Timme, Marc

[["dc.bibliographiccitation.artnumber","UNSP e1004002"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","11"],["dc.contributor.author","Arnoldt, Hinrich"],["dc.contributor.author","Chang, Shuwen"],["dc.contributor.author","Jahnke, Sven"],["dc.contributor.author","Urmersbach, Birk"],["dc.contributor.author","Taschenberger, Holger"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T10:01:14Z"],["dc.date.available","2018-11-07T10:01:14Z"],["dc.date.issued","2015"],["dc.description.abstract","Fundamental response properties of neurons centrally underly the computational capabilities of both individual nerve cells and neural networks. Most studies on neuronal input-output relations have focused on continuous-time inputs such as constant or noisy sinusoidal currents. Yet, most neurons communicate via exchanging action potentials (spikes) at discrete times. Here, we systematically analyze the stationary spiking response to regular spiking inputs and reveal that it is generically non-monotonic. Our theoretical analysis shows that the underlying mechanism relies solely on a combination of the discrete nature of the communication by spikes, the capability of locking output to input spikes and limited resources required for spike processing. Numerical simulations of mathematically idealized and biophysically detailed models, as well as neurophysiological experiments confirm and illustrate our theoretical predictions."],["dc.description.sponsorship","BMBF Germany [01GQ1005B]; Max Planck Society"],["dc.identifier.doi","10.1371/journal.pcbi.1004002"],["dc.identifier.isi","000352081000008"],["dc.identifier.pmid","25646860"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/13555"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/37971"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.relation.issn","1553-734X"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.mesh","Action Potentials"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Cells, Cultured"],["dc.subject.mesh","Computer Simulation"],["dc.subject.mesh","Models, Neurological"],["dc.subject.mesh","Neurons"],["dc.subject.mesh","Patch-Clamp Techniques"],["dc.subject.mesh","Rats"],["dc.subject.mesh","Rats, Wistar"],["dc.subject.mesh","Trapezoid Body"],["dc.title","When Less Is More: Non-monotonic Spike Sequence Processing in Neurons"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","224101"],["dc.bibliographiccitation.issue","22"],["dc.bibliographiccitation.journal","Physical Review Letters"],["dc.bibliographiccitation.volume","98"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2012-09-26T10:02:46Z"],["dc.date.accessioned","2021-10-27T13:22:29Z"],["dc.date.available","2012-09-26T10:02:46Z"],["dc.date.available","2021-10-27T13:22:29Z"],["dc.date.issued","2007-05-30"],["dc.description.abstract","We present a method to infer the complete connectivity of a network from its stable response dynamics. As a paradigmatic example, we consider networks of coupled phase oscillators and explicitly study their long-term stationary response to temporally constant driving. For a given driving condition, measuring the phase differences and the collective frequency reveals information about how the units are interconnected. Sufficiently many repetitions for different driving conditions yield the entire network connectivity (the absence or presence of each connection) from measuring the response dynamics only. For sparsely connected networks, we obtain good predictions of the actual connectivity even for formally underdetermined problems."],["dc.format.extent","4"],["dc.identifier.doi","10.1103/PhysRevLett.98.224101"],["dc.identifier.fs","53065"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7965"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92099"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.issn","1079-7114"],["dc.relation.orgunit","Bernstein Center for Computational Neuroscience Göttingen"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Revealing Network Connectivity from Response Dynamics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","UNSP 36"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","6"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:09:14Z"],["dc.date.available","2018-11-07T09:09:14Z"],["dc.date.issued","2012"],["dc.description.abstract","Conventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses toward stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recurrent neural networks, with plasticity and scaling. We show under which constraints an external input given to a feed-forward network forms an input trace similar to a cell assembly (Hebb, 1949) by enhancing synaptic weights to larger stable values as compared to the rest of the network. For instance, a weak input creates a less strong representation in the network than a strong input which produces a trace along large parts of the network. These processes are strongly influenced by the underlying connectivity. For example, when embedding recurrent structures (excitatory rings, etc.) into a feed-forward network, the input trace is extended into more distant layers, while inhibition shortens it. These findings provide a better understanding of the dynamics of generic network structures where plasticity is combined with scaling. This makes it also possible to use this rule for constructing an artificial network with certain desired storage properties."],["dc.identifier.doi","10.3389/fncom.2012.00036"],["dc.identifier.fs","597278"],["dc.identifier.isi","000305330100001"],["dc.identifier.pmid","22719724"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7780"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/26210"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Frontiers Res Found"],["dc.relation","info:eu-repo/grantAgreement/EC/FP7/270273/EU//Xperience"],["dc.relation.issn","1662-5188"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Analysis of synaptic scaling in combination with Hebbian plasticity in several simple networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","068101"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","Physical Review Letters"],["dc.bibliographiccitation.volume","102"],["dc.contributor.author","Kirst, Christoph"],["dc.contributor.author","Geisel, Theo"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T08:32:45Z"],["dc.date.available","2018-11-07T08:32:45Z"],["dc.date.issued","2009"],["dc.description.abstract","The response of a neuron to synaptic input strongly depends on whether or not the neuron has just emitted a spike. We propose a neuron model that after spike emission exhibits a partial response to residual input charges and study its collective network dynamics analytically. We uncover a desynchronization mechanism that causes a sequential desynchronization transition: In globally coupled neurons an increase in the strength of the partial response induces a sequence of bifurcations from states with large clusters of synchronously firing neurons, through states with smaller clusters to completely asynchronous spiking. We briefly discuss key consequences of this mechanism for more general networks of biophysical neurons."],["dc.identifier.doi","10.1103/PhysRevLett.102.068101"],["dc.identifier.fs","503776"],["dc.identifier.isi","000263389500068"],["dc.identifier.pmid","19257635"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/7964"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/17412"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","0031-9007"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Sequential Desynchronization in Networks of Spiking Neurons with Partial Reset"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.lastpage","2"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Jahnke, S."],["dc.contributor.author","Memmesheimer, R. M."],["dc.contributor.author","Timme, M."],["dc.date.accessioned","2011-04-06T10:10:35Z"],["dc.date.accessioned","2021-10-11T11:34:46Z"],["dc.date.available","2011-04-06T10:10:35Z"],["dc.date.available","2021-10-11T11:34:46Z"],["dc.date.issued","2009"],["dc.identifier.citation","Jahnke, S; Memmesheimer, RM; Timme, M (2009): How chaotic is the balanced state? - BMC Neuroscience, Vol. 10, Nr. Suppl 1, p. O20-"],["dc.identifier.doi","10.1186/1471-2202-10-S1-O20"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6049"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90698"],["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","balanced state"],["dc.subject.ddc","530"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","How chaotic is the balanced state?"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","011053"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Physical Review X"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Breuer, David"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.date.accessioned","2018-11-07T09:42:24Z"],["dc.date.available","2018-11-07T09:42:24Z"],["dc.date.issued","2014"],["dc.description.abstract","How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic processing on single-neuron responses and the performance of associative-memory tasks in artificial neural networks. First, we compute the effect of random input to a neuron incorporating nonlinear dendrites. This approach is independent of the details of the neuronal dynamics. Second, we use those results to study the impact of dendritic nonlinearities on the network dynamics in a paradigmatic model for associative memory, both numerically and analytically. We find that dendritic nonlinearities maintain network convergence and increase the robustness of memory performance against noise. Interestingly, an intermediate number of dendritic branches is optimal for memory functionality."],["dc.description.sponsorship","BMBF [01GQ1005B]; DFG [TI 629/3-1]"],["dc.identifier.doi","10.1103/PhysRevX.4.011053"],["dc.identifier.isi","000333584500002"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10142"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/33944"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","2160-3308"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 3.0"],["dc.title","Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","063038"],["dc.bibliographiccitation.journal","New Journal of Physics"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Bick, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T09:23:35Z"],["dc.date.available","2018-11-07T09:23:35Z"],["dc.date.issued","2013"],["dc.description.abstract","Since chaos control has found its way into many applications, the development of fast, easy-to-implement and universally applicable chaos control methods is of crucial importance. Predictive feedback control has been widely applied but suffers from a speed limit imposed by highly unstable periodic orbits. We show that this limit can be overcome by stalling the control, thereby taking advantage of the stable directions of the uncontrolled chaotic map. This analytical finding is confirmed by numerical simulations, giving a chaos-control method that is capable of successfully stabilizing periodic orbits of high period."],["dc.identifier.doi","10.1088/1367-2630/15/6/063038"],["dc.identifier.fs","597018"],["dc.identifier.isi","000320989400004"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10561"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/29615"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Iop Publishing Ltd"],["dc.relation.issn","1367-2630"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","CC BY 3.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/3.0/"],["dc.title","Stalling chaos control accelerates convergence"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.lastpage","2"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Chou, Wen-Chuang"],["dc.contributor.author","Fiala, André"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2014-07-02T10:46:47Z"],["dc.date.accessioned","2021-10-27T13:18:23Z"],["dc.date.available","2014-07-02T10:46:47Z"],["dc.date.available","2021-10-27T13:18:23Z"],["dc.date.issued","2013"],["dc.format.mimetype","application/pdf"],["dc.identifier.doi","10.1186/1471-2202-14-S1-P391"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10416"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91863"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.publisher","BioMed Central"],["dc.publisher.place","London"],["dc.relation.eissn","1471-2202"],["dc.relation.orgunit","Fakultät für Mathematik und Informatik"],["dc.rights","Goescholar"],["dc.rights.access","openAccess"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Heterogeneous connectivity can positively and negatively modulate the correlation between neural representations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1003307"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS Computational Biology"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Tsodyks, Misha"],["dc.contributor.author","Woergoetter, Florentin"],["dc.date.accessioned","2018-11-07T09:18:47Z"],["dc.date.available","2018-11-07T09:18:47Z"],["dc.date.issued","2013"],["dc.description.abstract","Memory storage in the brain relies on mechanisms acting on time scales from minutes, for long-term synaptic potentiation, to days, for memory consolidation. During such processes, neural circuits distinguish synapses relevant for forming a long-term storage, which are consolidated, from synapses of short-term storage, which fade. How time scale integration and synaptic differentiation is simultaneously achieved remains unclear. Here we show that synaptic scaling - a slow process usually associated with the maintenance of activity homeostasis - combined with synaptic plasticity may simultaneously achieve both, thereby providing a natural separation of short-from long-term storage. The interaction between plasticity and scaling provides also an explanation for an established paradox where memory consolidation critically depends on the exact order of learning and recall. These results indicate that scaling may be fundamental for stabilizing memories, providing a dynamic link between early and late memory formation processes."],["dc.identifier.doi","10.1371/journal.pcbi.1003307"],["dc.identifier.isi","000330355300055"],["dc.identifier.pmid","24204240"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9440"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/28483"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1553-7358"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","P372"],["dc.bibliographiccitation.issue","Suppl 1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Kolodziejski, Christoph"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Wörgötter, Florentin"],["dc.date.accessioned","2011-07-22T22:25:45Z"],["dc.date.accessioned","2011-07-23T15:34:56Z"],["dc.date.accessioned","2021-10-11T11:26:03Z"],["dc.date.available","2011-07-22T22:25:45Z"],["dc.date.available","2011-07-23T15:34:56Z"],["dc.date.available","2021-10-11T11:26:03Z"],["dc.date.issued","2011"],["dc.date.updated","2011-07-22T22:25:46Z"],["dc.identifier.doi","10.1186/1471-2202-12-S1-P372"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6832"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/90535"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Fakultät für Physik"],["dc.rights","CC BY 2.0"],["dc.rights.access","openAccess"],["dc.rights.holder","et al.; licensee BioMed Central Ltd."],["dc.rights.uri","http://creativecommons.org/licenses/by/2.0/"],["dc.subject.ddc","530"],["dc.subject.ddc","573"],["dc.subject.ddc","573.8"],["dc.subject.ddc","612"],["dc.subject.ddc","612.8"],["dc.title","Synaptic scaling generically stabilizes circuit connectivity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]