Statistical structure of locomotion and its modulation by odors

Most behaviors such as making tea are not stereotypical but have an obvious structure. However, analytical methods to objectively extract structure from non-stereotyped behaviors are immature. In this study, we analyze the locomotion of fruit flies and show that this non-stereotyped behavior is well-described by a Hierarchical Hidden Markov Model (HHMM). HHMM shows that a fly's locomotion can be decomposed into a few locomotor features, and odors modulate locomotion by altering the time a fly spends performing different locomotor features. Importantly, although all flies in our dataset use the same set of locomotor features, individual flies vary considerably in how often they employ a given locomotor feature, and how this usage is modulated by odor. This variation is so large that the behavior of individual flies is best understood as being grouped into at least 3-5 distinct clusters, rather than variations around an average fly.

诸如泡茶在内的多数行为并非刻板固化，却具备清晰可辨识的结构。然而，从非刻板行为中客观提取其内在结构的分析方法仍不成熟。本研究针对果蝇的运动行为展开分析，证实这类非刻板行为可通过分层隐马尔可夫模型（Hierarchical Hidden Markov Model, HHMM）进行精准刻画。该模型表明，果蝇的运动行为可拆解为若干运动特征，而气味通过改变果蝇在各运动特征上的停留时长，实现对其运动模式的调控。值得关注的是，尽管本数据集内的所有果蝇均采用同一套运动特征，但个体果蝇在特定运动特征的使用频率，以及该特征的使用如何受气味调控这两个维度上，均存在显著差异。这种差异幅度之显著，使得我们更适宜将果蝇个体的行为归为至少3至5个不同的聚类，而非以平均果蝇为基准的离散变异。