Nawrot, Martin Paul

[["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.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.contributor.author","Rapp, Hannes"],["dc.contributor.author","Nawrot, Martin Paul"],["dc.contributor.author","Stern, Merav"],["dc.date.accessioned","2022-08-19T12:25:07Z"],["dc.date.available","2022-08-19T12:25:07Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1101/662932"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/113069"],["dc.identifier.url","https://for2705.de/literature/publications/5"],["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","Numerical cognition based on precise counting with a single spiking neuron"],["dc.type","preprint"],["dc.type.internalPublication","unknown"],["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"]]