Local motion adaptation enhances the representation of spatial structure at EMD arrays

Li J, Lindemann JP, Egelhaaf M (2017)
PLOS Computational Biology 13(12): e1005919.

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Journal Article | Original Article | Published | English
Abstract
Neuronal representation and extraction of spatial information are essential for behavioral control. For flying insects, a plausible way to gain spatial information is to exploit distancedependent optic flow that is generated during translational self-motion. Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system. These motion detectors have adaptive response characteristics, i.e. their responses to motion with a constant or only slowly changing velocity decrease, while their sensitivity to rapid velocity changes is maintained or even increases. We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments. We focused on translational flight, because spatial information is only contained in the optic flow induced by translational locomotion. Indeed, flies, bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation (80% of the flight). With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation, while responses to foreground objects stay constant or even increase. This conclusion even holds under the dynamic flight conditions of insects.
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Article Processing Charge funded by the Deutsche Forschungsgemeinschaft and the Open Access Publication Fund of Bielefeld University.
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Li J, Lindemann JP, Egelhaaf M. Local motion adaptation enhances the representation of spatial structure at EMD arrays. PLOS Computational Biology. 2017;13(12): e1005919.
Li, J., Lindemann, J. P., & Egelhaaf, M. (2017). Local motion adaptation enhances the representation of spatial structure at EMD arrays. PLOS Computational Biology, 13(12), e1005919. doi:10.1371/journal.pcbi.1005919
Li, J., Lindemann, J. P., and Egelhaaf, M. (2017). Local motion adaptation enhances the representation of spatial structure at EMD arrays. PLOS Computational Biology 13:e1005919.
Li, J., Lindemann, J.P., & Egelhaaf, M., 2017. Local motion adaptation enhances the representation of spatial structure at EMD arrays. PLOS Computational Biology, 13(12): e1005919.
J. Li, J.P. Lindemann, and M. Egelhaaf, “Local motion adaptation enhances the representation of spatial structure at EMD arrays”, PLOS Computational Biology, vol. 13, 2017, : e1005919.
Li, J., Lindemann, J.P., Egelhaaf, M.: Local motion adaptation enhances the representation of spatial structure at EMD arrays. PLOS Computational Biology. 13, : e1005919 (2017).
Li, Jinglin, Lindemann, Jens Peter, and Egelhaaf, Martin. “Local motion adaptation enhances the representation of spatial structure at EMD arrays”. PLOS Computational Biology 13.12 (2017): e1005919.
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