Publications

• 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}
  }

• Kafle, T., Grub, M., Sakagiannis, P., Nawrot, M. P., & Arguello, R.. (2024). Fast and recurrent evolution of temperature preference among drosophilids. bioRxiv, 10.1101/2024.03.15.585210.
  [Bibtex]

  @article {Kafle2024.03.15.585210,
  author = {Tane Kafle and Manuel Grub and Panagiotis Sakagiannis and Martin Paul Nawrot and Roman Arguello},
  title = {Fast and recurrent evolution of temperature preference among drosophilids},
  elocation-id = {2024.03.15.585210},
  year = {2024},
  month = {mar},
  doi = {10.1101/2024.03.15.585210},
  pages = {10.1101/2024.03.15.585210},
  publisher = {Cold Spring Harbor Laboratory},
  abstract = {Small-bodied ectotherms are acutely vulnerable to temperature changes but diverse thermotactic behaviours have contributed to their ability to inhabit broad climatic niches. Understanding how - and how fast - these behaviours evolve are outstanding biological questions that are also relevant to conservation. Among insects, Drosophila melanogaster is a preeminent ectothermic model for temperate sensing and thermotaxis. However, little is known about how its temperature-related behaviours have evolved in comparison to its closely-related species. We have thermo-profiled over \~{}2400 larvae from eight closely related species of Drosophila from different thermal habitats. We found substantial variation in temperature preference and fine-scale navigational behaviours amongst these species, consistent with local adaptation. Agent-based modelling of the larval thermotaxis circuit indicate that it is the balance between cool and warm avoidance circuits, rather than changes in temperature sensitivity, that drive differences in temperature preference. Together, our findings highlight the fast evolution of temperature-related behaviours in an experimentally tractable cross-species system.Competing Interest StatementThe authors have declared no competing interest.},
  URL = {https://www.biorxiv.org/content/early/2024/03/16/2024.03.15.585210},
  eprint = {https://www.biorxiv.org/content/early/2024/03/16/2024.03.15.585210.full.pdf},
  journal = {bioRxiv}
  }

• Tredern, E. D., Manceau, D., Blanc, A., Sakagiannis, P., Barre, C., Sus, V., Viscido, F., Hasa, M. H., Autran, S., Nawrot, M. P., Masson, J., & Jovanic, T.. (2023). Feeding- state dependent modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila. bioRxiv, 10.1101/2023.12.26.573306.
  [Bibtex]

  @article{Tredern2023,
  author = {Tredern, Elo{\"{i}}se De and Manceau, Dylan and Blanc, Alexandre and Sakagiannis, Panagiotis and Barre, Chloe and Sus, Victoria and Viscido, Francesca and Hasa, Md Hamit and Autran, Sandra and Nawrot, Martin Paul and Masson, Jean-Baptiste and Jovanic, Tihana},
  doi = {10.1101/2023.12.26.573306},
  file = {:Users/springer/Downloads/2023.12.26.573306v1.full.pdf:pdf},
  journal = {bioRxiv},
  month = {dec},
  title = {{Feeding- state dependent modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila}},
  url = {https://doi.org/10.1101/2023.12.26.573306},
  pages = {10.1101/2023.12.26.573306},
  year = {2023}
  }

• 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.
  [Bibtex]

  @article{Strube-Bloss2021,
  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 = {http://biorxiv.org/content/early/2021/01/01/2020.12.30.424797.abstract},
  year = {2020}
  }

• Mana, P. P., Rostami, V., Torre, E., & Roudi, Y.. (2018). Maximum-entropy and representative samples of neuronal activity: a dilemma. arXiv, 1805.09084vl.
  [Bibtex]

  @article{Mana2018,
  abstract = {The present work shows that the maximum-entropy method can be applied to a sample of neuronal recordings along two different routes: (1) apply to the sample; or (2) apply to a larger, unsampled neuronal population from which the sample is drawn, and then marginalize to the sample. These two routes give inequivalent results. The second route can be further generalized to the case where the size of the larger population is unknown. Which route should be chosen? Some arguments are presented in favour of the second. This work also presents and discusses probability formulae that relate states of knowledge about a population and its samples, and that may be useful for sampling problems in neuroscience.},
  archivePrefix = {arXiv},
  arxivId = {1805.09084},
  author = {Mana, PierGianLuca Porta and Rostami, Vahid and Torre, Emiliano and Roudi, Yasser},
  doi = {10.31219/osf.io/uz29n},
  eprint = {1805.09084},
  file = {:Users/springer/Downloads/1805.09084.pdf:pdf},
  journal = {arXiv},
  pages = {1805.09084vl},
  title = {{Maximum-entropy and representative samples of neuronal activity: a dilemma}},
  url = {http://arxiv.org/abs/1805.09084},
  year = {2018}
  }

• Rostami, V., Rost, T., Schmitt, F. J., van Albada, S. J., Riehle, A., & Nawrot, M. P.. (2024). Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information. Nature Communications, 15, 6304.
  [Bibtex]

  @article{Rostami2024,
  abstract = {
  When preparing a movement, we often rely on partial or incomplete information, which can decrement task performance. In behaving monkeys we show that the degree of cued target information is reflected in both, neural variability in motor cortex and behavioral reaction times. We study the underlying mechanisms in a spiking motor-cortical attractor model. By introducing a biologically realistic network topology where excitatory neuron clusters are locally balanced with inhibitory neuron clusters we robustly achieve metastable network activity across a wide range of network parameters. In application to the monkey task, the model performs target-specific action selection and accurately reproduces the task-epoch dependent reduction of trial-to-trial variability in vivo where the degree of reduction directly reflects the amount of processed target information, while spiking irregularity remained constant throughout the task. In the context of incomplete cue information, the increased target selection time of the model can explain increased behavioral reaction times. We conclude that context-dependent neural and behavioral variability is a signum of attractor computation in the motor cortex.
  },
  author = {Vahid Rostami and Thomas Rost and Felix Johannes Schmitt and Sacha Jennifer van Albada and Alexa Riehle and Martin Paul Nawrot},
  doi = {10.1038/s41467-024-49889-4},
  issn = {2041-1723},
  issue = {1},
  journal = {Nature Communications},
  month = {jul},
  pages = {6304},
  title = {Spiking attractor model of motor cortex explains modulation of neural and behavioral variability by prior target information},
  volume = {15},
  url = {https://www.nature.com/articles/s41467-024-49889-4},
  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., 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}
  }

• 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.
  [Bibtex]

  @article{Arican2022,
  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 = {https://www.cell.com/current-biology/fulltext/S0960-9822(23)01059-X},
  volume = {33},
  year = {2023}
  }

• Schmitt, F. J., Rostami, V., & Nawrot, M. P.. (2023). Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST. Frontiers in Neuroinformatics, 17, 941696.
  [Bibtex]

  @article{Schmitt2022,
  author = {Schmitt, Felix Johannes and Rostami, Vahid and Nawrot, Martin Paul},
  doi = {10.3389/fninf.2023.941696},
  journal = {Frontiers in Neuroinformatics},
  keywords = {artificial agents,attractor neural network,computational neuroethology,computational neuroscience,metastability,neocortex,real-time simulation,spiking neural network},
  month = {jan},
  pages = {941696},
  title = {{Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST}},
  url = {https://www.frontiersin.org/articles/10.3389/fninf.2023.941696/abstract},
  volume = {17},
  year = {2023}
  }

• 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.
  [Bibtex]

  @article{Hancock2022a,
  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 = {https://doi.org/10.1038/s41598-022-14413-5},
  volume = {12},
  year = {2022}
  }

• Scharwächter, L., Schmitt, F. J., Pallast, N., Fink, G. R., & Aswendt, M.. (2022). Network analysis of neuroimaging in mice. NeuroImage, 119110.
  [Bibtex]

  @article{Scharwachter2022,
  author = {Scharw{\"{a}}chter, Leon and Schmitt, Felix J. and Pallast, Niklas and Fink, Gereon R. and Aswendt, Markus},
  doi = {10.1016/j.neuroimage.2022.119110},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Scharw{\"{a}}chter et al. - 2022 - Network analysis of neuroimaging in mice(2).pdf:pdf},
  issn = {10538119},
  journal = {NeuroImage},
  month = {mar},
  pages = {119110},
  title = {{Network analysis of neuroimaging in mice}},
  year = {2022}
  }

• Schmuker, M., Nawrot, M. P., & Chicca, E.. (2022). Neuromorphic Sensors, Olfaction. In Jaeger Dieter, & and Jung, R. (Eds.), Encyclopedia of Computational Neuroscience (2 ed., pp. 2334–2340). New York, NY: Springer New York.
  [Bibtex]

  @incollection{SchmukerMichael2022,
  address = {New York, NY},
  author = {Schmuker, Michael and Nawrot, Martin Paul and Chicca, Elisabetta},
  booktitle = {Encyclopedia of Computational Neuroscience},
  doi = {10.1007/978-1-0716-1006-0_119},
  edition = {2},
  editor = {{Jaeger Dieter} and and Jung, Ranu},
  isbn = {978-1-0716-1006-0},
  month = {jan},
  pages = {2334--2340},
  publisher = {Springer New York},
  title = {{Neuromorphic Sensors, Olfaction}},
  url = {https://doi.org/10.1007/978-1-0716-1006-0{\_}119},
  year = {2022}
  }

• 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}
  }

• Landgraf, T., Gebhardt, G. H. W., Bierbach, D., Romanczuk, P., Musiolek, L., Hafner, V. V., & Krause, J.. (2021). Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups. Annual Review of Control, Robotics, and Autonomous Systems, 4(1), 487–507.
  [Bibtex]

  @article{doi:10.1146/annurev-control-061920-103228,
  abstract = {Biomimetic robots that replace living social interaction partners can help elucidate the underlying interaction rules in animal groups. Our review focuses on the use of interactive robots that respond dynamically to animal behavior as part of a closed control loop. We discuss the most influential works to date and how they have contributed to our understanding of animal sociality. Technological advances permit the use of robots that can adapt to the situations they face and the conspecifics they encounter, or robots that learn to optimize their social performance from a set of experiences. We discuss how adaptation and learning may provide novel insights into group sociobiology and describe the technical challenges associatedwith these types of interactive robots. This interdisciplinary field provides a rich set of problems to be tackled by roboticists, machine learning engineers, and control theorists. By cultivating smarter robots, we can usher in an era of more nuanced exploration of animal behavior.},
  author = {Landgraf, Tim and Gebhardt, Gregor H W and Bierbach, David and Romanczuk, Pawel and Musiolek, Lea and Hafner, Verena V and Krause, Jens},
  doi = {10.1146/annurev-control-061920-103228},
  journal = {Annual Review of Control, Robotics, and Autonomous Systems},
  month = {may},
  number = {1},
  pages = {487--507},
  title = {{Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups}},
  url = {https://doi.org/10.1146/annurev-control-061920-103228},
  volume = {4},
  year = {2021}
  }

• 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}
  }

• Sakagiannis, P., Aguilera, M., & Nawrot, M. P.. (2020). A Plausible Mechanism for Drosophila Larva Intermittent Behavior. In Vouloutsi, V., Mura, A., Tauber, F., Speck, T., Prescott, T. J., & Verschure, P. F. M. J. (Eds.), In Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science .
  [Bibtex]

  @incollection{Sakagiannis2020,
  abstract = {The behavior of many living organisms is not continuous. Rather, activity emerges in bouts that are separated by epochs of rest, a phenomenon known as intermittent behavior. Although intermit- tency is ubiquitous across phyla, empirical studies are scarce and the underlying neural mechanisms remain unknown. Here we re- produce empirical evidence of intermittency during Drosophila larva free exploration. Our findings are in line with previously reported power-law distributed rest-bout durations while we report log-normal distributed activity-bout durations. We show that a stochastic net- work model can transition between power-law and non-power-law distributed states and we suggest a plausible neural mechanism for the alternating rest and activity in the larva. Finally, we discuss pos- sible implementations in behavioral simulations extending spatial Levy-walk or coupled-oscillator models with temporal intermittency},
  author = {Sakagiannis, Panagiotis and Aguilera, Miguel and Nawrot, Martin Paul},
  booktitle = {Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science},
  doi = {10.1007/978-3-030-64313-3_28},
  editor = {Vouloutsi, V. and Mura, A. and Tauber, F. and Speck, T. and Prescott, T.J. and Verschure, P.F.M.J.},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Sakagiannis, Aguilera, Nawrot - 2020 - A Plausible Mechanism for Drosophila Larva Intermittent Behavior(2).pdf:pdf},
  keywords = {larva crawling,levy-walks,neuronal avalanches},
  month = {dec},
  title = {{A Plausible Mechanism for Drosophila Larva Intermittent Behavior}},
  url = {https://doi.org/10.1007/978-3-030-64313-3{\_}28},
  year = {2020}
  }

• Rapp, H., & Nawrot, M. P.. (2020). A spiking neural program for sensorimotor control during foraging in flying insects. Proceedings of the National Academy of Sciences, 202009821.
  [Bibtex]

  @article{Rapp2020d,
  abstract = {Living organisms demonstrate remarkable abilities in mastering problems imposed by complex and dynamic environments, and they can generalize their experience in order to rapidly adapt behavior. This paper demonstrates the benefits of using biological spiking neural networks, sparse computations, and local learning rules. It highlights the functional roles of temporal- and population-sparse coding for rapid associative learning, precise memory recall, and transformation into navigational output. We show how memory formation generalizes to perform precise memory recall under dynamic, nonstationary conditions, giving rise to nontrivial foraging behavior in a complex natural environment. Results suggest how principles of biological computation could benefit agent-based machine learning to deal with nonstationary scenarios.Foraging is a vital behavioral task for living organisms. Behavioral strategies and abstract mathematical models thereof have been described in detail for various species. To explore the link between underlying neural circuits and computational principles, we present how a biologically detailed neural circuit model of the insect mushroom body implements sensory processing, learning, and motor control. We focus on cast and surge strategies employed by flying insects when foraging within turbulent odor plumes. Using a spike-based plasticity rule, the model rapidly learns to associate individual olfactory sensory cues paired with food in a classical conditioning paradigm. We show that, without retraining, the system dynamically recalls memories to detect relevant cues in complex sensory scenes. Accumulation of this sensory evidence on short time scales generates cast-and-surge motor commands. Our generic systems approach predicts that population sparseness facilitates learning, while temporal sparseness is required for dynamic memory recall and precise behavioral control. Our work successfully combines biological computational principles with spike-based machine learning. It shows how knowledge transfer from static to arbitrary complex dynamic conditions can be achieved by foraging insects and may serve as inspiration for agent-based machine learning.Code and datasets are available through our GitHub profile at https://github.com/nawrotlab. Datasets and source code data have been deposited on GitHub at https://github.com/nawrotlab/SpikingNeuralProgramForagingInsect-PNAS.},
  author = {Rapp, Hannes and Nawrot, Martin Paul},
  doi = {10.1073/pnas.2009821117},
  file = {:Users/springer/Downloads/2009821117.full.pdf:pdf},
  journal = {Proceedings of the National Academy of Sciences},
  month = {oct},
  pages = {202009821},
  title = {{A spiking neural program for sensorimotor control during foraging in flying insects}},
  url = {http://www.pnas.org/content/early/2020/10/27/2009821117.abstract},
  year = {2020}
  }

• Betkiewicz, R., Lindner, B., & Nawrot, M. P.. (2020). Circuit and cellular mechanisms facilitate the transformation from dense to sparse coding in the insect olfactory system. eNeuro, 7(2), 10.1523/ENEURO.0305–18.2020.
  [Bibtex]

  @article{Betkiewicz2018a,
  abstract = {Transformations between sensory representations are shaped by neural mechanisms at the cellular and the circuit level. In the insect olfactory system encoding of odor information undergoes a transition from a dense spatio-temporal population code in the antennal lobe to a sparse code in the mushroom body. However, the exact mechanisms shaping odor representations and their role in sensory processing are incompletely identified. Here, we investigate the transformation from dense to sparse odor representations in a spiking model of the insect olfactory system, focusing on two ubiquitous neural mechanisms: spike-frequency adaptation at the cellular level and lateral inhibition at the circuit level. We find that cellular adaptation is essential for sparse representations in time (temporal sparseness), while lateral inhibition regulates sparseness in the neuronal space (population sparseness). The interplay of both mechanisms shapes dynamical odor representations, which are optimized for discrimination of odors during stimulus onset and offset. In addition, we find that odor identity is stored on a prolonged time scale in the adaptation levels but not in the spiking activity of the principal cells of the mushroom body, providing a testable hypothesis for the location of the so-called odor trace.},
  author = {Betkiewicz, Rinaldo and Lindner, Benjamin and Nawrot, Martin P.},
  doi = {10.1523/ENEURO.0305-18.2020},
  file = {:Users/springer/Downloads/ENEURO.0305-18.2020.full.pdf:pdf},
  journal = {eNeuro},
  month = {feb},
  number = {2},
  pages = {10.1523/ENEURO.0305--18.2020},
  title = {{Circuit and cellular mechanisms facilitate the transformation from dense to sparse coding in the insect olfactory system}},
  url = {https://doi.org/10.1523/ENEURO.0305-18.2020{\%}0A},
  volume = {7},
  year = {2020}
  }

• Rapp, H., Nawrot, M. P., & Stern, M.. (2020). Numerical Cognition Based on Precise Counting with a Single Spiking Neuron. iScience, 23(2), 100852.
  [Bibtex]

  @article{Rapp2020,
  abstract = {Insects are able to solve basic numerical cognition tasks. We show that estimation of numerosity can be realized and learned by a single spiking neuron with an appropriate synaptic plasticity rule. This model can be efficiently trained to detect arbitrary spatiotemporal spike patterns on a noisy and dynamic background with high precision and low variance. When put to test in a task that requires counting of visual concepts in a static image it required considerably less training epochs than a convolutional neural network to achieve equal performance. When mimicking a behavioral task in free-flying bees that requires numerical cognition, the model reaches a similar success rate in making correct decisions. We propose that using action potentials to represent basic numerical concepts with a single spiking neuron is beneficial for organisms with small brains and limited neuronal resources.},
  author = {Rapp, Hannes and Nawrot, Martin Paul and Stern, Merav},
  doi = {10.1016/j.isci.2020.100852},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Rapp, Nawrot, Stern - 2020 - Numerical Cognition Based on Precise Counting with a Single Spiking Neuron.pdf:pdf},
  issn = {25890042},
  journal = {iScience},
  keywords = {Cognitive Neuroscience,In Silico Biology,Neuroscience},
  month = {feb},
  number = {2},
  pages = {100852},
  publisher = {Elsevier Inc.},
  title = {{Numerical Cognition Based on Precise Counting with a Single Spiking Neuron}},
  url = {https://doi.org/10.1016/j.isci.2020.100852},
  volume = {23},
  year = {2020}
  }

• 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.
  [Bibtex]

  @article{Arican2020,
  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 = {https://www.biorxiv.org/content/10.1101/825265v1?rss=1{\&}utm{\_}source=dlvr.it{\&}utm{\_}medium=twitter https://www.frontiersin.org/article/10.3389/fphys.2019.01539/full},
  volume = {10},
  year = {2020}
  }

• Seeholzer, A., Deger, M., & Gerstner, W.. (2019). Stability of working memory in continuous attractor networks under the control of shortterm plasticity. PLoS Computational Biology, 15(4), e1006928.
  [Bibtex]

  @article{Seeholzer2019,
  abstract = {Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes; and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory.},
  author = {Seeholzer, Alexander and Deger, Moritz and Gerstner, Wulfram},
  doi = {10.1371/journal.pcbi.1006928},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Seeholzer, Deger, Gerstner - 2019 - Stability of working memory in continuous attractor networks under the control of shortterm plastici.pdf:pdf},
  issn = {15537358},
  journal = {PLoS Computational Biology},
  number = {4},
  pages = {e1006928},
  publisher = {Public Library of Science},
  title = {{Stability of working memory in continuous attractor networks under the control of shortterm plasticity}},
  volume = {15},
  year = {2019}
  }

• Setareh, H., Deger, M., & Gerstner, W.. (2018). Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation. PLoS Computational Biology, 14(7), e1006216.
  [Bibtex]

  @article{Setareh2018,
  abstract = {The time scale of neuronal network dynamics is determined by synaptic interactions and neuronal signal integration, both of which occur on the time scale of milliseconds. Yet many behaviors like the generation of movements or vocalizations of sounds occur on the much slower time scale of seconds. Here we ask the question of how neuronal networks of the brain can support reliable behavior on this time scale. We argue that excitable neuronal assemblies with spike-frequency adaptation may serve as building blocks that can flexibly adjust the speed of execution of neural circuit function. We show in simulations that a chain of neuronal assemblies can propagate signals reliably, similar to the well-known synfire chain, but with the crucial difference that the propagation speed is slower and tunable to the behaviorally relevant range. Moreover we study a grid of excitable neuronal assemblies as a simplified model of the somatosensory barrel cortex of the mouse and demonstrate that various patterns of experimentally observed spatial activity propagation can be explained.},
  author = {Setareh, Hesam and Deger, Moritz and Gerstner, Wulfram},
  doi = {10.1371/journal.pcbi.1006216},
  file = {::},
  issn = {15537358},
  journal = {PLoS Computational Biology},
  month = {jul},
  number = {7},
  pages = {e1006216},
  publisher = {Public Library of Science},
  title = {{Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation}},
  volume = {14},
  year = {2018}
  }

• Nashef, A., Rapp, H., Nawrot, M. P., & Prut, Y.. (2018). Area-specific processing of cerebellar-thalamo-cortical information in primates. Biological Cybernetics, 112(1-2), 141–152.
  [Bibtex]

  @article{Nashef2018,
  abstract = {{\textcopyright} 2017, Springer-Verlag GmbH Germany. The cerebellar-thalamo-cortical (CTC) system plays a major role in controlling timing and coordination of voluntary movements. However, the functional impact of this system on motor cortical sites has not been documented in a systematic manner. We addressed this question by implanting a chronic stimulating electrode in the superior cerebellar peduncle (SCP) and recording evoked multiunit activity (MUA) and the local field potential (LFP) in the primary motor cortex (n= 926), the premotor cortex (n= 357) and the somatosensory cortex (n= 345). The area-dependent response properties were estimated using the MUA response shape (quantified by decomposing into principal components) and the time-dependent frequency content of the evoked LFP. Each of these signals alone enabled good classification between the somatosensory and motor sites. Good classification between the primary motor and premotor areas could only be achieved when combining features from both signal types. Topographical single-site representation of the predicted class showed good recovery of functional organization. Finally, the probability for misclassification had a broad topographical organization. Despite the area-specific response features to SCP stimulation, there was considerable site-to-site variation in responses, specifically within the motor cortical areas. This indicates a substantial SCP impact on both the primary motor and premotor cortex. Given the documented involvement of these cortical areas in preparation and execution of movement, this result may suggest a CTC contribution to both motor execution and motor preparation. The stimulation responses in the somatosensory cortex were sparser and weaker. However, a functional role of the CTC system in somatosensory computation must be taken into consideration.},
  author = {Nashef, Abdulraheem and Rapp, Hannes and Nawrot, Martin P. and Prut, Yifat},
  doi = {10.1007/s00422-017-0738-6},
  file = {::},
  issn = {14320770},
  journal = {Biological Cybernetics},
  keywords = {Cerebellum,Local field potential,Machine learning,Motor control,Multiunit activity,Thalamocortical,local field potential,machine learning,monkey,motor cortex,premotor cortex,somatosensory cortex},
  mendeley-tags = {local field potential,machine learning,monkey,motor cortex,premotor cortex,somatosensory cortex},
  number = {1-2},
  pages = {141--152},
  title = {{Area-specific processing of cerebellar-thalamo-cortical information in primates}},
  volume = {112},
  year = {2018}
  }

• The INCF Secretariat, Denker, M., Einevoll, G. T., Franke, F., Grün, S., Hagen, E., Kerr, J. N. D., Nawrot, M. P., Ness, T. B., Ritz, R., Smith, L., Wachtler, T., & Wójcik, D. K.. (2018). 1st INCF Workshop on Validation of Analysis Methods [version 1; not peer reviewed]. F1000Research, 7, 1226.
  [Bibtex]

  @article{Denker2012,
  author = {{The INCF Secretariat} and Denker, Michael and Einevoll, Gaute T and Franke, Felix and Gr{\"{u}}n, Sonja and Hagen, Espen and Kerr, Jason N D and Nawrot, Martin Paul and Ness, Torbjorn Baeko and Ritz, Raphael and Smith, Leslie and Wachtler, Thomas and W{\'{o}}jcik, Daniel K},
  doi = {10.7490/f1000research.1115887.1},
  file = {:Users/springer/Downloads/f1000research-213619.pdf:pdf},
  journal = {F1000Research},
  pages = {1226},
  title = {{1st INCF Workshop on Validation of Analysis Methods [version 1; not peer reviewed]}},
  url = {https://doi.org/10.7490/f1000research.1115887.1},
  volume = {7},
  year = {2018}
  }

• Riehle, A., Brochier, T., Nawrot, M., & Grün, S.. (2018). Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex. Frontiers in Neural Circuits, 12, 52.
  [Bibtex]

  @article{Riehle2018,
  abstract = {Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependence. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF≈CV². This expectation was met during movement, but not during wait where FF{\textgreater}{\textgreater}CV2². Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.},
  author = {Riehle, Alexa and Brochier, Thomas and Nawrot, Martin and Gr{\"{u}}n, Sonja},
  doi = {10.3389/fncir.2018.00052},
  file = {::},
  issn = {16625110},
  journal = {Frontiers in Neural Circuits},
  keywords = {Behavioral context,Monkey motor cortex,Renewal processes,Spike count variability,Spike time irregularity},
  pages = {52},
  title = {{Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex}},
  volume = {12},
  year = {2018}
  }

• Deger, M., Seeholzer, A., & Gerstner, W.. (2018). Multicontact co-operativity in spike-timing-dependent structural plasticity stabilizes networks. Cerebral Cortex, 28(4), 1396–1415.
  [Bibtex]

  @article{Deger2018,
  abstract = {Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of spine volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of structural and spike-timing-dependent plasticity that explains the multicontact configuration of synapses in adult neocortical networks under steady-state and lesion-induced conditions. Our plasticity rule with Hebbian and anti-Hebbian terms stabilizes both the postsynaptic firing rate and correlations between the pre-and postsynaptic activity at an active synaptic contact. Contacts appear spontaneously at a low rate and disappear if their strength approaches zero. Many presynaptic neurons compete to make strong synaptic connections onto a postsynaptic neuron, whereas the synaptic contacts of a given presynaptic neuron co-operate via postsynaptic firing. We find that cooperation of multiple synaptic contacts is crucial for stable, long-term synaptic memories. In simulations of a simplified network model of barrel cortex, our plasticity rule reproduces whisker-trimming-induced rewiring of thalamocortical and recurrent synaptic connectivity on realistic time scales.},
  author = {Deger, Moritz and Seeholzer, Alexander and Gerstner, Wulfram},
  doi = {10.1093/cercor/bhx339},
  file = {:Users/springer/Downloads/bhx339.pdf:pdf},
  issn = {14602199},
  journal = {Cerebral Cortex},
  keywords = {Barrel cortex model,Cortical reorganization,Dendritic spine motility,Synaptic plasticity},
  number = {4},
  pages = {1396--1415},
  title = {{Multicontact co-operativity in spike-timing-dependent structural plasticity stabilizes networks}},
  volume = {28},
  year = {2018}
  }

• 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.
  [Bibtex]

  @article{Muller2018,
  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}
  }

• Rost, T., Deger, M., & Nawrot, M. P.. (2018). Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick. Biological Cybernetics, 112(1-2), 81–98.
  [Bibtex]

  @article{Rost2018,
  abstract = {{\textcopyright} 2017 The Author(s) Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.},
  author = {Rost, Thomas and Deger, Moritz and Nawrot, Martin Paul},
  doi = {10.1007/s00422-017-0737-7},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Rost, Deger, Nawrot - 2018 - Winnerless competition in clustered balanced networks inhibitory assemblies do the trick.pdf:pdf},
  issn = {14320770},
  journal = {Biological Cybernetics},
  keywords = {Attractor networks,Binary networks,Cortical variability,Mean field theory,Multistability,attractor network,clustered network,spiking neural network},
  mendeley-tags = {attractor network,clustered network,spiking neural network},
  number = {1-2},
  pages = {81--98},
  publisher = {Springer Berlin Heidelberg},
  title = {{Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick}},
  volume = {112},
  year = {2018}
  }

• Nawrot, M. P., Kloppenburg, P., & Deger, M.. (2018). Foreword for the special issue on Neural Coding. Biological Cybernetics, 112(1-2), 11.
  [Bibtex]

  @article{Nawrot2018,
  author = {Nawrot, Martin P. and Kloppenburg, Peter and Deger, Moritz},
  doi = {10.1007/s00422-018-0754-1},
  file = {::},
  issn = {14320770},
  journal = {Biological Cybernetics},
  number = {1-2},
  pages = {11},
  title = {{Foreword for the special issue on Neural Coding}},
  volume = {112},
  year = {2018}
  }

• Schwalger, T., Deger, M., & Gerstner, W.. (2017). Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size. PLoS Computational Biology, 13(4), 1–63.
  [Bibtex]

  @article{Schwalger2017,
  abstract = {Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.},
  archivePrefix = {arXiv},
  arxivId = {1611.00294},
  author = {Schwalger, Tilo and Deger, Moritz and Gerstner, Wulfram},
  doi = {10.1371/journal.pcbi.1005507},
  eprint = {1611.00294},
  file = {:Users/springer/Downloads/journal.pcbi.1005507.pdf:pdf},
  isbn = {1111111111},
  issn = {15537358},
  journal = {PLoS Computational Biology},
  number = {4},
  pages = {1--63},
  title = {{Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size}},
  volume = {13},
  year = {2017}
  }

• Landgraf, T., & Nawrot, M. P.. (2017). Künstliche Mini‐Gehirne für Roboter. In Walkowiak, W., & Erber-Schropp, J. M. (Eds.), In Planen und Handeln: Neurowissenschaftliche, psychologische, medizinische und gesellschaftsrelevante Aspekte (, pp. 135–150). Wiesbaden: Springer Fachmedien Wiesbaden.
  [Bibtex]

  @inbook{Landgraf2017,
  abstract = {Auch Tiere mit relativ kleinen Gehirnen zeigen erstaunlich komplexe und robuste Wahrnehmungs‐ und Verhaltensleistungen. Dies gilt insbesondere f{\"{u}}r Insekten. Deren Mini‐Gehirne sind anpassungs‐ und lernf{\"{a}}hig, sie erm{\"{o}}glichen ihnen, langfristige Ged{\"{a}}chtnisse herauszubilden und sich kurzfristig an neue Gegebenheiten anzupassen.},
  address = {Wiesbaden},
  author = {Landgraf, Tim and Nawrot, Martin Paul},
  booktitle = {Planen und Handeln: Neurowissenschaftliche, psychologische, medizinische und gesellschaftsrelevante Aspekte},
  doi = {10.1007/978-3-658-16891-9_9},
  editor = {Walkowiak, Wolfgang and Erber-Schropp, Julia Maria},
  isbn = {978-3-658-16891-9},
  pages = {135--150},
  publisher = {Springer Fachmedien Wiesbaden},
  title = {{K{\"{u}}nstliche Mini‐Gehirne f{\"{u}}r Roboter}},
  url = {https://doi.org/10.1007/978-3-658-16891-9{\_}9},
  year = {2017}
  }

• Deger, M.. (2017). Spike Timing-Dependent Structural Plasticity of Multicontact Synaptic Connections. In The Rewiring Brain (pp. 261–273). .
  [Bibtex]

  @incollection{Deger2017,
  abstract = {Networks of neurons in the neocortex change. In the adult neocortex, structural plasticity causes continuous formation and removal of dendritic spines, the structural substrate of most excitatory synapses. Yet, despite this ongoing turnover, statistics of spine numbers remain constant and synaptically stored memories can persist for a lifetime. Several modeling approaches have been developed toward understanding structural plasticity. This chapter summarizes recent work on spike-timing-dependent plasticity models of dendritic spine plasticity and turnover. Such models provide a link between conceptual works rooted in artificial neural networks, and biophysical modeling approaches trying to identify and describe the biological substrate underlying plasticity. Recent modeling of synaptic connections made of multiple dendritic spines has demonstrated how cooperative synapse formation can be explained by the dependence of spine plasticity on pre- and postsynaptic spike timing, and how cooperation of spines may lead to long-term stable synaptic connections with highly reliable synaptic transmission.},
  author = {Deger, Moritz},
  booktitle = {The Rewiring Brain},
  chapter = {12},
  pages = {261--273},
  title = {{Spike Timing-Dependent Structural Plasticity of Multicontact Synaptic Connections}},
  url = {https://doi.org/10.1016/B978-0-12-803784-3.00012-3},
  year = {2017}
  }

• Lungu, I. A., Riehle, A., Nawrot, M. P., & Schmuker, M.. (2017). Predicting voluntary movements from motor cortical activity with neuromorphic hardware. IBM Journal of Research and Development, 61(2).
  [Bibtex]

  @article{Lungu2017,
  abstract = {Neurons in the mammalian motor cortices encode physical parameters of voluntary movements during planning and execution of a motor task. Brain-machine interfaces can decode limb movements from the activity of these neurons in real time. The future goal is to control prosthetic devices in severely paralyzed patients or to restore communication if the ability to speak or make gestures is lost. Here, we implemented a spiking neural network that decodes movement intentions from individual neuronal activity recorded in the motor cortex of a monkey. The network runs on neuromorphic hardware and performs its computations in a purely spike-based fashion. It incorporates an insect-brain-inspired, three-layer architecture with 176 neurons. Cortical signals are filtered using lateral inhibition, and the network is trained in a supervised fashion to predict two opposing directions of the monkey?s arm reaching movement before the movement is carried out. Our network operates on the actual spikes that have been emitted by motor cortical neurons, without the need to construct intermediate non-spiking representations. Using a pseudo-population of 12 manually-selected neurons, it reliably predicts the movement direction with an accuracy of 89.32 {\%} on unseen data after only 100 training trials. Our results provide a proof of concept for the first-time use of a neuromorphic device for decoding movement intentions.},
  author = {Lungu, I. A. and Riehle, A. and Nawrot, M. P. and Schmuker, M.},
  doi = {10.1147/JRD.2017.2656063},
  file = {::},
  issn = {21518556},
  journal = {IBM Journal of Research and Development},
  keywords = {brain machine interface,insect inspired,monkey,motor cortex,neuromorphic computing,spiking neural network},
  mendeley-tags = {brain machine interface,insect inspired,monkey,motor cortex,neuromorphic computing,spiking neural network},
  number = {2},
  title = {{Predicting voluntary movements from motor cortical activity with neuromorphic hardware}},
  volume = {61},
  year = {2017}
  }

• Chicca, E., Schmuker, M., & Nawrot, M. P.. (2015). Neuromorphic Sensors, Olfaction. In Jaeger, D., & Jung, R. (Eds.), In Encyclopedia of Computational Neuroscience (, pp. 1991–1997). New York, NY: Springer New York.
  [Bibtex]

  @inbook{Schmuker2015,
  address = {New York, NY},
  author = {Chicca, Elisabetta and Schmuker, Michael and Nawrot, Martin Paul},
  booktitle = {Encyclopedia of Computational Neuroscience},
  doi = {10.1007/978-1-4614-6675-8_119},
  editor = {Jaeger, Dieter and Jung, Ranu},
  isbn = {978-1-4614-6675-8},
  pages = {1991--1997},
  publisher = {Springer New York},
  title = {{Neuromorphic Sensors, Olfaction}},
  url = {https://doi.org/10.1007/978-1-4614-6675-8{\_}119},
  year = {2015}
  }

• Kumar, R., Kaur, R., Auffarth, B., & Bhondekar, A. P.. (2015). Understanding the odour spaces: A step towards solving olfactory stimulus-percept problem. PLoS ONE, 10(10), 1–19.
  [Bibtex]

  @article{Kumar2015,
  abstract = {Odours are highly complex, relying on hundreds of receptors, and people are known to disagree in their linguistic descriptions of smells. It is partly due to these facts that, it is very hard to map the domain of odour molecules or their structure to that of perceptual representations, a problem that has been referred to as the Structure-Odour-Relationship. We collected a number of diverse open domain databases of odour molecules having unorganised perceptual descriptors, and developed a graphical method to find the similarity between perceptual descriptors; which is intuitive and can be used to identify perceptual classes. We then separately projected the physico-chemical and perceptual features of these molecules in a non-linear dimension and clustered the similar molecules.We found a significant overlap between the spatial positioning of the clustered molecules in the physico-chemical and perceptual spaces.We also developed a statistical method of predicting the perceptual qualities of a novel molecule using its physico-chemical properties with high receiver operating characteristics(ROC).},
  author = {Kumar, Ritesh and Kaur, Rishemjit and Auffarth, Benjamin and Bhondekar, Amol P.},
  doi = {10.1371/journal.pone.0141263},
  file = {:Users/springer/Desktop/journal.pone.0141263.pdf:pdf},
  issn = {19326203},
  journal = {PLoS ONE},
  number = {10},
  pages = {1--19},
  title = {{Understanding the odour spaces: A step towards solving olfactory stimulus-percept problem}},
  volume = {10},
  year = {2015}
  }

• Meckenhäuser, G., Krämer, S., Farkhooi, F., Ronacher, B., & Nawrot, M. P.. (2014). Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system. Frontiers in Systems Neuroscience, 8, 183.
  [Bibtex]

  @article{Meckenhauser2014,
  abstract = {{\textcopyright} 2014 Meckenh{\"{a}}user, Kr{\"{a}}mer, Farkhooi, Ronacher and Nawrot. Acoustic communication plays a key role for mate attraction in grasshoppers. Males use songs to advertise themselves to females. Females evaluate the song pattern, a repetitive structure of sound syllables separated by short pauses, to recognize a conspecific male and as proxy to its fitness. In their natural habitat females often receive songs with degraded temporal structure. Perturbations may, for example, result from the overlap with other songs. We studied the response behavior of females to songs that show different signal degradations. A perturbation of an otherwise attractive song at later positions in the syllable diminished the behavioral response, whereas the same perturbation at the onset of a syllable did not affect song attractiveness. We applied na{\"{i}}ve Bayes classifiers to the spike trains of identified neurons in the auditory pathway to explore how sensory evidence about the acoustic stimulus and its attractiveness is represented in the neuronal responses. We find that populations of three or more neurons were sufficient to reliably decode the acoustic stimulus and to predict its behavioral relevance from the single-trial integrated firing rate. A simple model of decision making simulates the female response behavior. It computes for each syllable the likelihood for the presence of an attractive song pattern as evidenced by the population firing rate. Integration across syllables allows the likelihood to reach a decision threshold and to elicit the behavioral response. The close match between model performance and animal behavior shows that a spike rate code is sufficient to enable song pattern recognition.},
  author = {Meckenh{\"{a}}user, Gundula and Kr{\"{a}}mer, Stefanie and Farkhooi, Farzad and Ronacher, Bernhard and Nawrot, Martin P.},
  doi = {10.3389/fnsys.2014.00183},
  file = {::},
  issn = {16625137},
  journal = {Frontiers in Systems Neuroscience},
  keywords = {Acoustic communication,Decision making,Na{\"{i}}ve Bayes classifier,Neural information processing,Pattern recognition,Population coding},
  pages = {183},
  title = {{Neural representation of calling songs and their behavioral relevance in the grasshopper auditory system}},
  volume = {8},
  year = {2014}
  }

• 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.
  [Bibtex]

  @article{Pamir2014,
  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}
  }

• Schmuker, M., Pfeil, T., & Nawrot, M. P.. (2014). A neuromorphic network for generic multivariate data classification. Proceedings of the National Academy of Sciences of the United States of America, 111(6), 2081–2086.
  [Bibtex]

  @article{Schmuker2014,
  abstract = {Computational neuroscience has uncovered a number of computational principles used by nervous systems. At the same time, neuromorphic hardware has matured to a state where fast silicon implementations of complex neural networks have become feasible. En route to future technical applications of neuromorphic computing the current challenge lies in the identification and implementation of functional brain algorithms. Taking inspiration from the olfactory system of insects, we constructed a spiking neural network for the classification of multivariate data, a common problem in signal and data analysis. In this model, real-valued multivariate data are converted into spike trains using virtual receptors (VRs). Their output is processed by lateral inhibition and drives a winner-take-all circuit that supports supervised learning. VRs are conveniently implemented in software, whereas the lateral inhibition and classification stages run on accelerated neuromorphic hardware. When trained and tested on real-world datasets, we find that the classification performance is on par with a na{\"{i}}ve Bayes classifier. An analysis of the network dynamics shows that stable decisions in output neuron populations are reached within less than 100 ms of biological time, matching the time-to-decision reported for the insect nervous system. Through leveraging a population code, the network tolerates the variability of neuronal transfer functions and trial-totrial variation that is inevitably present on the hardware system. Our work provides a proof of principle for the successful implementation of a functional spiking neural network on a configurable neuromorphic hardware system that can readily be applied to realworld computing problems.},
  author = {Schmuker, Michael and Pfeil, Thomas and Nawrot, Martin Paul},
  doi = {10.1073/pnas.1303053111},
  file = {::},
  issn = {00278424},
  journal = {Proceedings of the National Academy of Sciences of the United States of America},
  keywords = {Bioinspired computing,Machine learning,Multivariate classification,Spiking networks,insect inspired,machine learning,neuromorphic computing,spiking neural network},
  mendeley-tags = {insect inspired,machine learning,neuromorphic computing,spiking neural network},
  number = {6},
  pages = {2081--2086},
  title = {{A neuromorphic network for generic multivariate data classification}},
  volume = {111},
  year = {2014}
  }

• Kloppenburg, P., & Nawrot, M. P.. (2014). Neural coding: Sparse but on time. Current Biology, 24(19), R957–R959.
  [Bibtex]

  @article{Kloppenburg2014,
  abstract = {To code information efficiently, sensory systems use sparse representations. In a sparse code, a specific stimulus activates only few spikes in a small number of neurons. A new study shows that the temporal pattern across sparsely activated neurons encodes information, suggesting that the sparse code extends into the time domain.},
  author = {Kloppenburg, Peter and Nawrot, Martin Paul},
  doi = {10.1016/j.cub.2014.08.041},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Kloppenburg, Nawrot - 2014 - Neural coding Sparse but on time.pdf:pdf},
  issn = {09609822},
  journal = {Current Biology},
  keywords = {insect olfaction,rate model,sparse coding},
  mendeley-tags = {insect olfaction,rate model,sparse coding},
  number = {19},
  pages = {R957--R959},
  title = {{Neural coding: Sparse but on time}},
  volume = {24},
  year = {2014}
  }

• Landgraf, T., Wild, B., Ludwig, T., Nowak, P., Helgadottir, L., Daumenlang, B., Breinlinger, P., Nawrot, M. P., & Rojas, R.. (2013). NeuroCopter: Neuromorphic computation of 6D ego-motion of a quadcopter. In Lepora, N. F., Mura, A., Krapp, H. G., Verschure, P. F. M. J., & Prescott, T. J. (Eds.), In Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science (Vol. 8064). Springer Berlin Heidelberg.
  [Bibtex]

  @incollection{Landgraf2013,
  abstract = {The navigation capabilities of honeybees are surprisingly complex. Experimental evidence suggests that honeybees rely on a map-like neuronal representation of the environment. Intriguingly, a honeybee brain exhibits approximately one million neurons only. In an interdisciplinary enterprise, we are investigating models of high-level processing in the nervous system of insects such as spatial mapping and decision making. We use a robotic platform termed NeuroCopter that is controlled by a set of functional modules. Each of these modules initially represents a conventional control method and, in an iterative process, will be replaced by a neural control architecture. This paper describes the neuromorphic extraction of the copter's ego motion from sparse optical flow fields. We will first introduce the reader to the system's architecture and then present a detailed description of the structure of the neural model followed by simulated and real-world results. {\textcopyright} 2013 Springer-Verlag Berlin Heidelberg.},
  author = {Landgraf, Tim and Wild, Benjamin and Ludwig, Tobias and Nowak, Philipp and Helgadottir, Lovisa and Daumenlang, Benjamin and Breinlinger, Philipp and Nawrot, Martin Paul and Rojas, Ra{\'{u}}l},
  booktitle = {Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science},
  doi = {10.1007/978-3-642-39802-5_13},
  editor = {Lepora, N.F. and Mura, A. and Krapp, H.G. and Verschure, P.F.M.J. and Prescott, T.J.},
  file = {::},
  isbn = {9783642398018},
  issn = {03029743},
  keywords = {biomimetics,neural networks,neuromorphic computation,robotics,self-localization},
  mendeley-tags = {robotics},
  publisher = {Springer Berlin Heidelberg},
  title = {{NeuroCopter: Neuromorphic computation of 6D ego-motion of a quadcopter}},
  url = {https://link.springer.com/chapter/10.1007/978-3-642-39802-5{\_}13{\#}citeas},
  volume = {8064},
  year = {2013}
  }

• Helgadottir, L. I., Haenicke, J., Landgraf, T., Rojas, R., & Nawrot, M. P.. (2013). Conditioned behavior in a robot controlled by a spiking neural network. Paper presented at the International IEEE/EMBS Conference on Neural Engineering, NER.
  [Bibtex]

  @inproceedings{Helgadottir2013,
  abstract = {Insects show a rich repertoire of goal-directed and adaptive behaviors that are still beyond the capabilities of today's artificial systems. Fast progress in our comprehension of the underlying neural computations make the insect a favorable model system for neurally inspired computing paradigms in autonomous robots. Here, we present a robotic platform designed for implementing and testing spiking neural network control architectures. We demonstrate a neuromorphic realtime approach to sensory processing, reward-based associative plasticity and behavioral control. This is inspired by the biological mechanisms underlying rapid associative learning and the formation of distributed memories in the insect. {\textcopyright} 2013 IEEE.},
  author = {Helgadottir, L.I. and Haenicke, J. and Landgraf, T. and Rojas, R. and Nawrot, M.P.},
  booktitle = {International IEEE/EMBS Conference on Neural Engineering, NER},
  doi = {10.1109/NER.2013.6696078},
  file = {::},
  isbn = {9781467319690},
  issn = {19483546},
  keywords = {insect olfaction,plasticity,robotics,spiking neural network},
  mendeley-tags = {insect olfaction,plasticity,robotics,spiking neural network},
  title = {{Conditioned behavior in a robot controlled by a spiking neural network}},
  year = {2013}
  }

• Farkhooi, F., Froese, A., Muller, E., Menzel, R., & Nawrot, M. P.. (2013). Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways. PLoS Computational Biology, 9(10), e1003251.
  [Bibtex]

  @article{Farkhooi2013,
  abstract = {Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.},
  author = {Farkhooi, Farzad and Froese, Anja and Muller, Eilif and Menzel, Randolf and Nawrot, Martin P.},
  doi = {10.1371/journal.pcbi.1003251},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Farkhooi et al. - 2013 - Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways.pdf:pdf},
  issn = {1553734X},
  journal = {PLoS Computational Biology},
  number = {10},
  pages = {e1003251},
  title = {{Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways}},
  volume = {9},
  year = {2013}
  }

• Häusler, C., Susemihl, A., & Nawrot, M. P.. (2013). Natural image sequences constrain dynamic receptive fields and imply a sparse code. Brain Research, 1536, 53–67.
  [Bibtex]

  @article{Hausler2013,
  abstract = {In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. {\textcopyright} 2013 The Authors.},
  author = {H{\"{a}}usler, Chris and Susemihl, Alex and Nawrot, Martin P.},
  doi = {10.1016/j.brainres.2013.07.056},
  file = {::},
  issn = {00068993},
  journal = {Brain Research},
  keywords = {Autoencoding,Lifetime sparseness,Machine learning,Population sparseness,Restricted Boltzmann Machine,Visual cortex},
  pages = {53--67},
  title = {{Natural image sequences constrain dynamic receptive fields and imply a sparse code}},
  volume = {1536},
  year = {2013}
  }

• Brill, M. F., Rosenbaum, T., Reus, I., Kleineidam, C. J., Nawrot, M. P., & Rössler, W.. (2013). Parallel processing via a dual olfactory pathway in the honeybee. Journal of Neuroscience, 33(6), 2443–2456.
  [Bibtex]

  @article{Brill2013,
  abstract = {In their natural environment, animals face complex and highly dynamic olfactory input. Thus vertebrates as well as invertebrates require fast and reliable processing of olfactory information. Parallel processing has been shown to improve processing speed and power in other sensory systems and is characterized by extraction of different stimulus parameters along parallel sensory information streams. Honeybees possess an elaborate olfactory system with unique neuronal architecture: a dual olfactory pathway comprising a medial projection-neuron (PN) antennal lobe (AL) protocerebral output tract (m-APT) and a lateral PN AL output tract (l-APT) connecting the olfactory lobes with higher-order brain centers. We asked whether this neuronal architecture serves parallel processing and employed a novel technique for simultaneous multiunit recordings from both tracts. The results revealed response profiles from a high number of PNs of both tracts to floral, pheromonal, and biologically relevant odor mixtures tested over multiple trials. PNs from both tracts responded to all tested odors, but with different characteristics indicating parallel processing of similar odors. Both PN tracts were activated by widely overlapping response profiles, which is a requirement for parallel processing. The l-APT PNs had broad response profiles suggesting generalized coding properties, whereas the responses of m-APT PNs were comparatively weaker and less frequent, indicating higher odor specificity. Comparison of response latencies within and across tracts revealed odor-dependent latencies. We suggest that parallel processing via the honeybee dual olfactory pathway provides enhanced odor processing capabilities serving sophisticated odor perception and olfactory demands associated with a complex olfactory world of this social insect.},
  author = {Brill, Martin F. and Rosenbaum, Tobias and Reus, Isabelle and Kleineidam, Christoph J. and Nawrot, Martin P. and R{\"{o}}ssler, Wolfgang},
  doi = {10.1523/JNEUROSCI.4268-12.2013},
  file = {:Users/springer/Documents/PhD/Lit/Brill (2013) Parallel Processing via a Dual Olfactory Pathway in the Honeybee.pdf:pdf},
  issn = {02706474},
  journal = {Journal of Neuroscience},
  keywords = {insect olfaction,review paper},
  mendeley-tags = {insect olfaction,review paper},
  number = {6},
  pages = {2443--2456},
  title = {{Parallel processing via a dual olfactory pathway in the honeybee}},
  volume = {33},
  year = {2013}
  }

• Auffarth, B.. (2013). Understanding smell-The olfactory stimulus problem. Neuroscience and Biobehavioral Reviews, 37(8), 1667–1679.
  [Bibtex]

  @article{Auffarth2013,
  abstract = {The main problem with sensory processing is the difficulty in relating sensory input to physiological responses and perception. This is especially problematic at higher levels of processing, where complex cues elicit highly specific responses. In olfaction, this relationship is particularly obfuscated by the difficulty of characterizing stimulus statistics and perception. The core questions in olfaction are hence the so-called stimulus problem, which refers to the understanding of the stimulus, and the structure-activity and structure-odor relationships, which refer to the molecular basis of smell. It is widely accepted that the recognition of odorants by receptors is governed by the detection of physico-chemical properties and that the physical space is highly complex. Not surprisingly, ideas differ about how odor stimuli should be classified and about the very nature of information that the brain extracts from odors. Even though there are many measures for smell, there is none that accurately describes all aspects of it. Here, we summarize recent developments in the understanding of olfaction. We argue that an approach to olfactory function where information processing is emphasized could contribute to a high degree to our understanding of smell as a perceptual phenomenon emerging from neural computations. Further, we argue that combined analysis of the stimulus, biology, physiology, and behavior and perception can provide new insights into olfactory function. We hope that the reader can use this review as a competent guide and overview of research activities in olfactory physiology, psychophysics, computation, and psychology. We propose avenues for research, particularly in the systematic characterization of receptive fields and of perception. {\textcopyright} 2013 Elsevier Ltd.},
  author = {Auffarth, Benjamin},
  doi = {10.1016/j.neubiorev.2013.06.009},
  file = {:Users/springer/Downloads/1-s2.0-S0149763413001644-main.pdf:pdf},
  issn = {01497634},
  journal = {Neuroscience and Biobehavioral Reviews},
  keywords = {Basic odors,Hedonics,Insect olfaction,Odor perception,Olfactory receptors,Olfactory stimulus-problem,Olfactory system,Spatial coding,Structure-activity relationship,Structure-odor relationship,Topography,Vertebrate olfaction},
  number = {8},
  pages = {1667--1679},
  pmid = {23806440},
  publisher = {Elsevier Ltd},
  title = {{Understanding smell-The olfactory stimulus problem}},
  url = {http://dx.doi.org/10.1016/j.neubiorev.2013.06.009},
  volume = {37},
  year = {2013}
  }

• Meckenhäuser, G., Hennig, M. R., & Nawrot, M. P.. (2013). Critical Song Features for Auditory Pattern Recognition in Crickets. PLoS ONE, 8(2).
  [Bibtex]

  @article{Meckenhauser2013,
  abstract = {Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females' phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale. {\textcopyright} 2013 Meckenh{\"{a}}user et al.},
  author = {Meckenh{\"{a}}user, Gundula and Hennig, R. Matthias and Nawrot, Martin P.},
  doi = {10.1371/journal.pone.0055349},
  file = {::},
  issn = {19326203},
  journal = {PLoS ONE},
  number = {2},
  title = {{Critical Song Features for Auditory Pattern Recognition in Crickets}},
  volume = {8},
  year = {2013}
  }

• Häusler, C., Susemihl, A., Nawrot, M. P., & Opper, M.. (2013). Temporal Autoencoding Improves Generative Models of Time Series. Paper presented at the JMLR: Workshop and Conference Proceedings.
  [Bibtex]

  @inproceedings{Hausler2013a,
  abstract = {Restricted Boltzmann Machines (RBMs) are generative models which can learn useful representations from samples of a dataset in an unsupervised fashion. They have been widely employed as an unsupervised pre-training method in machine learning. RBMs have been modified to model time series in two main ways: The Temporal RBM stacks a number of RBMs laterally and introduces temporal dependencies between the hidden layer units; The Conditional RBM, on the other hand, considers past samples of the dataset as a conditional bias and learns a representation which takes these into account. Here we propose a new training method for both the TRBM and the CRBM, which enforces the dynamic structure of temporal datasets. We do so by treating the temporal models as denoising autoencoders, considering past frames of the dataset as corrupted versions of the present frame and minimizing the reconstruction error of the present data by the model. We call this approach Temporal Autoencoding. This leads to a significant improvement in the performance of both models in a filling-in-frames task across a number of datasets. The error reduction for motion capture data is 56$\backslash${\%} for the CRBM and 80$\backslash${\%} for the TRBM. Taking the posterior mean prediction instead of single samples further improves the model's estimates, decreasing the error by as much as 91$\backslash${\%} for the CRBM on motion capture data. We also trained the model to perform forecasting on a large number of datasets and have found TA pretraining to consistently improve the performance of the forecasts. Furthermore, by looking at the prediction error across time, we can see that this improvement reflects a better representation of the dynamics of the data as opposed to a bias towards reconstructing the observed data on a short time scale.},
  archivePrefix = {arXiv},
  arxivId = {1309.3103},
  author = {H{\"{a}}usler, Chris and Susemihl, Alex and Nawrot, Martin P and Opper, Manfred},
  booktitle = {JMLR: Workshop and Conference Proceedings},
  eprint = {1309.3103},
  file = {:Users/springer/Downloads/1309.3103v1.pdf:pdf},
  keywords = {autoencoder,conditional,generative models,restricted boltzmann machine,temporal restricted boltzmann machine},
  pages = {1--14},
  title = {{Temporal Autoencoding Improves Generative Models of Time Series}},
  url = {http://arxiv.org/abs/1309.3103},
  year = {2013}
  }

• Häusler, C., & Susemihl, A.. (2012). Temporal Autoencoding Restricted Boltzmann Machine. arXiv, 1210.8353.
  [Bibtex]

  @article{Hausler2012,
  abstract = {Much work has been done refining and characterizing the receptive fields learned by deep learning algorithms. A lot of this work has focused on the development of Gabor-like filters learned when enforcing sparsity constraints on a natural image dataset. Little work however has investigated how these filters might expand to the temporal domain, namely through training on natural movies. Here we investigate exactly this problem in established temporal deep learning algorithms as well as a new learning paradigm suggested here, the Temporal Autoencoding Restricted Boltzmann Machine (TARBM).},
  archivePrefix = {arXiv},
  arxivId = {1210.8353},
  author = {H{\"{a}}usler, Chris and Susemihl, Alex},
  eprint = {1210.8353},
  file = {:Users/springer/Downloads/1210.8353.pdf:pdf},
  journal = {arXiv},
  pages = {1210.8353},
  title = {{Temporal Autoencoding Restricted Boltzmann Machine}},
  url = {http://arxiv.org/abs/1210.8353},
  year = {2012}
  }

• Berger, D., Pazienti, A., Flores, F. J., Nawrot, M. P., Maldonado, P. E., & Grün, S.. (2012). Viewing strategy of Cebus monkeys during free exploration of natural images. Brain Research, 1434, 34–46.
  [Bibtex]

  @article{Berger2012,
  abstract = {Humans and other primates move their eyes several times per second to foveate at different locations of a visual scene. What features of a scene guide eye movements in natural vision? We recorded eye movements of three monkeys during free exploration of natural scenes and propose a simple model to explain their dynamics. We use the spatial clustering of fixation positions to define the monkeys' subjective regions-of-interest (ROI) in natural scenes. For most images the subjective ROIs match significantly the computed saliency of the natural scene, except when the image contains human or primate faces. We also investigated the temporal sequence of eye movements by computing the probability that a fixation will be made inside or outside of the ROI, given the current fixation position. We fitted a Markov chain model to the sequence of fixation positions, and find that fixations made inside a ROI are more likely to be followed by another fixation in the same ROI. This is true, independent of the image saliency in the area of the ROI. Our results show that certain regions in a natural scene are explored locally before directing the focus to another local region. This strategy could allow for quick integration of the visual features that constitute an object, and efficient segmentation of objects from other objects and the background during free viewing of natural scenes. {\textcopyright} 2011 Elsevier B.V. All rights reserved.},
  author = {Berger, Denise and Pazienti, Antonio and Flores, Francisco J. and Nawrot, Martin P. and Maldonado, Pedro E. and Gr{\"{u}}n, Sonja},
  doi = {10.1016/j.brainres.2011.10.013},
  file = {::},
  issn = {00068993},
  journal = {Brain Research},
  keywords = {Eye movement,Fixation map,Monkey,Natural vision,Scan path},
  pages = {34--46},
  title = {{Viewing strategy of Cebus monkeys during free exploration of natural images}},
  volume = {1434},
  year = {2012}
  }

• Hausler, C., Nawrot, M. P., & Schmuker, M.. (2011). A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee. Paper presented at the 2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011.
  [Bibtex]

  @inproceedings{Hausler2011,
  abstract = {We decompose the honeybee's olfactory pathway into local circuits that represent successive processing stages and resemble a deep learning architecture. Using spiking neuronal network models, we infer the specific functional role of these microcircuits in odor discrimination, and measure their contribution to the performance of a spiking implementation of a probabilistic classifier, trained in a supervised manner. The entire network is based on a network of spiking neurons, suited for implementation on neuromorphic hardware. {\textcopyright} 2011 IEEE.},
  author = {Hausler, Chris and Nawrot, Martin Paul and Schmuker, Michael},
  booktitle = {2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011},
  doi = {10.1109/NER.2011.5910522},
  isbn = {9781424441402},
  pages = {198--202},
  title = {{A spiking neuron classifier network with a deep architecture inspired by the olfactory system of the honeybee}},
  year = {2011}
  }

• Fucke, T., Suchanek, D., Nawrot, M. P., Seamari, Y., Heck, D. H., Aertsen, A., & Boucsein, C.. (2011). Stereotypical spatiotemporal activity patterns during slow-wave activity in the neocortex. Journal of Neurophysiology, 106(6), 3035–3044.
  [Bibtex]

  @article{Fucke2011,
  abstract = {Alternating epochs of activity and silence are a characteristic feature of neocortical networks during certain sleep cycles and deep states of anesthesia. The mechanism and functional role of these slow oscillations ({\textless}1 Hz) have not yet been fully characterized. Experimental and theoretical studies show that slow-wave oscillations can be generated autonomously by neocortical tissue but become more regular through a thalamo-cortical feedback loop. Evidence for a functional role of slow-wave activity comes from EEG recordings in humans during sleep, which show that activity travels as stereotypical waves over the entire brain, thought to play a role in memory consolidation. We used an animal model to investigate activity wave propagation on a smaller scale, namely within the rat somatosensory cortex. Signals from multiple extracellular microelectrodes in combination with one intracellular recording in the anesthetized animal in vivo were utilized to monitor the spreading of activity. We found that activity propagation in most animals showed a clear preferred direction, suggesting that it often originated from a similar location in the cortex. In addition, the breakdown of active states followed a similar pattern with slightly weaker direction preference but a clear correlation to the direction of activity spreading, supporting the notion of a wave-like phenomenon similar to that observed after strong sensory stimulation in sensory areas. Taken together, our findings support the idea that activity waves during slow-wave sleep do not occur spontaneously at random locations within the network, as was suggested previously, but follow preferred synaptic pathways on a small spatial scale. {\textcopyright} 2011 the American Physiological Society.},
  author = {Fucke, Thomas and Suchanek, Dymphie and Nawrot, Martin P. and Seamari, Yamina and Heck, Detlef H. and Aertsen, Ad and Boucsein, Clemens},
  doi = {10.1152/jn.00811.2010},
  file = {::},
  issn = {00223077},
  journal = {Journal of Neurophysiology},
  keywords = {Extracellular electrode array,Ketamine/ xylazine,Slow-wave sleep,Traveling waves,Up/down state},
  number = {6},
  pages = {3035--3044},
  title = {{Stereotypical spatiotemporal activity patterns during slow-wave activity in the neocortex}},
  volume = {106},
  year = {2011}
  }

• Farkhooi, F., Muller, E., & Nawrot, M. P.. (2011). Adaptation reduces variability of the neuronal population code. Physical Review E – Statistical, Nonlinear, and Soft Matter Physics, 83(5).
  [Bibtex]

  @article{Farkhooi2011,
  abstract = {Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for generalized non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of neurons with spike-frequency adaptation, this results in the regularization of the population activity and an enhanced postsynaptic signal decoding. We confirm our theoretical results in a population of cortical neurons recorded in vivo. {\textcopyright} 2011 American Physical Society.},
  author = {Farkhooi, Farzad and Muller, Eilif and Nawrot, Martin P.},
  doi = {10.1103/PhysRevE.83.050905},
  file = {::},
  issn = {15393755},
  journal = {Physical Review E - Statistical, Nonlinear, and Soft Matter Physics},
  keywords = {spike train statistics,variability},
  mendeley-tags = {spike train statistics,variability},
  number = {5},
  title = {{Adaptation reduces variability of the neuronal population code}},
  volume = {83},
  year = {2011}
  }

• 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.
  [Bibtex]

  @article{Strube-Bloss2011,
  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}
  }

• Nawrot, M. P.. (2011). Neuroinformatik: Theorie Neuronaler Informationsverarbeitung.. In Speerspitzen (pp. 184–187). Heidelberger Akademie der Wissenschaften (Hrsg.), Universitätsverlag Winter, Heidelberg.
  [Bibtex]

  @incollection{Nawrot2011,
  author = {Nawrot, Martin Paul},
  booktitle = {Speerspitzen},
  pages = {184--187},
  publisher = {Heidelberger Akademie der Wissenschaften (Hrsg.), Universit{\"{a}}tsverlag Winter, Heidelberg},
  title = {{Neuroinformatik: Theorie Neuronaler Informationsverarbeitung.}},
  url = {https://www.bcp.fu-berlin.de/en/biologie/arbeitsgruppen/Archiv/ag{\_}nawrot/research/pdf/Nawrot11{\_}184.pdf},
  year = {2011}
  }

• Meckenhäuser, G., Hennig, M. R., & Nawrot, M. P.. (2011). Modeling phonotaxis in female Gryllus bimaculatus with artificial neural networks. Paper presented at the BMC Neuroscience.
  [Bibtex]

  @inproceedings{Meckenhauser2011,
  author = {Meckenh{\"{a}}user, Gundula and Hennig, Matthias R and Nawrot, Martin P},
  booktitle = {BMC Neuroscience},
  doi = {10.1186/1471-2202-12-s1-p234},
  file = {:Users/springer/Downloads/Meckenh{\~{A}}¤user2011{\_}Article{\_}ModelingPhonotaxisInFemaleGryl.pdf:pdf},
  issn = {1471-2202},
  number = {S1},
  pages = {P234},
  title = {{Modeling phonotaxis in female Gryllus bimaculatus with artificial neural networks}},
  volume = {12},
  year = {2011}
  }

• Boucsein, C., Nawrot, M. P., Schnepel, P., & Aertsen, A.. (2011). Beyond the cortical column: Abundance and physiology of horizontal connections imply a strong role for inputs from the surround. Frontiers in Neuroscience, 5, 32.
  [Bibtex]

  @article{Boucsein2011,
  abstract = {Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing.},
  author = {Boucsein, Clemens and Nawrot, Martin P. and Schnepel, Philipp and Aertsen, Ad},
  doi = {10.3389/fnins.2011.00032},
  file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Boucsein et al. - 2011 - Beyond the cortical column Abundance and physiology of horizontal connections imply a strong role for inputs fr.pdf:pdf},
  issn = {16624548},
  journal = {Frontiers in Neuroscience},
  keywords = {Cortical column,Dendritic integration,Reliability,Synaptic transmission,Temporal coding,cortex,in vitro,synaptic transmission},
  mendeley-tags = {cortex,in vitro,synaptic transmission},
  pages = {32},
  title = {{Beyond the cortical column: Abundance and physiology of horizontal connections imply a strong role for inputs from the surround}},
  volume = {5},
  year = {2011}
  }

• Schmuker, M., Häusler, C., Brüderle, D., & Nawrot, M. P.. (2011). Benchmarking the impact of information processing in the insect olfactory system with a spiking neuromorphic classifier. Paper presented at the BMC Neuroscience.
  [Bibtex]

  @inproceedings{Schmuker2011a,
  author = {Schmuker, Michael and H{\"{a}}usler, Chris and Br{\"{u}}derle, Daniel and Nawrot, Martin P},
  booktitle = {BMC Neuroscience},
  doi = {10.1186/1471-2202-12-s1-p233},
  file = {:Users/springer/Downloads/1471-2202-12-S1-P233.pdf:pdf},
  issn = {1471-2202},
  number = {S1},
  pages = {P233},
  publisher = {BioMed Central Ltd},
  title = {{Benchmarking the impact of information processing in the insect olfactory system with a spiking neuromorphic classifier}},
  url = {http://www.biomedcentral.com/1471-2202/12/S1/P233},
  volume = {12},
  year = {2011}
  }

• Nawrot, M. P.. (2010). Analysis and Interpretation of Interval and Count Variability in Neural Spike Trains. In Grün, S., & Rotter, S. (Eds.), In Analysis of Parallel Spike Trains (, pp. 37–58). Boston, MA: Springer US.
  [Bibtex]

  @inbook{Nawrot2010,
  abstract = {Understanding the nature and origin of neural variability at the level of single neurons and neural networks is fundamental to our understanding of how neural systems can reliably process information. This chapter provides a starting point to the empirical analysis and interpretation of the variability of single neuron spike trains. In the first part, we cover a number of practical issues of measuring the inter-spike interval variability with the coefficient of variation (CV) and the trial-by-trial count variability with the Fano factor (FF), including the estimation bias for finite observations, the measurement from rate-modulated spike trains, and the time-resolved analysis of variability dynamics. In the second part, we specifically explore the effect of serial interval correlation in nonrenewal spike trains and the impact of slow fluctuations of neural activity on the relation of interval and count variability in stochastic models and in in vivo recordings from cortical neurons. Finally, we discuss how we can interpret the empirical results with respect to potential neuron-intrinsic and neuron-extrinsic sources of single neuron output variability.},
  address = {Boston, MA},
  author = {Nawrot, Martin Paul},
  booktitle = {Analysis of Parallel Spike Trains},
  doi = {10.1007/978-1-4419-5675-0_3},
  editor = {Gr{\"{u}}n, Sonja and Rotter, Stefan},
  file = {::},
  isbn = {978-1-4419-5675-0},
  pages = {37--58},
  publisher = {Springer US},
  title = {{Analysis and Interpretation of Interval and Count Variability in Neural Spike Trains}},
  url = {https://doi.org/10.1007/978-1-4419-5675-0{\_}3},
  year = {2010}
  }

• Nawrot, M. P., Krofczik, S., Farkhooi, F., & Menzel, R.. (2010). Fast dynamics of odor rate coding in the insect antennal lobe. arXiv, 1101.0271.
  [Bibtex]

  @article{Nawrot2010,
  abstract = {Insects identify and evaluate behaviorally relevant odorants in complex natural scenes where odor concentrations and mixture composition can change rapidly. In the honeybee, a combinatorial code of activated and inactivated projection neurons (PNs) develops rapidly within tens of milliseconds at the first level of neural integration, the antennal lobe (AL). The phasic-tonic stimulus-response dynamics observed in the neural population code and in the firing rate profiles of single neurons is faithfully captured by two alternative models which rely either on short-term synaptic depression, or on spike frequency adaptation. Both mechanisms work independently and possibly in parallel to lateral inhibition. Short response latencies in local interneurons indicate that local processing within the AL network relies on fast lateral inhibition that can suppress effectively and specifically odor responses in single PNs. Reviewing recent findings obtained in different insect species, we conclude that the insect olfactory system implements a fast and reliable coding scheme optimized for time-varying input within the behaviorally relevant dynamic range.},
  archivePrefix = {arXiv},
  arxivId = {1101.0271},
  author = {Nawrot, Martin Paul and Krofczik, Sabine and Farkhooi, Farzad and Menzel, Randolf},
  eprint = {1101.0271},
  file = {:Users/springer/Downloads/1101.0271.pdf:pdf},
  journal = {arXiv},
  keywords = {combinatorial code,frequency adaptation,honeybee,latency code,lateral inhibition,olfaction,short term depression,spike},
  pages = {1101.0271},
  title = {{Fast dynamics of odor rate coding in the insect antennal lobe}},
  url = {http://arxiv.org/abs/1101.0271},
  year = {2010}
  }

• Farkhooi, F., Muller, E., & Nawrot, M. P.. (2009). Sequential sparsing by successive adapting neural populations. Paper presented at the BMC Neuroscience.
  [Bibtex]

  @inproceedings{Farkhooi2009a,
  abstract = {In the principal cells of the insect mushroom body, the Kenyon cells (KC), olfactory information is represented by a spatially and temporally sparse code. Each odor stimulus will activate only a small portion of neurons and each stimulus leads to only a short phasic response following stimulus onset irrespective of the actual duration of a constant stimulus. The mechanisms responsible for the sparse code in the KCs are yet unresolved. Here, we explore the role of the neuron-intrinsic mechanism of spike-frequency adaptation (SFA) in producing temporally sparse responses to sensory stimulation in higher processing stages. Our single neuron model is defined through a conductance-based integrate-and-fire neuron with spike-frequency adaptation [1]. We study a fully connected feed-forward network architecture in coarse analogy to the insect olfactory pathway. A first layer of ten neurons represents the projection neurons (PNs) of the antenna lobe. All PNs receive a step-like input from the olfactory receptor neurons, which was realized by independent Poisson processes. The second layer represents 100 KCs which converge onto ten neurons in the output layer which represents the population of mushroom body extrinsic neurons (ENs). Our simulation result matches with the experimental observations. In particular, intracellular recordings of PNs show a clear phasic-tonic response that outlasts the stimulus [2] while extracellular recordings from KCs in the locust express sharp transient responses [3]. We conclude that the neuron-intrinsic mechanism is can explain a progressive temporal response sparsening in the insect olfactory system. Further experimental work is needed to test this hypothesis empirically. [1] Muller et. al., Neural Comput, 19(11):2958-3010, 2007. [2] Assisi et. al., Nat Neurosci, 10(9):1176-1184, 2007. [3] Krofczik et. al. Front. Comput. Neurosci., 2(9), 2009.},
  author = {Farkhooi, Farzad and Muller, Eilif and Nawrot, Martin P},
  booktitle = {BMC Neuroscience},
  doi = {10.1186/1471-2202-10-s1-o10},
  file = {:Users/springer/Downloads/1471-2202-10-S1-O10.pdf:pdf},
  issn = {1471-2202},
  number = {S1},
  pages = {O10},
  title = {{Sequential sparsing by successive adapting neural populations}},
  url = {doi:10.1186/1471-2202-10-S1-O10},
  volume = {10},
  year = {2009}
  }

• Farkhooi, F., Strube-Bloss, M. F., & Nawrot, M. P.. (2009). Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability. Physical Review E – Statistical, Nonlinear, and Soft Matter Physics, 79(2), 21905.
  [Bibtex]

  @article{Farkhooi2009,
  abstract = {The activity of spiking neurons is frequently described by renewal point process models that assume the statistical independence and identical distribution of the intervals between action potentials. However, the assumption of independent intervals must be questioned for many different types of neurons. We review experimental studies that reported the feature of a negative serial correlation of neighboring intervals, commonly observed in neurons in the sensory periphery as well as in central neurons, notably in the mammalian cortex. In our experiments we observed the same short-lived negative serial dependence of intervals in the spontaneous activity of mushroom body extrinsic neurons in the honeybee. To model serial interval correlations of arbitrary lags, we suggest a family of autoregressive point processes. Its marginal interval distribution is described by the generalized gamma model, which includes as special cases the log-normal and gamma distributions, which have been widely used to characterize regular spiking neurons. In numeric simulations we investigated how serial correlation affects the variance of the neural spike count. We show that the experimentally confirmed negative correlation reduces single-neuron variability, as quantified by the Fano factor, by up to 50{\%}, which favors the transmission of a rate code. We argue that the feature of a negative serial correlation is likely to be common to the class of spike-frequency- adapting neurons and that it might have been largely overlooked in extracellular single-unit recordings due to spike sorting errors. {\textcopyright} 2009 The American Physical Society.},
  author = {Farkhooi, F. and Strube-Bloss, M.F. and Nawrot, M.P.},
  doi = {10.1103/PhysRevE.79.021905},
  file = {::},
  issn = {15393755},
  journal = {Physical Review E - Statistical, Nonlinear, and Soft Matter Physics},
  keywords = {Fano factor,spike train statistics,spiking irregularity},
  mendeley-tags = {Fano factor,spike train statistics,spiking irregularity},
  number = {2},
  pages = {021905},
  title = {{Serial correlation in neural spike trains: Experimental evidence, stochastic modeling, and single neuron variability}},
  volume = {79},
  year = {2009}
  }

• Nawrot, M. P., Schnepel, P., Aertsen, A., & Boucsein, C.. (2009). Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections. Frontiers in Neural Circuits, 3.
  [Bibtex]

  @article{Nawrot2009,
  abstract = {The mammalian neocortex has a remarkable ability to precisely reproduce behavioral sequences or to reliably retrieve stored information. In contrast, spiking activity in behaving animals shows a considerable trial-to-trial variability and temporal irregularity. The signal propagation and processing underlying these conflicting observations is based on fundamental neurophysiological processes like synaptic transmission, signal integration within single cells, and spike formation. Each of these steps in the neuronal signaling chain has been studied separately to a great extend, but it has been difficult to judge how they interact and sum up in active sub-networks of neocortical cells. In the present study, we experimentally assessed the precision and reliability of small neocortical networks consisting of trans-columnar, intermediate-range projections (200-1000 $\mu$m) on a millisecond time-scale. Employing photo-uncaging of glutamate in acute slices, we activated a number of distant presynaptic cells in a spatio-temporally precisely controlled manner, while monitoring the resulting membrane potential fluctuations of a postsynaptic cell. We found that signal integration in this part of the network is highly reliable and temporally precise. As numerical simulations showed, the residual membrane potential variability can be attributed to amplitude variability in synaptic transmission and may significantly contribute to trial-to-trial output variability of a rate signal. However, it does not impair the temporal accuracy of signal integration. We conclude that signals from intermediate-range projections onto neocortical neurons are propagated and integrated in a highly reliable and precise manner, and may serve as a substrate for temporally precise signal transmission in neocortical networks. {\textcopyright} 2009 Nawrot, Schnepel, Aertsen and Boucsein.},
  author = {Nawrot, Martin Paul and Schnepel, Philipp and Aertsen, Ad and Boucsein, Clemens},
  doi = {10.3389/neuro.04.001.2009},
  file = {::},
  issn = {16625110},
  journal = {Frontiers in Neural Circuits},
  keywords = {Cell assembly,Coincidence detector,Cortex,Dynamic photo stimulation,Rate coding,Synaptic variability,Temporal coding,cortex,in vitro,synaptic transmission},
  mendeley-tags = {cortex,in vitro,synaptic transmission},
  title = {{Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections}},
  volume = {3},
  year = {2009}
  }

• Krofczik, S., Menzel, R., & Nawrot, M. P.. (2009). Rapid odor processing in the honeybee antennal lobe network. Frontiers in Computational Neuroscience, 2, 9.
  [Bibtex]

  @article{Krofczik2009,
  abstract = {In their natural environment, many insects need to identify and evaluate behaviorally relevant odorants on a rich and dynamic olfactory background. Behavioral studies have demonstrated that bees recognize learned odors within {\textless}200 ms, indicating a rapid processing of olfactory input in the sensory pathway. We studied the role of the honeybee antennal lobe network in constructing a fast and reliable code of odor identity using in vivo intracellular recordings of individual projection neurons (PNs) and local interneurons (LNs). We found a complementary ensemble code where odor identity is encoded in the spatio-temporal pattern of response latencies as well as in the pattern of activated and inactivated PN firing. This coding scheme rapidly reaches a stable representation within 50-150 ms after stimulus onset. Testing an odor mixture versus its individual compounds revealed different representations in the two morphologically distinct types of lateral- and median PNs (l- and m-PNs). Individual m-PNs mixture responses were dominated by the most effective compound (elemental representation) whereas l-PNs showed suppressed responses to the mixture but not to its individual compounds (synthetic representation). The onset of inhibition in the membrane potential of l-PNs coincided with the responses of putative inhibitory interneurons that responded significantly faster than PNs. Taken together, our results suggest that processing within the LN network of the AL is an essential component of constructing the antennal lobe population code. {\textcopyright} 2009 Krofczik, Menzel and Nawrot.},
  author = {Krofczik, S. and Menzel, R. and Nawrot, M.P.},
  doi = {10.3389/neuro.10.009.2008},
  file = {:Users/springer/Documents/PhD/Lit/Krofczik (2009) Rapid odor processing in the honeybee antennal lobe network.pdf:pdf},
  issn = {16625188},
  journal = {Frontiers in Computational Neuroscience},
  keywords = {Antennal lobe,Apis mellifera,Latency code,Local interneurons,Odor mixture,Olfaction,Projection neurons,Temporal coding},
  pages = {9},
  title = {{Rapid odor processing in the honeybee antennal lobe network}},
  volume = {2},
  year = {2009}
  }

• Rybak, J., Kuß, A., Holler, W., Brandt, R., Hege, H., Nawrot, M. P., & Menzel, R.. (2009). The HoneyBee Standard Brain (HSB) – a versatile atlas tool for integrating data and data exchange in the neuroscience community. Paper presented at the BMC Neuroscience.
  [Bibtex]

  @inproceedings{Krofczik2009a,
  abstract = {In their natural environment, many insects need to identify and evaluate behaviorally relevant odorants on a rich and dynamic olfactory background. Behavioral studies have demonstrated that bees recognize learned odors within {\textless}200 ms, indicating a rapid processing of olfactory input in the sensory pathway. We studied the role of the honeybee antennal lobe network in constructing a fast and reliable code of odor identity using in vivo intracellular recordings of individual projection neurons (PNs) and local interneurons (LNs). We found a complementary ensemble code where odor identity is encoded in the spatio-temporal pattern of response latencies as well as in the pattern of activated and inactivated PN firing. This coding scheme rapidly reaches a stable representation within 50-150 ms after stimulus onset. Testing an odor mixture versus its individual compounds revealed different representations in the two morphologically distinct types of lateral- and median PNs (l- and m-PNs). Individual m-PNs mixture responses were dominated by the most effective compound (elemental representation) whereas l-PNs showed suppressed responses to the mixture but not to its individual compounds (synthetic representation). The onset of inhibition in the membrane potential of l-PNs coincided with the responses of putative inhibitory interneurons that responded significantly faster than PNs. Taken together, our results suggest that processing within the LN network of the AL is an essential component of constructing the antennal lobe population code. {\textcopyright} 2009 Krofczik, Menzel and Nawrot.},
  author = {Rybak, J{\"{u}}rgen and Ku{\ss}, Anja and Holler, Wolfgang and Brandt, Robert and Hege, Hans-Christian and Nawrot, Martin Paul and Menzel, Randolf},
  booktitle = {BMC Neuroscience},
  doi = {10.3389/neuro.10.009.2008},
  file = {:Users/springer/Downloads/1471-2202-10-S1-P1.pdf:pdf},
  issn = {16625188},
  keywords = {Antennal lobe,Apis mellifera,Latency code,Local interneurons,Odor mixture,Olfaction,Projection neurons,Temporal coding},
  number = {S1},
  pages = {P1},
  pmid = {19221584},
  title = {{The HoneyBee Standard Brain (HSB) – a versatile atlas tool for integrating data and data exchange in the neuroscience community}},
  url = {doi:10.1186/1471-2202-10-S1-P1},
  volume = {10},
  year = {2009}
  }

• Rickert, J., Riehle, A., Aertsen, A., Rotter, S., & Nawrot, M. P.. (2009). Dynamic encoding of movement direction in motor cortical neurons. Journal of Neuroscience, 29(44).
  [Bibtex]

  @article{Rickert2009,
  abstract = {When we perform a skilled movement such as reaching for an object, we can make use of prior information, for example about the location of the object in space. This helps us to prepare the movement, and we gain improved accuracy and speed during movement execution. Here, we investigate how prior information affects the motor cortical representation of movements during preparation and execution. We trained two monkeys in a delayed reaching task and provided a varying degree of prior information about the final target location. We decoded movement direction from multiple single-unit activity recorded from M1 (primary motor cortex) in one monkey and from PMd (dorsal premotor cortex) in a second monkey. Our results demonstrate that motor cortical cells in both areas exhibit individual encoding characteristics that change dynamically in time and dependent on prior information. On the population level, the information about movement direction is at any point in time accurately represented in a neuronal ensemble of time-varying composition. We conclude that movement representation in the motor cortex is not a static one, but one in which neurons dynamically allocate their computational resources to meet the demands defined by the movement task and the context of the movement. Consequently, we find that the decoding accuracy decreases if the precise task time, or the previous information that was available to the monkey, were disregarded in the decoding process. An optimal strategy for the readout of movement parameters from motor cortex should therefore take into account time and contextual parameters. Copyright {\textcopyright} 2009 Society for Neuroscience.},
  author = {Rickert, J. and Riehle, A. and Aertsen, A. and Rotter, S. and Nawrot, M.P.},
  doi = {10.1523/JNEUROSCI.5441-08.2009},
  file = {::},
  issn = {02706474},
  journal = {Journal of Neuroscience},
  keywords = {Fano factor,monkey,motor cortex,variability},
  mendeley-tags = {Fano factor,monkey,motor cortex,variability},
  number = {44},
  title = {{Dynamic encoding of movement direction in motor cortical neurons}},
  volume = {29},
  year = {2009}
  }

• Nawrot, M. P., Boucsein, C., Rodriguez-Molina, V., Riehle, A., Aertsen, A., & Rotter, S.. (2008). Measurement of variability dynamics in cortical spike trains. Journal of Neuroscience Methods, 169(2), 374–390.
  [Bibtex]

  @article{Nawrot2008,
  abstract = {We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV2) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average. The practical difficulty of measuring the CV2 in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF = CV2. We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV2 and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV2. {\textcopyright} 2007 Elsevier B.V. All rights reserved.},
  author = {Nawrot, Martin P. and Boucsein, Clemens and Rodriguez-Molina, Victor and Riehle, Alexa and Aertsen, Ad and Rotter, Stefan},
  doi = {10.1016/j.jneumeth.2007.10.013},
  file = {::},
  issn = {01650270},
  journal = {Journal of Neuroscience Methods},
  keywords = {Coefficient of variation,Cortical variability,Fano factor,Gamma process,Monkey motor cortex,Noise current injection,Renewal process,Spiking irregularity,in vitro,monkey,spike train statistics,spiking irregularity,variability},
  mendeley-tags = {Fano factor,in vitro,monkey,spike train statistics,spiking irregularity,variability},
  number = {2},
  pages = {374--390},
  title = {{Measurement of variability dynamics in cortical spike trains}},
  volume = {169},
  year = {2008}
  }

• Herz, A. V. M., Meier, R., Nawrot, M. P., Schiegel, W., & Zito, T.. (2008). G-Node: An integrated tool-sharing platform to support cellular and systems neurophysiology in the age of global neuroinformatics. Neural Networks, 21(8), 1070–1075.
  [Bibtex]

  @article{Herz2008,
  abstract = {The global scale of neuroinformatics offers unprecedented opportunities for scientific collaborations between and among experimental and theoretical neuroscientists. To fully harvest these possibilities, a set of coordinated activities is required that will improve three key ingredients of neuroscientific research: data access, data storage, and data analysis, together with supporting activities for teaching and training. Focusing on the development of tools aiming at neurophysiological data, the newly established German Neuroinformatics Node (G-Node) aims at addressing these aspects as part of the International Neuroinformatics Coordination Facility (INCF). Based on its technical and scientific scope, the Node could play a substantial role for cellular and systems neurophysiology as well as for the neuroscience community at large. {\textcopyright} 2008 Elsevier Ltd. All rights reserved.},
  author = {Herz, Andreas V M and Meier, Ralph and Nawrot, Martin P. and Schiegel, Willi and Zito, Tiziano},
  doi = {10.1016/j.neunet.2008.05.011},
  file = {::},
  issn = {08936080},
  journal = {Neural Networks},
  keywords = {Computational neuroscience,Data access,Data analysis,Data sharing,Data storage,Incf,International neuroinformatics coordination facili,National node,Neuroinformatics,Neurophysiology,Open source,Toolbox},
  number = {8},
  pages = {1070--1075},
  title = {{G-Node: An integrated tool-sharing platform to support cellular and systems neurophysiology in the age of global neuroinformatics}},
  volume = {21},
  year = {2008}
  }

• Nawrot, M. P., Boucsein, C., Rodriguez-Molina, V., Aertsen, A., Grün, S., & Rotter, S.. (2007). Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro. Neurocomputing, 70(10-12), 1717–1722.
  [Bibtex]

  @article{Nawrot2007,
  abstract = {Stationary spiking of single neurons is often modelled by a renewal point process. Here, we tested the underlying model assumption that the inter-spike intervals are mutually independent by analyzing stationary spike train recordings from individual rat neocortical neurons in vivo and in vitro. All neurons exhibited moderate (in vivo) or weak (in vitro) negative first order serial correlation of neighboring intervals which was found to be significant in most cases. No significant higher order serial correlations were detected. The observed negative correlation lead to a strong reduction of the spike count variability by about 30{\%} in vivo. {\textcopyright} 2006 Elsevier B.V. All rights reserved.},
  author = {Nawrot, Martin P. and Boucsein, Clemens and Rodriguez-Molina, Victor and Aertsen, Ad and Gr{\"{u}}n, Sonja and Rotter, Stefan},
  doi = {10.1016/j.neucom.2006.10.101},
  file = {::},
  issn = {09252312},
  journal = {Neurocomputing},
  keywords = {Fano factor,Markov order,Renewal process,Serial interval correlation},
  number = {10-12},
  pages = {1717--1722},
  title = {{Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro}},
  volume = {70},
  year = {2007}
  }

• Ball, T., Mehring, C., Nawrot, M. P., & Weiskopf, N.. (2006). Neuronale Kodierung von Bewegung bei Affe und Mensch: Von Einzelzellen und Zellenensembles zum Brain-Computer-Interface IV. In Jahrbuch der Heidelberger Akademie der Wissenschaften für 2005 .
  [Bibtex]

  @incollection{Ball2006,
  author = {Ball, Tonio and Mehring, Carsten and Nawrot, Martin Paul and Weiskopf, N},
  booktitle = {Jahrbuch der Heidelberger Akademie der Wissenschaften f{\"{u}}r 2005},
  title = {{Neuronale Kodierung von Bewegung bei Affe und Mensch: Von Einzelzellen und Zellenensembles zum Brain-Computer-Interface IV}},
  year = {2006}
  }

• Boucsein, C., Nawrot, M. P., Rotter, S., Aertsen, A., & Heck, D.. (2005). Controlling synaptic input patterns in vitro by dynamic photo stimulation. Journal of Neurophysiology, 94(4), 2948–2958.
  [Bibtex]

  @article{Boucsein2005,
  abstract = {Recent experimental and theoretical work indicates that both the intensity and the temporal structure of synaptic activity strongly modulate the integrative properties of single neurons in the intact brain. However, studying these effects experimentally is complicated by the fact that, in experimental systems, network activity is either absent, as in the acute slice preparation, or difficult to monitor and to control, as in in vivo recordings. Here, we present a new implementation of neurotransmitter uncaging in acute brain slices that uses functional projections to generate tightly controlled, spatio-temporally structured synaptic input patterns in individual neurons. For that, a set of presynaptic neurons is activated in a precisely timed sequence through focal photolytic release of caged glutamate with the help of a fast laser scanning system. Integration of synaptic inputs can be studied in postsynaptic neurons that are not directly stimulated with the laser, but receive input from the targeted neurons through intact axonal projections. Our new approach of dynamic photo stimulation employs functional synapses, accounts for their spatial distribution on the dendrites, and thus allows study of the integrative properties of single neurons with physiologically realistic input. Data obtained with our new technique suggest that, not only the neuronal spike generator, but also synaptic transmission and dendritic integration in neocortical pyramidal cells, can be highly reliable. Copyright {\textcopyright} 2005 The American Physiological Society.},
  author = {Boucsein, Clemens and Nawrot, Martin Paul and Rotter, Stefan and Aertsen, Ad and Heck, Detlef},
  doi = {10.1152/jn.00245.2005},
  file = {::},
  issn = {00223077},
  journal = {Journal of Neurophysiology},
  number = {4},
  pages = {2948--2958},
  title = {{Controlling synaptic input patterns in vitro by dynamic photo stimulation}},
  volume = {94},
  year = {2005}
  }

• Mehring, C., Nawrot, M. P., DeOliveira, S. C., Vaadia, E., Schulze-Bonhage, A., Aertsen, A., & Ball, T.. (2004). Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex. Journal of Physiology Paris, 98(4-6), 498–506.
  [Bibtex]

  @article{Mehring2004,
  abstract = {Cortical field potentials have been used for decades in neurophysiological studies to probe spatio-temporal activity patterns of local populations of neurons. Recently, however, interest in these signals was spurred as they were proposed as potential control signals for neuronal motor prostheses, i.e., for devices fit to record and decode brain activity to restore motor functions in paralyzed patients. Little is known, however, about the functional significance of these cortical field potentials. Here we compared information about arm movement direction in two types of movement related cortical field potentials, obtained during a four direction center-out arm reaching paradigm: local field potentials (LFPs) recorded with intracortical micro-electrodes from monkey motor cortex, and epicortical field potentials (EFPs) recorded with macro-electrode arrays subdurally implanted on the surface of the human cerebral cortex. While monkey LFPs showed a typical sequence of positive and negative potential peaks, an initial negative peak was the most salient feature of human EFPs. Individual contralateral LFPs from the monkey motor cortex carried approximately twice as much decoded information (DI) about arm movement direction (median 0.27 bit) as did individual EFPs from the contralateral hand/arm area of primary motor cortex in humans (median 0.12 bit). This relation was similar to the relation between median peak signal-to-noise ratios for directional modulation of movement related potentials (MRPs) of both types of signals. We discuss possible reasons for the observed differences, amongst them epi- vs. intracortical recording and the different electrode dimensions used to measure EFPs and LFPs. {\textcopyright} 2005 Elsevier Ltd. All rights reserved.},
  author = {Mehring, Carsten and Nawrot, Martin Paul and DeOliveira, Simone Cardoso and Vaadia, Eilon and Schulze-Bonhage, Andreas and Aertsen, Ad and Ball, Tonio},
  doi = {10.1016/j.jphysparis.2005.09.016},
  issn = {09284257},
  journal = {Journal of Physiology Paris},
  keywords = {Brain-machine interface,Motor control,Movement decoding,Neuronal signal types,Subdural electrodes,brain machine interface,human,machine learning,monkey,motor cortex},
  mendeley-tags = {brain machine interface,human,machine learning,monkey,motor cortex},
  number = {4-6},
  pages = {498--506},
  title = {{Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex}},
  volume = {98},
  year = {2004}
  }

• Nawrot, M. P., Pistohl, T., Schrader, S., Hehl, U., Rodriguez, V., & Aertsen, A.. (2003). Embedding living neurons into simulated neural networks. Paper presented at the International IEEE/EMBS Conference on Neural Engineering, NER.
  [Bibtex]

  @inproceedings{Nawrot2003a,
  abstract = {{\textcopyright} 2003 IEEE. We present a novel technique for interfacing between a neural network simulation and living neurons. In two experiments we demonstrate how such hybrid in vitro - in virtu networks can be used to investigate neuronal function and to test model predictions.},
  author = {Nawrot, M. P. and Pistohl, T. and Schrader, S. and Hehl, U. and Rodriguez, V. and Aertsen, A.},
  booktitle = {International IEEE/EMBS Conference on Neural Engineering, NER},
  doi = {10.1109/CNE.2003.1196800},
  file = {::},
  isbn = {0780375793},
  issn = {19483554},
  keywords = {Application software,Biological neural networks,Biological system modeling,Biomembranes,In vitro,Neural networks,Neurons,Predictive models,Testing,Timing,hybrid network,in vitro,spiking neural network},
  mendeley-tags = {hybrid network,in vitro,spiking neural network},
  pages = {229--232},
  title = {{Embedding living neurons into simulated neural networks}},
  year = {2003}
  }

• Nawrot, M. P., Aertsen, A., & Rotter, S.. (2003). Elimination of response latency variability in neuronal spike trains. Biological Cybernetics, 88(5), 321–334.
  [Bibtex]

  @article{Nawrot2003,
  abstract = {Neuronal activity in the mammalian cortex exhibits a considerable amount of trial-by-trial variability. This may be reflected by the magnitude of the activity as well as by the response latency with respect to an external event, such as the onset of a sensory stimulus, or a behavioral event. Here we present a novel nonparametric method for estimating trial-by-trial differences in response latency from neuronal spike trains. The method makes use of the dynamic rate profile for each single trial and maximizes their total pairwise correlation by appropriately shifting all trials in time. The result is a new alignment of trials that largely eliminates the variability in response latency and provides a new internal trigger that is independent of experiment time. To calibrate the method, we simulated spike trains based on stochastic point processes using a parametric model for phasic response profiles. We illustrate the method by an application to simultaneous recordings from a pair of neurons in the motor cortex of a behaving monkey. It is demonstrated how the method can be used to study the temporal relation of the neuronal response to the experiment, to investigate whether neurons share the same dynamics, and to improve spike correlation analysis. Differences between this and other, previously published methods are discussed.},
  author = {Nawrot, Martin P. and Aertsen, Ad and Rotter, Stefan},
  doi = {10.1007/s00422-002-0391-5},
  file = {::},
  issn = {03401200},
  journal = {Biological Cybernetics},
  keywords = {method paper,variability},
  mendeley-tags = {method paper,variability},
  number = {5},
  pages = {321--334},
  title = {{Elimination of response latency variability in neuronal spike trains}},
  volume = {88},
  year = {2003}
  }

• Egert, U., Knott, T., Schwarz, C., Nawrot, M. P., Brandt, A., Rotter, S., & Diesmann, M.. (2002). MEA-Tools: An open source toolbox for the analysis of multi-electrode data with MATLAB. Journal of Neuroscience Methods, 117(1), 33–42.
  [Bibtex]

  @article{Egert2002,
  abstract = {Recent advances in electrophysiological techniques have created new tools for the acquisition and storage of neuronal activity recorded simultaneously with numerous electrodes. These techniques support the analysis of the function as well as the structure of individual electrogenic cells in the context of surrounding neuronal or cardiac network. Commercially available tools for the analysis of such data, however, cannot be easily adapted to newly emerging requirements for data analysis and visualization, and cross compatibility between them is limited. In this report we introduce a free open source toolbox called microelectrode array tools (MEA-Tools) for the analysis of multi-electrode data based on the common data analysis environment MATLAB (version 5.3-6.1, The Mathworks, Natick, MA). The toolbox itself is platform independent. The file interface currently supports files recorded with MCRack (Multi Channel Systems, Reutlingen, Germany) under Microsoft Windows 95, 98, NT, and 2000, but can be adapted to other data acquisition systems. Functions are controlled via command line input and graphical user interfaces, and support common requirements for the analysis of local field potentials, extracellular spike activity, and continuous recordings, in addition to supplementary data acquired by additional instruments, e.g. intracellular amplifiers. Data may be processed as continuous recordings or time windows triggered to some event. {\textcopyright} 2002 Elsevier Science B.V. All rights reserved.},
  author = {Egert, U. and Knott, Th. and Schwarz, C. and Nawrot, M. P. and Brandt, A. and Rotter, S. and Diesmann, M.},
  doi = {10.1016/S0165-0270(02)00045-6},
  file = {::},
  issn = {01650270},
  journal = {Journal of Neuroscience Methods},
  keywords = {Data analysis,Electrophysiology,Field potential analysis,Microelectrode arrays,Multi-electrode recording,Spike analysis},
  number = {1},
  pages = {33--42},
  title = {{MEA-Tools: An open source toolbox for the analysis of multi-electrode data with MATLAB}},
  volume = {117},
  year = {2002}
  }

• Nawrot, M. P., Aertsen, A., & Rotter, S.. (1999). Single-trial estimation of neuronal firing rates: From single-neuron spike trains to population activity. Journal of Neuroscience Methods, 94(1), 81–92.
  [Bibtex]

  @article{Nawrot1999,
  abstract = {We present a method to estimate the neuronal firing rate from single-trial spike trains. The method, based on convolution of the spike train with a fixed kernel function, is calibrated by means of simulated spike trains for a representative selection of realistic dynamic rate functions. We derive rules for the optimized use and performance of the kernel method, specifically with respect to an effective choice of the shape and width of the kernel functions. An application of our technique to the on-line, single-trial reconstruction of arm movement trajectories from multiple single-unit spike trains using dynamic population vectors illustrates a possible use of the proposed method. Copyright (C) 1999 Elsevier Science B.V.},
  author = {Nawrot, Martin P. and Aertsen, Ad and Rotter, Stefan},
  doi = {10.1016/S0165-0270(99)00127-2},
  file = {::},
  issn = {01650270},
  journal = {Journal of Neuroscience Methods},
  keywords = {Dynamic population vector,Dynamic spike responses,Kernel estimator,Neural coding,Single-trial rate estimation,Spike train analysis,Stochastic point process,method paper},
  mendeley-tags = {method paper},
  number = {1},
  pages = {81--92},
  title = {{Single-trial estimation of neuronal firing rates: From single-neuron spike trains to population activity}},
  volume = {94},
  year = {1999}
  }

Jürgensen, Anna-Maria (2023)
Circuit motifs for sensory integration, learning, and the initiation of adaptive behavior in Drosophila.
https://kups.ub.uni-koeln.de/71836/

Arican, Cansu (2022)
Sensory to motor transformation during innate and adaptive behavior in the cockroach
https://kups.ub.uni-koeln.de/64251/

Rapp, Hannes (2020)
Spiking neural models & machine learning for systems neuroscience: Learning, Cognition and Behavior.
https://kups.ub.uni-koeln.de/11360/

Rost, Thomas (2016) 
Modeling Cortical Variability Dynamics From Inhibitory Clustering to Context-Dependent Modulation
http://dx.doi.org/10.17169/refubium-11083

Dezhdar, Tara (2016)
Unmixing sensory channels encoding mechanical and thermal stimuli
http://dx.doi.org/10.17169/refubium-7549

Haenicke, Joachim (2015)
Modeling insect-inspired mechanisms of neural and behavioral plasticity
http://dx.doi.org/10.17169/refubium-12322

Sölter, Jan (2015)
Topographic coding principles in olfactory systems
http://dx.doi.org/10.17169/refubium-14370

Meckenhäuser, Gundula (2014)
Temporal aspects of the processing of calling songs in Orthoptera.
https://refubium.fu-berlin.de/handle/fub188/10195

Häusler, Christopher John (2014)
Data Science for Neuroscience – The Brain as Inspiration, Model and Data Source.
https://refubium.fu-berlin.de/handle/fub188/7401

Pamir, Evren (2013)
From behavioral plasticity to neuronal computation. An investigation of associative learning in the honeybee brain.
https://refubium.fu-berlin.de/handle/fub188/12681

Farkhooi, Farzad (2011)
Emergent properties of spike frequency adaptation in neuronal systems.
https://refubium.fu-berlin.de/handle/fub188/5870

Meyer, Anneke (2011)
Characterisation of Local Interneurons in the Antennal Lobe of the Honeybee.
http://kops.uni-konstanz.de/handle/123456789/16253

Berger, Denise (2009)
Intrinsic and functional aspects of neuronal synchrony in primary visual cortex.
https://refubium.fu-berlin.de/handle/fub188/7453

Denker, Michael (2009)
Interpreting the local field potential as a reflection of cooperative neuronal spiking dynamics.
https://refubium.fu-berlin.de/handle/fub188/3438

Abbas Khan, Akhunzada (2024)
Machine learning based behavioral prediction from whole-brain imaging of Drosophila melanogaster.

Müsellim, Celine (2023)
The role of peripheral dynamics of olfactory sensation in chemotactic behavior of Drosophila larva

Leyla Weyermann (2021)
Dimensionality in cortical spiking attractor neural networks using unsupervised machine learning

Schmitt, Felix (2020)
Effects of cellular adaptation in clustered spiking network topology on single neuron dynamics

Kuschmann, Malvina (2020)
Variables influencing individual learning performance of Periplaneta Americana in an olfactory classical conditioning task

Springer, Magdalena (2020)
A mechanistic model for reward prediction and extinction learning in Drosophila melanogaster

Neukirchen, Saskia (2020)
Intracranial Fat Bodies in Crested Ducks (Anas platyrhynchos f.d.)

Razizadeh, Nasima Sophia (2019)
Representation of isometric wrist movement in the motor and somatosensory cortices of primates

Faxel, Miriam (2019)
Modeling a Biologically Realistic Microcircuit of the Drosophila Mushroom Body Calyx

Jürgensen, Anna Maria (2019)
Learning Induced Plasticity in a Spiking Network Model Inspired by Drosophila Olfactory System

Hindennach, Susanne (2017)
Dynamical Processing of Sensory Information in Antennal Lobe Projection Neurons of the American Cockroach

Schaffrath, Stephan (2017)
Automatic evaluation of scorpion venom fingerprints based on binary statistics

Möller, Timo (2017)
Weight Initialization in Deep Belief Networks

Gabler, Stephan (2012)
Representation of chemical features in the insect antennal lobe (supervised by Dr. Michael Schmuker)

Helgadottir, Lovisa Irpa (2012)
Controlling an autonomous robot using spiking neural networks (supervised by Prof. Dr. Martin Nawrot)

Kasap, Bahadir (2012)
Inhibitory Spike-Timing Dependent Plasticity in a Spiking Neural Network Model Inspired by Honeybee Antennal Lobe
(supervised by Dr. Michael Schmuker)

Rost, Thomas (2011)
Modelling Pattern Recognition in Cricket Phonotaxis
(supervised by Prof. Dr. Martin Nawrot and Prof. Dr. Matthias Hennig)

Rosenbaum, Tobias (2011)
Analysis of networking capabilities in the olfactory system of the honeybee
(supervised by Prof. Wolfgang Roessler and Prof. Dr. Martin Nawrot)

Häusler, Christopher John (2010)
Of Neuromorphic Classifiers and Insect Inspired Microcircuits
(supervised by Dr. Michael Schmuker)

Philip Baxter Aßmann (2024)
On the Creation of Memory Patterns across Synaptic Boutons. A Computational Framework for Modeling Compartmentalized Dopamine Effects and Other Regulatory Mechanisms along the Mushroom Body Gamma Lobe

Safria, Lawin (2024)
Time-resolved decoding of movement directions using a random forest classifier

Nurmagambetov, Xenia (2023)
Different Sensory Modalities Elicit a Highly Variable Individual Learning Performance in the American Cockroach Periplaneta americana

Morozova, Anna (2022)
A Computational Model of Second Order Conditioning of Drosophila melanogaster

Völk, Max (2020)
Hand Movements and Mental Rotation of Three-Dimensional Objects

Bulk, Janice (2018)
Classical olfactory conditioning in harnessed cockroaches Periplaneta americana

Pinger, Katharina (2017)
Operante Konditionierung an der Kakerlake Periplaneta americana

Dahlhoff, Alice (2017)
Conditioning and Operant Testing in the Cockroach Periplaneta americana in a Forced-Choice Task

Krex, F. (2012)
Virtuelles Screening von Duftmolekülen mit dem EVA Deskriptor (supervised by Dr. Michael Schmuker)

Winter, C. (2012)
Identifizierung von funktionellen Regionen im lateralen Horn von Drosophila melanogaster
(supervised by Jan Soelter, examination by Prof. Martin Nawrot und Dr. Renard)

Kolbe, S. (2012)
Implementierung und Testung von Kohärenzmethoden
zur Analyse neurokognitiver Signale
(supervised by Dr. Sascha Tamm and Prof. Dr. Martin Nawrot)

Meyer, J. (2011)
Echtzeitanbindung von Sensorelektronik an die Simulation neuronaler Netzwerke
(externally supervised by Prof. Martin Nawrot)

Stiller, S. (2007)
Olfactory Coding in extrinsic neurons of the honey bee.
(supervised by Prof. Martin Nawrot)