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.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"]]