Timme, Marc

[["dc.bibliographiccitation.artnumber","188101"],["dc.bibliographiccitation.issue","18"],["dc.bibliographiccitation.journal","Physical Review Letters"],["dc.bibliographiccitation.volume","97"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T08:58:56Z"],["dc.date.available","2018-11-07T08:58:56Z"],["dc.date.issued","2006"],["dc.description.abstract","Precise timing of spikes and temporal locking are key elements of neural computation. Here we demonstrate how even strongly heterogeneous, deterministic neural networks with delayed interactions and complex topology can exhibit periodic patterns of spikes that are precisely timed. We develop an analytical method to find the set of all networks exhibiting a predefined pattern dynamics. Such patterns may be arbitrarily long and of complicated temporal structure. We point out that the same pattern can exist in very different networks and have different stability properties."],["dc.identifier.doi","10.1103/PhysRevLett.97.188101"],["dc.identifier.isi","000241757600063"],["dc.identifier.pmid","17155580"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/23768"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","American Physical Soc"],["dc.relation.issn","0031-9007"],["dc.title","Designing the dynamics of spiking neural networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1555"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Discrete and Continuous Dynamical Systems"],["dc.bibliographiccitation.lastpage","1588"],["dc.bibliographiccitation.volume","28"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T08:36:08Z"],["dc.date.available","2018-11-07T08:36:08Z"],["dc.date.issued","2010"],["dc.description.abstract","Is a periodic orbit underlying a periodic pattern of spikes in a heterogeneous neural network stable or unstable? We analytically assess this question in neural networks with delayed interactions by explicitly studying the microscopic time evolution of perturbations. We show that in purely inhibitorily coupled networks of neurons with normal dissipation (concave rise function), such as common leaky integrate-and-fire neurons, a l l orbits underlying non-degenerate periodic spike patterns are stable. In purely inhibitorily coupled networks with strongly connected topology and normal dissipation (strictly concave rise function), they are even asymptotically stable. In contrast, for the same type of individual neurons, all orbits underlying such patterns are unstable if the coupling is excitatory. For networks of neurons with anomalous dissipation ((strictly) convex rise function), the reverse statements hold. For the stable dynamics, we give an analytical lower bound on the local size of the basin of attraction. Numerical simulations of networks with different integrate-and-fire type neurons illustrate our results."],["dc.identifier.doi","10.3934/dcds.2010.28.1555"],["dc.identifier.isi","000279827200011"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18238"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Inst Mathematical Sciences"],["dc.relation.issn","1078-0947"],["dc.title","STABLE AND UNSTABLE PERIODIC ORBITS IN COMPLEX NETWORKS OF SPIKING NEURONS WITH DELAYS"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","030701"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PHYSICAL REVIEW E"],["dc.bibliographiccitation.volume","89"],["dc.contributor.author","Jahnke, Sven"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T09:42:38Z"],["dc.date.available","2018-11-07T09:42:38Z"],["dc.date.issued","2014"],["dc.description.abstract","A wide range of networked systems exhibit highly connected nodes (hubs) as prominent structural elements. The functional roles of hubs in the collective nonlinear dynamics of many such networks, however, are not well understood. Here, we propose that hubs in neural circuits may activate local signal transmission along sequences of specific subnetworks. Intriguingly, in contrast to previous suggestions of the functional roles of hubs, here, not the hubs themselves, but nonhub subnetworks transfer the signals. The core mechanism relies on hubs and nonhubs providing activating feedback to each other. It may, thus, induce the propagation of specific pulse and rate signals in neuronal and other communication networks."],["dc.description.sponsorship","BMBF [01GQ1005B]; DFG [TI 629/3-1]"],["dc.identifier.doi","10.1103/PhysRevE.89.030701"],["dc.identifier.isi","000332672200001"],["dc.identifier.pmid","24730779"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/34003"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","1550-2376"],["dc.relation.issn","1539-3755"],["dc.title","Hub-activated signal transmission in complex networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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","Jahnke, Sven"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2014-07-02T10:46:48Z"],["dc.date.accessioned","2021-10-27T13:18:23Z"],["dc.date.available","2014-07-02T10:46:48Z"],["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-P390"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10418"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91864"],["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","Oscillation induced propagation of synchrony in structured neural networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","16236"],["dc.bibliographiccitation.issue","49"],["dc.bibliographiccitation.journal","Journal of Neuroscience"],["dc.bibliographiccitation.lastpage","16258"],["dc.bibliographiccitation.volume","35"],["dc.contributor.author","Jahnke, Sven"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.date.accessioned","2018-11-07T09:47:35Z"],["dc.date.available","2018-11-07T09:47:35Z"],["dc.date.issued","2015"],["dc.description.abstract","Hippocampal activity is fundamental for episodic memory formation and consolidation. During phases of rest and sleep, it exhibits sharp-wave/ripple (SPW/R) complexes, which are short episodes of increased activity with superimposed high-frequency oscillations. Simultaneously, spike sequences reflecting previous behavior, such as traversed trajectories in space, are replayed. Whereas these phenomena are thought to be crucial for the formation and consolidation of episodic memory, their neurophysiological mechanisms are not well understood. Here we present a unified model showing how experience may be stored and thereafter replayed in association with SPW/Rs. We propose that replay and SPW/Rs are tightly interconnected as they mutually generate and support each other. The underlying mechanism is based on the nonlinear dendritic computation attributable to dendritic sodium spikes that have been prominently found in the hippocampal regions CA1 and CA3, where SPW/Rs and replay are also generated. Besides assigning SPW/Rs a crucial role for replay and thus memory processing, the proposed mechanism also explains their characteristic features, such as the oscillation frequency and the overall wave form. The results shed a new light on the dynamical aspects of hippocampal circuit learning."],["dc.identifier.doi","10.1523/JNEUROSCI.3977-14.2015"],["dc.identifier.isi","000366057000019"],["dc.identifier.pmid","26658873"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/35144"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Soc Neuroscience"],["dc.relation.issn","0270-6474"],["dc.title","A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","182"],["dc.bibliographiccitation.issue","1-2"],["dc.bibliographiccitation.journal","Physica D Nonlinear Phenomena"],["dc.bibliographiccitation.lastpage","201"],["dc.bibliographiccitation.volume","224"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T08:55:30Z"],["dc.date.available","2018-11-07T08:55:30Z"],["dc.date.issued","2006"],["dc.description.abstract","We suggest a new perspective of research towards understanding the relations between the structure and dynamics of a complex network: can we design a network, e.g. by modifying the features of its units or interactions, such that it exhibits a desired dynamics? Here we present a case study where we positively answer this question analytically for networks of spiking neural oscillators. First, we present a method of finding the set of all networks (defined by all mutual coupling strengths) that exhibit an arbitrary given periodic pattern of spikes as an invariant solution. In such a pattern, all the spike times of all the neurons are exactly predefined. The method is very general, as it covers networks of different types of neurons, excitatory and inhibitory couplings, interaction delays that may be heterogeneously distributed, and arbitrary network connectivities. Second, we show how to design networks if further restrictions are imposed, for instance by predefining the detailed network connectivity. We illustrate the applicability of the method by examples of Erdbs-Renyi and power-law random networks. Third, the method can be used to design networks that optimize network properties. To illustrate this idea, we design networks that exhibit a predefined pattern dynamics while at the same time minimizing the networks' wiring costs. (c) 2006 Elsevier B.V. All rights reserved."],["dc.identifier.doi","10.1016/j.physd.2006.09.037"],["dc.identifier.isi","000242974000021"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/22924"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Science Bv"],["dc.relation.issn","0167-2789"],["dc.title","Designing complex networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Neuroscience"],["dc.bibliographiccitation.lastpage","1"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Breuer, David"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.date.accessioned","2014-07-02T10:46:42Z"],["dc.date.accessioned","2021-10-27T13:18:24Z"],["dc.date.available","2014-07-02T10:46:42Z"],["dc.date.available","2021-10-27T13:18:24Z"],["dc.date.issued","2013"],["dc.format.mimetype","application/pdf"],["dc.identifier.doi","10.1186/1471-2202-14-S1-P273"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10410"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91867"],["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","Computational optimum of recurrent neural circuits at intermediate numbers of nonlinear dendritic branches"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","048102"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Physical Review Letters"],["dc.bibliographiccitation.volume","100"],["dc.contributor.author","Jahnke, Sven"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.contributor.author","Timme, Marc"],["dc.date.accessioned","2018-11-07T11:18:54Z"],["dc.date.available","2018-11-07T11:18:54Z"],["dc.date.issued","2008"],["dc.description.abstract","Irregular dynamics in multidimensional systems is commonly associated with chaos. For infinitely large sparse networks of spiking neurons, mean field theory shows that a balanced state of highly irregular activity arises under various conditions. Here we analytically investigate the microscopic irregular dynamics in finite networks of arbitrary connectivity, keeping track of all individual spike times. For delayed, purely inhibitory interactions we demonstrate that any irregular dynamics that characterizes the balanced state is not chaotic but rather stable and convergent towards periodic orbits. These results highlight that chaotic and stable dynamics may be equally irregular."],["dc.identifier.doi","10.1103/PhysRevLett.100.048102"],["dc.identifier.isi","000252863400095"],["dc.identifier.pmid","18352336"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/55144"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Amer Physical Soc"],["dc.relation.issn","0031-9007"],["dc.title","Stable irregular dynamics in complex neural networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","041016"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Physical Review X"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Jahnke, Sven"],["dc.contributor.author","Timme, Marc"],["dc.contributor.author","Memmesheimer, Raoul-Martin"],["dc.date.accessioned","2018-11-07T09:02:19Z"],["dc.date.available","2018-11-07T09:02:19Z"],["dc.date.issued","2012"],["dc.description.abstract","Sparse random networks contain structures that can be considered as diluted feed-forward networks. Modeling of cortical circuits has shown that feed-forward structures, if strongly pronounced compared to the embedding random network, enable reliable signal transmission by propagating localized (subnetwork) synchrony. This assumed prominence, however, is not experimentally observed in local cortical circuits. Here, we show that nonlinear dendritic interactions, as discovered in recent single-neuron experiments, naturally enable guided synchrony propagation already in random recurrent neural networks that exhibit mildly enhanced, biologically plausible substructures. DOI: 10.1103/PhysRevX.2.041016"],["dc.description.sponsorship","BMBF [01GQ1005B]; DFG [TI 629/3-1]; Swartz Foundation"],["dc.identifier.doi","10.1103/PhysRevX.2.041016"],["dc.identifier.isi","000312452200001"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8458"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/24654"],["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.rights.uri","https://creativecommons.org/licenses/by/3.0"],["dc.title","Guiding Synchrony through Random 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"]]