different network models and motivated by experimental observations, synaptic plasticity was proposed as a possible mechanism to self-organize brain dynamics toward a critical point. In this review, we discuss how various biologically plausible plasticity rules operating across multiple timescales are implemented in the models and how they alter the network’s dynamical state through modification of number and strength of the connections between the neurons. Some of these rules help to stabilize criticality, some need additional mechanisms to prevent divergence from the critical state. We propose that rules that are capable of bringing the network to criticality can be classified by how long the near-critical dynamics persists after their disabling. Finally, we discuss the role of self-organization and criticality in computation. Overall, the concept of criticality helps to shed light on brain function and self-organization, yet the overall dynamics of living neural networks seem to harnesses not only criticality for computation, but also deviations thereof."],["dc.identifier.doi","10.3389/fphy.2021.619661"],["dc.identifier.eissn","2296-424X"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85256"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","2296-424X"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.rights.uri","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Self-Organization Toward Criticality by Synaptic Plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]