Flies

  1. Sensory neuron population expansion enhances odour tracking through relaxed projection neuron adaptation.
    Suguru Takagi, Gizem Sancer, Liliane Abuin, S. David Stupski, J. Roman Arguello, Lucia L. Prieto-Godino, David L. Stern, Steeve Cruchet, Raquel Álvarez-Ocaña, Carl F. R. Wienecke, Floris van Breugel, James M. Jeanne, Thomas O. Auer & Richard Benton. (2023). Nature Communications [bioRxiv]

  2. Wind Gates Olfaction-Driven Search States in Free Flight
    Stupski, S. D., and van Breugel, F. (2024). Current Biology [    bioRxiv].

  3. Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects.
    Houle, J. and van Breugel, F. (2023). Physics of Fluids [bioRxiv].

  4. Flies catch wind of where smells come from.
    van Breugel, F. and Brunton, B. (2022). Nature News and Views.

  5. Active Anemosensing Hypothesis: How Flying Insects Could Estimate Ambient Wind Direction Through Sensory Integration & Active Movement.
    van Breugel, F, Jewell, J., Houle, J. (2022). Royal Society Interface. [    bioRxiv]

  6. Correlated decision making across multiple phases of olfactory guided search in Drosophila
    van Breugel, F. (2021). J. Exp. Biol.

  7. The long-distance flight behavior of Drosophilasupports an agent-based model for wind-assisted dispersal in insects
    Leitch, K., Ponce, F., Dickson, W., van Breugel, F., Dickinson, M. H. (2021). PNAS.

  8. Visual-olfactory integration in the human disease vector mosquito, Aedes aegypti
    Vinauger, C., van Breugel, F., Locke, L., Tobin, K., Dickinson, M., Fairhall, A., Akbari, O., Riffell, J. (2019) Current Biology.

  9. Distinct activity-gated pathways mediate attraction and aversion to CO2 in Drosophila.
    van Breugel, F., Huda, A., and Dickinson, M. (2018) Nature.

  10. History dependence in insect flight decisions during odor tracking.
    Pang, R., van Breugel, F., Dickinson, M., Riffell, J., and Fairhall, A. (2018). PLOS Comp. Biol.

  11. Superhydrophobic diving flies (Ephydra hians) and the hypersaline waters of Mono Lake.
    van Breugel, F and Dickinson, M. (2017) PNAS.

  12. Mosquitoes use vision to associate odor plumes with thermal targets.
    van Breugel, F., Riffell, J., Fairhall, A., and Dickinson, M. H. (2015). Current Biology.

  13. Plume-Tracking behavior of flying Drosophila emerges from a set of distinct sensory-motor reflexes.
    van Breugel, F. and Dickinson, M. H. (2014). Current Biology.

  14. Octopaminergic modulation of the visual flight speed regulator of Drosophila.
    van Breugel, F., Suver, M. P., and Dickinson, M. H. (2014). J. Exp. Biol.

  15. The visual control of landing and obstacle avoidance in the fruit fly, Drosophila melanogaster.
    van Breugel, F. and Dickinson, M. H. (2012). J. Exp. Biol.

ROBOTS

  1. Emergent behavior and neural dynamics in artificial agents tracking turbulent plumes.
    Singh, S., van Breugel., F., Rao, R., Brunton, B. W. (2023). Nature Machine Intelligence. [arXiv]

  2. A Nonlinear Observability Analysis of Ambient Wind Estimation with Uncalibrated Sensors, Inspired by Insect Neural Encoding
    van Breugel (2021). Control and Decisions Conference.     arXiv

  3. Insect inspired vision-based velocity estimation through spatial pooling of optic flow during linear motion
    Lingenfelter, B., Nag, A., van Breugel, F. (2021). Bioinspiration & Biomimetics.

  4. Upwind Detection of Ambient Wind Using Biomimetic Antenna Sensors for Aerial Vehicles through Active Sensing
    Lopez, A. P., Tung, R., van Breugel, F. (2020). AIAA Aviation Forum.

  5. Monocular distance estimation from optic flow during active landing maneuvers.
    van Breugel, F., Morgansen, K. A., and Dickinson, M. H. (2014). Bioinspiration and Biomimetics.

  6. Strategies for the stabilization of longitudinal forward flapping flight revealed using a dynamically-scaled robotic fly.
    Elzinga, M., van Breugel, F., and Dickinson, M. H. (2014). Bioinspiration and Biomimetics.

  7. From insects to machines.
    van Breugel, F., Regan, W., and Lipson, H. (2008). Robotics and Automation Magazine.

  8. Towards evolvable hovering flight on a physical ornithopter.
    Regan, W., van Breugel, F., and Lipson, H. (2006). Alife X conference proceedings.

  9. Evolving buildable flapping ornithopters.
    van Breugel, F., and Lipson, H. (2005). GECCO conference proceedings.

DATA

  1. Empirical Individual State Observability
    Cellini, B., Boyacioglu, B., and van Breugel, F. (2023). Control and Decisions Conference. [    arXiv]

  2. Odour source distance is predictable from a time history of odour statistics for large scale outdoor plumes.
    Arunava Nag & Floris van Breugel. (2024). Royal Society Interface. [    bioRxiv]

  3. A method for classifying snow using ski-mounted strain sensors
    Mclelland, F., and van Breugel, F. (2023). Cold Regions Science and Technology.

  4. PyNumDiff: A Python package for numerical differentiation of noisy time-series data
    van Breugel, F., Liu, Y., Brunton, B. W., Kutz, J. N. (2022). JOSS. Github.

  5. Numerical differentiation of noisy data: A unifying multi-objective optimization framework
    van Breugel, F., Kutz, J. N., Brunton, B. W. (2020). IEEE Access.

  6. Understanding biological plume tracking behavior using deep reinforcement-learning
    Singh, S. H., van Breugel, F., Rao, R. P. N., Brunton, B. W. (2020). ALife 2020.

  7. FigureFirst: A Layout-first Approach for Scientific Figures.
    Lindsay, T., Weir, P., and van Breugel, F. (2017). Scipy 2017.