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Anna-Maria Jürgensen

PhD Student

Room 2.315
Phone: +49-221-470-4152
Fax:      +49-221-470-4889

My work is centered around understanding the mechanisms underlying associative learning. I use computer simulations of rate and spiking network models of the Drosophila olfactory system to study learning through synaptic plasticity. In collaborations with experimental labs I focus on implementing the brain areas of interest with a high level of anatomical and functional accuracy to discover ‘how the brain actually does it’. I am especially interested in where and how prediction error signals are a biologically plausible driving force of plasticity in this circuit. Additionally I study how sparse code of higher-level sensory representations supports pattern discrimination and learning in the restricted coding space with its very limited number of neurons.


  • [DOI] Sakagiannis, P. P., Jürgensen, A., & Nawrot, M. P.. (2021). A realistic locomotory model of Drosophila larva for behavioral simulations.. bioRxiv, 2021.07.07.451470.
    abstract = {The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and top-down modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Inter-individual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.Competing Interest StatementThe authors have declared no competing interest.},
    author = {Sakagiannis, Panagiotis Parthenios and J{\"{u}}rgensen, Anna-Maria and Nawrot, Martin Paul},
    doi = {10.1101/2021.07.07.451470},
    file = {:Users/springer/Downloads/2021.07.07.451470v1.full.pdf:pdf},
    journal = {bioRxiv},
    keywords = {sensor},
    mendeley-tags = {sensor},
    month = {jan},
    pages = {2021.07.07.451470},
    title = {{A realistic locomotory model of Drosophila larva for behavioral simulations.}},
    url = {},
    year = {2021}
  • [DOI] Jürgensen, A., Khalili, A., Chicca, E., Indiveri, G., & Nawrot, M. P.. (2021). A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain. Neuromorphic Computing and Engineering.
    author = {J{\"{u}}rgensen, Anna-Maria and Khalili, Afshin and Chicca, Elisabetta and Indiveri, Giacomo and Nawrot, Martin Paul},
    doi = {10.1088/2634-4386/ac3ba6},
    file = {:Users/springer/Downloads/J{\"{u}}rgensen+et+al{\_}2021{\_}Neuromorph.{\_}Comput.{\_}Eng.{\_}10.1088{\_}2634-4386{\_}ac3ba6.pdf:pdf},
    isbn = {9781509008964},
    issn = {13616579},
    journal = {Neuromorphic Computing and Engineering},
    month = {nov},
    title = {{A neuromorphic model of olfactory processing and sparse coding in the Drosophila larva brain}},
    url = {},
    year = {2021}