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Discriminating external and internal causes for heading changes in freely flying Drosophila.

Censi A, Straw AD, Sayaman RW, Murray RM, Dickinson MH - PLoS Comput. Biol. (2013)

Bottom Line: The remaining turning decisions, not explained by this feature of the visual input, may be attributed to a combination of deterministic processes based on unobservable internal states and purely stochastic behavior.We cannot distinguish these contributions using external observations alone, but we are able to provide a quantitative bound of their relative importance with respect to stimulus-triggered decisions.We discuss how this technique could be generalized for use in other systems and employed as a tool for classifying effects into sensory, decision, and motor categories when used to analyze data from genetic behavioral screens.

View Article: PubMed Central - PubMed

Affiliation: Control & Dynamical Systems, California Institute of Technology, Pasadena, California, United States of America.

ABSTRACT
As animals move through the world in search of resources, they change course in reaction to both external sensory cues and internally-generated programs. Elucidating the functional logic of complex search algorithms is challenging because the observable actions of the animal cannot be unambiguously assigned to externally- or internally-triggered events. We present a technique that addresses this challenge by assessing quantitatively the contribution of external stimuli and internal processes. We apply this technique to the analysis of rapid turns ("saccades") of freely flying Drosophila melanogaster. We show that a single scalar feature computed from the visual stimulus experienced by the animal is sufficient to explain a majority (93%) of the turning decisions. We automatically estimate this scalar value from the observable trajectory, without any assumption regarding the sensory processing. A posteriori, we show that the estimated feature field is consistent with previous results measured in other experimental conditions. The remaining turning decisions, not explained by this feature of the visual input, may be attributed to a combination of deterministic processes based on unobservable internal states and purely stochastic behavior. We cannot distinguish these contributions using external observations alone, but we are able to provide a quantitative bound of their relative importance with respect to stimulus-triggered decisions. Our results suggest that comparatively few saccades in free-flying conditions are a result of an intrinsic spontaneous process, contrary to previous suggestions. We discuss how this technique could be generalized for use in other systems and employed as a tool for classifying effects into sensory, decision, and motor categories when used to analyze data from genetic behavioral screens.

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Data collection, saccade detection, reduced coordinate space, time histogram, number of saccades histogram.Panel A shows the experimental setup: the fly is tracked in a circular arena of 1 m radius. The retro-illuminated checkerboard pattern gives a uniform stimulus to the fly. Panel B-i shows some of the trajectories recorded in the arena. The trajectory can be interpreted as a mix of smooth turns and rapid turns, called “saccades”, which are responsible for most of the total angular displacement of the animal. We wrote software to detect these saccades events, based on two different algorithms, documented in the Supplemental Materials. In this paper, we only consider these discrete saccade events (Panel B-ii). Panel C shows the two coordinate systems we use in this paper. We take advantage of the circular symmetry of the environment, along with a hypothesis of planarity, to reduce the degrees of freedom to 2. Panel C-i shows the choice of the axis-angle/distance from the wall coordinates. Panel C-ii shows the “fly-centric view”. The fly configuration is reduced to 2 spatial coordinates by rotating the configuration so that the animal points “up” with respect to the diagram. We remark that all the results in this paper do not depend on the choice of coordinates. Panel D shows a density plot of , which is the time spent at each configuration . Panel E shows the number of saccades (both left and right) detected at each configuration.
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pcbi-1002891-g001: Data collection, saccade detection, reduced coordinate space, time histogram, number of saccades histogram.Panel A shows the experimental setup: the fly is tracked in a circular arena of 1 m radius. The retro-illuminated checkerboard pattern gives a uniform stimulus to the fly. Panel B-i shows some of the trajectories recorded in the arena. The trajectory can be interpreted as a mix of smooth turns and rapid turns, called “saccades”, which are responsible for most of the total angular displacement of the animal. We wrote software to detect these saccades events, based on two different algorithms, documented in the Supplemental Materials. In this paper, we only consider these discrete saccade events (Panel B-ii). Panel C shows the two coordinate systems we use in this paper. We take advantage of the circular symmetry of the environment, along with a hypothesis of planarity, to reduce the degrees of freedom to 2. Panel C-i shows the choice of the axis-angle/distance from the wall coordinates. Panel C-ii shows the “fly-centric view”. The fly configuration is reduced to 2 spatial coordinates by rotating the configuration so that the animal points “up” with respect to the diagram. We remark that all the results in this paper do not depend on the choice of coordinates. Panel D shows a density plot of , which is the time spent at each configuration . Panel E shows the number of saccades (both left and right) detected at each configuration.

Mentions: The flight arena was a 2 meter diameter, 80 cm high cylinder (see Figure 1A). 10 cm×10 cm red and green gel filters (Roscolux) were attached to the arena in a regular checkerboard arrangement and provided a high contrast visual stimulus to flies near the wall. One meter from the wall (i.e., at the center of the arena), the angular wavelength of this pattern was ∼11°, and consequently would be twice the inter-ommatidial spacing of a ∼5.5° in Drosophila[58]. The particular red and green filters were chosen to have similar infrared transmission to facilitate tracking using cameras outfitted with long (IR) pass filters. The arena was illuminated from outside with a circular array of eight 750W Fresnel stage lights pointing towards the arena center. These lights provided both visible and infrared light for fly visual responses and machine vision tracking, respectively.


Discriminating external and internal causes for heading changes in freely flying Drosophila.

Censi A, Straw AD, Sayaman RW, Murray RM, Dickinson MH - PLoS Comput. Biol. (2013)

Data collection, saccade detection, reduced coordinate space, time histogram, number of saccades histogram.Panel A shows the experimental setup: the fly is tracked in a circular arena of 1 m radius. The retro-illuminated checkerboard pattern gives a uniform stimulus to the fly. Panel B-i shows some of the trajectories recorded in the arena. The trajectory can be interpreted as a mix of smooth turns and rapid turns, called “saccades”, which are responsible for most of the total angular displacement of the animal. We wrote software to detect these saccades events, based on two different algorithms, documented in the Supplemental Materials. In this paper, we only consider these discrete saccade events (Panel B-ii). Panel C shows the two coordinate systems we use in this paper. We take advantage of the circular symmetry of the environment, along with a hypothesis of planarity, to reduce the degrees of freedom to 2. Panel C-i shows the choice of the axis-angle/distance from the wall coordinates. Panel C-ii shows the “fly-centric view”. The fly configuration is reduced to 2 spatial coordinates by rotating the configuration so that the animal points “up” with respect to the diagram. We remark that all the results in this paper do not depend on the choice of coordinates. Panel D shows a density plot of , which is the time spent at each configuration . Panel E shows the number of saccades (both left and right) detected at each configuration.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3585425&req=5

pcbi-1002891-g001: Data collection, saccade detection, reduced coordinate space, time histogram, number of saccades histogram.Panel A shows the experimental setup: the fly is tracked in a circular arena of 1 m radius. The retro-illuminated checkerboard pattern gives a uniform stimulus to the fly. Panel B-i shows some of the trajectories recorded in the arena. The trajectory can be interpreted as a mix of smooth turns and rapid turns, called “saccades”, which are responsible for most of the total angular displacement of the animal. We wrote software to detect these saccades events, based on two different algorithms, documented in the Supplemental Materials. In this paper, we only consider these discrete saccade events (Panel B-ii). Panel C shows the two coordinate systems we use in this paper. We take advantage of the circular symmetry of the environment, along with a hypothesis of planarity, to reduce the degrees of freedom to 2. Panel C-i shows the choice of the axis-angle/distance from the wall coordinates. Panel C-ii shows the “fly-centric view”. The fly configuration is reduced to 2 spatial coordinates by rotating the configuration so that the animal points “up” with respect to the diagram. We remark that all the results in this paper do not depend on the choice of coordinates. Panel D shows a density plot of , which is the time spent at each configuration . Panel E shows the number of saccades (both left and right) detected at each configuration.
Mentions: The flight arena was a 2 meter diameter, 80 cm high cylinder (see Figure 1A). 10 cm×10 cm red and green gel filters (Roscolux) were attached to the arena in a regular checkerboard arrangement and provided a high contrast visual stimulus to flies near the wall. One meter from the wall (i.e., at the center of the arena), the angular wavelength of this pattern was ∼11°, and consequently would be twice the inter-ommatidial spacing of a ∼5.5° in Drosophila[58]. The particular red and green filters were chosen to have similar infrared transmission to facilitate tracking using cameras outfitted with long (IR) pass filters. The arena was illuminated from outside with a circular array of eight 750W Fresnel stage lights pointing towards the arena center. These lights provided both visible and infrared light for fly visual responses and machine vision tracking, respectively.

Bottom Line: The remaining turning decisions, not explained by this feature of the visual input, may be attributed to a combination of deterministic processes based on unobservable internal states and purely stochastic behavior.We cannot distinguish these contributions using external observations alone, but we are able to provide a quantitative bound of their relative importance with respect to stimulus-triggered decisions.We discuss how this technique could be generalized for use in other systems and employed as a tool for classifying effects into sensory, decision, and motor categories when used to analyze data from genetic behavioral screens.

View Article: PubMed Central - PubMed

Affiliation: Control & Dynamical Systems, California Institute of Technology, Pasadena, California, United States of America.

ABSTRACT
As animals move through the world in search of resources, they change course in reaction to both external sensory cues and internally-generated programs. Elucidating the functional logic of complex search algorithms is challenging because the observable actions of the animal cannot be unambiguously assigned to externally- or internally-triggered events. We present a technique that addresses this challenge by assessing quantitatively the contribution of external stimuli and internal processes. We apply this technique to the analysis of rapid turns ("saccades") of freely flying Drosophila melanogaster. We show that a single scalar feature computed from the visual stimulus experienced by the animal is sufficient to explain a majority (93%) of the turning decisions. We automatically estimate this scalar value from the observable trajectory, without any assumption regarding the sensory processing. A posteriori, we show that the estimated feature field is consistent with previous results measured in other experimental conditions. The remaining turning decisions, not explained by this feature of the visual input, may be attributed to a combination of deterministic processes based on unobservable internal states and purely stochastic behavior. We cannot distinguish these contributions using external observations alone, but we are able to provide a quantitative bound of their relative importance with respect to stimulus-triggered decisions. Our results suggest that comparatively few saccades in free-flying conditions are a result of an intrinsic spontaneous process, contrary to previous suggestions. We discuss how this technique could be generalized for use in other systems and employed as a tool for classifying effects into sensory, decision, and motor categories when used to analyze data from genetic behavioral screens.

Show MeSH
Related in: MedlinePlus