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Magdalena Anna Springer

PhD Student

Publications

• Springer, M., Sigrist, S., & Nawrot, M. P.. (2025). A model investigation of short-term synaptic plasticity tuned via Unc13 isoforms. bioRxiv, 2025.06.18.660326.
  [Bibtex]

  @article {Springer2025.06.18.660326,
  author = {Springer, Magdalena and Sigrist, Stephan and Nawrot, Martin Paul},
  title = {A model investigation of short-term synaptic plasticity tuned via Unc13 isoforms},
  elocation-id = {2025.06.18.660326},
  year = {2025},
  doi = {10.1101/2025.06.18.660326},
  publisher = {Cold Spring Harbor Laboratory},
  abstract = {Short-term synaptic plasticity (STP) is a fundamental mechanism of neural computation supporting a variety of nervous system functions from sensory adaptation and gain control to working memory and decision making. At the presynaptic release site, an interplay between distinct (M)Unc13 protein isoforms is suggested to orchestrate depressing and facilitating components of STP. In this study, we introduce a modification of the well-established TsodyksMarkram Model (TMM) for STP. We constrain our model by in vivo intracellular recordings in the olfactory system of the fruit fly, where previous work suggested Unc13A to provide a phasic, depressing and Unc13B a tonic, facilitating release component. A combination of a facilitating and a depressing model component indeed allowed for accurate model fits. Differential knock-down experiments of the Unc13A and Unc13B gene variants provide biological model interpretation, linking the protein-specific molecular mechanisms to synaptic function and STP. Our mathematical formulation of protein-dependent STP can be readily and efficiently used to design biologically realistic spiking neural network models that feature different genetically defined synapse types.Competing Interest StatementThe authors have declared no competing interest.Deutsche Forschungsgemeinschaft, https://ror.org/018mejw64, 430592330, 431549029, 365082554},
  URL = {https://www.biorxiv.org/content/early/2025/06/19/2025.06.18.660326},
  eprint = {https://www.biorxiv.org/content/early/2025/06/19/2025.06.18.660326.full.pdf},
  journal = {bioRxiv},
  pages = {2025.06.18.660326}
  }

• Rachad, E. Y., Deimel, S. H., Epple, L., Gadgil, Y. V., Jürgensen, A., Springer, M., Lin, C., Nawrot, M. P., Lin, S., & Fiala, A.. (2025). Functional dissection of a neuronal brain circuit mediating higher-order associative learning. Cell Reports, 44(5), 115593.
  [Bibtex]

  @article{RACHAD2025115593,
  title = {Functional dissection of a neuronal brain circuit mediating higher-order associative learning},
  journal = {Cell Reports},
  volume = {44},
  number = {5},
  pages = {115593},
  year = {2025},
  issn = {2211-1247},
  doi = {https://doi.org/10.1016/j.celrep.2025.115593},
  url = {https://www.sciencedirect.com/science/article/pii/S221112472500364X},
  author = {Rachad, El Yazid and Deimel, Stephan Hubertus and Epple, Lisa and Gadgil, Yogesh Vasant and Jürgensen, Anna-Maria and Springer, Magdalena and Lin, Chen-Han and Nawrot, Martin Paul and Lin, Suewei and Fiala, André},
  keywords = {associative learning, higher-order conditioning, mushroom body, , neuronal circuit dissection, learning and memory, insect brain, odor coding, dopamine, prediction error},
  abstract = {A central feature characterizing the neural architecture of many species’ brains is their capacity to form associative chains through learning. In elementary forms of associative learning, stimuli coinciding with reward or punishment become attractive or repulsive. Notably, stimuli previously learned as attractive or repulsive can themselves serve as reinforcers, establishing a cascading effect whereby they become associated with additional stimuli. When this iterative process is perpetuated, it results in higher-order associations. Here, we use odor conditioning in Drosophila and computational modeling to dissect the architecture of neuronal networks underlying higher-order associative learning. We show that the responsible circuit, situated in the mushroom bodies of the brain, is characterized by parallel processing of odor information and by recurrent excitatory and inhibitory feedback loops that empower odors to gain control over the dopaminergic valence-signaling system. Our findings establish a paradigmatic framework of a neuronal circuit diagram enabling the acquisition of associative chains.}
  }

• Springer, M., & Nawrot, M. P.. (2021). A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly. eNeuro, 8(3), ENEURO.0549–20.2021.
  [Bibtex]

  @article{SpringerENEURO.0549-20.2021,
  abstract = {Extinction learning, the ability to update previously learned information by integrating novel contradictory information, is of high clinical relevance for therapeutic approaches to the modulation of maladaptive memories. Insect models have been instrumental in uncovering fundamental processes of memory formation and memory update. Recent experimental results in Drosophila melanogaster suggest that, after the behavioral extinction of a memory, two parallel but opposing memory traces coexist, residing at different sites within the mushroom body (MB). Here, we propose a minimalistic circuit model of the Drosophila MB that supports classical appetitive and aversive conditioning and memory extinction. The model is tailored to the existing anatomic data and involves two circuit motives of central functional importance. It employs plastic synaptic connections between Kenyon cells (KCs) and MB output neurons (MBONs) in separate and mutually inhibiting appetitive and aversive learning pathways. Recurrent modulation of plasticity through projections from MBONs to reinforcement-mediating dopaminergic neurons (DAN) implements a simple reward prediction mechanism. A distinct set of four MBONs encodes odor valence and predicts behavioral model output. Subjecting our model to learning and extinction protocols reproduced experimental results from recent behavioral and imaging studies. Simulating the experimental blocking of synaptic output of individual neurons or neuron groups in the model circuit confirmed experimental results and allowed formulation of testable predictions. In the temporal domain, our model achieves rapid learning with a step-like increase in the encoded odor value after a single pairing of the conditioned stimulus (CS) with a reward or punishment, facilitating single-trial learning.},
  author = {Springer, Magdalena and Nawrot, Martin Paul},
  doi = {10.1523/ENEURO.0549-20.2021},
  file = {:Users/springer/Downloads/ENEURO.0549-20.2021.full-2.pdf:pdf},
  journal = {eNeuro},
  number = {3},
  pages = {ENEURO.0549--20.2021},
  publisher = {Society for Neuroscience},
  title = {{A Mechanistic Model for Reward Prediction and Extinction Learning in the Fruit Fly}},
  url = {https://www.eneuro.org/content/8/3/ENEURO.0549-20.2021},
  volume = {8},
  year = {2021}
  }