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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Teller, M.V.A.: Neural Programming and an Internal Reinforcement Policy
Bernardet, U., Blanchard, M., Verschure, P.F.M.J.: IQR: a distributed system for real-time real-world neuronal simulation. Neurocomputing 44-46(null), 1043–1048 (2002)
Bobrowski, O., Meir, R., Eldar, Y.C.: Bayesian filtering in spiking neural networks: noise, adaptation, and multisensory integration. Neural Computation 21(5), 1277–1320 (2009)
Collett, T.S., Collett, M.: Memory use in insect visual navigation. Nature Reviews. Neuroscience 3(7), 542–552 (2002)
Cruse, H., Wehner, R.: No need for a cognitive map: decentralized memory for insect navigation. PLoS Computational Biology 7(3), e1002009 (2011)
Indiveri, G., Linares-Barranco, B., Hamilton, T.J., Van Schaik, A., Etienne-Cummings, R., Delbruck, T., Liu, S.-C., Dudek, P., Häfliger, P., Renaud, S., et al: Neuromorphic silicon neuron circuits. Frontiers in Neuroscience 5 (2011)
Landgraf, T., Wild, B., Ludwig, T., Nowak, P., Helgadottir, B., Daumenlang, L., Breinlinger, P., Nawrot, M., Rojas, R.: Test Flight for Living Machines 2013 (2013)
Lucas, B.D., Kanade, T.: An Iterative Image Registration Technique with an Application to Stereo Vision. Imaging 130(x), 674–679 (1981)
Menzel, R.: The honeybee as a model for understanding the basis of cognition. Nature Reviews Neuroscience 13(11), 758–768 (2012)
Menzel, R., Greggers, U., Smith, A., Berger, S., Brandt, R., Brunke, S., Bundrock, G., Hülse, S., Plümpe, T., Schaupp, F., Schüttler, E., Stach, S., Stindt, J., Stollhoff, N., Watzl, S.: Honey bees navigate according to a map-like spatial memory. Proceedings of the National Academy of Sciences of the United States of America 102(8), 3040–3045 (2005)
Menzel, R., Kirbach, A., Haass, W.-D., Fischer, B., Fuchs, J., Koblofsky, M., Lehmann, K., Reiter, L., Meyer, H., Nguyen, H., et al.: A common frame of reference for learned and communicated vectors in honeybee navigation. Current Biology 21(8), 645–650 (2011)
Menzel, R., Lehmann, K., Manz, G., Fuchs, J., Koblofsky, M., Greggers, U.: Vector integration and novel shortcutting in honeybee navigation. Apidologie 43(3), 229–243 (2012)
Pfeil, T., Grübl, A., Jeltsch, S., Müller, E., Müller, P., Petrovici, M.A., Schmuker, M., Brüderle, D., Schemmel, J., Meier, K.: Six networks on a universal neuromorphic computing substrate. Frontiers in Neuroscience 7 (2013)
Poon, C.-S., Zhou, K.: Neuromorphic silicon neurons and large-scale neural networks: challenges and opportunities. Frontiers in Neuroscience 5, 108 (2011)
Shi, J., Tomasi, C.: Good features to track. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition CVPR 1994, pp. 593–600. IEEE Comput. Soc. Press (1994)
Wehner, R., Srinivasan, M.V.: Path integration in insects (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Landgraf, T. et al. (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) Biomimetic and Biohybrid Systems. Living Machines 2013. Lecture Notes in Computer Science(), vol 8064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39802-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-39802-5_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39801-8
Online ISBN: 978-3-642-39802-5
eBook Packages: Computer ScienceComputer Science (R0)