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Peripheral Processing Facilitates Optic Flow-Based Depth Perception

Overview of attention for article published in Frontiers in Computational Neuroscience, October 2016
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Title
Peripheral Processing Facilitates Optic Flow-Based Depth Perception
Published in
Frontiers in Computational Neuroscience, October 2016
DOI 10.3389/fncom.2016.00111
Pubmed ID
Authors

Jinglin Li, Jens P. Lindemann, Martin Egelhaaf

Abstract

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.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 4%
Germany 1 4%
Unknown 25 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 33%
Researcher 5 19%
Student > Master 4 15%
Student > Bachelor 3 11%
Lecturer > Senior Lecturer 1 4%
Other 0 0%
Unknown 5 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 22%
Neuroscience 4 15%
Computer Science 3 11%
Engineering 3 11%
Immunology and Microbiology 1 4%
Other 5 19%
Unknown 5 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2016.
All research outputs
#20,349,664
of 22,896,955 outputs
Outputs from Frontiers in Computational Neuroscience
#1,162
of 1,347 outputs
Outputs of similar age
#273,568
of 316,323 outputs
Outputs of similar age from Frontiers in Computational Neuroscience
#22
of 31 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
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We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.