Tetzlaff, Christian

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Lehr, Andrew B."],["dc.contributor.author","Luboeinski, Jannik"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2022-12-01T08:30:56Z"],["dc.date.available","2022-12-01T08:30:56Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\r\n Events that are important to an individual’s life trigger neuromodulator release in brain areas responsible for cognitive and behavioral function. While it is well known that the presence of neuromodulators such as dopamine and norepinephrine is required for memory consolidation, the impact of neuromodulator concentration is, however, less understood. In a recurrent spiking neural network model featuring neuromodulator-dependent synaptic tagging and capture, we study how synaptic memory consolidation depends on the amount of neuromodulator present in the minutes to hours after learning. We find that the storage of rate-based and spike timing-based information is controlled by the level of neuromodulation. Specifically, we find better recall of temporal information for high levels of neuromodulation, while we find better recall of rate-coded spatial patterns for lower neuromodulation, mediated by the selection of different groups of synapses for consolidation. Hence, our results indicate that in minutes to hours after learning, the level of neuromodulation may alter the process of synaptic consolidation to ultimately control which type of information becomes consolidated in the recurrent neural network."],["dc.description.sponsorship"," Natural Sciences and Engineering Research Council of Canada http://dx.doi.org/10.13039/501100000038"],["dc.description.sponsorship"," Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.description.sponsorship"," Georg-August-Universität Göttingen http://dx.doi.org/10.13039/501100003385"],["dc.identifier.doi","10.1038/s41598-022-22430-7"],["dc.identifier.pii","22430"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118021"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.relation.eissn","2045-2322"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","606"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Network Neuroscience"],["dc.bibliographiccitation.lastpage","634"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Herpich, Juliane"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2020-12-10T18:38:11Z"],["dc.date.available","2020-12-10T18:38:11Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1162/netn_a_00086"],["dc.identifier.pmid","31157312"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77207"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/5"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C01: Die plastizitätsabhängige räumliche und zeitliche Organisation von AMPA-Rezeptoren und Gerüstproteinen"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.title","Principles underlying the input-dependent formation and organization of memories"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","7298"],["dc.bibliographiccitation.issue","19"],["dc.bibliographiccitation.journal","International Journal of Molecular Sciences"],["dc.bibliographiccitation.volume","21"],["dc.contributor.affiliation","Reshetniak, Sofiia; \t\t \r\n\t\t Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, 37073 Göttingen, Germany, sofiia.reshetniak@med.uni-goettingen.de\t\t \r\n\t\t International Max Planck Research School for Molecular Biology, 37077 Göttingen, Germany, sofiia.reshetniak@med.uni-goettingen.de"],["dc.contributor.affiliation","Fernández-Busnadiego, Rubén; \t\t \r\n\t\t Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, 37077 Göttingen, Germany, ruben.fernandezbusnadiego@med.uni-goettingen.de\t\t \r\n\t\t Institute for Neuropathology, University Medical Center Göttingen, 37075 Göttingen, Germany, ruben.fernandezbusnadiego@med.uni-goettingen.de"],["dc.contributor.affiliation","Müller, Marcus; \t\t \r\n\t\t Institute for Theoretical Physics, University of Göttingen, 37077 Göttingen, Germany, mmueller@theorie.physik.uni-goettingen.de"],["dc.contributor.affiliation","Rizzoli, Silvio O.; \t\t \r\n\t\t Institute for Neuro- and Sensory Physiology and Biostructural Imaging of Neurodegeneration (BIN) Center, University Medical Center Göttingen, 37073 Göttingen, Germany, srizzol@gwdg.de\t\t \r\n\t\t Cluster of Excellence “Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells” (MBExC), University of Göttingen, 37077 Göttingen, Germany, srizzol@gwdg.de"],["dc.contributor.affiliation","Tetzlaff, Christian; \t\t \r\n\t\t Third Institute of Physics, University of Göttingen, 37077 Göttingen, Germany, tetzlaff@phys.uni-goettingen.de"],["dc.contributor.author","Reshetniak, Sofiia"],["dc.contributor.author","Fernández Busnadiego, Rubén"],["dc.contributor.author","Müller, Marcus"],["dc.contributor.author","Rizzoli, Silvio O."],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2021-04-14T08:31:06Z"],["dc.date.available","2021-04-14T08:31:06Z"],["dc.date.issued","2020"],["dc.date.updated","2022-09-06T17:07:09Z"],["dc.identifier.doi","10.3390/ijms21197298"],["dc.identifier.pmid","33023247"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/83487"],["dc.identifier.url","https://mbexc.uni-goettingen.de/literature/publications/152"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/99"],["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.eissn","1422-0067"],["dc.relation.workinggroup","RG Fernández-Busnadiego (Structural Cell Biology)"],["dc.relation.workinggroup","RG Rizzoli (Quantitative Synaptology in Space and Time)"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Quantitative Synaptic Biology: A Perspective on Techniques, Numbers and Expectations"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","174"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Network Neuroscience"],["dc.bibliographiccitation.lastpage","199"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Krüppel, Steffen"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2021-04-14T08:27:03Z"],["dc.date.available","2021-04-14T08:27:03Z"],["dc.date.issued","2020"],["dc.description.sponsorship","Open-Access-Publikationsfonds 2019"],["dc.identifier.doi","10.1162/netn_a_00118"],["dc.identifier.pmid","32166207"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17211"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/82153"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/76"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-399"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","Merged from goescholar"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/732266/EU//Plan4Act"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C01: Die plastizitätsabhängige räumliche und zeitliche Organisation von AMPA-Rezeptoren und Gerüstproteinen"],["dc.relation.eissn","2472-1751"],["dc.relation.orgunit","Fakultät für Physik"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Krüppel, Steffen"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2022-08-26T07:15:45Z"],["dc.date.available","2022-08-26T07:15:45Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1101/612341"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113245"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/31"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C01: Die plastizitätsabhängige räumliche und zeitliche Organisation von AMPA-Rezeptoren und Gerüstproteinen"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.title","The self-organized learning of noisy environmental stimuli requires distinct phases of plasticity"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Cognitive Computation"],["dc.contributor.author","Luboeinski, Jannik"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2022-09-01T09:49:24Z"],["dc.date.available","2022-09-01T09:49:24Z"],["dc.date.issued","2022"],["dc.description.abstract","Abstract\r\n \r\n Background / Introduction\r\n In recurrent neural networks in the brain, memories are represented by so-called Hebbian cell assemblies. Such assemblies are groups of neurons with particularly strong synaptic connections formed by synaptic plasticity and consolidated by synaptic tagging and capture (STC). To link these synaptic mechanisms to long-term memory on the level of cognition and behavior, their functional implications on the level of neural networks have to be understood.\r\n \r\n \r\n Methods\r\n We employ a biologically detailed recurrent network of spiking neurons featuring synaptic plasticity and STC to model the learning and consolidation of long-term memory representations. Using this, we investigate the effects of different organizational paradigms, and of priming stimulation, on the functionality of multiple memory representations. We quantify these effects by the spontaneous activation of memory representations driven by background noise.\r\n \r\n \r\n Results\r\n We find that the learning order of the memory representations significantly biases the likelihood of activation towards more recently learned representations, and that hub-like overlap structure counters this effect. We identify long-term depression as the mechanism underlying these findings. Finally, we demonstrate that STC has functional consequences for the interaction of long-term memory representations: 1. intermediate consolidation in between learning the individual representations strongly alters the previously described effects, and 2. STC enables the priming of a long-term memory representation on a timescale of minutes to hours.\r\n \r\n \r\n Conclusion\r\n Our findings show how synaptic and neuronal mechanisms can provide an explanatory basis for known cognitive effects."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft CRC1286"],["dc.description.sponsorship","Horizon 2020 FETPROACT"],["dc.description.sponsorship"," Georg-August-Universität Göttingen http://dx.doi.org/10.13039/501100003385"],["dc.identifier.doi","10.1007/s12559-022-10021-7"],["dc.identifier.pii","10021"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113416"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-597"],["dc.relation.eissn","1866-9964"],["dc.relation.issn","1866-9956"],["dc.relation.orgunit","III. Physikalisches Institut - Biophysik"],["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","Organization and Priming of Long-term Memory Representations with Two-phase Plasticity"],["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","26"],["dc.bibliographiccitation.journal","Frontiers in Computational Neuroscience"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Lappalainen, Janne"],["dc.contributor.author","Herpich, Juliane"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2019-07-09T11:51:17Z"],["dc.date.available","2019-07-09T11:51:17Z"],["dc.date.issued","2019"],["dc.description.abstract","Synaptic plasticity serves as an essential mechanism underlying cognitive processes as learning and memory. For a better understanding detailed theoretical models combine experimental underpinnings of synaptic plasticity and match experimental results. However, these models are mathematically complex impeding the comprehensive investigation of their link to cognitive processes generally executed on the neuronal network level. Here, we derive a mathematical framework enabling the simplification of such detailed models of synaptic plasticity facilitating further mathematical analyses. By this framework we obtain a compact, firing-rate-dependent mathematical formulation, which includes the essential dynamics of the detailed model and, thus, of experimentally verified properties of synaptic plasticity. Amongst others, by testing our framework by abstracting the dynamics of two well-established calcium-dependent synaptic plasticity models, we derived that the synaptic changes depend on the square of the presynaptic firing rate, which is in contrast to previous assumptions. Thus, the here-presented framework enables the derivation of biologically plausible but simple mathematical models of synaptic plasticity allowing to analyze the underlying dependencies of synaptic dynamics from neuronal properties such as the firing rate and to investigate their implications in complex neuronal networks."],["dc.identifier.doi","10.3389/fncom.2019.00026"],["dc.identifier.pmid","31133837"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16096"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59918"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/10"],["dc.language.iso","en"],["dc.relation","info:eu-repo/grantAgreement/EC/H2020/732266/EU//Plan4Act"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C01: Die plastizitätsabhängige räumliche und zeitliche Organisation von AMPA-Rezeptoren und Gerüstproteinen"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.subject.ddc","530"],["dc.title","A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","1015624"],["dc.bibliographiccitation.journal","Frontiers in Neuroinformatics"],["dc.bibliographiccitation.volume","16"],["dc.contributor.affiliation","Michaelis, Carlo; 1Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Lehr, Andrew B.; 1Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Oed, Winfried; 1Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany"],["dc.contributor.affiliation","Tetzlaff, Christian; 1Department of Computational Neuroscience, University of Göttingen, Göttingen, Germany"],["dc.contributor.author","Michaelis, Carlo"],["dc.contributor.author","Lehr, Andrew B."],["dc.contributor.author","Oed, Winfried"],["dc.contributor.author","Tetzlaff, Christian"],["dc.date.accessioned","2022-12-01T08:31:34Z"],["dc.date.available","2022-12-01T08:31:34Z"],["dc.date.issued","2022"],["dc.date.updated","2022-11-28T10:47:14Z"],["dc.description.abstract","Developing intelligent neuromorphic solutions remains a challenging endeavor. It requires a solid conceptual understanding of the hardware's fundamental building blocks. Beyond this, accessible and user-friendly prototyping is crucial to speed up the design pipeline. We developed an open source Loihi emulator based on the neural network simulator Brian that can easily be incorporated into existing simulation workflows. We demonstrate errorless Loihi emulation in software for a single neuron and for a recurrently connected spiking neural network. On-chip learning is also reviewed and implemented, with reasonable discrepancy due to stochastic rounding. This work provides a coherent presentation of Loihi's computational unit and introduces a new, easy-to-use Loihi prototyping package with the aim to help streamline conceptualization and deployment of new algorithms."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.3389/fninf.2022.1015624"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/118202"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-621"],["dc.publisher","Frontiers Media S.A."],["dc.relation.eissn","1662-5196"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Frontiers in Synaptic Neuroscience"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Bonilla-Quintana, Mayte"],["dc.contributor.author","Wörgötter, Florentin"],["dc.contributor.author","Tetzlaff, Christian"],["dc.contributor.author","Fauth, Michael"],["dc.date.accessioned","2020-12-10T18:44:35Z"],["dc.date.available","2020-12-10T18:44:35Z"],["dc.date.issued","2020"],["dc.description.abstract","Dendritic spines are the morphological basis of excitatory synapses in the cortex and their size and shape correlates with functional synaptic properties. Recent experiments show that spines exhibit large shape fluctuations that are not related to activity-dependent plasticity but nonetheless might influence memory storage at their synapses. To investigate the determinants of such spontaneous fluctuations, we propose a mathematical model for the dynamics of the spine shape and analyze it in 2D—related to experimental microscopic imagery—and in 3D. We show that the spine shape is governed by a local imbalance between membrane tension and the expansive force from actin bundles that originates from discrete actin polymerization foci. Experiments have shown that only few such polymerization foci co-exist at any time in a spine, each having limited life time. The model shows that the momentarily existing set of such foci pushes the membrane along certain directions until foci are replaced and other directions may now be affected. We explore these relations in depth and use our model to predict shape and temporal characteristics of spines from the different biophysical parameters involved in actin polymerization. Approximating the model by a single recursive equation we finally demonstrate that the temporal evolution of the number of active foci is sufficient to predict the size of the model-spines. Thus, our model provides the first platform to study the relation between molecular and morphological properties of the spine with a high degree of biophysical detail."],["dc.identifier.doi","10.3389/fnsyn.2020.00009"],["dc.identifier.pmid","32218728"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17387"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78517"],["dc.identifier.url","https://sfb1286.uni-goettingen.de/literature/publications/75"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.relation","SFB 1286: Quantitative Synaptologie"],["dc.relation","SFB 1286 | C03: Modellierung der Fluktuation dendritischer Dornenfortsätze"],["dc.relation.eissn","1663-3563"],["dc.relation.workinggroup","RG Tetzlaff (Computational Neuroscience - Learning and Memory)"],["dc.relation.workinggroup","RG Wörgötter (Computational Neuroscience)"],["dc.rights","http://creativecommons.org/licenses/by/4.0/"],["dc.title","Modeling the Shape of Synaptic Spines by Their Actin Dynamics"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]