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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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Simple models of decision making processes and relative experimental predictions.This figure shows, on the left, several simplified saccade generation schemes, and their prediction in terms of the observed statistics. All models assume that the visual stimulus  is processed as to extract a one-dimensional feature  on which the animal decisions are based. The models presented are meant to represent a sample of qualitatively different functional models of behavior generations, and not necessarily biologically plausible models of neural computation. Panel A-i shows a “hard threshold” model: if the feature  is below a threshold, no event is generated, otherwise, the event is generated stochastically with a certain rate. Panel A-ii shows what would be the prediction of the model if we were to plot the saccade generation rate (an observable quantity) as a function of the feature , assuming we knew how to compute . Panel B shows the same model, but with noise affecting the computation of the feature. The effect on the observed rate would be to transform the hard threshold in a soft threshold. Panel C shows a model in which there is a parallel saccade generation mechanism, which generates saccades randomly independently of the stimulus. The effect of this on the measured rate is to raise uniformly the curves. Also the contribution of some internal processing based on internal neural states which were not a function of the instantantaneous stimulus would have the same effect on the rate statistics. Panel D shows the case where the behavior depends also on some other feature of the stimulus  in addition to . In this case, if we plotted the rates as a function of , ignoring the dependency on , we would see that it is not possible for  to explain the rates by itself. Therefore, once we have identified the curves , , and the feature , we are able to identify the contribution of a random generation process (or based on an internal state) as a uniform baseline saccade rate; and we can infer whether another feature is necessary to explain the behavior by the vertical spread of the rates.
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pcbi-1002891-g003: Simple models of decision making processes and relative experimental predictions.This figure shows, on the left, several simplified saccade generation schemes, and their prediction in terms of the observed statistics. All models assume that the visual stimulus is processed as to extract a one-dimensional feature on which the animal decisions are based. The models presented are meant to represent a sample of qualitatively different functional models of behavior generations, and not necessarily biologically plausible models of neural computation. Panel A-i shows a “hard threshold” model: if the feature is below a threshold, no event is generated, otherwise, the event is generated stochastically with a certain rate. Panel A-ii shows what would be the prediction of the model if we were to plot the saccade generation rate (an observable quantity) as a function of the feature , assuming we knew how to compute . Panel B shows the same model, but with noise affecting the computation of the feature. The effect on the observed rate would be to transform the hard threshold in a soft threshold. Panel C shows a model in which there is a parallel saccade generation mechanism, which generates saccades randomly independently of the stimulus. The effect of this on the measured rate is to raise uniformly the curves. Also the contribution of some internal processing based on internal neural states which were not a function of the instantantaneous stimulus would have the same effect on the rate statistics. Panel D shows the case where the behavior depends also on some other feature of the stimulus in addition to . In this case, if we plotted the rates as a function of , ignoring the dependency on , we would see that it is not possible for to explain the rates by itself. Therefore, once we have identified the curves , , and the feature , we are able to identify the contribution of a random generation process (or based on an internal state) as a uniform baseline saccade rate; and we can infer whether another feature is necessary to explain the behavior by the vertical spread of the rates.

Mentions: In Figure 3 we illustrate the predictions of four qualitatively different functional models in terms of the observed statistics. On the left side we show the functional model, and on the right we show the expected observed event rates as a function of the feature . This exercise assumes that we know how to estimate the feature, which we will show later. Here we describe what we would expect to find, before embarking on the actual computation of .


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)

Simple models of decision making processes and relative experimental predictions.This figure shows, on the left, several simplified saccade generation schemes, and their prediction in terms of the observed statistics. All models assume that the visual stimulus  is processed as to extract a one-dimensional feature  on which the animal decisions are based. The models presented are meant to represent a sample of qualitatively different functional models of behavior generations, and not necessarily biologically plausible models of neural computation. Panel A-i shows a “hard threshold” model: if the feature  is below a threshold, no event is generated, otherwise, the event is generated stochastically with a certain rate. Panel A-ii shows what would be the prediction of the model if we were to plot the saccade generation rate (an observable quantity) as a function of the feature , assuming we knew how to compute . Panel B shows the same model, but with noise affecting the computation of the feature. The effect on the observed rate would be to transform the hard threshold in a soft threshold. Panel C shows a model in which there is a parallel saccade generation mechanism, which generates saccades randomly independently of the stimulus. The effect of this on the measured rate is to raise uniformly the curves. Also the contribution of some internal processing based on internal neural states which were not a function of the instantantaneous stimulus would have the same effect on the rate statistics. Panel D shows the case where the behavior depends also on some other feature of the stimulus  in addition to . In this case, if we plotted the rates as a function of , ignoring the dependency on , we would see that it is not possible for  to explain the rates by itself. Therefore, once we have identified the curves , , and the feature , we are able to identify the contribution of a random generation process (or based on an internal state) as a uniform baseline saccade rate; and we can infer whether another feature is necessary to explain the behavior by the vertical spread of the rates.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1002891-g003: Simple models of decision making processes and relative experimental predictions.This figure shows, on the left, several simplified saccade generation schemes, and their prediction in terms of the observed statistics. All models assume that the visual stimulus is processed as to extract a one-dimensional feature on which the animal decisions are based. The models presented are meant to represent a sample of qualitatively different functional models of behavior generations, and not necessarily biologically plausible models of neural computation. Panel A-i shows a “hard threshold” model: if the feature is below a threshold, no event is generated, otherwise, the event is generated stochastically with a certain rate. Panel A-ii shows what would be the prediction of the model if we were to plot the saccade generation rate (an observable quantity) as a function of the feature , assuming we knew how to compute . Panel B shows the same model, but with noise affecting the computation of the feature. The effect on the observed rate would be to transform the hard threshold in a soft threshold. Panel C shows a model in which there is a parallel saccade generation mechanism, which generates saccades randomly independently of the stimulus. The effect of this on the measured rate is to raise uniformly the curves. Also the contribution of some internal processing based on internal neural states which were not a function of the instantantaneous stimulus would have the same effect on the rate statistics. Panel D shows the case where the behavior depends also on some other feature of the stimulus in addition to . In this case, if we plotted the rates as a function of , ignoring the dependency on , we would see that it is not possible for to explain the rates by itself. Therefore, once we have identified the curves , , and the feature , we are able to identify the contribution of a random generation process (or based on an internal state) as a uniform baseline saccade rate; and we can infer whether another feature is necessary to explain the behavior by the vertical spread of the rates.
Mentions: In Figure 3 we illustrate the predictions of four qualitatively different functional models in terms of the observed statistics. On the left side we show the functional model, and on the right we show the expected observed event rates as a function of the feature . This exercise assumes that we know how to estimate the feature, which we will show later. Here we describe what we would expect to find, before embarking on the actual computation of .

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