Nawrot, Martin Paul

[["dc.bibliographiccitation.artnumber","ENEURO.0549-20.2021"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","eNeuro"],["dc.bibliographiccitation.volume","8"],["dc.contributor.author","Springer, Magdalena"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T11:49:40Z"],["dc.date.available","2022-08-19T11:49:40Z"],["dc.date.issued","2021"],["dc.description.abstract","Extinction learning, the ability to update previously learned information by integrating novel contradictory information, is of high clinical relevance for therapeutic approaches to the modulation of maladaptive memories. Insect models have been instrumental in uncovering fundamental processes of memory formation and memory update. Recent experimental results in Drosophila melanogaster suggest that, after the behavioral extinction of a memory, two parallel but opposing memory traces coexist, residing at different sites within the mushroom body (MB). Here, we propose a minimalistic circuit model of the Drosophila MB that supports classical appetitive and aversive conditioning and memory extinction. The model is tailored to the existing anatomic data and involves two circuit motives of central functional importance. It employs plastic synaptic connections between Kenyon cells (KCs) and MB output neurons (MBONs) in separate and mutually inhibiting appetitive and aversive learning pathways. Recurrent modulation of plasticity through projections from MBONs to reinforcement-mediating dopaminergic neurons (DAN) implements a simple reward prediction mechanism. A distinct set of four MBONs encodes odor valence and predicts behavioral model output. Subjecting our model to learning and extinction protocols reproduced experimental results from recent behavioral and imaging studies. Simulating the experimental blocking of synaptic output of individual neurons or neuron groups in the model circuit confirmed experimental results and allowed formulation of testable predictions. In the temporal domain, our model achieves rapid learning with a step-like increase in the encoded odor value after a single pairing of the conditioned stimulus (CS) with a reward or punishment, facilitating single-trial learning."],["dc.identifier.doi","10.1523/ENEURO.0549-20.2021"],["dc.identifier.pmid","33785523"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113054"],["dc.identifier.url","https://for2705.de/literature/publications/27"],["dc.language.iso","en"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation","FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies"],["dc.relation.issn","2373-2822"],["dc.relation.workinggroup","RG Nawrot"],["dc.title","A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Jürgensen, Anna-Maria"],["dc.contributor.author","Khalili, Afshin"],["dc.contributor.author","Chicca, Elisabetta"],["dc.contributor.author","Indiveri, Giacomo"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T12:43:01Z"],["dc.date.available","2022-08-19T12:43:01Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1101/2021.06.29.450278"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113076"],["dc.identifier.url","https://for2705.de/literature/publications/40"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation.workinggroup","RG Gerber"],["dc.relation.workinggroup","RG Nawrot"],["dc.title","A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Strube-Bloss, Martin Fritz"],["dc.contributor.author","D’Albis, Tiziano"],["dc.contributor.author","Menzel, Randolf"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T12:39:09Z"],["dc.date.available","2022-08-19T12:39:09Z"],["dc.date.issued","2021"],["dc.identifier.doi","10.1101/2020.12.30.424797"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113074"],["dc.identifier.url","https://for2705.de/literature/publications/32"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation.workinggroup","RG Nawrot"],["dc.title","Single neuron activity predicts behavioral performance of individual animals during memory retention"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","024008"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Neuromorphic Computing and Engineering"],["dc.bibliographiccitation.volume","1"],["dc.contributor.author","Jürgensen, Anna-Maria"],["dc.contributor.author","Khalili, Afshin"],["dc.contributor.author","Chicca, Elisabetta"],["dc.contributor.author","Indiveri, Giacomo"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T12:07:56Z"],["dc.date.available","2022-08-19T12:07:56Z"],["dc.date.issued","2021"],["dc.description.abstract","Animal nervous systems are highly efficient in processing sensory input. The neuromorphic computing paradigm aims at the hardware implementation of neural network computations to support novel solutions for building brain-inspired computing systems. Here, we take inspiration from sensory processing in the nervous system of the fruit fly larva. With its strongly limited computational resources of <200 neurons and <1.000 synapses the larval olfactory pathway employs fundamental computations to transform broadly tuned receptor input at the periphery into an energy efficient sparse code in the central brain. We show how this approach allows us to achieve sparse coding and increased separability of stimulus patterns in a spiking neural network, validated with both software simulation and hardware emulation on mixed-signal real-time neuromorphic hardware. We verify that feedback inhibition is the central motif to support sparseness in the spatial domain, across the neuron population, while the combination of spike frequency adaptation and feedback inhibition determines sparseness in the temporal domain. Our experiments demonstrate that such small, biologically realistic neural networks, efficiently implemented on neuromorphic hardware, can achieve parallel processing and efficient encoding of sensory input at full temporal resolution."],["dc.identifier.doi","10.1088/2634-4386/ac3ba6"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113062"],["dc.identifier.url","https://for2705.de/literature/publications/51"],["dc.language.iso","en"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation","FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies"],["dc.relation.issn","2634-4386"],["dc.relation.workinggroup","RG Nawrot"],["dc.rights","CC BY 4.0"],["dc.title","A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Scientific Reports"],["dc.bibliographiccitation.volume","12"],["dc.contributor.author","Hancock, Clare E."],["dc.contributor.author","Rostami, Vahid"],["dc.contributor.author","Rachad, El Yazid"],["dc.contributor.author","Deimel, Stephan H."],["dc.contributor.author","Nawrot, Martin P."],["dc.contributor.author","Fiala, André"],["dc.date.accessioned","2022-07-01T07:34:53Z"],["dc.date.available","2022-07-01T07:34:53Z"],["dc.date.issued","2022"],["dc.description.abstract","By learning, through experience, which stimuli coincide with dangers, it is possible to predict outcomes and act pre-emptively to ensure survival. In insects, this process is localized to the mushroom body (MB), the circuitry of which facilitates the coincident detection of sensory stimuli and punishing or rewarding cues and, downstream, the execution of appropriate learned behaviors. Here, we focused our attention on the mushroom body output neurons (MBONs) of the γ-lobes that act as downstream synaptic partners of the MB γ-Kenyon cells (KCs) to ask how the output of the MB γ-lobe is shaped by olfactory associative conditioning, distinguishing this from non-associative stimulus exposure effects, and without the influence of downstream modulation. This was achieved by employing a subcellularly localized calcium sensor to specifically monitor activity at MBON postsynaptic sites. Therein, we identified a robust associative modulation within only one MBON postsynaptic compartment (MBON-γ1pedc > α/β), which displayed a suppressed postsynaptic response to an aversively paired odor. While this MBON did not undergo non-associative modulation, the reverse was true across the remainder of the γ-lobe, where general odor-evoked adaptation was observed, but no conditioned odor-specific modulation. In conclusion, associative synaptic plasticity underlying aversive olfactory learning is localized to one distinct synaptic γKC-to-γMBON connection."],["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.description.sponsorship","Open-Access-Publikationsfonds 2022"],["dc.identifier.doi","10.1038/s41598-022-14413-5"],["dc.identifier.pii","14413"],["dc.identifier.pmid","35729203"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/112033"],["dc.identifier.url","https://for2705.de/literature/publications/53"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-581"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation.eissn","2045-2322"],["dc.relation.workinggroup","RG Fiala"],["dc.relation.workinggroup","RG Nawrot"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Visualization of learning-induced synaptic plasticity in output neurons of the Drosophila mushroom body γ-lobe"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Sakagiannis, Panagiotis"],["dc.contributor.author","Aguilera, Miguel"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T12:30:53Z"],["dc.date.available","2022-08-19T12:30:53Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1101/2020.09.19.304774"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113072"],["dc.identifier.url","https://for2705.de/literature/publications/29"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation","FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies"],["dc.relation.workinggroup","RG Nawrot"],["dc.title","A plausible mechanism for Drosophila larva intermittent behavior"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.contributor.author","Rapp, Hannes"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T12:41:23Z"],["dc.date.available","2022-08-19T12:41:23Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1101/2020.04.05.026203"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113075"],["dc.identifier.url","https://for2705.de/literature/publications/37"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation","FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies"],["dc.relation.workinggroup","RG Nawrot"],["dc.title","Neural computation underlying rapid learning and dynamic memory recall for sensori-motor control in insects"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1539"],["dc.bibliographiccitation.journal","Frontiers in Physiology"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Arican, Cansu"],["dc.contributor.author","Bulk, Janice"],["dc.contributor.author","Deisig, Nina"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T10:10:22Z"],["dc.date.available","2022-08-19T10:10:22Z"],["dc.date.issued","2019"],["dc.description.abstract","Animal personality and individuality are intensively researched in vertebrates and both concepts are increasingly applied to behavioral science in insects. However, only few studies have looked into individuality with respect to performance in learning and memory tasks. In vertebrates, individual learning capabilities vary considerably with respect to learning speed and learning rate. Likewise, honeybees express individual learning abilities in a wide range of classical conditioning protocols. Here, we study individuality in the learning and memory performance of cockroaches, both in classical and operant conditioning tasks. We implemented a novel classical (olfactory) conditioning paradigm where the conditioned response is established in the maxilla-labia response (MLR). Operant spatial learning was investigated in a forced two-choice task using a T-maze. Our results confirm individual learning abilities in classical conditioning of cockroaches that was reported for honeybees and vertebrates but contrast long-standing reports on stochastic learning behavior in fruit flies. In our experiments, most learners expressed a correct behavior after only a single learning trial showing a consistent high performance during training and test. We can further show that individual learning differences in insects are not limited to classical conditioning but equally appear in operant conditioning of the cockroach."],["dc.identifier.doi","10.3389/fphys.2019.01539"],["dc.identifier.pmid","31969831"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113046"],["dc.identifier.url","https://for2705.de/literature/publications/16"],["dc.language.iso","en"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation","FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies"],["dc.relation.issn","1664-042X"],["dc.relation.workinggroup","RG Nawrot"],["dc.rights","CC BY 4.0"],["dc.title","Cockroaches Show Individuality in Learning and Memory During Classical and Operant Conditioning"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","100852"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","iScience"],["dc.bibliographiccitation.volume","23"],["dc.contributor.author","Rapp, Hannes"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.contributor.author","Stern, Merav"],["dc.date.accessioned","2022-08-19T12:05:07Z"],["dc.date.available","2022-08-19T12:05:07Z"],["dc.date.issued","2020-02-21"],["dc.description.abstract","Insects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatiotemporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free-flying bees that requires numerical cognition, the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources."],["dc.identifier.doi","10.1016/j.isci.2020.100852"],["dc.identifier.pmid","32058964"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113061"],["dc.identifier.url","https://for2705.de/literature/publications/46"],["dc.language.iso","en"],["dc.relation","FOR 2705: Dissection of a Brain Circuit: Structure, Plasticity and Behavioral Function of the Drosophila Mushroom Body"],["dc.relation","FOR 2705 | TP 4: From molecular computation to adaptive behavior: Across level modeling of memory computation in the mushroom bodies"],["dc.relation.eissn","2589-0042"],["dc.relation.workinggroup","RG Nawrot"],["dc.rights","CC BY 4.0"],["dc.title","Numerical Cognition Based on Precise Counting with a Single Spiking Neuron"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","498"],["dc.bibliographiccitation.issue","4-6"],["dc.bibliographiccitation.journal","Journal of Physiology-Paris"],["dc.bibliographiccitation.lastpage","506"],["dc.bibliographiccitation.volume","98"],["dc.contributor.author","Mehring, Carsten"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.contributor.author","de Oliveira, Simone Cardoso"],["dc.contributor.author","Vaadia, Eilon"],["dc.contributor.author","Schulze-Bonhage, Andreas"],["dc.contributor.author","Aertsen, Ad"],["dc.contributor.author","Ball, Tonio"],["dc.date.accessioned","2022-10-06T13:33:13Z"],["dc.date.available","2022-10-06T13:33:13Z"],["dc.date.issued","2004"],["dc.identifier.doi","10.1016/j.jphysparis.2005.09.016"],["dc.identifier.pii","S0928425705000173"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/115574"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-602"],["dc.relation.issn","0928-4257"],["dc.relation.orgunit","Deutsches Primatenzentrum"],["dc.title","Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dspace.entity.type","Publication"]]