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.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"]]