Control & Dynamical Systems, California Institute of Technology, Pasadena, California, United States of America.
PLoS Comput Biol. 2013;9(2):e1002891. doi: 10.1371/journal.pcbi.1002891. Epub 2013 Feb 28.
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.
当动物在世界上寻找资源时,它们会根据外部感官线索和内部生成的程序改变方向。阐明复杂搜索算法的功能逻辑具有挑战性,因为动物的可观察行为不能明确地归因于外部或内部触发的事件。我们提出了一种技术,通过定量评估外部刺激和内部过程的贡献来解决这个问题。我们将该技术应用于分析自由飞行的黑腹果蝇的快速转弯(“扫视”)。我们表明,从动物所经历的视觉刺激中计算出的单个标量特征足以解释大部分(93%)的转弯决策。我们自动从可观察的轨迹中估计这个标量值,而不考虑任何关于感官处理的假设。事后,我们表明,从其他实验条件下测量的可观察特征场是一致的。无法用这种视觉输入的特征来解释的其余转弯决策可能归因于基于不可观察的内部状态的确定性过程和纯粹的随机行为的组合。我们不能仅通过外部观察来区分这些贡献,但我们能够提供相对于刺激触发决策的它们的相对重要性的定量界限。我们的结果表明,在自由飞行条件下,相对较少的扫视是内在自发过程的结果,这与之前的建议相反。我们讨论了如何将这种技术推广到其他系统中,并在用于分析遗传行为筛选数据时,将其用作将效应分类为感觉、决策和运动类别的工具。