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Can fly tangential neurons be used to estimate self-motion?

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83919

Franz,  MO
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84104

Neumann,  TR
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84072

Plagge M, Mallot,  HA
Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Franz, M., Neumann, T., Plagge M, Mallot, H., & Zell, A. (1999). Can fly tangential neurons be used to estimate self-motion? In Ninth International Conference on Artificial Neural Networks (ICANN 99) (pp. 994-999). London, UK: Institute of Electrical Engineers.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-E65F-1
Abstract
The so-called tangential neurons in the fly brain are sensitive to the typical optic flow patterns generated during self-motion. This suggests a possible involvement in the self-motion estimation process. In this study, we examine whether a simplified matched filter model of these neurons can be used to estimate self-motion from the optic flow. We present a theory for the construction of an optimal matched filter incorporating both the noise properties of the motion signal, and prior knowledge about the distance distribution of the invironment. Tests on a mobile robot demonstrate that the matched filter approach works for real time camera input and the noisy motion fields computed by Reichardt motion detectors.