Tchumatchenko, Tatjana

[["dc.bibliographiccitation.artnumber","058102"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Physical Review Letters"],["dc.bibliographiccitation.volume","104"],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Malyshev, Aleksey"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Volgushev, Maxim"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:12Z"],["dc.date.available","2017-09-07T11:46:12Z"],["dc.date.issued","2010"],["dc.description.abstract","We study how threshold models and neocortical neurons transfer temporal and interneuronal input correlations to correlations of spikes. In both, we find that the low common input regime is governed by firing rate dependent spike correlations which are sensitive to the detailed structure of input correlation functions. In the high common input regime, the spike correlations are largely insensitive to the firing rate and exhibit a universal peak shape. We further show that pairs with different firing rates driven by common inputs in general exhibit asymmetric spike correlations."],["dc.identifier.doi","10.1103/physrevlett.104.058102"],["dc.identifier.gro","3151856"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8686"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","0031-9007"],["dc.title","Correlations and Synchrony in Threshold Neuron Models"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.lastpage","10"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Volgushev, Maxim"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:17Z"],["dc.date.available","2017-09-07T11:46:17Z"],["dc.date.issued","2010"],["dc.description.abstract","Concerted neural activity can reflect specific features of sensory stimuli or behavioral tasks. Correlation coefficients and count correlations are frequently used to measure correlations between neurons, design synthetic spike trains and build population models. But are correlation coefficients always a reliable measure of input correlations? Here, we consider a stochastic model for the generation of correlated spike sequences which replicate neuronal pairwise correlations in many important aspects. We investigate under which conditions the correlation coefficients reflect the degree of input synchrony and when they can be used to build population models. We find that correlation coefficients can be a poor indicator of input synchrony for some cases of input correlations. In particular, count correlations computed for large time bins can vanish despite the presence of input correlations. These findings suggest that network models or potential coding schemes of neural population activity need to incorporate temporal properties of correlated inputs and take into consideration the regimes of firing rates and correlation strengths to ensure that their building blocks are an unambiguous measures of synchrony.Introduction"],["dc.identifier.doi","10.3389/neuro.10.001.2010"],["dc.identifier.gro","3151883"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8714"],["dc.language.iso","en"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1662-5188"],["dc.title","Signatures of synchrony in pairwise count correlations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","P249"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.lastpage","2"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Malyshev, Aleksey"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Volgushev, Maxim"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2011-04-14T14:08:59Z"],["dc.date.accessioned","2021-10-11T11:31:09Z"],["dc.date.available","2011-04-14T14:08:59Z"],["dc.date.available","2021-10-11T11:31:09Z"],["dc.date.issued","2009"],["dc.identifier.citation","Tchumatchenko, Tatjana; Malyshev, Aleksey; Geisel, Theo; Volgushev, Maxim; Wolf, Fred (2009): On the temporal structure of correlated activity in a pair of neurons - BMC Neuroscience, Vol. 10, Nr. Suppl 1, p. P249-"],["dc.identifier.doi","10.1186/1471-2202-10-S1-P249"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6132"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90593"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","http://goedoc.uni-goettingen.de/licenses"],["dc.subject","correlated activity; neurons"],["dc.subject.ddc","530"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","On the temporal structure of correlated activity in a pair of neurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","68"],["dc.bibliographiccitation.journal","Frontiers in Neuroscience"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Volgushev, Maxim"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:13Z"],["dc.date.available","2017-09-07T11:46:13Z"],["dc.date.issued","2011"],["dc.description.abstract","Sensory and cognitive processing relies on the concerted activity of large populations of neurons. The advent of modern experimental techniques like two-photon population calcium imaging makes it possible to monitor the spiking activity of multiple neurons as they are participating in specific cognitive tasks. The development of appropriate theoretical tools to quantify and interpret the spiking activity of multiple neurons, however, is still in its infancy. One of the simplest and widely used measures of correlated activity is the pairwise correlation coefficient. While spike correlation coefficients are easy to compute using the available numerical toolboxes, it has remained largely an open question whether they are indeed a reliable measure of synchrony. Surprisingly, despite the intense use of correlation coefficients in the design of synthetic spike trains, the construction of population models and the assessment of the synchrony level in live neuronal networks very little was known about their computational properties. We showed that many features of pairwise spike correlations can be studied analytically in a tractable threshold model. Importantly, we demonstrated that under some circumstances the correlation coefficients can vanish, even though input and also pairwise spike cross correlations are present. This finding suggests that the most popular and frequently used measures can, by design, fail to capture the neuronal synchrony."],["dc.identifier.doi","10.3389/fnins.2011.00068"],["dc.identifier.gro","3151852"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8681"],["dc.language.iso","en"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.issn","1662-4548"],["dc.title","Spike Correlations – What Can They Tell About Synchrony?"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]