Tchumatchenko, Tatjana

[["dc.bibliographiccitation.artnumber","062707"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Physical Review. E"],["dc.bibliographiccitation.volume","91"],["dc.contributor.author","Gatys, Leon A."],["dc.contributor.author","Ecker, Alexander S."],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Bethge, Matthias"],["dc.date.accessioned","2020-04-01T11:34:52Z"],["dc.date.available","2020-04-01T11:34:52Z"],["dc.date.issued","2015"],["dc.description.abstract","Synaptic unreliability is one of the major sources of biophysical noise in the brain. In the context of neural information processing, it is a central question how neural systems can afford this unreliability. Here we examine how synaptic noise affects signal transmission in cortical circuits, where excitation and inhibition are thought to be tightly balanced. Surprisingly, we find that in this balanced state synaptic response variability actually facilitates information transmission, rather than impairing it. In particular, the transmission of fast-varying signals benefits from synaptic noise, as it instantaneously increases the amount of information shared between presynaptic signal and postsynaptic current. Furthermore we show that the beneficial effect of noise is based on a very general mechanism which contrary to stochastic resonance does not reach an optimum at a finite noise level."],["dc.identifier.doi","10.1103/PhysRevE.91.062707"],["dc.identifier.pmid","26172736"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63430"],["dc.language.iso","en"],["dc.relation.eissn","1550-2376"],["dc.relation.issn","1539-3755"],["dc.title","Synaptic unreliability facilitates information transmission in balanced cortical populations"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","12171"],["dc.bibliographiccitation.issue","34"],["dc.bibliographiccitation.journal","The Journal of neuroscience"],["dc.bibliographiccitation.lastpage","12179"],["dc.bibliographiccitation.volume","31"],["dc.contributor.author","Tchumatchenko, T."],["dc.contributor.author","Malyshev, A."],["dc.contributor.author","Wolf, F."],["dc.contributor.author","Volgushev, M."],["dc.date.accessioned","2021-06-01T10:48:23Z"],["dc.date.available","2021-06-01T10:48:23Z"],["dc.date.issued","2011"],["dc.description.abstract","The processing speed of the brain depends on the ability of neurons to rapidly relay input changes. Previous theoretical and experimental studies of the timescale of population firing rate responses arrived at controversial conclusions, some advocating an ultrafast response scale but others arguing for an inherent disadvantage of mean encoded signals for rapid detection of the stimulus onset. Here we assessed the timescale of population firing rate responses of neocortical neurons in experiments performed in the time domain and the frequency domain in vitro and in vivo. We show that populations of neocortical neurons can alter their firing rate within 1 ms in response to somatically delivered weak current signals presented on a fluctuating background. Signals with amplitudes of miniature postsynaptic currents can be robustly and rapidly detected in the population firing. We further show that population firing rate of neurons of rat visual cortex in vitro and cat visual cortex in vivo can reliably encode weak signals varying at frequencies up to ∼200–300 Hz, or ∼50 times faster than the firing rate of individual neurons. These results provide coherent evidence for the ultrafast, millisecond timescale of cortical population responses. Notably, fast responses to weak stimuli are limited to the mean encoding. Rapid detection of current variance changes requires extraordinarily large signal amplitudes. Our study presents conclusive evidence showing that cortical neurons are capable of rapidly relaying subtle mean current signals. This provides a vital mechanism for the propagation of rate-coded information within and across brain areas."],["dc.identifier.doi","10.1523/JNEUROSCI.2182-11.2011"],["dc.identifier.gro","3151850"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/85920"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-425"],["dc.notes.status","public"],["dc.notes.submitter","chake"],["dc.relation.eissn","1529-2401"],["dc.relation.issn","0270-6474"],["dc.title","Ultrafast Population Encoding by Cortical Neurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["dspace.entity.type","Publication"]]

[["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","e1002239"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLOS Computational Biology"],["dc.bibliographiccitation.volume","7"],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2017-09-07T11:46:14Z"],["dc.date.available","2017-09-07T11:46:14Z"],["dc.date.issued","2011"],["dc.description.abstract","Many sensory or cognitive events are associated with dynamic current modulations in cortical neurons. This raises an urgent demand for tractable model approaches addressing the merits and limits of potential encoding strategies. Yet, current theoretical approaches addressing the response to mean- and variance-encoded stimuli rarely provide complete response functions for both modes of encoding in the presence of correlated noise. Here, we investigate the neuronal population response to dynamical modifications of the mean or variance of the synaptic bombardment using an alternative threshold model framework. In the variance and mean channel, we provide explicit expressions for the linear and non-linear frequency response functions in the presence of correlated noise and use them to derive population rate response to step-like stimuli. For mean-encoded signals, we find that the complete response function depends only on the temporal width of the input correlation function, but not on other functional specifics. Furthermore, we show that both mean- and variance-encoded signals can relay high-frequency inputs, and in both schemes step-like changes can be detected instantaneously. Finally, we obtain the pairwise spike correlation function and the spike triggered average from the linear mean-evoked response function. These results provide a maximally tractable limiting case that complements and extends previous results obtained in the integrate and fire framework."],["dc.identifier.doi","10.1371/journal.pcbi.1002239"],["dc.identifier.fs","585670"],["dc.identifier.gro","3151862"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8630"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/8692"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","final"],["dc.notes.submitter","chake"],["dc.relation.issn","1553-7358"],["dc.relation.orgunit","Fakultät für Physik"],["dc.title","Representation of Dynamical Stimuli in Populations of Threshold Neurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","no"],["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"]]

[["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Tchumatchenko, Tatjana"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Wolf, Fred"],["dc.date.accessioned","2012-05-07T11:43:41Z"],["dc.date.accessioned","2021-10-27T13:21:14Z"],["dc.date.available","2012-05-07T11:43:41Z"],["dc.date.available","2021-10-27T13:21:14Z"],["dc.date.issued","2011"],["dc.format.extent","19"],["dc.identifier.doi","10.1186/1471-2202-12-S1-P376"],["dc.identifier.fs","585670"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7584"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92003"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Representation of dynamical stimuli in threshold neuron models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","44"],["dc.contributor.author","Gatys, L."],["dc.contributor.author","Ecker, A. S."],["dc.contributor.author","Tchumatchenko, T."],["dc.contributor.author","Bethge, M."],["dc.date.accessioned","2020-04-03T11:18:59Z"],["dc.date.available","2020-04-03T11:18:59Z"],["dc.date.issued","2013"],["dc.description.abstract","Neural activity in the cortex appears to be notoriously noisy. A widely accepted explanation for this finding is that excitatory and inhibitory inputs to downstream neurons are balanced in a way that the upstream population activity does not affect the mean but only the variance of the input current. This can be thought of as a multiplicative noise channel. However, the capacity limits imposed by this information channel are not known. Here we develop a general understanding of the encoding process in terms of scale mixture processes and derive information-theoretic bounds on their performance. Our results show that signal transmission via instantaneous changes in the variance can behave quite differently from the common additive noise channel. We perform systematic numerical analyses to maximize the information across the variance channel and thus obtain tight lower bounds to its capacity. Furthermore, we found that additional noise, resembling the unreliable synaptic transmission of spikes, can surprisingly enhance the coding performance of the channel."],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63590"],["dc.language.iso","en"],["dc.notes.preprint","yes"],["dc.relation.conference","Bernstein Conference 2013"],["dc.relation.eventend","2013-09-27"],["dc.relation.eventlocation","Tübingen"],["dc.relation.eventstart","2013-09-24"],["dc.relation.iserratumof","yes"],["dc.title","Information Coding in the Variance of Neural Activity"],["dc.type","conference_paper"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Gatys, L. A."],["dc.contributor.author","Ecker, A. S."],["dc.contributor.author","Tchumatchenko, T."],["dc.contributor.author","Bethge, M."],["dc.date.accessioned","2020-04-03T11:13:25Z"],["dc.date.available","2020-04-03T11:13:25Z"],["dc.date.issued","2014"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/63588"],["dc.language.iso","en"],["dc.title","How much signal is there in the noise?"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]