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Anna-Maria Jürgensen

PhD Student

Publications (ORCID profile):

Articles

• Jürgensen, A., Schmitt, F. J., & Nawrot, M. P.. (2024). Minimal circuit motifs for second-order conditioning in the insect mushroom body. Frontiers in Physiology, 14, 1326307.
  [Bibtex]

  @article{10.3389/fphys.2023.1326307,
  abstract = {In well-established first-order conditioning experiments, the concurrence of a sensory cue with reinforcement forms an association, allowing the cue to predict future reinforcement. In the insect mushroom body, a brain region central to learning and memory, such associations are encoded in the synapses between its intrinsic and output neurons. This process is mediated by the activity of dopaminergic neurons that encode reinforcement signals. In second-order conditioning, a new sensory cue is paired with an already established one that presumably activates dopaminergic neurons due to its predictive power of the reinforcement. We explored minimal circuit motifs in the mushroom body for their ability to support second-order conditioning using mechanistic models. We found that dopaminergic neurons can either be activated directly by the mushroom body's intrinsic neurons or via feedback from the output neurons via several pathways. We demonstrated that the circuit motifs differ in their computational efficiency and robustness. Beyond previous research, we suggest an additional motif that relies on feedforward input of the mushroom body intrinsic neurons to dopaminergic neurons as a promising candidate for experimental evaluation. It differentiates well between trained and novel stimuli, demonstrating robust performance across a range of model parameters.},
  author = {J{\"{u}}rgensen, Anna-Maria and Schmitt, Felix Johannes and Nawrot, Martin Paul},
  doi = {10.3389/fphys.2023.1326307},
  file = {:Users/springer/Downloads/fphys-14-1326307.pdf:pdf},
  issn = {1664-042X},
  journal = {Frontiers in Physiology},
  month = {jan},
  pages = {1326307},
  title = {{Minimal circuit motifs for second-order conditioning in the insect mushroom body}},
  url = {https://www.frontiersin.org/articles/10.3389/fphys.2023.1326307},
  volume = {14},
  year = {2024}
  }

• Jürgensen, A., Sakagiannis, P., Schleyer, M., Gerber, B., & Nawrot, M. P.. (2024). Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva. iScience, 27(1).
  [Bibtex]

  @article{Jurgensen2024,
  abstract = {Predicting reinforcement from sensory cues is beneficial for goal-directed behavior. In insect brains, underlying associations between cues and reinforcement, encoded by dopaminergic neurons, are formed in the mushroom body. We propose a spiking model of the Drosophila larva mushroom body. It includes a feedback motif conveying learned reinforcement expectation to dopaminergic neurons, which can compute prediction error as the difference between expected and present reinforcement. We demonstrate that this can serve as a driving force in learning. When combined with synaptic homeostasis, our model accounts for theoretically derived features of acquisition and loss of associations that depend on the intensity of the reinforcement and its temporal proximity to the cue. From modeling olfactory learning over the time course of behavioral experiments and simulating the locomotion of individual larvae toward or away from odor sources in a virtual environment, we conclude that learning driven by prediction errors can explain larval behavior.},
  annote = {doi: 10.1016/j.isci.2023.108640},
  author = {J{\"{u}}rgensen, Anna-Maria and Sakagiannis, Panagiotis and Schleyer, Michael and Gerber, Bertram and Nawrot, Martin Paul},
  doi = {10.1016/j.isci.2023.108640},
  issn = {2589-0042},
  journal = {iScience},
  month = {jan},
  number = {1},
  publisher = {Elsevier},
  title = {{Prediction error drives associative learning and conditioned behavior in a spiking model of Drosophila larva}},
  url = {https://doi.org/10.1016/j.isci.2023.108640},
  volume = {27},
  year = {2024}
  }

• 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}
  }

Preprints

• Sakagiannis, P., Jürgensen, A., & Nawrot, M. P.. (2024). A behavioral architecture for realistic simulations of Drosophila larva locomotion and foraging. bioRxiv, 10.1101/2021.07.07.451470.
  [Bibtex]

  @article {Sakagiannis2021.07.07.451470,
  author = {Sakagiannis, Panagiotis and J{\"u}rgensen, Anna-Maria and Nawrot, Martin Paul},
  title = {A behavioral architecture for realistic simulations of Drosophila larva locomotion and foraging},
  elocation-id = {2021.07.07.451470},
  year = {2024},
  month={oct},
  doi = {10.1101/2021.07.07.451470},
  pages = {10.1101/2021.07.07.451470},
  publisher = {Cold Spring Harbor Laboratory},
  abstract = {The Drosophila larva is extensively used as model organism in neuroethological studies where precise behavioral tracking enables the statistical analysis of individual and population-level behavioral metrics that can inform mathematical models of larval behavior. Here, we propose a hierarchical model architecture comprising three layers to facilitate modular model construction, closed-loop simulations, and direct comparisons between empirical and simulated data. At the basic layer, the autonomous locomotory model is capable of performing exploration. Based on novel kinematic analyses our model features intermittent forward crawling that is phasically coupled to lateral bending. At the second layer, navigation is achieved via active sensing in a simulated environment and top-down modulation of locomotion. At the top layer, behavioral adaptation entails associative learning. We evaluate virtual larval behavior across agent-based simulations of autonomous free exploration, chemotaxis, and odor preference testing. Our behavioral architecture is ideally suited for the modular combination of neuromechanical, neural or mere statistical model components, facilitating their evaluation, comparison, extension and integration into multifunctional control architectures.Competing Interest StatementThe authors have declared no competing interest.},
  URL = {https://www.biorxiv.org/content/early/2024/10/09/2021.07.07.451470},
  eprint = {https://www.biorxiv.org/content/early/2024/10/09/2021.07.07.451470.full.pdf},
  journal = {bioRxiv}
  }