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Anna-Maria Jürgensen

PhD Student

Publications

• Jürgensen, A., Schmitt, F. J., & Nawrot, M. P.. (2023). Minimal circuit motifs for second-order conditioning in the insect mushroom body. bioRxiv, 10.1101/2023.09.11.557174.
  [Bibtex]

  @article{Jurgensen2023,
  author = {J{\"{u}}rgensen, Anna-Maria and Schmitt, Felix Johannes and Nawrot, Martin Paul},
  doi = {10.1101/2023.09.11.557174},
  file = {:Users/springer/Downloads/2023.09.11.557174v1.full.pdf:pdf},
  journal = {bioRxiv},
  month = {sep},
  pages = {10.1101/2023.09.11.557174},
  title = {{Minimal circuit motifs for second-order conditioning in the insect mushroom body}},
  url = {https://www.biorxiv.org/content/10.1101/2023.09.11.557174v1},
  year = {2023}
  }

• Jürgensen, A., Sakagiannis, P., Schleyer, M., Gerber, B., & Nawrot, M. P.. (2022). Prediction error drives associative olfactory learning and conditioned behavior in a spiking model of Drosophila larva. bioRxiv, 2022.12.21.521372.
  [Bibtex]

  @article{Juergensen2022,
  abstract = {Predicting positive or negative reinforcement from environmental clues is essential to guide decision making and goal-directed behavior. In insect brains the mushroom body is a central structure for learning such valuable associations between sensory signals and reinforcement. We propose a biologically realistic spiking network model of the Drosophila larval olfactory pathway for the association of odors and reinforcement to bias behavior towards either approach or avoidance. We demonstrate that prediction error coding through integration of present and expected reinforcement in dopaminergic neurons can serve as a driving force in learning that can, combined with synaptic homeostasis, account for the experimentally observed features of acquisition and extinction of associations that depend on the intensity of odor and reward, as well as temporal features of the odor/reward pairing. To allow for a direct comparison of our simulation results with behavioral data we model learning-induced plasticity over the full time course of behavioral experiments and simulate locomotion of individual larvae towards or away from odor sources in a virtual environment.Competing Interest StatementThe authors have declared no competing interest.},
  author = {J{\"{u}}rgensen, Anna-Maria and Sakagiannis, Panagiotis and Schleyer, Michael and Gerber, Bertram and Nawrot, Martin Paul},
  doi = {10.1101/2022.12.21.521372},
  file = {:Users/springer/Downloads/2022.12.21.521372v1.full.pdf:pdf},
  journal = {bioRxiv},
  month = {dec},
  pages = {2022.12.21.521372},
  title = {{Prediction error drives associative olfactory learning and conditioned behavior in a spiking model of Drosophila larva}},
  url = {http://biorxiv.org/content/early/2022/12/21/2022.12.21.521372.abstract},
  year = {2022}
  }

• Sakagiannis, P. P., Jürgensen, A., & Nawrot, M. P.. (2021). A realistic locomotory model of Drosophila larva for behavioral simulations.. bioRxiv, 2021.07.07.451470.
  [Bibtex]

  @article{Sakagiannis2021,
  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 = {http://biorxiv.org/content/early/2021/07/08/2021.07.07.451470.abstract},
  year = {2021}
  }

• 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.
  [Bibtex]

  @article{Anna-MariaJurgensen*aAfshinKhalili*abElisabettaChiccacGiacomoIndiverid,
  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 = {https://doi.org/10.1088/2634-4386/ac3ba6},
  year = {2021}
  }