Nawrot, Martin Paul

[["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.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","28412"],["dc.bibliographiccitation.issue","45"],["dc.bibliographiccitation.journal","Proceedings of the National Academy of Sciences of the United States of America"],["dc.bibliographiccitation.lastpage","28421"],["dc.bibliographiccitation.volume","117"],["dc.contributor.author","Rapp, Hannes"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.date.accessioned","2022-08-19T11:48:06Z"],["dc.date.available","2022-08-19T11:48:06Z"],["dc.date.issued","2020"],["dc.description.abstract","Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning."],["dc.identifier.doi","10.1073/pnas.2009821117"],["dc.identifier.pmid","33122439"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113053"],["dc.identifier.url","https://for2705.de/literature/publications/25"],["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","1091-6490"],["dc.relation.issn","0027-8424"],["dc.relation.workinggroup","RG Nawrot"],["dc.rights","CC BY-NC-ND 4.0"],["dc.title","A spiking neural program for sensorimotor control during foraging in flying insects"],["dc.type","journal_article"],["dc.type.internalPublication","no"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]