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Magdalena Anna Springer

PhD Student

Room 2.314
Email:
magdalena.springer@uni-koeln.de
Phone: +49-221-470-6463

I am interested in investigating the neural circuits that form the olfactory system in arthropods. At first glance, an arthropod brain seems to be rather simple to understand in comparison to a mammalian brain. However, a closer look raises a lot of questions regarding those actually complex and sophisticated brain structures. How are neurons connected with each other to shape behavior? Why is there structural variance in the olfactory pathways of arthropod species and what benefits could those differences have? In order to come closer to answering these questions, I build detailed computational models of the underlying brain structures. By studying the precise connectivity of single neurons and short-term plasticity at the synaptic clefts, my goal is to contribute to the understanding of the basic principles of neural coding that shape decision making and behavior.

Publications

  • [DOI] 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}
}