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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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Related in: MedlinePlus

Receptive fields of wide-field motion detection consistent with the feature . These pictures show several receptive fields of wide field motion sensitive cells, the spatial feature that they compute, as well as a comparison with the feature  identified from the data. The pictures in first column show the kernel ; the pictures in the second column show the corresponding feature field. The panels A through D show the kernels obtained as solutions of an optimization problem, respectively by solving a linear least-squares problem (panel A), and three different regularization problems: by penalizing the norm of the solution (panel B), by penalizing the norm of the spatial derivative (panel C), and by penalizing the curvature of the solution (panel D). Note all solutions are asymmetric due to the noise in the data. Panel E shows the kernel , which is the closest harmonic function to the regularized solution in panel D. Panel F shows the result obtained by setting to zero this kernel in the back of the field of view. This shows that the contribution of the back of the field of view is necessary to recreate the small sidelobes of the estimated feature field.
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pcbi-1002891-g006: Receptive fields of wide-field motion detection consistent with the feature . These pictures show several receptive fields of wide field motion sensitive cells, the spatial feature that they compute, as well as a comparison with the feature identified from the data. The pictures in first column show the kernel ; the pictures in the second column show the corresponding feature field. The panels A through D show the kernels obtained as solutions of an optimization problem, respectively by solving a linear least-squares problem (panel A), and three different regularization problems: by penalizing the norm of the solution (panel B), by penalizing the norm of the spatial derivative (panel C), and by penalizing the curvature of the solution (panel D). Note all solutions are asymmetric due to the noise in the data. Panel E shows the kernel , which is the closest harmonic function to the regularized solution in panel D. Panel F shows the result obtained by setting to zero this kernel in the back of the field of view. This shows that the contribution of the back of the field of view is necessary to recreate the small sidelobes of the estimated feature field.

Mentions: We tried to improve the results by penalizing large values and large spatial variations of (measured either by the spatial derivatives or ). The modified error function is:(7)for and different values of . In general, by varying and , we found a multitude of solutions, all very different from each other, having approximately the same predictive power (Figure 6). We noticed that for increasing regularization values the estimated linear kernel tended to be shaped as an harmonic function, as illustrated by the kernel obtained by regularizing the second derivative () and using a large value of (), shown in Figure 6D. This kernel is still asymmetric. If we impose that the kernel must be symmetric, we find that the best approximation using one harmonic is:(8)This kernel and relative feature field is shown in Figure 6E, and it is a good approximation of the feature estimated from the data.


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)

Receptive fields of wide-field motion detection consistent with the feature . These pictures show several receptive fields of wide field motion sensitive cells, the spatial feature that they compute, as well as a comparison with the feature  identified from the data. The pictures in first column show the kernel ; the pictures in the second column show the corresponding feature field. The panels A through D show the kernels obtained as solutions of an optimization problem, respectively by solving a linear least-squares problem (panel A), and three different regularization problems: by penalizing the norm of the solution (panel B), by penalizing the norm of the spatial derivative (panel C), and by penalizing the curvature of the solution (panel D). Note all solutions are asymmetric due to the noise in the data. Panel E shows the kernel , which is the closest harmonic function to the regularized solution in panel D. Panel F shows the result obtained by setting to zero this kernel in the back of the field of view. This shows that the contribution of the back of the field of view is necessary to recreate the small sidelobes of the estimated feature field.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585425&req=5

pcbi-1002891-g006: Receptive fields of wide-field motion detection consistent with the feature . These pictures show several receptive fields of wide field motion sensitive cells, the spatial feature that they compute, as well as a comparison with the feature identified from the data. The pictures in first column show the kernel ; the pictures in the second column show the corresponding feature field. The panels A through D show the kernels obtained as solutions of an optimization problem, respectively by solving a linear least-squares problem (panel A), and three different regularization problems: by penalizing the norm of the solution (panel B), by penalizing the norm of the spatial derivative (panel C), and by penalizing the curvature of the solution (panel D). Note all solutions are asymmetric due to the noise in the data. Panel E shows the kernel , which is the closest harmonic function to the regularized solution in panel D. Panel F shows the result obtained by setting to zero this kernel in the back of the field of view. This shows that the contribution of the back of the field of view is necessary to recreate the small sidelobes of the estimated feature field.
Mentions: We tried to improve the results by penalizing large values and large spatial variations of (measured either by the spatial derivatives or ). The modified error function is:(7)for and different values of . In general, by varying and , we found a multitude of solutions, all very different from each other, having approximately the same predictive power (Figure 6). We noticed that for increasing regularization values the estimated linear kernel tended to be shaped as an harmonic function, as illustrated by the kernel obtained by regularizing the second derivative () and using a large value of (), shown in Figure 6D. This kernel is still asymmetric. If we impose that the kernel must be symmetric, we find that the best approximation using one harmonic is:(8)This kernel and relative feature field is shown in Figure 6E, and it is a good approximation of the feature estimated from the data.

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