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Plasticity, Learning & Memory

Insects are well-suited model organisms to study fundamental principles of neural plasticity that underlie memory formation, decision making and behavioral control. We contribute to this research field by investigating different forms of learning mainly in the olfactory pathway with a specific interest in inter-individual differences. To this end, we study the center for learning and memory (mushroom body) in fruit flies and cockroaches with computational and experimental methods to target different levels of plasticity from molecular processing to behavioral outcomes. On the computational side, we design biologically realistic neural networks with a range of rate- and spike based modelling approaches. On the experimental side, a combination of behavioral and electrophysiological methods is used.

  • [DOI] 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).
    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 = {},
    volume = {27},
    year = {2024}
  • [DOI] Arican, C., Schmitt, F. J., Rössler, W., Strube-Bloss, M. F., & Nawrot, M. P.. (2023). The mushroom body output encodes behavioral decision during sensory-motor transformation. Current Biology, 33, 1–8.
    abstract = {Animal behavioral decisions are dynamically formed by evaluating momentary sensory evidence on the background of individual experience and the acute motivational state. In insects, the mushroom body (MB) has been implicated in forming associative memories and in assessing the appetitive or aversive valence of sensory stimuli to bias approach versus avoidance behavior. To study the MB involvement in innate feeding behavior we performed extracellular single-unit recordings from MB output neurons (MBONs) while simultaneously monitoring a defined feeding behavior in response to timed odor stimulation in naive cockroaches. All animals expressed the feeding behavior almost exclusively in response to food odors. Likewise, MBON responses were invariably and strongly tuned to the same odors. Importantly, MBON responses were restricted to behaviorally responded trials, which allowed the accurate prediction of the occurrence versus non-occurrence of the feeding behavior in individual trials from the neuronal population activity. During responded trials the neuronal activity generally preceded the onset of the feeding behavior, indicating a causal relation. Our results contest the predominant view that MBONs encode stimulus valence. Rather, we conclude that the MB output dynamically encodes the behavioral decision to inform downstream motor networks.Competing Interest StatementThe authors have declared no competing interest.},
    author = {Arican, Cansu and Schmitt, Felix Johannes and R{\"{o}}ssler, Wolfgang and Strube-Bloss, Martin Fritz and Nawrot, Martin Paul},
    doi = {10.1016/j.cub.2023.08.016},
    file = {:Users/springer/Downloads/PIIS096098222301059X.pdf:pdf},
    journal = {Current Biology},
    month = {sep},
    pages = {1--8},
    title = {{The mushroom body output encodes behavioral decision during sensory-motor transformation}},
    url = {},
    volume = {33},
    year = {2023}
  • [DOI] Hancock, C. E., Rostami, V., Rachad, E. Y., Deimel, S. H., Nawrot, M. P., & Fiala, A.. (2022). Visualization of learning-induced synaptic plasticity in output neurons of the Drosophila mushroom body gamma-lobe. Scientific Reports, 12(1), 10421.
    abstract = {By learning, through experience, which stimuli coincide with dangers, it is possible to predict outcomes and act pre-emptively to ensure survival. In insects, this process is localized to the mushroom body (MB), the circuitry of which facilitates the coincident detection of sensory stimuli and punishing or rewarding cues and, downstream, the execution of appropriate learned behaviors. Here, we focused our attention on the mushroom body output neurons (MBONs) of the $\gamma$-lobes that act as downstream synaptic partners of the MB $\gamma$-Kenyon cells (KCs) to ask how the output of the MB $\gamma$-lobe is shaped by olfactory associative conditioning, distinguishing this from non-associative stimulus exposure effects, and without the influence of downstream modulation. This was achieved by employing a subcellularly localized calcium sensor to specifically monitor activity at MBON postsynaptic sites. Therein, we identified a robust associative modulation within only one MBON postsynaptic compartment (MBON-$\gamma$1pedc {\textgreater} $\alpha$/$\beta$), which displayed a suppressed postsynaptic response to an aversively paired odor. While this MBON did not undergo non-associative modulation, the reverse was true across the remainder of the $\gamma$-lobe, where general odor-evoked adaptation was observed, but no conditioned odor-specific modulation. In conclusion, associative synaptic plasticity underlying aversive olfactory learning is localized to one distinct synaptic $\gamma$KC-to-$\gamma$MBON connection.},
    author = {Hancock, Clare E and Rostami, Vahid and Rachad, El Yazid and Deimel, Stephan H and Nawrot, Martin P and Fiala, Andr{\'{e}}},
    doi = {10.1038/s41598-022-14413-5},
    file = {:Users/springer/Downloads/Hancock{\_}et{\_}al-2022-Scientific{\_}Reports.pdf:pdf},
    issn = {2045-2322},
    journal = {Scientific Reports},
    month = {jun},
    number = {1},
    pages = {10421},
    title = {{Visualization of learning-induced synaptic plasticity in output neurons of the Drosophila mushroom body gamma-lobe}},
    url = {},
    volume = {12},
    year = {2022}
  • [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.
    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 = {},
    volume = {8},
    year = {2021}
  • [DOI] Strube-Bloss, M. F., D’Albis, T., Menzel, R., & Nawrot, M. P.. (2020). Single neuron activity predicts behavioral performance of individual animals during memory retention. bioRxiv, 2020.12.30.424797.
    abstract = {In 1972 Rescorla and Wagner formulated their model of classical Pavlovian conditioning postulating that the associative strength of a stimulus is expressed directly in the behavior it elicits1. Many biologists and psychologists were inspired by this model, and numerous experiments thereafter were interpreted assuming that the magnitude of the conditioned response (CR) reflects an associative effect at the physiological level. However, a correlation between neural activity and the expression of the CR in individual animals has not yet been reported. Here we show that, following differential odor conditioning, the change in activity of single mushroom body output neurons (MBON) of the honeybee predicts the behavioral performance of the individual during memory retention. The encoding of the stimulus-reward association at the mushroom body output occurs about 600 ms before the initiation of the CR. We conclude that the MB provides a stable representation of the stimulus-reward associative strength, and that this representation is required for behavioral decision-making during memory retention.Competing Interest StatementThe authors have declared no competing interest.},
    author = {Strube-Bloss, Martin Fritz and D'Albis, Tiziano and Menzel, Randolf and Nawrot, Martin Paul},
    doi = {10.1101/2020.12.30.424797},
    file = {::},
    journal = {bioRxiv},
    month = {dec},
    pages = {2020.12.30.424797},
    title = {{Single neuron activity predicts behavioral performance of individual animals during memory retention}},
    url = {},
    year = {2020}
  • [DOI] Arican, C., Bulk, J., Deisig, N., & Nawrot, M. P.. (2020). Cockroaches Show Individuality in Learning and Memory During Classical and Operant Conditioning. Frontiers in Physiology, 10, 825265.
    abstract = {Animal personality and individuality are intensively researched in vertebrates and both concepts are increasingly applied to behavioral science in insects. However, only few studies have looked into individuality with respect to performance in learning and memory tasks. In vertebrates individual learning capabilities vary considerably with respect to learning speed and learning rate. Likewise, honeybees express individual learning abilities in a wide range of classical conditioning protocols. Here, we study individuality in the learning and memory performance of cockroaches, both in classical and operant conditioning tasks. We implemented a novel classical (olfactory) conditioning paradigm where the conditioned response is established in the maxilla-labia response (MLR). Operant spatial learning was investigated in a forced two-choice task using a T-maze. Our results confirm individual learning abilities in classical conditioning of cockroaches that was reported for honeybees and vertebrates but contrast long-standing reports on stochastic learning behavior in fruit flies. In our experiments, most learners expressed a correct behavior after only a single learning trial showing a consistent high performance during training and test. We can further show that individual learning differences in insects are not limited to classical conditioning but equally appear in operant conditioning of the cockroach.},
    author = {Arican, Cansu and Bulk, Janice and Deisig, Nina and Nawrot, Martin Paul},
    doi = {10.3389/fphys.2019.01539},
    file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Arican et al. - 2020 - Cockroaches Show Individuality in Learning and Memory During Classical and Operant Conditioning.pdf:pdf},
    issn = {1664-042X},
    journal = {Frontiers in Physiology},
    keywords = {classical conditioning,classical conditioning,cockroach,insect behavior,insect cognition,learning and memory,operant conditioning,personality,cockroach,insect behavior,insect cognition,learning and memory,operant conditioning,personality},
    month = {jan},
    pages = {825265},
    title = {{Cockroaches Show Individuality in Learning and Memory During Classical and Operant Conditioning}},
    url = {{\&}utm{\_}{\&}utm{\_}medium=twitter},
    volume = {10},
    year = {2020}
  • [DOI] Müller, J., Nawrot, M., Menzel, R., & Landgraf, T.. (2018). A neural network model for familiarity and context learning during honeybee foraging flights. Biological Cybernetics, 112(1-2), 113–126.
    abstract = {{\textcopyright} 2017 Springer-Verlag GmbH Germany How complex is the memory structure that honeybees use to navigate? Recently, an insect-inspired parsimonious spiking neural network model was proposed that enabled simulated ground-moving agents to follow learned routes. We adapted this model to flying insects and evaluate the route following performance in three different worlds with gradually decreasing object density. In addition, we propose an extension to the model to enable the model to associate sensory input with a behavioral context, such as foraging or homing. The spiking neural network model makes use of a sparse stimulus representation in the mushroom body and reward-based synaptic plasticity at its output synapses. In our experiments, simulated bees were able to navigate correctly even when panoramic cues were missing. The context extension we propose enabled agents to successfully discriminate partly overlapping routes. The structure of the visual environment, however, crucially determines the success rate. We find that the model fails more often in visually rich environments due to the overlap of features represented by the Kenyon cell layer. Reducing the landmark density improves the agents route following performance. In very sparse environments, we find that extended landmarks, such as roads or field edges, may help the agent stay on its route, but often act as strong distractors yielding poor route following performance. We conclude that the presented model is valid for simple route following tasks and may represent one component of insect navigation. Additional components might still be necessary for guidance and action selection while navigating along different memorized routes in complex natural environments.},
    author = {M{\"{u}}ller, Jurek and Nawrot, Martin and Menzel, Randolf and Landgraf, Tim},
    doi = {10.1007/s00422-017-0732-z},
    file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/M{\"{u}}ller et al. - 2018 - A neural network model for familiarity and context learning during honeybee foraging flights.pdf:pdf},
    issn = {14320770},
    journal = {Biological Cybernetics},
    keywords = {Artificial agent,Insect cognition,Insect navigation,Learning and plasticity,Mushroom body,Spiking neural network model,insect inspired,learning and memory,navigation,plasticity,spiking neural network},
    mendeley-tags = {insect inspired,learning and memory,navigation,plasticity,spiking neural network},
    number = {1-2},
    pages = {113--126},
    title = {{A neural network model for familiarity and context learning during honeybee foraging flights}},
    volume = {112},
    year = {2018}
  • [DOI] Pamir, E., Szyszka, P., Scheiner, R., & Nawrot, M. P.. (2014). Rapid learning dynamics in individual honeybees during classical conditioning. Frontiers in Behavioral Neuroscience, 8, 313.
    abstract = {{\textcopyright} 2014 Pamir, Szyszka, Scheiner and Nawrot. Associative learning in insects has been studied extensively by a multitude of classical conditioning protocols. However, so far little emphasis has been put on the dynamics of learning in individuals. The honeybee is a well-established animal model for learning and memory. We here studied associative learning as expressed in individual behavior based on a large collection of data on olfactory classical conditioning (25 datasets, 3298 animals). We show that the group-averaged learning curve and memory retention score confound three attributes of individual learning: the ability or inability to learn a given task, the generally fast acquisition of a conditioned response (CR) in learners, and the high stability of the CR during consecutive training and memory retention trials. We reassessed the prevailing view that more training results in better memory performance and found that 24 h memory retention can be indistinguishable after single-trial and multiple-trial conditioning in individuals. We explain how inter-individual differences in learning can be accommodated within the Rescorla-Wagner theory of associative learning. In both data-analysis and modeling we demonstrate how the conflict between population-level and single-animal perspectives on learning and memory can be disentangled.},
    author = {Pamir, Evren and Szyszka, Paul and Scheiner, Ricarda and Nawrot, Martin P.},
    doi = {10.3389/fnbeh.2014.00313},
    file = {:Users/springer/Documents/Nawrot/Lit/Pamir (2014) Rapidlearningdynamicsinindividualhoneybeesduringclassicalconditioning.pdf:pdf},
    issn = {16625153},
    journal = {Frontiers in Behavioral Neuroscience},
    keywords = {Apis mellifera,Classical conditioning,Learning curve,Proboscis extension response (PER),Rescorla-Wagner model,Single-trial learning,Sucrose responsiveness,Sucrose sensitivity,classical conditioning,honeybee,learning and memory},
    mendeley-tags = {classical conditioning,honeybee,learning and memory},
    pages = {313},
    title = {{Rapid learning dynamics in individual honeybees during classical conditioning}},
    volume = {8},
    year = {2014}
  • [DOI] Strube-Bloss, M. F., Nawrot, M. P., & Menzel, R.. (2011). Mushroom body output neurons encode odor-reward associations. Journal of Neuroscience, 31(8), 3129–3140.
    abstract = {Neural correlates of learning and memory formation have been reported at different stages of the olfactory pathway in both vertebrates and invertebrates. However, the contribution of different neurons to the formation of a memory trace is little understood. Mushroom bodies (MBs) in the insect brain are higher-order structures involved in integration of olfactory, visual, and mechanosensory information and in memory formation. Here we focus on the ensemble spiking activity of single MB output neurons (ENs) when honeybees learned to associate an odor with reward. A large group of ENs (∼50{\%}) changed their odor response spectra by losing or gaining sensitivity for specific odors. This response switching was dominated by the rewarded stimulus (CS+), which evoked exclusively recruitment. The remaining ENs did not change their qualitative odor spectrum but modulated their tuning strength, again dominated by increased responses to the CS+. While the bees showed a conditioned response (proboscis extension) after a few acquisition trials, no short-term effects were observed in the neuronal activity. In both EN types, associative plastic changes occurred only during retention 3 h after conditioning. Thus, long-term but not short-term memory was reflected by increased EN activity to the CS+. During retention, the EN ensemble separated the CS+ most differently from the CS- and control odors ∼140 ms after stimulus onset. The learned behavioral response appeared ∼330 ms later. It is concluded that after memory consolidation, the ensemble activity of the MB output neurons predicts the meaning of the stimulus (reward) and may provide the prerequisite for the expression of the learned behavior.},
    author = {Strube-Bloss, Martin Fritz and Nawrot, Martin Paul and Menzel, Randolf},
    doi = {10.1523/JNEUROSCI.2583-10.2011},
    file = {::},
    issn = {02706474},
    journal = {Journal of Neuroscience},
    keywords = {classical conditioning,honeybee,learning and memory,mushroom body},
    mendeley-tags = {classical conditioning,honeybee,learning and memory,mushroom body},
    number = {8},
    pages = {3129--3140},
    title = {{Mushroom body output neurons encode odor-reward associations}},
    volume = {31},
    year = {2011}
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