Peripheral Processing Facilitates Optic Flow-Based Depth Perception

Li J, Lindemann JP, Egelhaaf M (2016)
Frontiers in Computational Neuroscience 10(10): 111.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
OA 2.58 MB
Bio-inspired lightweight omni-directional visual system with local gain control
Abstract / Bemerkung
Flying insects, such as flies or bees, rely on consistent information regarding the depth structure of the environment when performing their flight maneuvers in cluttered natural environments. These behaviors include avoiding collisions, approaching targets or spatial navigation. Insects are thought to obtain depth information visually from the retinal image displacements (“optic flow”) during translational ego-motion. Optic flow in the insect visual system is processed by a mechanism that can be modeled by correlation-type elementary motion detectors (EMDs). However, it is still an open question how spatial information can be extracted reliably from the responses of the highly contrast- and pattern-dependent EMD responses, especially if the vast range of light intensities encountered in natural environments is taken into account. This question will be addressed here by systematically modeling the peripheral visual system of flies, including various adaptive mechanisms. Different model variants of the peripheral visual system were stimulated with image sequences that mimic the panoramic visual input during translational ego-motion in various natural environments, and the resulting peripheral signals were fed into an array of EMDs. We characterized the influence of each peripheral computational unit on the representation of spatial information in the EMD responses. Our model simulations reveal that information about the overall light level needs to be eliminated from the EMD input as is accomplished under light-adapted conditions in the insect peripheral visual system. The response characteristics of large monopolar cells (LMCs) resemble that of a band-pass filter, which reduces the contrast dependency of EMDs strongly, effectively enhancing the representation of the nearness of objects and, especially, of their contours. We furthermore show that local brightness adaptation of photoreceptors allows for spatial vision under a wide range of dynamic light conditions.
spatial vision; optic flow; brightness adaptation; photoreceptors; LMCs; computational modeling; fly; natural environments
Frontiers in Computational Neuroscience
Open-Access-Publikationskosten wurden durch die Deutsche Forschungsgemeinschaft und die Universität Bielefeld gefördert.
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Li J, Lindemann JP, Egelhaaf M. Peripheral Processing Facilitates Optic Flow-Based Depth Perception. Frontiers in Computational Neuroscience. 2016;10(10): 111.
Li, J., Lindemann, J. P., & Egelhaaf, M. (2016). Peripheral Processing Facilitates Optic Flow-Based Depth Perception. Frontiers in Computational Neuroscience, 10(10), 111. doi:10.3389/fncom.2016.00111
Li, Jinglin, Lindemann, Jens Peter, and Egelhaaf, Martin. 2016. “Peripheral Processing Facilitates Optic Flow-Based Depth Perception”. Frontiers in Computational Neuroscience 10 (10): 111.
Li, J., Lindemann, J. P., and Egelhaaf, M. (2016). Peripheral Processing Facilitates Optic Flow-Based Depth Perception. Frontiers in Computational Neuroscience 10:111.
Li, J., Lindemann, J.P., & Egelhaaf, M., 2016. Peripheral Processing Facilitates Optic Flow-Based Depth Perception. Frontiers in Computational Neuroscience, 10(10): 111.
J. Li, J.P. Lindemann, and M. Egelhaaf, “Peripheral Processing Facilitates Optic Flow-Based Depth Perception”, Frontiers in Computational Neuroscience, vol. 10, 2016, : 111.
Li, J., Lindemann, J.P., Egelhaaf, M.: Peripheral Processing Facilitates Optic Flow-Based Depth Perception. Frontiers in Computational Neuroscience. 10, : 111 (2016).
Li, Jinglin, Lindemann, Jens Peter, and Egelhaaf, Martin. “Peripheral Processing Facilitates Optic Flow-Based Depth Perception”. Frontiers in Computational Neuroscience 10.10 (2016): 111.
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