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A neural network model for familiarity and context learning during honeybee foraging flights

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Abstract

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.

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Acknowledgements

We thank Barbara Webb for fruitful discussions and Julian Petrasch and Sebastian Krieger for creating the drone map. This work was partially funded by the Bundesministerium für Bildung und Forschung (BMBF) through Grant No. 01GQ0941 to the Bernstein Focus Learning and Memory Berlin (Insect Inspired Robots: Towards an Understanding of Memory in Decision Making) and the Dr. Klaus Tschira Stiftung through Grant No. 00.300.2016 (Robotik in der Biologie: Ziele finden mit einem winzigen Gehirn. Die neuronalen Grundlagen der Navigation der Bienen).

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Authors and Affiliations

  1. Institute for Computer Science, Free University Berlin, Berlin, Germany

    Jurek Müller & Tim Landgraf

  2. Computational Systems Neuroscience, Institute for Zoology, University of Cologne, Cologne, Germany

    Martin Nawrot

  3. Institute for Neurobiology, Free University Berlin, Berlin, Germany

    Randolf Menzel

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  1. Jurek Müller
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Correspondence to Tim Landgraf.

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This article belongs to a Special Issue on Neural Coding.

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Müller, J., Nawrot, M., Menzel, R. et al. A neural network model for familiarity and context learning during honeybee foraging flights. Biol Cybern 112, 113–126 (2018). https://doi.org/10.1007/s00422-017-0732-z

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